Machines

20 pages, 10930 KiB

Open AccessEditor’s ChoiceArticle

Development of the E-Portal for the Design of Freeform Varifocal Lenses Using Shiny/R Programming Combined with Additive Manufacturing

by Negin Dianat, Shangkuan Liu, Kai Cheng and Kevin Lu

Machines 2025, 13(4), 298; https://doi.org/10.3390/machines13040298 - 3 Apr 2025

Viewed by 320

Abstract

This paper presents an interactive online e-portal development and application using Shiny/R version 4.4.0 programming for personalised varifocal lens surface design and manufacturing in an agile and responsive manner. Varifocal lenses are specialised lenses that provide clear vision at both far and near [...] Read more.

This paper presents an interactive online e-portal development and application using Shiny/R version 4.4.0 programming for personalised varifocal lens surface design and manufacturing in an agile and responsive manner. Varifocal lenses are specialised lenses that provide clear vision at both far and near distances. The user interface (UI) of the e-portal application creates an environment for customers to input their eye prescription data and geometric parameters to visualise the result of the designed freeform varifocal lens surface, which includes interactive 2D contour plots and 3D-rendered diagrams for both left and right eyes simultaneously. The e-portal provides a unified interactive platform where users can simultaneously access both the specialised Copilot demo web for lenses and the main Shiny/R version 4.4.0 programming app, ensuring seamless integration and an efficient process flow. Additionally, the data points of the 3D-designed surface are automatically saved. In order to check the performance of the designed varifocal lens before production, it is remodelled in the COMSOL Multiphysics 6.2 modelling and analysis environment. Ray tracing is built in the environment for the lens design assessment and is then integrated with the lens additive manufacturing (AM) using a Formlabs 3D printer (Digital Fabrication Center (DFC), London, UK). The results are then analysed to further validate the e-portal-driven personalised design and manufacturing approach. Full article

(This article belongs to the Section Advanced Manufacturing)

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25 pages, 16138 KiB

Open AccessEditor’s ChoiceArticle

Tool Condition Monitoring in the Milling of Low- to High-Yield-Strength Materials

by Sohan Nagaraj and Nancy Diaz-Elsayed

Machines 2025, 13(4), 276; https://doi.org/10.3390/machines13040276 - 27 Mar 2025

Viewed by 455

Abstract

The preservation and continuous monitoring of cutting tools in a computer numerical control (CNC) machine is essential for ensuring seamless transitions in the manufacturing workflow, as well as maintaining adequate part quality. The implementation of tool condition monitoring (TCM) when milling can provide [...] Read more.

The preservation and continuous monitoring of cutting tools in a computer numerical control (CNC) machine is essential for ensuring seamless transitions in the manufacturing workflow, as well as maintaining adequate part quality. The implementation of tool condition monitoring (TCM) when milling can provide the user with necessary data regarding tool life, wear, and part quality. However, it is important to broaden the application of the TCM process across a much broader class of workpiece materials to understand the effects of material properties on the condition of the tool. The aim of this paper is to investigate the efficacy of tool condition monitoring techniques while milling low- and high-yield-strength materials across varied process parameters. A Fast Fourier Transform (FFT) analysis was conducted in this research. Vibration data were acquired from both uniaxial and triaxial accelerometers to investigate irregularities in vibrational amplitudes between new and worn milling tools. The experimental results show that there is a significant increase in vibrational amplitudes for the worn tool when compared to the new tool across various frequencies, which affirms the expected increase in vibrations and cutting forces at the tool–workpiece interface from using a worn tool. The F-values and p-values calculated using an F-test with a 95% confidence interval indicated statistically significant differences in vibration data between new and worn tools across various materials, including polyurethane foam, aluminum 6061, mild steel, and stainless steel, under different cutting conditions (low, medium, and high). These results further validate the findings obtained from the FFT analysis and highlight the effectiveness of vibration-based monitoring in distinguishing tool wear under varying material characteristics and machining conditions. Full article

(This article belongs to the Special Issue Innovations in the Design, Simulation, and Manufacturing of Production Systems)

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20 pages, 6341 KiB

Open AccessEditor’s ChoiceArticle

Development and Application of a Dual-Robot Fabrication System in Figuring of a 2.4 m × 4.58 m CFRP Antenna Reflector Surface

by Qiang Xin, Haitao Liu, Jieli Wu, Liming Lu, Xufeng Hao, Zhige Zeng and Yongjian Wan

Machines 2025, 13(4), 268; https://doi.org/10.3390/machines13040268 - 25 Mar 2025

Viewed by 364

Abstract

The demand for large-scale components continues to grow with the development of frontier technologies. Traditionally, these components are machined using machine tools, which are costly and have functional limitations. High-flexibility robots provide a cost-effective solution for machining large-scale components. This research proposes a [...] Read more.

