Machines doi: 10.3390/machines12030200
Authors: Gabriel G. R. de Castro Tatiana M. B. Santos Fabio A. A. Andrade José Lima Diego B. Haddad Leonardo de M. Honório Milena F. Pinto
This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.
]]>Machines doi: 10.3390/machines12030199
Authors: Alessandro Giorgetti Filippo Ceccanti Niccolò Baldi Simon Kemble Gabriele Arcidiacono Paolo Citti
Powder Bed Fusion Laser Melting (PBF-LM) additive manufacturing technology is expected to have a remarkable impact on the industrial setting, making possible the realization of a metallic component with very complex designs to enhance product performance. However, the industrial use of the PBF-LM system needs a capability monitoring system to ensure product quality. Among the various studies developed, the investigation of methodology for the actual machine capability determination has been faced and still represents an open point. There are multiple authors and institutes proposing different investigation methods, ranging from the realization of samples (ex situ analysis) to installing monitoring devices on the machine (in situ analysis). Compared to other approaches, sample realization allows for assessing how the machine works through specimen analysis, but it is sensitive to the sample design. In this article, we first present an analysis of a well-known test artifact from an Axiomatic Design perspective. Second, based on the customer needs analysis and adjustments with respect to the use of hypothetical additive production lines, a new test artifact with an uncoupled design matrix is introduced. The proposed design has been experimentally tested and characterized using artifact made of Inconel 718 superalloy to evaluate its performance and representativeness in machine capability assessment. The results show an accurate identification of beam offset and scaling factor considering all the building platform positions. In addition, the artifact is characterized by a reduced building time (more than 90% with respect to the reference NIST artifact) and a halved inspection time (from 16 h to 8 h).
]]>Machines doi: 10.3390/machines12030198
Authors: Xiangyu Zhang Bowen Xie Yang Yang Yongbin Liu Pan Jiang
The wheeled chassis, which is the carrying device of the existing handling robot, is mostly only suitable for flat indoor environments and does not have the ability to work on outdoor rugged terrain, greatly limiting the development of chassis driven handling robots. On this basis, this paper innovatively designs a four-wheel-driving Ackerman chassis with strong vibration absorption and obstacle surmounting capabilities and conducts performance research and optimization on it through quantitative experiments and dynamic simulation. Firstly, based on the introduction of the working principle and structure of the four-wheel-driving Ackerman carrier chassis, a multi-sensor distributed dynamic performance test system is constructed through the analysis of the chassis performance evaluation index. Then, according to the quantitative operation experiment of the chassis, the vibration and acceleration characteristics of the chassis at different positions of the chassis, the amount of slip and straightness of the chassis under different running distance, and the operating characteristics of the chassis under different road conditions and different damping springs conditions were analyzed respectively, which verified the rationality of the chassis design. Finally, by constructing the chassis dynamics simulation model; the influence law of chassis structure; and performance parameters such as chassis wheelbase, guide rod structure, and parameters, wheel friction coefficient and assembly error on the dynamic characteristics of the chassis is studied, and the optimal structure of the four-wheel-driving Ackerman chassis is determined while it is verified based on the simulation results. The research shows that the four-wheel-driving Ackerman chassis has good vibration performance and stability and has strong adaptability to different roads. After optimization, the vibration performance, stability, amount of slip, and straightness of the chassis structure are significantly improved, and the straightness is reduced to 0.399%, which is suitable for precise carriage applications on the chassis. The research has important guiding significance for promoting the development and application of wheeled chassis.
]]>Machines doi: 10.3390/machines12030197
Authors: Cunxiang He Yufei Liu Yuhan Liu
Having emerged as strategic focal points in industrial transformation and technological innovation, intelligent machine tools are pivotal in the field of intelligent manufacturing. Accurately forecasting emerging technologies within this domain is crucial for guiding intelligent manufacturing’s evolution and fostering rapid innovation. However, prevailing research methodologies exhibit limitations, often concentrating on popular topics at the expense of lesser-known yet significant areas, thereby impacting the accurate identification of research priorities. The complex, systemic, and interdisciplinary nature of intelligent machine tool technology challenges traditional research approaches, particularly in assessing technological maturity and intricate interactions. To overcome these challenges, we propose a novel framework that leverages technological communities for a comprehensive analysis. This approach clusters data into specific topics which are reflective of the technology system, facilitating detailed investigations within each area. By refining community analysis methods and integrating structural and interactive community features, our framework significantly improves the precision of emerging technology predictions. Our research not only validates the framework but also projects key emerging technologies in intelligent machine tools, offering valuable insights for business leaders and scholars alike.
]]>Machines doi: 10.3390/machines12030196
Authors: Mirco Polonara Alessandra Romagnoli Gianfranco Biancini Luca Carbonari
Incorporating collaborative applications constitutes a challenging and increasingly intricate objective within the context of small and medium-sized enterprises (SMEs). This challenge stems from the shortage of highly specialized personnel in these types of companies when it comes to adopting cutting-edge technologies. The lack of innovation in production processes, however, increases the risk that SMEs will not be able to adapt to rapid changes in the market and the growing needs of consumers, who today are evolving at an unprecedented pace. The importance of adopting collaborative applications can be found in their capacity to harmonize human adaptability with the precision of robotic technology. This synergy contributes to the establishment of a safer work environment while guaranteeing effective and efficient performance. These features not only lead to improved production line performance compared to traditional manual or stationary operations, but also highlight new perspectives in the design, production, and customization of new products. This, in turn, helps companies strengthen their competitiveness in the global market. In this scenario, the primary challenge centers around effectively putting these solutions into practice. Our research aims to highlight how significant benefits can be achieved, both in terms of performance improvements and economically, through the analysis of a simple yet illuminating case study.
]]>Machines doi: 10.3390/machines12030195
Authors: Andreas Dörner Marek Bures Michal Simon Gerald Pirkl
Cognitive ergonomics and the mental health of production workers have attracted increasing interest in industrial companies. However, there is still not much research available as it is regarding physical ergonomics and muscular load. This paper designs an experiment to analyze the cognitive ergonomics and mental stress of shop floor production workers interacting with different user interfaces of a Manufacturing Execution System (MES) that is adjustable for analyzing the influence of other assistive systems, too. This approach is going to be designed with the Design of Experiments (DoE) method. Therefore, the respective goals and factors are going to be determined. The environment will be the laboratories of the University of Applied Sciences Amberg-Weiden and its Campus for Digitalization in Amberg. In detail, there will be a sample assembly process from the automotive supplier industry for demonstration purposes. At this laboratory, the MES software from the European benchmark SAP is installed, and the respective standard Production Operator Desk is going to be used with slight adaptions. In order to make the cognitive ergonomics measurable, different approaches are going to be used. For instance, body temperature, heart rate and skin conductance as well as subjective methods of self-assessment are planned. The result of this paper is a ready-to-run experiment with sample data for each classification of participants. Further, possible limitations and adjustments are going to be discussed. Finally, an approach to validating the expected results is going to be shown and future intentions are going to be discussed.
]]>Machines doi: 10.3390/machines12030194
Authors: Milos Knezev Robert Cep Luka Mejic Branislav Popovic Aco Antic Branko Strbac Aleksandar Zivkovic
Understanding the temperature–working condition relationship is crucial for optimizing machining processes to ensure dimensional accuracy, surface finish quality, and overall spindle longevity. Monitoring and controlling spindle temperature through appropriate cooling systems and operational parameters are essential for efficient and reliable machining operations. This paper presents an in-depth analysis of the thermal equilibrium and deformation characteristics of a high-speed motorized spindle unit utilized in grinding machine tools. Through a series of thermal equilibrium experiments and meticulous data acquisition, the study investigates the nuanced influence of various working conditions, including spindle speeds, coolant types, and coolant flow rates, on spindle temperatures and thermal deformations. Leveraging the power of Artificial Neural Networks (ANNs), predictive models are meticulously developed to accurately forecast spindle behavior. Subsequently, the models are seamlessly transitioned to a cloud computing infrastructure to ensure remote accessibility and scalability, facilitating real-time monitoring and forecasting of spindle performance. The validity and reliability of the predictive models are rigorously assessed through comparison with experimental data, demonstrating excellent agreement and high accuracy in forecasting spindle thermal behavior. Furthermore, the study underscores the critical role of key working condition variables as precise predictors of spindle temperature and thermal deformation, emphasizing their significance in optimizing overall spindle efficiency and performance. This comprehensive analysis offers valuable insights and practical implications for enhancing spindle operation and advancing the field of grinding machine tools.
]]>Machines doi: 10.3390/machines12030193
Authors: Dehuang Gong Xueqian Wei Hongli Liu Fengming Li
A satellite with two solar wings can be modeled using a pair of symmetric flexible cantilever beams connected to a central rigid body. Due to certain reasons, the symmetric flexible cantilever beams may be turned into asymmetric ones, which will inevitably influence the vibration properties of the structural system. By changing the structural sizes and adding local mass on one side of the two beams, a structural system with asymmetric mass distribution is obtained and its vibration characteristics are investigated. Hamilton’s principle with the assumed mode method is employed to establish the equation of motion of the asymmetric structural system. The natural frequencies, mode shapes, frequency response curves and displacement time histories of the system are calculated, and they are compared with those of the structural system with a symmetric mass distribution. The correctness and feasibility of the present analytical method are verified by means of the finite element method (FEM) and a vibration experiment. The analytical results show that the mass asymmetry of the two beams leads to the mode localization phenomenon, and the coupling effect between the two beams and the central rigid body is enhanced. The larger the mass asymmetry is and the closer the position of the added local mass to the end of the cantilever beam is, the more obvious of the mode localization phenomenon is and the more obvious of the coupling effect between the two beams and the central rigid body is. The present investigation results are helpful for the dynamic analysis and design of spacecraft structures composed of flexible solar wings and a central rigid body.
]]>Machines doi: 10.3390/machines12030192
Authors: Abdulmajeed Dabwan Husam Kaid Abdulrahman Al-Ahmari Khaled N. Alqahtani Wadea Ameen
The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment maintenance. Currently, UFMSs with consistently large queues of parts awaiting service employ a repair-after-failure approach as a standard maintenance procedure. This method may require unexpected resources, incur costs, consume time, and potentially disrupt the operations of other UFMSs, either partially or fully. This study suggests using a predictive maintenance (PdM) strategy that utilizes the Internet of Things (IoT) to predict and avoid early mechanical equipment failures before they happen in UFMSs, thereby reducing unplanned downtime and enhancing reliability. Therefore, the objective of this paper is to construct timed Petri net (TPN) models using the IoT for the PdM configuration of mechanical equipment in the dynamic scheduling problem of UFMSs. This necessitates that users represent the specific problem using TPNs. The process of PN modeling requires the utilization of domain knowledge pertaining to the target problems as well as to machine information. However, it is important to note that the modeling rules for PNs are straightforward and limited in number. Consequently, the TPN model is applied to generate and formulate mixed-integer linear programming (MILP) instances accurately. This is done to identify the optimal production cycle time, which may be implemented in real-life scenarios. Several UFMS instances are used to demonstrate the applications and effectiveness of the proposed method. The computational results demonstrate that the proposed method shows superior solution quality, effectively solves instances for a total of 10 parts and 6 machines, and achieves a solution in a reasonable CPU time.
]]>Machines doi: 10.3390/machines12030191
Authors: Mingjie Feng Jianbo Dai Wenbo Zhou Haozhi Xu Zhongbin Wang
Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator’s lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm.
]]>Machines doi: 10.3390/machines12030190
Authors: Di Yuan Dong Wang Qiang Wan
A novel penalty contact constitution was developed to replicate the hysteresis memory effect observed in contact interfaces. On this basis, a refined finite element analysis (FEA) was performed to study the stick–slip friction contact behavior of bolted joint interfaces. The analysis was validated by comparing it with the experimental hysteresis loops in the literature. The simulated hysteresis loops were subsequently used to identify four parameters of the Iwan model. Additionally, the effects of bolt clamping, friction coefficient, and excitation amplitude were individually examined. It was found that the deterioration in bolt clamping performance resulted in a decrease in both the equivalent joint stiffness and energy dissipation. Similarly, the reduction in the friction coefficient yielded a comparable impact. Furthermore, the identified model parameters of critical stick–slip force and displacement exhibited a quasi-linear relationship to the bolt preload and friction coefficient.
]]>Machines doi: 10.3390/machines12030189
Authors: Michal Bučko Lucie Krejčí Ivo Hlavatý Jiří Lorenčík
Businesses are constantly trying to improve their production by looking for bottlenecks to improve their market position. The introduction and innovation of automated production lines is necessary for both labor shortages and productivity and quality reasons. A combination of precision, fluidity, and speed, that is the basic definition of a production line. With the advent of new technologies, production lines have also begun to continuously speed up and innovate. Innovation is the subject of this paper, where the problem of designing a completely new layout for a new production line in the food industry has been addressed. The aim of this paper was to create a design for the optimal layout of the production line in preselected production areas. Optimal use of the space allocated for production is very important for every company today.
]]>Machines doi: 10.3390/machines12030188
Authors: Petr Baron Oleksandr Pivtorak Ján Ivan Marek Kočiško
The present paper describes a study conducted at the request of the operator of machining center equipment. The operator observed undesirable indicators in terms of increased backlash and vibration of the milling head and poor quality of the machined surfaces. Vibration measurements and vibrodiagnostics were carried out before disassembling the milling head in the idle state. The bearings, lubricant, and friction regime were analyzed in the next step. The vibrodiagnostic methods used included VEL, ACC, EN2, EN3, and EN4, with recommended limits conforming to STN ISO 10816-3. The vibration values obtained indicated a problem with the bearings, exceeding the limit values. After disassembly of the bearings, abrasive wear, corrosion, and improper lubricant conditions were detected. Lubricant analysis showed the presence of abrasive and corrosive particles, indicating an unsatisfactory friction regime. Determining the optimum lubricant temperature and the effect on friction torque constituted other aspects of the study. Inspection of the bearing microgeometry confirmed unsatisfactory roundness. Furthermore, the assembly of tapered roller bearings with axial preload was analyzed with a focus on bearing stiffness, accuracy, and life. The results showed that preload improves shaft guidance accuracy and load distribution, promoting reliable operation and extending bearing life.
]]>Machines doi: 10.3390/machines12030187
Authors: Anurag Balayan Rajnish Mallick Stuti Dwivedi Sahaj Saxena Bisheshwar Haorongbam Anshul Sharma
This research addresses the imperative challenge of a lightweight design for an Unmanned Aerial Vehicle (UAV) chassis to enhance the thrust-to-weight and power-to-weight ratios, crucial for optimal flight performance, focused on developing an intriguing lightweight yet robust quadcopter chassis. Advanced generative design techniques, integrated with topology optimization, using Autodesk Fusion 360 software (v. 16.5. 0.2083), 3D-printing methods and lightweight materials like Polylactic Acid (P.L.A.), Acrylonitrile Butadiene Styrene (A.B.S.), and Nylon 6/6 play a significant role in achieving the desired balance between structural integrity and weight reduction. The study showcases successful outcomes, presenting quadcopter chassis designs that significantly improve structural efficiency and overall performance metrics. The findings contribute to aerial robotics and hold promise for precision agriculture applications with relevant performed simulations, emphasizing the importance of tailored design methodologies for other engineering domains. In conclusion, this research provides a foundational step toward advancing drone technology, with weight reductions of almost 50%, P/W and T/W ratios increment of 6.08% and 6.75%, respectively, at least an 11.8% increment in Factor of Safety, at least a 70% reduction in stress values and reduced manufacturing time from its comparative DJI F450 drone, demonstrating the critical role of innovative design approaches in optimizing operational efficiency for targeted applications.
]]>Machines doi: 10.3390/machines12030186
Authors: Xiaoyang Zhou
With the increasing demand for processing precision in the manufacturing industry, feed-rate scheduling is a crucial component in achieving the processing quality of complex surfaces. A smooth feed-rate profile not only guarantees machining quality but also improves machining efficiency. Although the typical offline feed-rate scheduling method possesses good processing efficiency, it may not provide an optimal solution due to the NP-hard problem caused by the feed-rate scheduling of continuous curve segments, which easily results in excess kinetic limitations and feed-rate fluctuations in a real-time interpolation. Instead, the FIR (Finite Impulse Response) method is widely used to realize interpolation in real-time processing. However, the FIR method will filter out a large number of high-frequency signals, leading to a low-processing efficiency. Further, greater acceleration or deceleration is required to ensure the interpolation passes through the segment end at a predefined feed rate and the deceleration in the feed rate profile appears earlier, which allows the interpolation to easily exceed the kinetic limitation. At present, a simple offline or online method cannot realize the global optimization of the feed-rate profile and guarantee the machining efficiency. Moreover, the current feed-rate scheduling that considers both offline and online methods does not consider the situation that the call of offline data and online prediction data will lead to a decrease in the real-time performance of the CNC system. Further, real-time feed-rate scheduling data tend to dominate the whole interpolation process, thus reducing the effect of the offline feed-rate scheduling data. Hence, based on the tool path with C3 continuity (Cubic Continuously Differentiable), this paper first presents a basic interpolation unit relevant to the S-type interpolation feed-rate profile. Then, an offline local smooth strategy is proposed to smooth the feed-rate profile and reduce the exceeding of kinetic limitations and feed-rate fluctuations caused by frequent acceleration and deceleration. Further, a global online smoothing strategy based on the data generated by offline pre-interpolation is presented. What is more, FIR login and logout conditions are proposed to further smooth the feed-rate profile and improve the real-time performance and machining efficiency. The case study validates that the proposed method performs better in kinetic results compared with the typical offline and FIR methods in both the simulation experiment and actual machining experiments. Especially, in actual processing experiments, the proposed method obtains a 28% reduction in contour errors. Further, the proposed method compared with the FIR method obtains a 15% increase in machining efficiency but only a 4% decrease compared with the typical offline method.
]]>Machines doi: 10.3390/machines12030185
Authors: Xiaohui Liu Haofeng Liu Hui Qiao Sihan Zhou Liang Qin
This paper focus on direct current (DC) filter grounding faults to propose a novel dilated normalized residual convolutional neural network (DRNCNN) fault diagnosis model for high-voltage direct current (HVDC) transmission systems. To address the insufficiency of the traditional model’s receptive field in dealing with high-dimensional and nonlinear data, this paper incorporates dilated convolution and batch normalization (BN), significantly enhancing the CNN’s capability to capture complex spatial features. Furthermore, this paper integrates residual connections and parameter rectified linear units (PReLU) to optimize gradient propagation and mitigate the issue of gradient vanishing during training. These innovative improvements, embodied in the DRNCNN model, substantially increase the accuracy of fault detection, achieving a diagnostic accuracy rate of 99.28%.
]]>Machines doi: 10.3390/machines12030184
Authors: Xing Shui Zhijun Rong Binbin Dan Qiangjian He Xin Yang
Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making it difficult to detect wear stages. In response to this issue, a system for monitoring milling cutter wear has been presented, which is based on parameterized Variational Mode Decomposition (VMD) Multiscale Permutation Entropy. Initially, an updated whale optimization technique is used, with the joint correlation coefficient serving as the fitness value for determining the VMD parameters. The improved VMD technique is then used to break down the original signal into a series of intrinsic mode functions, and the Multiscale Permutation Entropy of each effective mode is determined to generate a feature vector. Finally, a 1D Convolutional Neural Network (1D CNN) is employed as the input model for state monitoring using the feature vector. The experimental findings show that the suggested technique can efficiently extract characteristics indicating the wear condition of milling cutters, allowing for the precise monitoring of milling cutter wear states. The recognition rate is as high as 98.4375%, which is superior to those of comparable approaches.
]]>Machines doi: 10.3390/machines12030183
Authors: Luca Landi Giulia Morettini Massimiliano Palmieri Stefano Benicchi Filippo Cianetti Claudio Braccesi
In recent years, polymeric materials have gained prominence as a competitive option for gear manufacturing. Nevertheless, the absence of comprehensive literature addressing the wear due to the coupling of these materials presents a real challenge in response to this innovative trend. Wear of plastic gearwheels represents, in fact, a key issue, traditionally assessed using standard formulations under optimal dry operating conditions. These calculations often rely on coefficients derived from specialized gear tests, but their applicability is constrained to specific polymer–metal combinations. This research was dedicated to the development of a test bench tailored to evaluate the wear of glass fiber-reinforced self-lubricating polymer gearwheels under different operating conditions. This study commenced with a comprehensive exploration of wear phenomena in thermoplastic gearwheels and the inherent challenges associated with utilizing existing standards and the scientific literature for wear analysis. This was followed by a careful evaluation of the operational needs of the test bench, which, starting from a basic solution already implemented, improved its use in various aspects. Finally, this study introduced an optical-based methodology for average linear wear control. This research strived to establish a testing approach that minimizes uncertainties when assessing the wear of thermoplastic gears.
]]>Machines doi: 10.3390/machines12030182
Authors: Sean Mather Arthur Erdman
Gears are foundational tools used to transmit or modify mechanical energy or motion. Implementing gears into planar linkage mechanisms is less common but can be a similarly valuable technique that takes advantage of the high efficiency of gears while producing complex and precise motions. While recent numerical methods for designing these geared planar linkage mechanisms (GPLMs) have proliferated in the literature, analytical approaches have their merits and have received less attention. Here, an analytical alternative is presented as a modification of the complex-number loop-based synthesis method for designing multiloop mechanisms. Some of the base topologies for geared dyad, triad, and quadriad chains are presented, along with a numerical example demonstrating the solution procedure’s effectiveness.
]]>Machines doi: 10.3390/machines12030181
Authors: Carsten Knoll Julius Fiedler Stefan Ecklebe
In this paper, we introduce a novel method to formally represent elements of control engineering knowledge in a suitable data structure. To this end, we first briefly review existing representation methods (RDF, OWL, Wikidata, ORKG). Based on this, we introduce our own approach: The Python-based imperative representation of knowledge (PyIRK) and its application to formulate the Ontology of Control Systems Engineering (OCSE). One of its main features is the possibility to represent the actual content of definitions and theorems as nodes and edges of a knowledge graph, which is demonstrated by selected theorems from Lyapunov’s theory. While the approach is still experimental, the current result already allows the application of methods of automated quality assurance and a SPARQL-based semantic search mechanism. The feature set of the framework is demonstrated by various examples. The paper concludes with a discussion of the limitations and directions for further development.
]]>Machines doi: 10.3390/machines12030180
Authors: Cosmin Constantin Grigoras Valentin Zichil Vlad Andrei Ciubotariu Stefan Marius Cosa
This review focuses on the complex connections between machine learning, mechatronics, and stretch forming, offering valuable insights that can lay the groundwork for future research. It provides an overview of the origins and fundamentals of these fields, emphasizes notable progress, and explores the influence of these fields on society and industry. Also highlighted is the progress of robotics research and particularities in the field of sheet metal forming and its various applications. This review paper focuses on presenting the latest technological advancements and the integrations of these fields from their beginnings to the present days, providing insights into future research directions.
]]>Machines doi: 10.3390/machines12030179
Authors: Jin Yan Jianbin Liao Weiwei Zhang Jinliang Dai Chaoming Huang Hanlin Li Hongliang Yu
In this paper, a graph convolutional network is constructed and applied for bearing fault diagnosis. Specifically, the constant-Q transform (CQT) is first adopted for spectral analysis of vibration signals, where the frequencies are distributed in the logarithmic scale. Varied frequency resolutions can be obtained to satisfy the spectral resolution requirement and reduce signal dimension. Afterwards, the CQT spectrum is modeled by a graph, where nodes are frequency bins and edges reflect the inner relationship of different bins. There are edges between the fundamental and harmonic components. Then, a two-layer graph convolutional network (GCN) is utilized to assess the significance of vibration sources within the mixed signals. Finally, the bearing faults are determined according to the output of the GCN. To the best of our knowledge, this is the first work to model the vibration signal in this graph structure. The advantage of this approach lies in the simplification of edge definitions, facilitating shared connectivity relationships between the fundamental frequency and harmonics. Its performance was compared with another state-of-the-art fault diagnosis model. Experimental results demonstrate that the proposed model obtains higher accuracy, and it is more effective in extracting discriminative features.
]]>Machines doi: 10.3390/machines12030178
Authors: Magno Ayala Jesus Doval-Gandoy Jorge Rodas Osvaldo Gonzalez Raúl Gregor Larizza Delorme Carlos Romero Ariel Fleitas
The predictive current controller has arisen as a practicable technique for operating multiphase machines due to its fast dynamic response, control flexibility, and overall good performance. However, this type of controller has limitations, e.g., it tends to suffer from steady-state tracking errors in (d−q) currents; high computational burden; and high (x−y) currents, which become more pronounced at higher speeds, thereby worsening its sustainability. While some proposals have addressed these limitations by incorporating modulation stages and new cost functions, there is still room for improvement, particularly at higher speeds. In line with the pursuit of sustainable advancements, this article explores the integration of a field-weakening strategy with a modulated predictive current controller applied to a six-phase induction machine to improve its performance at current tracking for higher speed ranges. Experimental tests were conducted to validate the effectiveness of the proposed controller, assessing stator current tracking, reduction in the (x−y) currents, and the total harmonic distortion.
]]>Machines doi: 10.3390/machines12030177
Authors: Xiaoning Song Kaifu Mi Yu Lei Zhengyang Li Dongjia Yan
Erosion of solid particles in a pipe elbow containing a 90° angle is investigated by simulation methods. In the process of shale gas exploitation, the impact of solid particles carried by fluid on the inner surface wall of pipes, as well as the turbulent flow, cause the erosion of pipes, which brings about heavy economic losses for the oil and gas industry. In the impact erosion of the inner surface wall of the pipe, the worst erosion occurs at the elbow. In this study, the erosion of a pipe elbow which has been widely used in actual production is analyzed, and the influence of the fluid velocity, the solid particle size, and the wall roughness on the erosion is investigated. Additionally, the simulation results of the erosion with the rebound and freeze boundary conditions are compared, indicating that setting the freeze boundary condition could significantly improve the computational efficiency by 74% with the acceptable accuracy. In order to reduce the impact erosion in the pipe elbow containing a 90° angle, an optimal design is proposed that can reduce the maximum erosion rate by 52.4%. These results complement the research of elbow erosion and provide ideas for the optimization problem of a pipe elbow containing a 90° angle.
]]>Machines doi: 10.3390/machines12030176
Authors: Zhiqiang He Fugang Zhai Changyu Tan Xiaojun Chen Tianshuo Chen Pengpeng Ma
With the increasing demand for lightweight construction machinery, it is of great significance to study non-metallic materials that can replace steel plates to make hydraulic oil tanks (HOTs). To explore the feasibility of making HOTs with three materials—cross-linked polyethylene (XLPE), polypropylene (PP), and nylon (PA)—this paper takes 28 L and 115 L volumes commonly used in construction machinery, such as forklifts and loaders, as the design volume and obtains non-metal HOT products of good forming quality by regulating the process parameters. Based on the test methods and evaluation bases of the fuel tank in the national standard, the normal-temperature pressure test, high-temperature pressure test, and low-temperature impact test are designed according to the working conditions of the HOTs. Finally, the non-metallic HOT products are tested. The results show that the rotational molding of XLPE material is the easiest, and products of all sizes can be molded, but the mechanical properties and thermal stability of the products are poor. The low-temperature impact resistance of PP products is poor. PA material can be used to create small HOTs, and the product performance is excellent. This research serves as a valuable reference for the non-metallic and lightweight design of HOTs.