The demand for large-scale components continues to grow with the development of frontier technologies. Traditionally, these components are machined using machine tools, which are costly and have functional limitations. High-flexibility robots provide a cost-effective solution for machining large-scale components. This research proposes a dual-robot fabrication system for producing a 2.4 m × 4.58 m carbon fiber reinforced polymer (CFRP) antenna reflector. First, the kinematic model of the in-house developed robot was established to compute its theoretical workspace, which was subsequently used to partition the machining regions. Based on laser tracker measurements and theoretical calculations, a method and procedure for calibrating the Tool Center Point and Tool Control Frame of the robot were proposed. Subsequently, the dual-robot fabrication system was configured based on the determined machining regions for each robot. To further improve the figuring accuracy of the system, the support structure and figuring path were investigated and determined. Finally, processing experiments were conducted, and the material removal function for the flexible processing tool was computed to shape the reflector surface. The final results achieved the required surface figure accuracies for areas ≤ φ1750 mm, ≤φ2400 mm, and the whole surface were improved to 13.5 μm RMS, 23.4 μm RMS, and 45.8 μm RMS, respectively. This validates the processing capability and demonstrates the potential application of the dual-robot fabrication system in producing large-scale components with high accuracy. Full article

(This article belongs to the Section Advanced Manufacturing)

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18 pages, 4666 KiB

Open AccessEditor’s ChoiceArticle

A Novel Lateral Control System for Autonomous Vehicles: A Look-Down Strategy

by Farzad Nadiri and Ahmad B. Rad

Machines 2025, 13(3), 211; https://doi.org/10.3390/machines13030211 - 6 Mar 2025

Viewed by 884

Abstract

This paper introduces a robust yet straightforward lane detection and lateral control approach via the deployment of a dual camera based on the look-down strategy for autonomous vehicles. Unlike traditional single-camera systems that rely on the look-ahead methodology and a single front-facing preview, [...] Read more.

This paper introduces a robust yet straightforward lane detection and lateral control approach via the deployment of a dual camera based on the look-down strategy for autonomous vehicles. Unlike traditional single-camera systems that rely on the look-ahead methodology and a single front-facing preview, the proposed algorithm leverages two downward-facing cameras mounted beneath the vehicle’s driver and the passenger side mirror, respectively. This configuration captures the road surface, enabling precise detection of the lateral boundaries, particularly during lane changes and in narrow lanes. A Proportional-Integral-Derivative (PID) controller is designed to maintain the vehicle’s position in the center of the road. We compare this system’s accuracy, lateral steadiness, and computational efficiency against (1) a conventional bird’s-eye view lane detection method and (2) a popular deep learning-based lane detection framework. Experiments in the CARLA simulator under varying road geometries, lighting conditions, and lane marking qualities confirm that the proposed look-down system achieves superior real-time performance, comparable lane detection accuracy, and reduced computational overhead relative to both traditional bird’s-eye and advanced neural approaches. These findings underscore the practical benefits of a straightforward, explainable, and resource-efficient solution for robust autonomous vehicle lane-keeping. Full article

(This article belongs to the Special Issue Trajectory Planning for Autonomous Vehicles: State of the Art)

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23 pages, 21742 KiB

Open AccessEditor’s ChoiceArticle

Modular Design and Layout Planning of Tooling Structures for Aircraft Assembly

by Zhanghu Shi, Chengyu Li, Junshan Hu, Xingtao Su, Hancheng Wang and Wei Tian

Machines 2025, 13(3), 185; https://doi.org/10.3390/machines13030185 - 25 Feb 2025

Viewed by 732

Abstract

Aircraft structures consist of numerous complex components that require a high level of precision to assemble. Tooling plays a crucial role in the assembly of aircraft components, providing the functions of positioning, shape maintenance, and support to guarantee the accuracy of the product. [...] Read more.

Aircraft structures consist of numerous complex components that require a high level of precision to assemble. Tooling plays a crucial role in the assembly of aircraft components, providing the functions of positioning, shape maintenance, and support to guarantee the accuracy of the product. Aiming to obtain reusable assembly tooling that can be rapidly reconfigured, this study focuses on the modular design and layout of tooling structures. The concept of functional elements for the characterization of tooling parts is proposed, and the relationship between each pair of elements is established to clarify the similarities and dependencies among various tooling structures. Based on the analysis of functional elements and their relationships, the tooling structures are divided and recombined into several modules. The detailed module designs are demonstrated by using typical structures such as platforms, columns, and locators as examples. A parametric representation of the multi-source information of tooling modules is proposed, and optimization methods for the layout and configuration of locators and platforms are developed using their parametric information. A reconfigurable tooling process integrated with a monitoring system is designed, realized, and successfully applied to the assembly of a practical type of fuselage. The results from verifying these methods’ efficiencies show that the modular design and reconfiguration planning of tooling only takes about 10 min and a few seconds, respectively, which is far less than the time consumed during traditional tooling design (from several days to weeks). The work in this study provides an engineering paradigm for the serialization and reconfiguration of assembly tooling in aviation manufacturing. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Assembly Technologies for Aerospace Production Systems)