]]>Machines doi: 10.3390/machines12030175
Authors: Yi-Fan Cui Ying-Hui Zhang Wei-Dong He Lian-Jun Dong
Focusing on the investigation of a 3 MW wind-turbine gearbox, this paper’s aim is to address the challenge of turbine shutdown due to the internal oil temperature exceeding its limits. Additionally, there is the difficulty in measuring the internal temperature. To tackle these issues, a thermal network model for the entire gearbox was developed. This model is based on an analysis of the thermodynamic behavior of the three-stage transmission in the wind-turbine gearbox and internal oil-spray lubrication. Through this model, thermal balance equations were established to predict the steady-state temperatures under different operating conditions. This study delved into the calculation methods for the power loss of heat sources in thermodynamic balance equations and the calculation methods for different types of thermal resistance between nodes, forming an adapted computational process. Applying this model, simulated analyses yielded temperatures at various nodes and bearing temperatures under different operating conditions. These results were compared with actual SCADA data, and steady-state thermal simulations of the high-speed stages were conducted, demonstrating the model’s effectiveness in predicting steady-state temperatures for a large-megawatt wind-turbine gearbox. Furthermore, the model-based analysis explored the impact of the oil spray parameters on the gearbox temperature, providing a theoretical foundation for further anticipating overheating malfunctions and optimizing the internal cooling systems.
]]>Machines doi: 10.3390/machines12030174
Authors: Tatsuhiko Aizawa Tomohiro Miyata Kiyoyuki Endoh
The two-step PM (powder metallurgy)-route procedure was proposed to fabricate a super-engineering plastic gear directly from powder feedstock. Its lightweight, fully dense integrity and high-stiffness has been found to be suitable for reducers in robotics and electric vehicles, as they work even in severe environmental conditions. In this study, the green compaction and sinter-forging processes were used to consolidate the polyimide powder feedstock and to sinter forge the solid preform into the final products. To demonstrate the high density of preforms and sinter-forged gears, a hardness measurement and X-ray computer tomography were employed. The gear-grade balancing was also evaluated to describe the effect of fine sinter-forging conditions on the dimensional quality of polyimide gears. High gear grade with JIS-2 class proved that the polyimide was useful as a matrix of lightweight and high-strength gears.
]]>Machines doi: 10.3390/machines12030173
Authors: Binrui Zhang Min Ye Gaoqi Lian Yan Li Baozhou Xia
The comprehensive performance of unmanned excavators is crucial for the development and optimization of the field of construction machinery. To effectively improve the unmanned excavator to meet the needs of the market, it is imperative to quantify the evaluation method of the comprehensive performance of unmanned excavators. In this study, an evaluation method combining a fuzzy analytic hierarchy process and multivariate image area analysis method is proposed. Firstly, based on the feature extraction of the signal stability of the unmanned excavators, fifteen evaluation indexes were proposed. Then, the case study is used to obtain the scores corresponding to these indexes. The fuzzy analytic hierarchy process is applied to determine the relative weight of the selected evaluation criteria, in which the uncertain and imprecise judgments of decision makers are converted into fuzzy numbers. At the same time, the braking performance of the three types of unmanned excavators was comprehensively evaluated and ranked using the multivariate image area analysis method as an empirical example. Finally, a weight analysis is performed to check the robustness of the ranking results. The results show that the proposed method is effective and feasible. It provides a reference for the performance improvement and efficiency optimization of unmanned excavators.
]]>Machines doi: 10.3390/machines12030172
Authors: Larisa Rybak Giuseppe Carbone Dmitry Malyshev Artem Voloshkin
Aliquoting of biological samples refers to the process of dividing a larger biological sample into smaller, representative portions known as aliquots. This procedure is commonly employed in laboratories, especially in fields like molecular biology, genetics, and clinical research. Currently, manual dosing devices are commonplace in laboratories, but they demand a significant amount of time for their manual operation. The automated dosing devices available are integrated into narrowly focused aliquoting systems and lack versatility as manipulator equipment. Addressing this limitation, a novel technical solution is proposed in this paper for a modular dosing device compatible with robotic manipulators. The paper introduces and details a mathematical model, optimizes its parameters, and constructs a detailed 3D model using the NX environment to demonstrate the engineering feasibility of our concept. It further outlines the development of a three-dimensional dynamic simulation model for the dosing device, comparing analytical calculations with simulation results. The construction of a dosing device prototype is discussed, followed by a comprehensive experimental validation.
]]>Machines doi: 10.3390/machines12030171
Authors: Anton Hoyer Eckart Uhlmann
Brushing with bonded abrasives is a finishing process used for deburring, edge rounding, and roughness reduction. However, due to the complex motion, chipping, and wear behavior of abrasive filaments, industrial brushing processes have historically relied on empirical knowledge. To gain a better understanding of filament interactions, a physical model based on the discrete element method was developed to simulate process forces and contact areas. Filament patterns of round brushes were determined through the use of laser line triangulation and image processing. These filament patterns showed interlocked filaments and yielded more accurate results when used in brushing simulations than the oversimplified square patterns, which were used in previous research. Simulation confirms the occurrence of filament interactions, distinguishes between sweeping and striking filament motions, and reveals dynamic behavior at high brushing velocities that may increase undesirable tool wear.
]]>Machines doi: 10.3390/machines12030170
Authors: Jiahao Wang Zhengqing Liu Yang Wu Qiucheng Wang Dayu Shu
Tantalum–tungsten alloys have been widely used in different industrial sectors—for example, in chemical, medical, aerospace, and military equipment. However, they are usually difficult to cut because of the large cutting force, rapid tool wear, and poor surface finish during machining. This paper presents the machining performance and cutting tool wear of AlCrN/TiAlN-coated carbide tools during the milling process of Ta-2.5W. The effects of cutting parameters on the cutting forces and surface roughness of AlCrN/TiAlN-coated carbide tools were obtained and analyzed. The results show that the wear resistance of AlCrN-coated tools is better than that of TiAlN-coated tools, and that the main wear mechanisms of both cutting tools are crater wear, adhesive wear, and diffusion wear. Compared to TiAlN-coated tools, AlCrN-coated tools reduced the cutting forces by 1% to 15% and decreased the surface roughness by 6% to 20%. A cutting speed within the range of 80–120 m/min can ensure a low cutting force while maintaining good surface roughness, which is more conducive to machining Ta-2.5W.
]]>Machines doi: 10.3390/machines12030169
Authors: Wang-Su Jeon Sang-Yong Rhee
The advancement of smart factories has brought about small quantity batch production. In multi-variety production, both materials and processing methods change constantly, resulting in irregular changes in the progression of tool wear, which is often affected by processing methods. This leads to changes in the timing of tool replacement, and failure to correctly determine this timing may result in substantial damage and financial loss. In this study, we sought to address the issue of incorrect timing for tool replacement by using a Seq2Seq model to predict tool wear. We also trained LSTM and GRU models to compare performance by using R2, mean absolute error (MAE), and mean squared error (MSE). The Seq2Seq model outperformed LSTM and GRU with an R2 of approximately 0.03~0.037 in step drill data, 0.540.57 in top metal data, and 0.16~0.45 in low metal data. Confirming that Seq2Seq exhibited the best performance, we established a real-time monitoring system to verify the prediction results obtained using the Seq2Seq model. It is anticipated that this monitoring system will help prevent accidents in advance.
]]>Machines doi: 10.3390/machines12030168
Authors: Nikolaos E. Karkalos Panagiotis Karmiris-Obratański
Non-conventional processes are considerably important for the machining of hard-to-cut alloys in various demanding applications. Given that the surface quality and integrity, dimensional accuracy, and productivity are important considerations in industrial practice, the prediction of the outcome of the material removal process should be able to be conducted with sufficient accuracy, taking into consideration the computational cost and difficulty of implementation of the relevant models. In the case of AWJ, various types of approaches have been already proposed, both relying on analytical or empirical models and developed by solving partial differential equations. As the creation of a model for AWJ pocket milling is rather demanding, given the number of parameters involved, in the present work, it is intended to compare the use of three different types of efficient modeling approaches for the prediction of the dimensions of pockets milled by AWJ technology. The models are developed and evaluated based on experimental results of AWJ pocket milling of a titanium workpiece by an eco-friendly walnut shell abrasive. The results indicate that a semi-empirical approach performs better than a two-step hybrid analytical/semi-empirical method regarding the selected cases, but both methods show promising results regarding the realistic representation of the pocket shape, which can be further improved by a probabilistic approach.
]]>Machines doi: 10.3390/machines12030167
Authors: Hoang-Long Dang Sangshin Kwak Seungdeog Choi
DC microgrids are vital for integrating renewable energy sources into the grid, but they face the threat of DC arc faults, which can lead to malfunctions and fire hazards. Therefore, ensuring the secure and efficient operation of DC systems necessitates a comprehensive understanding of the characteristics of DC arc faults and the implementation of a reliable arc fault detection technique. Existing arc-fault detection methods often rely on time–frequency domain features and machine learning algorithms. In this study, we propose an advanced detection technique that utilizes a novel approach based on feature differences between moving intervals and advanced learning techniques (ALTs). The proposed method employs a unique approach by utilizing a time signal derived from power supply-side signals as a reference input. To operationalize the proposed method, a meticulous feature extraction process is employed on each dataset. Notably, the difference between features within distinct moving intervals is calculated, forming a set of differentials that encapsulate critical information about the evolving arc-fault conditions. These differentials are then channeled as inputs for advanced learning techniques, enhancing the model’s ability to discern intricate patterns indicative of DC arc faults. The results demonstrate the effectiveness and consistency of our approach across various scenarios, validating its potential to improve fault detection in DC systems.
]]>Machines doi: 10.3390/machines12030166
Authors: Jing Zhou Haili Li Lin Lu Ying Cheng
A set of online inspection systems for surface defects based on machine vision was designed in response to the issue that extrusion molding ceramic 3D printing is prone to pits, bubbles, bulges, and other defects during the printing process that affect the mechanical properties of the printed products. The inspection system automatically identifies and locates defects in the printing process by inspecting the upper surface of the printing blank, and then feeds back to the control system to produce a layer of adjustment or stop the printing. Due to the conflict between the position of the camera and the extrusion head of the printer, the camera is placed at an angle, and the method of identifying the points and fitting the function to the data was used to correct the camera for aberrations. The region to be detected is extracted using the Otsu method (OSTU) on the acquired image, and the defects are detected using methods such as the Canny algorithm and Fast Fourier Transform, and the three defects are distinguished using the double threshold method. The experimental results show that the new aberration correction method can effectively minimize the effect of near-large selection caused by the tilted placement of the camera, and the accuracy of this system in detecting surface defects reached more than 97.2%, with a detection accuracy of 0.051 mm, which can meet the detection requirements. Using the weighting function to distinguish between its features and defects, and using the confusion matrix with the recall rate and precision as the evaluation indexes of this system, the results show that the detection system has accurate detection capability for the defects that occur during the printing process.
]]>Machines doi: 10.3390/machines12030165
Authors: Yong Hoon Jang Han Sol Kim
This study aims to propose a sampled-data control technique, utilizing a linear matrix inequality (LMI) approach, to achieve string-stable vehicle platooning in a cooperative adaptive cruise control (CACC) system with communication delays. To do this, a decentralized sampled-data controller design technique that combines one controller using sensor measurements and another one utilizing vehicle-to-vehicle (V2V) communication, ensuring both individual and string stability, is proposed first. Next, a memory sampled-data control (MSC) approach is presented to account for transmission delays in V2V communication. Additionally, an improved Lyapunov–Krasovskii functional (LKF) is presented to improve computational complexity and sampling performance. The design conditions are formulated as linear matrix inequalities (LMIs) in the time domain, facilitating efficient stability analysis and optimization. Finally, vehicle platooning simulations are provided to validate the effectiveness and feasibility of the proposed technique.
]]>Machines doi: 10.3390/machines12030164
Authors: Chungeng Sun Jipeng Li Ying Tan Zhijie Duan
High-precision tracking of an electro-hydraulic servo material testing machine’s force control system was achieved using a proposed integral sliding mode control method based on feedback linearization to improve the machine’s force control performance and anti-interference ability. First, the electro-hydraulic servo system’s nonlinear mathematical model was established, and its input–output linearization was realized using differential geometry theory. Second, integral sliding mode control was introduced into the controller and the feedback-linearized integral sliding mode controller was designed. The controller’s stability was proven based on the Lyapunov stability principle. Finally, a simulation model of the electro-hydraulic servo material testing machine’s force control system was established using AMESim/Simulink software. The designed controller was simulated and verified, and the control effects of the system’s different amplitudes and frequency signals were analyzed. The results showed that the feedback-linearized integral sliding mode control algorithm could effectively improve the system’s force tracking accuracy and parameter adaptability, yielding better robustness and a better control effect.
]]>Machines doi: 10.3390/machines12030163
Authors: Erick Axel Padilla-García Raúl Dalí Cruz-Morales Jaime González-Sierra David Tinoco-Varela María R. Lorenzo-Gerónimo
Although additive manufacturing is a relatively new technology, it has been widely accepted by industry and academia due to the wide variety of prototypes that can be built. Furthermore, using mobile robots to carry out different tasks allows greater flexibility than using manipulator robots. In that sense, and based on those above, this article focuses on the design and assembly of a multi-configurable mobile robot that is capable of changing from a differential to an omnidirectional configuration. For this purpose, a sequential mechatronic design/control methodology was implemented to obtain an affordable platform via additive manufacturing which is easily scalable and allows the user to change from one configuration to another. As a proof of concept, this change is made manually. Fabrication, construction, and assembly processes for both structures are presented. Then, a hierarchical control law is designed. In this sense and based on Lyapunov’s method, a low-level controller is developed to control the angular speed of the wheels to a desired angular speed, and a medium-level controller controls the robot’s attitude to follow a desired Cartesian trajectory. Finally, the control strategies are implemented in both prototype configurations, and through experimental results, the theoretical analysis and the construction of the mobile robot are validated.
]]>Machines doi: 10.3390/machines12030162
Authors: Chunjiang He Jinxu Zhang Chao Lin
An atypical face gear pair with complex transmission motion can be used in intermittent reciprocating mechanisms with more precise transmission and a much higher capacity than conventional mechanisms, such as cams and linkages. In this study, we derive a mathematical equation for the complex tooth surface of this gear pair. We indicate the change in root cutting, top sharpening and the effective width of the tooth surface with different parameters. Additionally, we derive the governing equation for the kinematical characteristics of this eccentric curve-face gear pair with a rigid–flexible coupling system, revealing the continuous intermittent contact principle of this gear type with different parameters. Boundary conditions for the gear pair are proposed, demonstrating that the vibration of the gear pair is more obvious, even at a low velocity. In addition, the critical velocity, which mostly ranges from 300 rpm to 400 rpm, is affected by the stiffness of the frames and the parameters of the tooth surfaces. The interval space and interval time of the intermittent contact system are Δd≤0.3 mm and Δt≤5.6×10−4 s, with visible surface sliding on the contact area. It is shown that the contact points are firstly concentrated at the outer part of the tooth surface and that the meshing will break off at the first tooth with the minimum inner radius RGi−min. These theoretical results, which have been verified experimentally, provide theoretical support for further analysis and the better application of this unconventional gear pair.
]]>Machines doi: 10.3390/machines12030161
Authors: Wu Wang Hua Li Pei Liu Botong Niu Jing Sun Boge Wen
Using optimal assembly relationships, companies can enhance product quality without significantly increasing production costs. However, predicting Assembly Geometric Errors presents a challenging real-world problem in the manufacturing domain. To address this challenge, this paper introduces a highly efficient Transformer-based neural network model known as Predicting Assembly Geometric Errors based on Transformer (PAGEformer). This model accurately captures long-range assembly relationships and predicts final assembly errors. The proposed model incorporates two unique features: firstly, an enhanced self-attention mechanism to more effectively handle long-range dependencies, and secondly, the generation of positional information regarding gaps and fillings to better capture assembly relationships. This paper collected actual assembly data for folding rudder blades for unmanned aerial vehicles and established a Mechanical Assembly Relationship Dataset (MARD) for a comparative study. To further illustrate PAGEformer performance, we conducted extensive testing on a large-scale dataset and performed ablation experiments. The experimental results demonstrated a 15.3% improvement in PAGEformer accuracy compared to ARIMA on the MARD. On the ETH, Weather, and ECL open datasets, PAGEformer accuracy increased by 15.17%, 17.17%, and 9.5%, respectively, compared to the mainstream neural network models.
]]>Machines doi: 10.3390/machines12030160
Authors: Michele Asperti Michele Vignati Edoardo Sabbioni
Torque vectoring is a widely known technique to improve vehicle handling and to increase stability in limit conditions. With the advent of electric vehicles, this is becoming a key topic since it is possible to have distributed powertrains, i.e., multiple motors are adopted, in which each motor is controlled separately from the others. Moreover, electric motors deliver the torque required by the controller faster and more precisely than internal combustion engines, active differentials and conventional hydraulic brakes. The state of the art of Direct Yaw Moment Control (DYC) techniques, ranging from classical to modern control theories, are analyzed and discussed in this paper. The aim is to give an overview of the currently available approaches while identifying their drawbacks regarding performances and robustness when dealing with common issues like model uncertainties, external disturbances, friction limit and common state estimation problems. This contribution analyzes all the steps from the lateral dynamics reference generation to the desired control action computation and allocation to the available actuators. In addition, some of the presented control logic is evaluated in a simulation environment for a passenger car. Results of both open-loop and closed-loop maneuvers allow the comparison and clarification of each control strategy’s key advantages.
]]>Machines doi: 10.3390/machines12030159
Authors: Bassam Hasanain
The study and implementation of ergonomics are vital for the growth of industries and improvement in work cultures. Sustainable manufacturing cannot be achieved without the implementation of human-factor ergonomics. Ergonomics is used to analyze the link between research studies and industrial practices in order to maximize the efficiency of processes by keeping in view the well-being of workforce. Designing tools, tasks, machines, systems, jobs, and settings for efficient, safe, and successful human usage involves applying knowledge about human behavior, abilities, and limitations. Workers are the backbone of the manufacturing economy. The review outlines significant advancements in preventing ergonomic problems during the design stage of the manufacturing process to achieve sustainability. The bibliometric analysis is used to identify the literature base for ergonomics. To maximize the benefits of ergonomics and to integrate sustainable practices, various methods are required to organize existing processes and technologies. The human-centered design identifies problems and aligns the output with the intended objectives of sustainability. The goal of human factors and ergonomics is to successfully integrate people into systems and develop the manufacturing processes around the well-being of workers and sustainability principles. Similarly, ergoecology, eco-ergonomics, and green ergonomics are frequently used for sustainable manufacturing. Achieving sustainability in manufacturing is not possible without considering human ergonomics. Ergonomists frequently research management, planning, and other topics to increase the efficiency of the manufacturing process. Efficient worker performance and quality of life can be enhanced through work design, management, and organizational ergonomics. Human ergonomics relates sustainability with cognitive variables such as situational awareness, human reliability, and decision-making abilities. This review explains the role of human factors and ergonomics for sustainable manufacturing.
]]>Machines doi: 10.3390/machines12030158
Authors: Ashish Kumar Sahu Reemon Z. Haddad Dhafar Al-Ani Berker Bilgin
Interior permanent magnet synchronous motors (IPMSMs) are extensively used as traction motors today because of their exceptional torque, power density, and wide, constant power operating range. Under real-world usage, an IPMSM rotor undergoes varying electromagnetic, thermal, and mechanical loads. Under such conditions, fatigue life-based design criteria should be used over stress-based design criteria to ensure the structural integrity of the rotor. Moreover, the driving dynamics can change the rotor temperature continuously, which affects the electromagnetic, mechanical, and fatigue properties of the rotor material. This paper proposes a robust thermomechanical rotor fatigue simulation workflow considering significant loads acting on an IPMSM rotor and the temperature variation throughout a drive cycle. It discusses an accelerated fatigue life estimation approach based on the peak valley extraction method to reduce the simulation time significantly for the stress and fatigue analysis. Then, it presents a method for a stress-life curve generation for variable loading. It also presents a sensitivity study with a median S-N curve, and a 90% reliability and 95% confidence (R90C95) S-N curve.
]]>Machines doi: 10.3390/machines12030157
Authors: Nicolás Mendoza Mahdi Haghshenas-Jaryani
This paper presents the design, development, and testing of a robot that combines soft-body grasping and crawling locomotion to navigate tubular objects. Inspired by the natural snakes’ climbing locomotion of tubular objects, the soft robot includes proximal and distal modules with radial expansion/contraction for grasping around the objects and a longitudinal contractile–expandable driving module in-between for providing a bi-directional crawling movement along the length of the object. The robot’s grasping modules are made of fabrics, and the crawling module is made of an extensible pneumatic soft actuator (ePSA). Conceptual designs and CAD models of the robot parts, textile-based inflatable structures, and pneumatic driving mechanisms were developed. The mechanical parts were fabricated using advanced and conventional manufacturing techniques. An Arduino-based electro-pneumatic control board was developed for generating cyclic patterns of grasping and locomotion. Different reinforcing patterns and materials characterize the locomotor actuators’ dynamical responses to the varying input pressures. The robot was tested in a laboratory setting to navigate a cable, and the collected data were used to modify the designs and control software and hardware. The capability of the soft robot for navigating cables in vertical, horizontal, and curved path scenarios was successfully demonstrated. Compared to the initial design, the forward speed is improved three-fold.
]]>Machines doi: 10.3390/machines12030156
Authors: Nikolaos A. Fountas Ioannis Papantoniou Dimitrios E. Manolakos Nikolaos M. Vaxevanidis
Advances in machining technology and materials science impose the identification of optimal settings for process-related parameters to maintain high quality and process efficiency. Given the available resources, manufacturers should determine an advantageous process parameter range for their settings. In this work, the machinability of a special tool steel (UNIMAX® by Uddeholm, Sweden) under dry CNC turning is investigated. The working material is examined under two states; annealed and hardened. As major machinability indicators, main cutting force Fz (N) and mean surface roughness Ra (μm) were selected and studied under different values for the cutting conditions of cutting speed, feed rate, and depth of cut. A systematic experimental design was established as per the response surface methodology (RSM). The experimental design involved twenty base runs with eight cube points, four center points in the cube, six axial points, and two center points in the axial direction. Corresponding statistical analysis was based on analysis of variance and normal probability plots for residuals. Two regression models referring to main cutting force and surface roughness for both the annealed and hardened states of the material were developed and used as objective functions for subsequent evaluations by three modern meta-heuristics under the goal of machinability optimization, namely multi-objective grey wolf algorithm, multi-objective multi-verse algorithm and multi-objective ant lion algorithm. All algorithms were found capable of providing beneficial Pareto-optimal solutions for both main cutting force and surface roughness simultaneously whilst regression models achieved high correlation among input variables and optimization responses.
]]>Machines doi: 10.3390/machines12030155
Authors: Yongqi Xia Shibo Deng Mingtao Wu Binkun Ni
The coarse-grained electroplated diamond grinding wheels is increasingly favored in precision grinding of hard and brittle materials owing to its high material removal efficiency, high wear resistance and steady surface contour accuracy. However, how to determine whether the dressed grinding wheel surface topography can achieve the desired precision ground surface quality is still a huge challenge to this day. In this paper, a novel numerical simulation model, which was established basing on the statistical features of actual electroplated coarse-grained diamond grinding wheel and the kinetics of the grinding process, was proposed for theoretically and thoroughly studying the influence of the surface dressing depth of coarse-grained electroplated diamond grinding wheel on ground workpiece surface morphology. At first, the statistical features of actual electroplated coarse-grained diamond grinding wheel was acquired and a novel numerical grinding wheel surface model was established. Subsequently, a numerical ground workpiece surface simulation model was also developed. And then, the evolving mechanism of the grinding wheel surface topography with the dressed wheel surface abrasive grain protrusion height was theoretically studied by numerical simulation. Moreover, the influence of the wheel surface abrasive grain protrusion height on the ground surface roughness was thoroughly researched by means of theoretical model and experiments. The simulation and experiments results in this paper indicated that precision ground workpiece surface with nano-scale surface roughness can be acquired by grinding with a dressed grinding wheel with a certain abrasive grain protrusion height of 25% of the typical abrasive size. Comparing with the undressed grinding wheel (grinding wheel with original surface topography and not be dressed), the surface roughness Sa and Sq of the surface ground with a well-dressed wheel can achieving a significant decrease of 97.75–99.77% and 97.57–99.73%, respectively. Therefore, carefully dressing the electroplated coarse-grained diamond grinding wheel is of great significance for obtaining a precision ground workpiece surface quality.
]]>Machines doi: 10.3390/machines12030154
Authors: Zian Wu Wenxian Yang Xiaoping Song Kexiang Wei
Pitch bearings in wind turbines are crucial components that enable safe blade pitching, optimize electrical power output, and ensure turbine protection. Traditional vibration analysis-based methods used for high-speed bearings are not applicable to monitoring pitch bearings, due to its slow non-integer cycle rotation. To address this issue, a stress-based pitch bearing monitoring method is proposed in this paper. First, finite element analysis is conducted to establish the relationship between the maximum surface stress on the outer race of the pitch bearing and the presence of cracks. This relationship allows the identification of cracks on the outer race and an assessment of their severity based on the value of the maximum surface stress. Second, the outer race of the pitch bearing is divided into several segments, and a singularity detection technique is employed to locate the position of cracks on the outer race based on the stresses measured from the segments. To verify the proposed method, a wind turbine pitch bearing test rig was developed in a laboratory. Experimental results have shown that the proposed method can effectively and accurately identify and locate cracks on the outer race of the bearing, thereby demonstrating its great potential as a reliable approach for monitoring the condition of wind turbine pitch bearings.
]]>Machines doi: 10.3390/machines12030153
Authors: Alexander Bott Simon Anderlik Robin Ströbel Jürgen Fleischer Andreas Worthmann
This study addresses the challenge of the optimization of milling in industrial production, focusing on developing and applying a novel framework for optimising manufacturing processes. Recognising a gap in current methods, the research primarily targets the underutilisation of advanced data analysis and machine learning techniques in industrial settings. The proposed framework integrates these technologies to refine machining parameters more effectively than conventional approaches. The research method involved the development of the framework for the realisation and analysis of measurement data from milling machines, focusing on six machine parts and employing a machine learning system for optimization and evaluation. The developed and realised framework in the form of a software demonstrator showed its applicability in different experiments. This research enables easy deployment of data-driven techniques for sustainable industrial practices, highlighting the potential of this framework for transforming manufacturing processes.
]]>Machines doi: 10.3390/machines12030152
Authors: Hao Lin Haipeng Geng Ling Li Leiming Song Xiaojun Hu
High-speed direct-drive permanent magnet synchronous motors (PMSMs), supported by elastic foil gas bearings, have broad applications, such as in microcompressors. However, some problems remain to be solved for the electrical performance analysis of PMSMs. For example, there is presently no related analytical model that can be used in rotor dynamics expression for this type of PMSM. This study aimed to establish theoretical models for electromagnetic force density and torque. The process involved both theoretical and experimental research. The analytic models of air gap magnetic density, electromagnetic force density, and electromagnetic performance were established for a PMSM with a parallel magnetized cylindrical permanent magnet. The analytic calculation was conducted, and the results of the analytic model were obtained. The analytical model of the electromagnetic torque and force can be applied in theoretical research on rotor dynamics. The model provides a theoretical basis and method for studying the influence of the electromagnetic load on rotor dynamics. A finite element simulation analysis of the electrical performance of the PMSM was carried out. An electrical performance experiment was conducted. The deviation between the experimental result and the theoretical value was less than 4%. This result indicated that the analytic models could be used in a dynamics analysis of compressors that are directly driven by a PMSM for application in engineering and industrial contexts.