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21 pages, 12471 KiB

Open AccessEditor’s ChoiceArticle

Layout Optimization of Multi-Robot Manufacturing Processing Systems: Applications in Directed Energy Deposition–Arc Additive Manufacturing and Jig-Less Welding

by Michail Aggelos Terzakis, Christos Papaioannou, Iñaki Sainz, Jonatan Rodriguez Vazquez, Panagiotis Lagios, Enrique Gil Illescas and Panagiotis Stavropoulos

Machines 2025, 13(3), 172; https://doi.org/10.3390/machines13030172 - 21 Feb 2025

Viewed by 825

Abstract

Layout design is the process in which industrial robots and other manufacturing components are positioned within a manufacturing system so that the intended operations can be handled appropriately. The traditional layout design process presents several challenges. It involves numerous iterations of testing different [...] Read more.

Layout design is the process in which industrial robots and other manufacturing components are positioned within a manufacturing system so that the intended operations can be handled appropriately. The traditional layout design process presents several challenges. It involves numerous iterations of testing different manually generated manufacturing layouts, requiring extensive trial and error to achieve an optimal solution. This process is highly time-consuming and demands significant expertise and cognitive effort from the designer. Within this publication, a flexible, scalable, and efficient function-block-based solution is presented for the optimization of manufacturing system layouts, especially in the field of multi-robot cells in two different use cases: one in additive manufacturing and one in jig-less welding. The findings showcase that the methodology followed enabled the efficient allocation of industrial robots in a workspace, minimizing the cognitive effort required in comparison to the traditional manual trial-and-error layout design procedure. Full article

(This article belongs to the Special Issue Innovations in the Design, Simulation, and Manufacturing of Production Systems)

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14 pages, 6790 KiB

Open AccessEditor’s ChoiceArticle

Lightweight Design of a Connecting Rod Using Lattice-Structure Parameter Optimisation: A Test Case for L-PBF

by Michele Amicarelli, Michele Trovato and Paolo Cicconi

Machines 2025, 13(3), 171; https://doi.org/10.3390/machines13030171 - 21 Feb 2025

Cited by 1 | Viewed by 627

Abstract

Lightweight design is a common way of reducing mass while enhancing the performance of mechanical components. The paper proposes a method to analyse the substitution of bulk volume with optimised lattice structures. The approach considers an early DoE analysis to explore the design [...] Read more.

Lightweight design is a common way of reducing mass while enhancing the performance of mechanical components. The paper proposes a method to analyse the substitution of bulk volume with optimised lattice structures. The approach considers an early DoE analysis to explore the design space, Finite Element Analysis to evaluate the feasibility of possible design solutions, and Artificial Intelligence tools to look for optimal design solutions, including Genetic Algorithms and Response Surface Methods. To validate the methodological approach, this work proposes the design optimisation of a lightweight diesel engine connecting rod, redesigned using Triply Periodic Minimal Surface (TPMS) lattice structures where they are functionally convenient. The TPMS cells analysed are gyroid, diamond, and SplitP. Laser-Powder Bed Fusion (L-PBF) is the Additive Manufacturing process considered during the redesign phase. The resulting connecting rod achieves a mass of roughly 614 g, obtaining a lightweight of more than 50% of the initial weight, using gyroid lattice structures and titanium alloy powders such as Ti6Al4V. Full article

(This article belongs to the Special Issue Novel Manufacturing Processes and Their Innovation for Industries)

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20 pages, 718 KiB

Open AccessEditor’s ChoiceArticle

Closed-Loop Transient Longitudinal Trajectory Tracking for Connected Vehicles

by Lingyun Hua and Guoming Zhu

Machines 2025, 13(2), 163; https://doi.org/10.3390/machines13020163 - 19 Feb 2025

Cited by 1 | Viewed by 349

Abstract

Vehicle longitudinal trajectory tracking plays a significant role in developing ecorouting and autonomous driving systems to handle various disturbances and uncertainties (e.g., road grade, gust wind, etc.) that are often ignored by the optimization strategies used to generate reference controls and trajectories. In [...] Read more.