]]>Machines doi: 10.3390/machines12030151
Authors: Sadaf Zeeshan Tauseef Aized Fahid Riaz
Using modern machines like robots comes with its set of challenges when encountered with unstructured scenarios like occlusion, shadows, poor illumination, and other environmental factors. Hence, it is essential to consider these factors while designing harvesting robots. Fruit harvesting robots are modern automatic machines that have the ability to improve productivity and replace labor for repetitive and laborious harvesting tasks. Therefore, the aim of this paper is to design an improved orange-harvesting robot for a real-time unstructured environment of orchards, mainly focusing on improved efficiency in occlusion and varying illumination. The article distinguishes itself with not only an efficient structural design but also the use of an enhanced convolutional neural network, methodologically designed and fine-tuned on a dataset tailored for oranges integrated with position visual servoing control system. Enhanced motion planning uses an improved rapidly exploring random tree star algorithm that ensures the optimized path for every robot activity. Moreover, the proposed machine design is rigorously tested to validate the performance of the fruit harvesting robot. The unique aspect of this paper is the in-depth evaluation of robots to test five areas of performance that include not only the accurate detection of the fruit, time of fruit picking, and success rate of fruit picking, but also the damage rate of fruit picked as well as the consistency rate of the robot picking in varying illumination and occlusion. The results are then analyzed and compared with the performance of a previous design of fruit harvesting robot. The study ensures improved results in most aspects of the design for performance in an unstructured environment.
]]>Machines doi: 10.3390/machines12030150
Authors: Qiang Li Markus Heß
The third-body particle-involved sliding contact between two rough rubbers with wavy surfaces is experimentally studied. The experiment is designed to isolate the direct contact between the first bodies so that friction resistance is induced completely by the interactions between the third-body particle and the surfaces of the rubbers. In dry contact of a single particle, it is found that the particle exhibits pure rolling during the sliding of the first bodies, and the macroscopic friction resistance for overcoming sliding does not depend on the particle size, but it is significantly influenced by the initial position of the surface waviness relative to the particle’s position. The behavior of the particle under lubricated conditions exhibited significant differences. Due to the low local friction at the interface, the particle rapidly glided down to the valley of the waviness during compression. This abrupt motion of the particle resulted in it coming to rest in a stable position, awaiting a substantial force to push it forward. The friction resistance in the case with lubrication was found to be independent of the initial position of the waviness, and its value consistently remained at the maximum found in dry contact. Therefore, lubrication actually increases the macroscopic friction resistance. An approximate solution for the specific case of dry contact is proposed to understand the friction behavior.
]]>Machines doi: 10.3390/machines12030149
Authors: Yingbo Wang Fengyuan Zuo Shuai Zhang Zhen Zhao
This article proposes a progressive frequency domain-guided depth model with adaptive preprocessing to solve the problem of defect detection with weak features based on X-ray images. In distinct intuitive surface defect detection tasks, non-destructive testing of castings using X-rays presents more complex and weak defect features, leading to lower accuracy and insufficient robustness on the part of current casting defect detection methods. To address these challenges, the proposed method establishes four specialized mechanisms to improve model accuracy. First, an adaptive image contrast enhancement method is proposed to enhance the features of defects in casting images to promote subsequent feature extraction and prediction. Second, a subtle clue mining module based on frequency domain attention is proposed to fully extract the discriminative features of casting defects. Third, a feature refinement module based on progressive learning is proposed to achieve a balance between feature resolution and semantic information. Finally, a refined deep regression supervision mechanism is designed to improve defect detection accuracy under strict intersection-to-union ratio standards. We established extensive ablation studies using casting defect images in GDXray, conducted detailed comparative experiments with other methods, and performed experiments to analyze the robustness of the resulting models. Compared with other X-ray defect detection methods, our framework achieves an average +4.6 AP. Compared to the baseline, our proposed refined deep regression supervision mechanism results in an improvement of 5.3 AP.
]]>Machines doi: 10.3390/machines12030148
Authors: Jiman Kim Hyunsu Kim
With the recent conversion of internal combustion engines to electric vehicles, new noise issues have arisen, and among them, the noise generated by internal vehicle auxiliary systems is being considered. This study introduces an electronic filter designed with a motor model featuring vibration components, aiming to minimize the noise and vibrations generated by a Brushed DC (BDC) motor commonly employed in vehicle internal systems. It introduces a method to identify the connectors and internal parameters used in the motor for the matching of the model and experimental motor, and to measure and estimate these parameters. The model is separated and executed to ensure convergence, and it is validated by comparing the analysis results with the measured values. A filter is designed using the model to reduce current oscillations in the motor, confirming a subsequent reduction in noise and vibration. This research suggests the potential to attenuate noise and vibration in already produced motors by attaching only a filter without modifying the internal motor structure. Moreover, it is anticipated that a filter can be designed to predict and mitigate the noise and vibration components of the motor based on changes in load.
]]>Machines doi: 10.3390/machines12020147
Authors: Mattia Maltauro Roberto Meneghello Gianmaria Concheri
In tolerancing activities focusing on the allocation of geometrical tolerances, many critical issues originate from the non-optimal assignment of responsibilities among the organization units involved. This paper aims to depict relations between different tolerancing activities and relevant specifications, assigning them to the proper actor and, therefore, expanding the ISO 8015:2011 “responsibility principle”. A classification among tolerancing activities, specifications, and media is proposed; a horizontal hierarchical framework among functional, manufacturing, and verification specifications and a vertical hierarchical framework along the supply chain are discussed. Examples of both hierarchical structures are presented.
]]>Machines doi: 10.3390/machines12020146
Authors: Junwoo Kim Moustafa El-Gindy Zeinab El-Sayegh
In this research, an 8 × 8 scaled electric combat vehicle (SECV) is built. The scaled vehicle is evaluated in both experimental and simulated methods to analyze its performance. The scaled vehicle is developed to apply the Ackermann condition by implementing the individual steering and individual wheel speed control system at low speed. Individual eight-wheel rotational velocity control and individual eight-wheel steering angle control in real time are developed and installed on the remotely controlled scaled vehicle to meet a perfect Ackermann condition. Three different steering scenarios are developed and applied: a traditional steering scenario (first and second axle steering), fixed third axle steering scenario (first, second, and fourth axle steering), and all-wheel steering scenario. Stationary evaluation, turn radius evaluation, and double lane change evaluation are conducted to verify the application of the Ackermann condition. The differences between the experimental results and the simulated data are within an acceptable range. An important demonstration of this research is the novel validation of physical and simulated data in the application of the Ackermann condition for eight-wheel steering and velocity control for the three steering scenarios.
]]>Machines doi: 10.3390/machines12020145
Authors: Marco Ceccarelli Susana Sanz Vicente Díaz Matteo Russo
A new portable arm exercise device is presented as a laboratory prototype to assist arm movements in rehabilitation therapies and movement exercises. Unlike the devices currently used, a portable design is proposed, with easy assembly and operational characteristics that enable it to be used by users in the home and in a familiar environment. Sensors are also provided on the rotating crank to validate and monitor the efficiency of the arm exercise. A low-cost prototype is assembled using off-the-shelf components and 3D-printed parts. Design issues are discussed and elaborated on to build a prototype for future laboratory testing using fairly simple experimental methodology. Preliminary testing by one author shows good feasibility of the device. The findings from the experimental results can be summarized as effective smooth-monitored cyclic motion in the crank rotation with limited values for acceleration less than 1 g and for acting user forces less than 22 N. The values detected are significantly lower in the left hand, with the testing subject being right-handed and healthy, without injury to her upper limbs.
]]>Machines doi: 10.3390/machines12020144
Authors: Paula Bastida-Molina Yago Rivera César Berna-Escriche David Blanco Lucas Álvarez-Piñeiro
The recharging of electric vehicles will undoubtedly entail an increase in demand. Traditionally, efforts have been made to shift their recharging to off-peak hours of the consumption curve, where energy demand is lower, typically during nighttime hours. However, the introduction of photovoltaic solar energy presents a new scenario to consider when synchronizing generation and demand curves. High-generation surpluses are expected during the central day hours, due to the significant contribution of this generation; these surpluses could be utilized for electric vehicle recharging. Hence, these demand-side management analyses present important challenges for electricity systems and markets. This research explores this overdemand avenue and presents a method for determining the ideal recharge curve of the electric vehicle. Consequently, with this objective of maximizing photovoltaic generation to cover as much of the foreseeable demand for electric vehicles as possible in future scenarios of the electrification of the economy, the six fundamental electric vehicle charging profiles have been analyzed. A practical scenario for 2040 is projected for the Canary Islands, estimating the potential levels of demand-side management and associated coverage. The coverage ranges from less than 20% to over 40%, considering the absence of demand-side management measures and the maximum displacement achievable through such measures.
]]>Machines doi: 10.3390/machines12020143
Authors: Jihao Duan Zhuofan Wu Jianbo Ren Gaochen Zhang
Abrasive disc grinding is currently a key manufacturing process to achieve better accuracy and high-quality surfaces of TC17 components. Grinding force, which results from the friction and elastic–plastic deformation during the contact and interaction between the abrasive grains and the workpiece, is a critical parameter that represents the grinding accuracy and efficiency. In order to understand the influence factors of grinding force, the characteristics of the flexible abrasive disc grinding process were studied. Considering the contact state between the abrasive tool and the workpiece, the theoretical model of normal grinding force was established in detail, from macro- and micro-perspectives. By conducting single-factor and orthogonal grinding experiments of TC17 components, the influence of different process parameters on the normal grinding force was revealed. The normal grinding force prediction models of the abrasive disc grinding process were developed based on the Box–Behnken design (BBD) and particle swarm optimization–back propagation (PSO-BP) neural networks, respectively. The results showed that the normal grinding force was negatively correlated with the disc rotational speed, and positively correlated with the contact angle, grinding depth, and feed rate, and the interaction of the factor feed rate and grinding depth was the more influential factor. Both the BBD and PSO-BP force models had good reliability and accuracy, and the mean absolute error (MAE) and mean relative error (MRE) of the above two prediction models were 0.22 N and 0.16 N, and 13.3% and 10.9%, respectively.
]]>Machines doi: 10.3390/machines12020142
Authors: Aleksandra Müller Steffen Wurm Phil Willecke Oliver Petrovic Werner Herfs Christian Brecher
The Industry 4.0 research initiative strives to facilitate globally interconnected, flexible, and highly adaptable production systems. The use of skill-based control mechanisms such as OPC UA skills offers the prospect of a straightforward and flexible interchange, as well as the seamless integration of individual participants and processes through standardized interfaces. Furthermore, by enhancing these skills with evaluation parameters pertinent to the processes, such as CO2 equivalents or the duration of specific skill executions, a foundation is laid for creating a customizable and adaptable composition of processes based on specific production process needs. In this article, the OPC UA skill concept is expanded with process-relevant properties, and a structured procedure for the introduction of skill-based process control is presented. The developed concept was implemented and tested on an industrial use case of glass pane completion. The aim of this publication is to demonstrate the potential of skill-based process control that has an integrated assessment of skills.
]]>Machines doi: 10.3390/machines12020141
Authors: Marko Jamšek Gal Sajko Jurij Krpan Jan Babič
This paper focuses on the development of a novel climbing robot that is designed for autonomous maintenance of vertical gardens in urban environments. The robot, designed with a unique five-legged structure, is equipped with a range of electrical and mechanical components, enabling it to autonomously navigate and maintain a specially designed vertical garden wall facilitating interactive maintenance and growth monitoring. The motion planning and control of the robot were developed to ensure precise and adaptive movement across the vertical garden wall. Advanced algorithms were employed to manage the complex dynamics of the robot’s movements, optimizing its efficiency and effectiveness in navigating and maintaining the garden structure. The operation of the robot in maintaining the vertical garden was evaluated during a two-week trial where the robot successfully performed nearly 8000 leg movements, with only 0.6% requiring human intervention. This demonstrates a high level of autonomy and reliability. This study concludes that the pentapod robot demonstrates significant potential for automating the maintenance of vertical gardens, offering a promising tool for enhancing urban green spaces.
]]>Machines doi: 10.3390/machines12020140
Authors: Petrica Radu Carol Schnakovszky
Milling parts with low rigidity (thin-walled parts) are increasingly attracting the interest of the academic and industrial environment, due to the applicability of these components in industrial sectors of strategic interest at the international level in the aerospace industry, nuclear industry, defense industry, automotive industry, etc. Their low rigidity and constantly changing strength during machining lead on the one hand to instability of the cutting process and on the other hand to part deformation. Solving both types of problems (dynamic and static) must be preceded by prediction of cutting forces as accurately as possible, as they have a significant meaning for machining condition identification and process performance evaluation. Since there are plenty of papers dealing with this topic in the literature, the current research attempts to summarize the models used for prediction of force in milling of thin-walled parts and to identify which are the trends in addressing this issue from the perspective of intelligent production systems.
]]>Machines doi: 10.3390/machines12020139
Authors: Anna Mičietová Mária Čilliková Robert Čep Branislav Mičieta Juraj Uríček Miroslav Neslušan
This study is focused on analysing residual stresses (RSs) after turning high-tempered bearing steel through the use of the X-ray diffraction (XRD) technique. Phase transformations expressed in terms of the near-surface white layer (WL) and the corresponding microhardness profiles are correlated with the RSs as well as the depth of the RS profiles. Normal and shear components of RS and FWHM (full width at half maximum) of the diffraction peaks are analysed as a function of cutting insert flank wear as well as the cutting speed. It was found that the influence of tool wear prevails over cutting speed, RSs tend to shift into the compressive region with increasing tool flank wear, and the valuable shear components of RSs can be found in the near-surface region when the cutting inserts of lower flank wear are employed. The increasing flank wear also increases the depth in which the compressive RSs can be found. Furthermore, surface RSs are affected by the phase transformation process (formation of re-hardened WL) as well as the superimposing mechanical and thermal load.
]]>Machines doi: 10.3390/machines12020138
Authors: Mariusz Deja Angelos P. Markopoulos
Advances and Trends in Non-conventional, Abrasive and Precision Machining 2021 [...]
]]>Machines doi: 10.3390/machines12020137
Authors: Tarik Zarrouk Mohammed Nouari Jamal-Eddine Salhi Abdelkader Benbouaza
Nomex honeycomb composite (NHC) cores have seen significant growth in recent years, particularly in the aeronautics, aerospace, naval and automotive industries. This development presents significant challenges in terms of improving machining quality, requiring the use of specialized cutting tools and favorable cutting techniques. In this context, experimental studies have been carried out to highlight the characteristics of the milling of NHCs by rotary ultrasonic machining (RUM). However, the rapid motion of the cutting tool and the inaccessibility of the tool/part interface prevent the visualization of the chip formation process. For this purpose, a three-dimensional numerical model for milling the NHC structure using RUM technology was developed by Abaqus Explicit software. On the basis of this model, the components of the cutting force, the quality of the machined surface and the chip accumulation in front of the cutting tool were analyzed. The numerical results agree with the experimental tests, demonstrating that the use of RUM technology effectively reduces the cutting force components. An in-depth analysis of the influence of feed component Fy on the quality of the generated surface was carried out, revealing that the surface quality improved with low values of feed component Fy. Furthermore, the impact of ultrasonic vibrations on the accumulation of chips in front of the cutting tool is particularly optimized, in particular for large amplitudes.
]]>Machines doi: 10.3390/machines12020135
Authors: Luca Vecchiato Matteo Negri Giulio Picci Luca Viale Giulio Zaltron Stefano Giacometti Giovanni Meneghetti
The optimization of the brake systems is crucial for vehicle performance and safety of Formula SAE (FSAE) race cars. This study introduces a specialized brake test bench designed to enhance the understanding and testing of these systems. The bench integrates a rotating mechanical system mounting a brake disc-caliper group, which is driven by an electric motor, a pneumatic brake pedal assembly to simulate real braking conditions, and a comprehensive array of sensors that facilitate the measurement of critical parameters, such as rotation speed, braking torque, oil pressure, and disc temperature. Its structure, sensor integration, and control electronics are fully described, demonstrating the capability to replicate on-track scenarios in a controlled environment. The results underscore the utility of the bench in providing precise and consistent testing conditions essential for analyzing the efficiency, durability, and safety of the braking systems of FSAE race cars.
]]>Machines doi: 10.3390/machines12020136
Authors: Yuqian Yang Xin Chen Maolin Yang Wei Guo Pingyu Jiang
The Industrial Product Service System (IPS2) is considered a sustainable and efficient business model, which has been gradually popularized in manufacturing fields since it can reduce costs and satisfy customization. However, a comprehensive design method for IPS2 is absent, particularly in terms of requirement perception, resource allocation, and service activity arrangement of specific industrial fields. Meanwhile, the planning and scheduling of multiple parallel service activities throughout the delivery of IPS2 are also in urgent need of resolution. This paper proposes a method containing service order design, service resource configuration, and service flow modeling to establish an IPS2 for robot-driven sanding processing lines. In addition, we adopt the modified Deep Q-network (DQN) to realize a scheduling scheme aimed at minimizing the total tardiness of multiple parallel service flows. Finally, our industrial case study validates the effectiveness of our methods for IPS2 design, demonstrating that the modified deep reinforcement learning algorithm reliably generates robust scheduling schemes.
]]>Machines doi: 10.3390/machines12020134
Authors: Lei Song Chunguang Lu Chen Li Yongjin Xu Jiangming Zhang Lin Liu Wei Liu Xianbo Wang
With the rapid growth of the photovoltaic industry, fire incidents in photovoltaic systems are becoming increasingly concerning as they pose a serious threat to their normal operation. Research findings indicate that direct current (DC) fault arcs are the primary cause of these fires. DC arcs are characterized by high temperature, intense heat, and short duration, and they lack zero crossing or periodicity features. Detecting DC fault arcs in intricate photovoltaic systems is challenging. Hence, researching DC fault arcs in photovoltaic systems is of crucial significance. This paper discusses the application of mathematical morphology for detecting DC fault arcs. The system utilizes a multi-stage mathematical morphology filter, and experimental results have shown its effective extraction of fault arc features. Subsequently, we propose a method for detecting DC fault arcs in photovoltaic systems using a cyclic neural network, which is well-suited for time series processing tasks. By combining multiple features extracted from experiments, we trained the neural network and achieved high accuracy. This experiment demonstrates that our recurrent neural network (RNN) based scheme for DC fault arc recognition has significant reference value and implications for future research. The ROC curve on the test set approaches 1 from the initial state, and the accuracy on the test set remains at 98.24%, indicating the strong robustness of the proposed model.
]]>Machines doi: 10.3390/machines12020133
Authors: Wadah Talal Abdulrazzak Akroot
This study aims to develop, evaluate, and improve a polygeneration system that combines solar and Brayton cycle technologies and focuses on the sequential integration of heat. In this configuration, the exhaust gases from the Al-Qayyarah gas turbine power plant and the parabolic trough collector (PTC) array generate steam through a high recovery steam generation process. An absorption refrigeration system also supplies the Brayton circuit with low-temperature air. This process is evaluated from a 3E perspective, which includes exergy, energy, and exergoeconomic analyses for two different configurations. These configurations are integrated solar combined cycle (ISCC) with and without absorption systems (ISCC and ISCC-ARC). In addition, a comprehensive analysis was carried out to assess the impact of critical factors on the output generated, the unit cost of the products, and the exergy and energy efficiency for each configuration. The results revealed that the power produced by the ISCC-ARC and ISCC systems is 580.6 MW and 547.4 MW, respectively. Accordingly, the total energy and exergy efficiencies for the ISCC-ARC are 51.15% and 49.4%, respectively, while for the ISCC system, they are 50.89% and 49.14%, respectively. According to the results, the total specific costs for the ISCC-ARC system increased from 69.09 $/MWh in June to 79.05 $/MWh in December. ISCC’s total specific costs also fluctuate throughout the year, from 72.56 $/MWh in June to 78.73 $/MWh in December.
]]>Machines doi: 10.3390/machines12020132
Authors: Jaewook An Hamin Lee Chang-Wan Kim
In recent years, increased sales of fuel cell electric vehicles (FCEVs) have required composite overwrapped pressure vessel (COPV) designs to be lightweight and allow safe high-pressure hydrogen storage. In this study, we propose the weight minimization of Type 2 COPVs for FCEVs considering mechanical safety. Steel liner thickness, ply thickness, ply orientation, and the number of plies were set as design variables, and weight minimization was performed. For the constraints of optimization, the Tsai–Wu failure index of the composite layer and von Mises stress of the steel liner are considered. The design of experiments (DoE) was conducted to generate kriging model and perform sensitivity analysis. The optimized design of Type 2 COPVs was determined by satisfying all constraints, with significant weight reduction and preserved mechanical safety of the structure.
]]>Machines doi: 10.3390/machines12020131
Authors: Renato Brancati Domenico De Falco Giandomenico Di Massa Stefano Pagano Ernesto Rocca
Periodic monitoring of large industrial and civil structures is carried out through static and dynamic measurements. The monitoring, carried out over many years, offers important information for evaluating the health of structures and their management. Dynamic tests are carried out starting from measurements of the vibrations of the structure induced by mechanical devices or by the surrounding environment. If a ground support element is available, it is possible to exert a forcing action on the structure using actuators fixed to the support. When a ground support is unavailable, the structure can be forced using devices comprised of masses with rotary or reciprocating translational motion. These masses must be large enough to excite appreciable mechanical vibrations of the structure. In this paper, a vibration exciter, based on a mass suspended on an air spring and forced to vibrate at the resonant frequency, is proposed. Thanks to the resonant condition, the force transmitted to the structure is amplified compared to that applied to the mass. The excitation frequency can be adjusted by altering the inflation pressure of the air spring to modify the natural frequency of the system. In the paper, after the presentation of some mechanical devices used as vibration exciters for large structures, the proposed device is described and the first experimental results are reported.
]]>Machines doi: 10.3390/machines12020130
Authors: Austeja Dapkute Vytautas Siozinys Martynas Jonaitis Mantas Kaminickas Milvydas Siozinys
This study delves into the EA-SAS platform, a digital twin environment developed by our team, with a particular focus on the EA-SAS Cloud Scheduler, our bespoke program designed to optimize ETL (extract, transform, and load) scheduling and thereby enhance automation within industrial systems. We elucidate the architectural intricacies of the EA-SAS Cloud Scheduler, demonstrating its adeptness in efficiently managing computationally heavy tasks, a capability underpinned by our empirical benchmarks. The architecture of the scheduler incorporates Docker to create isolated task environments and leverages RabbitMQ for effective task distribution. Our analysis reveals the EA-SAS Cloud Scheduler’s prowess in maintaining minimal overhead times, even in scenarios characterized by high operational loads, underscoring its potential to markedly bolster operational efficiency in industrial settings. While acknowledging the limitations inherent in our current assessment, particularly in simulating real-world industrial complexities, the study also charts potential future research pathways. These include a thorough exploration of the EA-SAS Cloud Scheduler’s adaptability across diverse industrial scenarios and an examination of the integration challenges associated with its reliance on specific technological frameworks.
]]>Machines doi: 10.3390/machines12020129
Authors: Giovanni Gerardo Muscolo Paolo Fiorini
This paper presents a planar cable-driven model of a simple mechanism that is able to measure forces and displacements. Recently, a preliminary study based on a cable-driven sensitive mechanism was presented to the research community, underlining the innovative characteristics of the model in under-actuation and under-sensing. The core of the research work was to conceive a compliant system able to measure forces and displacements from a point located in a different zone with respect to the one where the force is applied, and this is possible thanks to cable-driven systems. In this paper, a new simplified model with respect to our published work is presented, reducing the number of cables and including the calculation of friction in the developed test bench. The formulation to calculate the displacement of the point of the applied force and the formulation to calculate the force are presented and validated with a simulation and by using a real test bench for experimentation. A multi-body system is used for the simulation, and the results are compared and discussed. Four cases are analysed to test the formulation, including the friction in pulleys and in the joint connection between the mobile part and the fixed part of the mechanism. Future works will be oriented toward reducing the dimensions of the conceived mechanism in order to implement the model in minimally invasive robotic surgery instruments.
]]>Machines doi: 10.3390/machines12020128
Authors: Erich Wehrle Veit Gufler
In this paper, the direct differentiation of generalized-α time integration is derived, equations are introduced and results are shown. Although generalized-α time integration has found usage, the derivation and the resulting equations for the analytical sensitivity analysis via direct differentiation are missing. Thus, here, the sensitivity equations of generalized-α time integration via direct differentiation are provided. Results with generalized-α are compared with Newmark-β time integration and their sensitivities with numerical sensitivities via forward finite differencing in terms of accuracy and performance. An example is shown for each linear structural dynamics and flexible multibody dynamics.
]]>Machines doi: 10.3390/machines12020127
Authors: Myung-Kyo Seo Won-Young Yun
Data-based equipment fault detection and diagnosis is an important research area in the smart factory era, which began with the Fourth Industrial Revolution. Steel manufacturing is a typical processing industry, and efficient equipment operation can improve product quality and cost. Steel production systems require precise control of the equipment, which is a complex process. A gearbox transmits power between shafts and is an essential piece of mechanical equipment. A gearbox malfunction can cause serious problems not only in production, quality, and delivery but in safety. Many researchers are developing methods for monitoring gearbox condition and for diagnosing failures in order to resolve problems. In most data-driven methods, the analysis data set is derived from a distribution of identical data with failure mode labels. Industrial sites, however, often collect data without information on the failure type or failure status due to varying operating conditions and periodic repair. Therefore, the data sets not only include frequent false alarms, but they cannot explain the causes of the alarms. In this paper, a framework called the Reduced Lagrange Method (R-LM) periodically assigns pseudolabels to vibration signals collected without labels and creates an input data set. In order to monitor the status of equipment and to diagnose failures, the input data set is fed into a supervised learning classifier. To verify the proposed method, we build a test rig using motors and gearboxes that are used on production sites in order to artificially simulate defects in the gears and to operate them to collect vibration data. Data features are extracted from the frequency domain and time domain, and pseudolabeling is applied. There were fewer false alarms when applying R-LM, and it was possible to explain which features were responsible for equipment status changes, which improved field applicability. It was possible to detect changes in equipment conditions before a catastrophic failure, thus providing meaningful alarm and warning information, as well as further promising research topics.
]]>Machines doi: 10.3390/machines12020126
Authors: Kai Zhang Yang Bai Zhimin Zhang
Any 3D AFM image is a convolution of the geometry of the AFM tip and the profile of the scanned sample, especially when the dimensions of the scanned sample are comparable to those of the AFM tip shape. The precise profile of the scanned sample can be extracted from the 3D AFM image if the geometry of the AFM tip is known. Therefore, in order to separate the geometry of the AFM probe tip from the 3D AFM image of a diffraction grating with a rectangular profile and to correct for the topographic convolutions induced by the AFM probe tip, a method is used to quantitatively evaluate the geometry of the AFM probe tip, including the tip radius and the included angle. A model for reconstructing the measured AFM image is proposed to correct topography convolutions caused by the AFM tip shape when scanning a diffraction grating with rectangular profiles. A series of experiments were performed to verify the effectiveness of the proposed AFM tip geometry evaluation method, and comparison experiments were conducted to demonstrate the feasibility and reliability of the proposed reconstruction model.
]]>Machines doi: 10.3390/machines12020125
Authors: Xu Li Gangjun Li Zhuming Bi
Computer-aided engineering (CAE) is an essential tool in a digital twin not only to verify and validate a virtual twin before it is transformed into a physical twin, but also to monitor the use of the physical twin for enhanced sustainability. This paper aims to develop a CAE model for a digital twin to predict the fatigue life of materials. Fatigue damage is represented by the size of a macro-crack that grows with a cluster of micro-cracks subjected to three different loads. The growth angle is related to the maximum circumferential tensile stress, and the growth rate is determined by the stress intensity factor (SIF) at the crack tip. The prediction model takes into consideration the main factors, including micro-cracks, crack closures, and initial configurations. Simulations are developed for the growth of macro-cracks with radially distributed micro-cracks and randomly distributed micro-cracks, and we find that (1) the macro-crack in the second case grows faster than that in the first case; (2) a pure shear load affects the macro-crack propagation more than a combined shear and tensile load or a tensional load; (3) the external stresses required to propagate are reduced when the inclination angle of the micro-crack is small and within (−25° < β < 25°); (4) micro-cracks affect the propagating path of the macro-crack and generally guide the direction of propagation. The developed model has been verified and validated experimentally for its effectiveness in predicting the fracture or fatigue damage of a structure.
]]>Machines doi: 10.3390/machines12020124
Authors: Manuel A. Montoya Martínez Rafael Torres-Córdoba Evgeni Magid Edgar A. Martínez-García
This study introduces a cybernetic control and architectural framework for a robotic fish avatar operated by a human. The behavior of the robot fish is influenced by the electromyographic (EMG) signals of the human operator, triggered by stimuli from the surrounding objects and scenery. A deep artificial neural network (ANN) with perceptrons classifies the EMG signals, discerning the type of muscular stimuli generated. The research unveils a fuzzy-based oscillation pattern generator (OPG) designed to emulate functions akin to a neural central pattern generator, producing coordinated fish undulations. The OPG generates swimming behavior as an oscillation function, decoupled into coordinated step signals, right and left, for a dual electromagnetic oscillator in the fish propulsion system. Furthermore, the research presents an underactuated biorobotic mechanism of the subcarangiform type comprising a two-solenoid electromagnetic oscillator, an antagonistic musculoskeletal elastic system of tendons, and a multi-link caudal spine composed of helical springs. The biomechanics dynamic model and control for swimming, as well as the ballasting system for submersion and buoyancy, are deduced. This study highlights the utilization of EMG measurements encompassing sampling time and μ-volt signals for both hands and all fingers. The subsequent feature extraction resulted in three types of statistical patterns, namely, Ω,γ,λ, serving as inputs for a multilayer feedforward neural network of perceptrons. The experimental findings quantified controlled movements, specifically caudal fin undulations during forward, right, and left turns, with a particular emphasis on the dynamics of caudal fin undulations of a robot prototype.
]]>Machines doi: 10.3390/machines12020123
Authors: Florian Oexle Fabian Heimberger Alexander Puchta Jürgen Fleischer
The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tools to support the remaining skilled workers and optimize processes in virtual space. Existing methods for modeling the dynamic behavior of machine tools rely on the use of expert knowledge and require a significant amount of manual effort. In this paper, a concept is proposed for individualized and lifetime-adaptive modeling of the dynamic behavior of machine tools with the focus on the machine’s tool center point. Therefore, existing and proven algorithms are combined and applied to this use case. Additionally, it eliminates the need for detailed information about the machine’s kinematic structure and utilizes automated data collection, which reduces the dependence on expert knowledge. In preliminary tests, the algorithm for the initial model setup shows a fit of 99.88% on simulation data. The introduced re-fit approach for online parameter actualization is promising, as in preliminary tests, an accuracy of 95.23% could be reached.
]]>Machines doi: 10.3390/machines12020122
Authors: Filipe Pereira Luís Magalhães Adriano A. Santos António Ferreira da Silva Katarzyna Antosz José Machado
Manual counting and packaging processes often involve repetitive, error-prone tasks. Specifically, packaging wooden handles, utilized in gardening tools and cutlery, typically relies on labor-intensive methods with dimensions varying in diameter, length and mass. These variations complicate packaging, requiring precise counting and diverse handling solutions. This article introduces an automated counting structure tailored for a wide array of wooden handles manufactured by a company in northern Portugal. Employing standardized mechanical design methodologies, we delineate crucial stages encompassing the design, development, implementation and testing of this specialized counting equipment. The machine has been partially integrated into the management system of the company, taking into account future global integration according to the Industry 4.0 concept.
]]>Machines doi: 10.3390/machines12020121
Authors: Ardeshir Shojaeinasab Masoud Jalayer Amirali Baniasadi Homayoun Najjaran
Condition monitoring (CM) is essential for maintaining operational reliability and safety in complex machinery, particularly in robotic systems. Despite the potential of deep learning (DL) in CM, its ‘black box’ nature restricts its broader adoption, especially in mission-critical applications. Addressing this challenge, our research introduces a robust, four-phase framework explicitly designed for DL-based CM in robotic systems. (1) Feature extraction utilizes advanced Fourier and wavelet transformations to enhance both the model’s accuracy and explainability. (2) Fault diagnosis employs a specialized Convolutional Long Short-Term Memory (CLSTM) model, trained on the features to classify signals effectively. (3) Model refinement uses SHAP (SHapley Additive exPlanation) values for pruning nonessential features, thereby simplifying the model and reducing data dimensionality. (4) CM interpretation develops a system offering insightful explanations of the model’s decision-making process for operators. This framework is rigorously evaluated against five existing fault diagnosis architectures, utilizing two distinct datasets: one involving torque measurements from a robotic arm for safety assessment and another capturing vibration signals from an electric motor with multiple fault types. The results affirm our framework’s superior optimization, reduced training and inference times, and effectiveness in transparently visualizing fault patterns.
]]>Machines doi: 10.3390/machines12020120
Authors: Kamel Mehdi Peter Pavol Monka Katarina Monkova Zied Sahraoui Nawel Glaa Jakub Kascak
During machining, the surface of the machined materials is damaged and tool wear occurs, sometimes even to complete failure. Machining of thin-walled parts is generally cumbersome due to their low structural rigidity. The study deals with the effect of the feed rate and the thickness of the thin-walled part on the dynamic behavior and stability of the turning process during the roughing and finishing of thin-walled tubular workpieces made of steel alloy 42CrMo4. At the same time, the cutting forces and deformations of the workpiece were also evaluated via numerical and experimental approaches. The numerical study is based on a three-dimensional (3D) finite element model (FEM) developed using the ABAQUS/Explicit frame. In the model, the workpiece material is governed by the behavior law of Johnson–Cook. Numerical and experimental results show that the cutting forces and the quality of the machined surface depend not only on the choice of cutting parameters but also on the dynamic behavior of thin-walled parts due to their low rigidity and low structural damping during the machining operation. Cutting forces are proportional to the feed rate and inversely proportional to the thickness of the part. Their variations around the average values are low for roughing tests where the wall-part thickness is higher or equal to 3.5 mm. However, these variations intensify for finishing tests where the wall thickness is less or equal to 1.5 mm. Indeed, the recorded FFT spectra for a finishing operation show several harmonics that occurred at around 550 Hz, and the amplitude of the peaks, which describes the level of power contained in the signals, shows an increase similar to that of the amplitudes of the temporal signal. The flexibility of the part generates instability in the cutting process, but the frequencies of the vibrations are higher than the frequency of rotation of the part.
]]>Machines doi: 10.3390/machines12020119
Authors: Chen Yang Wei Cai Ying Xie Baicheng Shao
In this paper, the cooling performance of oil-cooling PMSM with hairpin winding under various oil parameters is analyzed via a simulation and an experiment. The effects of oil jet positions, oil temperatures, and oil flow rates on the cooling performance are analyzed. It is found that increasing the oil temperature in the range of 20 °C to 60 °C, increasing the flow rate of oil jets whose position angle is from 15° to 45°, and increasing the flow rate in the range of 1 L/min to 2 L/min will significantly improve the cooling performance. The apertures of the oil spray ring are optimized using the Taguchi algorithm. The cooling performance is the best when the flow ratio is m(0°):m(15°):m(30°):m(45°):m(60°):m(75°) = 4%:19%:10%:10%:4%:4%. This study provides a guide for the design of the oil-cooling system for the hairpin winding of the PMSM.
]]>Machines doi: 10.3390/machines12020118
Authors: Anas Charroud Karim El Moutaouakil Vasile Palade Ali Yahyaouy Uche Onyekpe Eyo U. Eyo
The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicle localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains.
]]>Machines doi: 10.3390/machines12020117
Authors: Mouna Oukrid Nicolas Bernard Mohamed-Fouad Benkhoris Djamel Ziane
This paper deals with the design of five-phase permanent magnet synchronous machines (PMSMs) exploiting the third harmonic for torque generation. Through the optimization of the stator size and rotor structure, the objective functions related to mass and electric losses are minimized for a targeted electromagnetic power (10 kW and 400 rpm) and a given volume. The study takes into account saturation, thermal, electrical and mechanical constraints. On that note, a 1D analytical magnetic model, considering the existence and use of the third harmonic, is presented. The design optimization then shows how the use of harmonic 3 can improve the machine’s performance. It will be shown that, for a given electromagnetic torque, taking the third harmonic into account in the sizing process leads to a mass reduction that can reach 20% and electrical losses that can go up to 21%. A finite element analysis model of the five-phase PMSM is then established in order to verify the results of the optimization and validate them.
]]>Machines doi: 10.3390/machines12020116
Authors: Yuejian Chen Xuemei Liu Wenkun Fan Ningyuan Duan Kai Zhou
The timely detection of faults that occur in industrial machines and components can avoid possible catastrophic machine failure, prevent large financial losses, and ensure the safety of machine operators. A solution to tackle the fault detection problem is to start with modeling the condition monitoring signals and then examine any deviation of real-time monitored data from the baseline model. The newly developed deep long short-term memory (LSTM) neural network has a high nonlinear flexibility and can simultaneously store long- and short-term memories. Thus, deep LSTM is a good option for representing underlying data-generating processes. This paper presents a deep-LSTM-based fault detection method. A goodness-of-fit criterion is innovatively used to quantify the deviation between the baseline model and the newly monitored vibrations as opposed to the mean squared value of the LSTM residual used in many reported works. A railway suspension fault detection case is studied. Benchmark studies have shown that the deep-LSTM-based fault detection method performs better than the vanilla-LSTM-based and linear-autoregression-model-based methods. Using the goodness-of-fit criterion, railway suspension faults can be better detected than when using the mean squared value of the LSTM residual.
]]>Machines doi: 10.3390/machines12020115
Authors: Juan Jose Aciego Ignacio Gonzalez-Prieto Mario Javier Duran Angel Gonzalez-Prieto Juan Carrillo-Rios
A diverse group of so-called multi-vector techniques has recently appeared to enhance the control performance of multiphase drives when a direct control strategy is implemented. With different numbers of switching states and approaches for estimating the application times, each multi-vector solution has its own nature and merits. Previous studies have individually tested each version of the proposed finite-control-set model predictive control (FCS-MPC) strategies using a single experimental setup with specific parameters and, in some cases, using a limited range of operating conditions and focusing exclusively on some control aspects. Although such works provide partial contributions, the control performance is highly affected by the test and rig conditions, being dependent on the machine parameters, the switching frequency and the range of operation. Consequently, it becomes difficult to extract some universal conclusions that guide the control designer on the best alternative for each application. Aiming to enrich the knowledge in this field and provide a broader picture, this work performs a global analysis with different multi-vector techniques, various machine parameters, multiple operating points and a complete set of indices. Experimental results confirm that the selection of the most adequate control strategy is not a trivial task because the degree to which multi-vector techniques are affected by the test conditions is variable and complex. Some tables with a qualitative analysis, based on the extensive empirical tests, contribute with a more complete insight and guide eventual control designers on the decision about the optimal regulation approach to be chosen.
]]>Machines doi: 10.3390/machines12020114
Authors: Yiqiang An Jiazhe Mao Chengwei Tong Xiaoyun Zhou Jian Ruan Sheng Li
The electro-hydrostatic actuator (EHA) is a new type of high-performance servo actuator that originated in the field of aerospace, and it is gradually becoming a common basic component for various types of large equipment. A miniature EHA, mainly composed of a micro two-dimensional (2D) piston pump and a brushless DC motor, is designed in this article by simplifying the system structure. This paper analyzes the structure and working principle of this EHA and establishes the mathematical models of the brushless DC motor, micro two-dimensional pump, and hydraulic cylinder. Field-oriented control (FOC) is used to drive the brushless DC motor, and the models of the controller are established in Simulink. Furthermore, the models of the mechanical and hydraulic systems of the miniature EHA are established in AMESim. In addition to this, a prototype of this miniature EHA was fabricated in this paper and an experimental platform was built for experiments. In the joint simulation environment, the rise time of the EHA system at 6000 r/min is 0.158 s and the frequency response amplitude attenuation to −3 dB has a bandwidth of 20 Hz. On the other hand, the constructed miniature EHA prototype was dynamically characterized to obtain a rise time of 0.242 s at 6000 r/min and a bandwidth of 13 Hz. In this paper, the feasibility of the design scheme of the miniature EHA system is verified, and its excellent dynamic properties are verified with simulation and experiment.
]]>Machines doi: 10.3390/machines12020112
Authors: Weiguo Hai Yingming He Qilong Xue
The swing of the riser in deep-water drilling can significantly impact the drill string. In this study, we establish a riser model that considers the combined disturbance of periodic dynamic wind and wave loads. By coupling it with the drill string model, we develop a dynamic model for deep-water drilling systems. Through analyzing multiple sets of different drilling parameters, we examine displacements and impact forces at various positions along the drill string system. Specifically, our focus lies on velocity, acceleration, and rotational speed information of BHA. We investigate how WOB and rotational speed influence motion trajectory and vibration characteristics of the drill string within the dynamic model of deep-water drilling systems. Simulation results reveal slight differences in whirling trajectories between inside the riser and below mud line for the drill string. Rotational speed has a greater impact on the drill string compared to WOB; higher rotational speeds lead to increased collision forces between the drill string system and both riser and wellbore. Our findings identify specific combinations of WOB and rotational speed parameters that can stabilize drilling operations within dynamic models for deep-water drilling systems. These research results provide valuable insights for adjusting WOB and rotational speed parameters in deep-water drilling.
]]>Machines doi: 10.3390/machines12020113
Authors: Michele Gabrio Antonelli Pierluigi Beomonte Zobel Costanzo Manes Enrico Mattei Nicola Stampone
In collaborative robotics, to improve human–robot interaction (HRI), it is necessary to avoid accidental impacts. In this direction, several works reported how to modify the trajectories of collaborative robots (cobots), monitoring the operator’s position in the cobot workspace by industrial safety devices, cameras, or wearable tracking devices. The detection of the emotional state of the operator could further prevent possible dangerous situations. This work aimed to increase the predictability of anomalous behavior on the part of human operators by the implementation of emotional intelligence (EI) that allows a cobot to detect the operator’s Level of Attention (LoA), implicitly associated with the emotional state, and to decide the safest trajectory to complete a task. Consequently, the operator is induced to pay due attention, the safety rate of the HRI is improved, and the cobot downtime is reduced. The approach was based on a vision transformer (ViT) architecture trained and validated by the Level of Attention Dataset (LoAD), the ad hoc dataset created and developed on facial expressions and hand gestures. ViT was integrated into a digital twin of the Omron TM5-700 cobot, suitably developed within this project, and the effectiveness of the EI was tested on a pick-and-place task. Then, the proposed approach was experimentally validated with the physical cobot. The results of the simulation and experimentation showed that the goal of the work was achieved and the decision-making process can be successfully integrated into existing robot control strategies.
]]>Machines doi: 10.3390/machines12020111
Authors: Bo Li Yan Wang Yipeng Liu Jianguo Tao Hui Ren Hui Yang
A space in-orbit service simulation experiment platform is a type of equipment platform that allows spacecraft such as satellites and deep-space explorers to be adequately ground tested before launch. The function of the crane system is to drive the target spacecraft to perform a large-scale movement. This study focuses on the dynamics of a space in-orbit service simulation experiment platform with suspension rope and column quadrilateral truss structure as connecting devices. A space in-orbit service simulation experiment platform with a column quadrilateral truss structure as a connecting device is studied, modeled as a crane system–column quadrilateral truss structure–target spacecraft system. For the column quadrilateral truss structure, the equivalent beam model is used to make it equivalent based on the Timoshenko beam theory. The required equivalent stiffness parameters are determined and adjusted. The relative error between the finite element model and the corrected equivalent beam model of the column quadrilateral truss structure is no more than 4.7%. The results indicate that the accuracy of the modified equivalent beam model is sufficient. The improved equivalent beam model has excellent precision according to numerical calculations, and the derived equivalent stiffness parameters may be employed directly in dynamic modeling.
]]>Machines doi: 10.3390/machines12020110
Authors: Piotr Sender Irene Buj-Corral Jesús Álvarez-Flórez
In this work, the analysis of the acoustic emission (AE) signal in grinding processes is addressed. The proposed analysis method decomposes the acoustic signal into three frequency ranges. The total energy of each range is determined, as well as the highest frequency. Different grinding experiments were carried out, according to a full factorial design of experiments (DOE), in which feed speed, depth of cut, and transversal step (table cross feed) were varied. Arithmetic average roughness Ra and the material removal rate (MRR) were determined. It was observed that Ra depends mainly on the transversal step, followed by feed speed and the interaction between the transversal step and depth of cut, while MRR is greatly influenced by the transversal step. According to multi-objective optimization with the Derringer–Suich function, in order to simultaneously minimize Ra and maximize MRR, a transversal step of 9 mm per longitudinal pass, feed speed of 20 m/min, and depth of cut of 0.020 mm should be selected.
]]>Machines doi: 10.3390/machines12020109
Authors: Chan Roh Hyeon-min Jeon Seong-wan Kim Jong-su Kim Na-young Lee Sung-woo Song
Global interest in environmentally friendly ships has surged as a result of greenhouse gas reduction policies and the demand for carbon neutrality. Despite growing demand for electric propulsion systems, there is a lack of research and development on crucial components. Efficiency and stability are primarily influenced by the performance of inverters, which are essential for driving propulsion motors. Existing inverter control techniques can be of two types: continuous-PWM (pulse width modulation) methods for harmonic performance enhancement and discontinuous-PWM methods for efficiency improvement by reducing losses. However, there are limitations in that each PWM method exhibits substantial variations in inverter performance based on its operating conditions. To address these challenges, this study proposes the hybrid pulse-width-modulation (HPWM) method for optimal inverter operation. By analyzing the inverter’s operating conditions, the proposed HPWM method adopts various pulse-width-modulation (PWM) strategies based on a modulation index to achieve harmonic improvement and loss reduction. Our focus is on comparing and analyzing diverse PWM techniques under varying modulation indices and frequency conditions to attain the optimal operating conditions. Experimental validation of the proposed method was conducted using a 2.2 kW dynamometer. In comparison with existing PWM methods, the proposed method demonstrated superior performance.
]]>Machines doi: 10.3390/machines12020108
Authors: Christyan Cruz Ulloa David Orbea Jaime del Cerro Antonio Barrientos
The technological advancements in sensory systems and robotics over the past decade have facilitated the innovation of centralized systems for optimizing resource utilization and monitoring efficiency in inspection applications. This paper presents a novel system designed for gas concentration sensing in environments by implementing a modular artificial nose (emulating the inhalation and exhalation process) equipped with a strategically designed air capture centralization system based on computational fluid dynamics analysis (CFD). The system incorporates three gas identification sensors distributed within the artificial nose, and their information is processed in real-time through embedded systems. The artificial nose is hardware–software integrated with a quadruped robot capable of traversing the environment to collect samples, maximizing coverage area through its mobility and locomotion capabilities. This integration provides a comprehensive perspective on gas distribution in a specific area, enabling the efficient detection of substances in the surrounding environment. The robotic platform employs a graphical interface for real-time gas concentration data map visualization. System integration is achieved using the Robot Operating System (ROS), leveraging its modularity and flexibility advantages. This innovative robotic approach offers a promising solution for enhanced environmental inspection and monitoring applications.
]]>Machines doi: 10.3390/machines12020107
Authors: Hongyu Shu Yijie Yu Ran Shu Wenjie Wang Changjiang Pan
This paper presents the design and development of a new type of planetary traction drive bearing-type reducer. In this design, the transmission outer ring is replaced with an elastic ring. The design constructs a circular arc at the axial end of the rolling body’s contour line. This ensures that the contact point of this arc with the reducer’s outer ring and the inner ring’s axial end face is maintained on the radial traction contact line. As a result, it can replace the thrust bearing and provide an axial support function. It has the advantages of simple structure, easy processing, smooth transmission, and low noise. This paper first introduces the design and development process of this bearing-type reducer and presents systematic research on its transmission principle and dynamics. Subsequently, in response to the edge effect phenomenon of the outer ring contact line, the contour line of the outer ring is refined by adopting the shaping method used for bearing rollers, establishing a full circular arc profile shaping method, which significantly improves its edge effect. Finally, in our investigations, combined with experimental tests, a prototype of the bearing-type reducer was fabricated, and the speed ratio, torque, and transmission efficiency of the reducer were studied. The results demonstrate that the bearing-type reducer can achieve high transmission accuracy and efficiency. The transmission performance varies significantly under different lubrication conditions, with the peak efficiency reaching as high as 99.97% when using Santotrac 50 traction oil. The results verify the feasibility of the proposed design method and have the potential to be applied in wheel hub motors and robot joints.
]]>Machines doi: 10.3390/machines12020106
Authors: Alaa Saadah Donald Medlin Jad Saud Levente Menyhárt Xiaoran Zheng Géza Husi
This paper offers a comprehensive examination of the development and implementation of advanced safety protocols in the Patient Positioning System (PPS) for radiosurgery. In an era where precision and safety are increasingly crucial in medical procedures, particularly radiosurgery, the implementation of sophisticated safety measures in PPS is vital. This research delves into the detailed design of the system, emphasizing the sensor and controller mechanisms employed. A significant focus is placed on comparing single-loop and dual-loop control systems, assessing their impact on the precision, accuracy, and repeatability of the PPS. The study showcases how dual-loop control demonstrates superior performance in these areas, leading to enhanced patient safety and treatment outcomes. Additionally, the paper discusses the integration of these safety protocols within the system’s architecture, underscoring the practical implications of these advanced measures in augmenting patient safety and treatment effectiveness.
]]>Machines doi: 10.3390/machines12020105
Authors: Insu Bae Suan Lee
This paper addresses the critical issue of fault detection and prediction in electric motor machinery, a prevalent challenge in industrial applications. Faults in these machines, stemming from mechanical or electrical issues, often lead to performance degradation or malfunctions, manifesting as abnormal signals in vibrations or currents. Our research focuses on enhancing the accuracy of fault classification in electric motor facilities, employing innovative image transformation methods—recurrence plots (RPs), the Gramian angular summation field (GASF), and the Gramian angular difference field (GADF)—in conjunction with a multi-input convolutional neural network (CNN) model. We conducted comprehensive experiments using datasets encompassing four types of machinery components: bearings, belts, shafts, and rotors. The results reveal that our multi-input CNN model exhibits exceptional performance in fault classification across all machinery types, significantly outperforming traditional single-input models. This study not only demonstrates the efficacy of advanced image transformation techniques in fault detection but also underscores the potential of multi-input CNN models in industrial fault diagnosis, paving the way for more reliable and efficient monitoring of electric motor machinery.
]]>Machines doi: 10.3390/machines12020104
Authors: Dmitry Malyshev Victoria Perevuznik Marco Ceccarelli
This paper presents the structure and model of a hybrid modular structure of a robotic system for lower limb rehabilitation. It is made of two modules identical in structure, including an active 3-PRRR manipulator for moving the patient’s foot and a passive orthosis based on the RRR mechanism for supporting the lower limb. A mathematical model has been developed to describe the positions for the links of the active and passive mechanisms of two modules, as a function of the angles in the joints of the passive orthosis, considering constraints for attaching the active manipulators to the moving platform and their configurations. A method has been formulated for a parametric synthesis of the hybrid robotic system proposed with modular structure, taking into account the generated levels of parametric constraints depending on the ergonomic and manufacturability features. The proposed design is based on a criterion in the form of a convolution, including two components, one of which is based on minimizing unattainable points of the trajectory, considering the characteristics of anthropometric data, and the other is based on the compactness of the design. The results of the mathematical modeling are discussed as well as the analysis results towards a prototype validation.
]]>Machines doi: 10.3390/machines12020103
Authors: Gaetano Lettera Ciro Natale
Aeronautical robotic applications use quite large, heavy robots with huge end effectors that are frequently multifunctional. An assembly jig to hold a fuselage panel and two medium-sized six-axis robots fixed on linear axes, referred to as the internal and the external robot with respect to the curvature of the panel, make up the Lean robotized AssemBly and cOntrol of composite aeRostructures (LABOR) work cell. A distributed software architecture is proposed in which individual modules are developed to execute specific subprocesses, each implementing innovative algorithms that solve the main drawbacks of state-of-the-art solutions. Real-time referencing adopts a point-cloud-based strategy to reconstruct and process the part before drilling, avoiding hole positioning errors. Accurate concentric countersink diameters are made possible through the automatic adjustment of the drilling tool with respect to the skin panel, which guarantees its orthogonality, as well as the implementation of process parameter optimization algorithms based on historical results that compensate for the wear of the drilling bits. Automatic sealing and fastening strategies that involve the measurement of the main fastener quality parameters allow for the complete verification of the entire assembly process of each part. Additionally, an advanced multimodal perception system continuously monitors the collaborative workspace to ensure safe human–robot collaboration (HRC) tasks. Through this integrated architecture, LABOR substantially reduces expenses and facilitates maintenance and programming.
]]>Machines doi: 10.3390/machines12020102
Authors: Hoang-Long Dang Sangshin Kwak Seungdeog Choi
DC series arc faults pose a significant threat to the reliability of DC systems, particularly in DC generation units where aging components and high voltage levels contribute to their occurrence. Recognizing the severity of this issue, this study aimed to enhance DC arc fault detection by proposing an advanced recognition procedure. The methodology involves a sophisticated combination of current filtering using the Three-Sigma Rule in the time domain and the removal of switching noise in the frequency domain. To further enhance the diagnostic capabilities, the proposed method utilizes time and frequency signals generated from power supply-side signals as a reference input. The time–frequency features extracted from the filtered signals are then combined with artificial learning models. This fusion of advanced signal processing and machine learning techniques aims to capitalize on the strengths of both domains, providing a more comprehensive and effective means of detecting arc faults. The results of this detection process validate the effectiveness and consistency of the proposed DC arc failure identification schematic. This research contributes to the advancement of fault detection methodologies in DC systems, particularly by addressing the challenges associated with distinguishing arc-related distortions, ultimately enhancing the safety and dependability of DC electrical systems.
]]>Machines doi: 10.3390/machines12020101
Authors: Kun Wang Yukun Huang Baoqiang Zhang Huageng Luo Xiang Yu Dawei Chen Zhiqiang Zhang
Synchronous analysis is one of the most effective and practical techniques in rotating machinery diagnostics, especially in cases with variable speed operations. A modern analog-to-digital convertor (ADC) usually digitizes an analog signal to an equal time interval data series. Synchronous resampling converts the data series from an equal time interval data series to an equal shaft rotation angle interval data series. This conversion is usually achieved in the digital domain with the aid of shaft speed information, through either direct measurement or identification from a measured vibration signal, which is a time-consuming process. In order to improve the computational efficiency as well as the data processing accuracy, in this paper, a fast synchronous time-point calculation method based on an inverse function interpolation procedure is proposed. By identifying the inverse function of the instantaneous phase with respect to time, the calculation process of synchronous time points is optimized, which results in improved calculation efficiency and accuracy. These advantages are demonstrated by numerical simulations as well as experimental verifications. The numerical simulation results show that the proposed method can improve calculation speed by about five times. The synchronous analysis based on the proposed method was applied to a bearing fault detection in a high-speed rail carriage, which demonstrated the advantages of the proposed algorithm in improving the signal-to-noise ratio (SNR) for bearing damage feature extraction.
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