Vehicle longitudinal trajectory tracking plays a significant role in developing ecorouting and autonomous driving systems to handle various disturbances and uncertainties (e.g., road grade, gust wind, etc.) that are often ignored by the optimization strategies used to generate reference controls and trajectories. In this paper, based on a linearized vehicle model with the help of feedback linearization, a linear quadratic integral tracking (LQIT) control is utilized to generate regulation laws to minimize the tracking error of optimal speed or brake distance trajectories, respectively, and maintain brake safety. A unified Kalman filter is used to estimate system states based on noisy measurements. Both acceleration and deceleration LQIT controls are designed to handle the change of upperlevel optimal control strategies to varying traffic. Simulation and co-simulation studies validated the proposed LQIT control strategies in Simulink with the SUMO traffic model using a real-world map under manipulated driving conditions. The simulation results show that under changing traffic conditions, the LQIT acceleration control is able to reduce the static tracking error by 99.8%, compared with the vehicle controlled only by the high-level optimal acceleration control without a trajectory tracker, achieving less tracking error and overshoot than using a PI control. The LQIT deceleration control reduces the brake distance error by 48% over the optimal deceleration control alone and ensures a safer brake distance than a coupled PI control. The traffic model used in the SUMO co-simulation confirms the capability of handling varying traffic for the developed LQIT control strategies. Full article

(This article belongs to the Section Vehicle Engineering)

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45 pages, 3966 KiB

Open AccessEditor’s ChoiceReview

A Comprehensive Study of Cooling Rate Effects on Diffusion, Microstructural Evolution, and Characterization of Aluminum Alloys

by Atiqur Rahman, Sriram Praneeth Isanaka and Frank Liou

Machines 2025, 13(2), 160; https://doi.org/10.3390/machines13020160 - 18 Feb 2025

Viewed by 1817

Abstract

Cooling Rate (CR) definitively influences the microstructure of metallic parts manufactured through various processes. Factors including cooling medium, surface area, thermal conductivity, and temperature control can influence both predicted and unforeseen impacts that then influence the results of mechanical properties. This comprehensive study [...] Read more.

Cooling Rate (CR) definitively influences the microstructure of metallic parts manufactured through various processes. Factors including cooling medium, surface area, thermal conductivity, and temperature control can influence both predicted and unforeseen impacts that then influence the results of mechanical properties. This comprehensive study explores the impact of CRs in diffusion, microstructural development, and the characterization of aluminum alloys and the influence of various manufacturing processes and post-process treatments, and it studies analytical models that can predict their effects. It examines a broad range of CRs encountered in diverse manufacturing methods, such as laser powder bed fusion (LPBF), directed energy deposition (DED), casting, forging, welding, and hot isostatic pressing (HIP). For example, varying CRs might result in different types of solidification and microstructural evolution in aluminum alloys, which thereby influence their mechanical properties during end use. The study further examines the effects of post-process heat treatments, including quenching, annealing, and precipitation hardening, on the microstructure and mechanical properties of aluminum alloys. It discusses numerical and analytical models, which are used to predict and optimize CRs for achieving targeted material characteristics of specific aluminum alloys. Although understanding CR and its effects is crucial, there is a lack of literature on how CR affects alloy properties. This comprehensive review aims to bridge the knowledge gap through a thorough literature review of the impact of CR on microstructure and mechanical properties. Full article

(This article belongs to the Special Issue Innovations in the Design, Simulation, and Manufacturing of Production Systems)

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33 pages, 16520 KiB

Open AccessEditor’s ChoiceArticle

Enhanced Non-Destructive Testing of Small Wind Turbine Blades Using Infrared Thermography

by Majid Memari, Mohammad Shekaramiz, Mohammad A. S. Masoum and Abdennour C. Seibi

Machines 2025, 13(2), 108; https://doi.org/10.3390/machines13020108 - 29 Jan 2025

Viewed by 1080

Abstract

This study presents a foundational step in a broader initiative aimed at leveraging thermal imaging technology to enhance wind turbine maintenance, particularly focusing on the challenges of detecting defects and object localization in small wind turbine blades. Serving as a preliminary experiment, this [...] Read more.

This study presents a foundational step in a broader initiative aimed at leveraging thermal imaging technology to enhance wind turbine maintenance, particularly focusing on the challenges of detecting defects and object localization in small wind turbine blades. Serving as a preliminary experiment, this research project tested methodologies and technologies on a smaller scale before advancing to more complex applications involving large, operational wind turbines using drone-mounted cameras. Utilizing thermal cameras suitable for both handheld and drone use, alongside advanced image processing applications, we navigated the significant challenge of acquiring high-quality thermal images to detect small defects. This required a concentrated analysis of a select subset of data and a methodological shift towards object detection and localization using the You Only Look Once (YOLO) model versions 8 and 9. This effort not only paves the way for applying these techniques to larger-scale turbines but also contributes to the ongoing development of an integrated maintenance strategy in the wind energy sector. Highlighting the critical impact of environmental conditions on thermal imaging, our research underscores the importance of continued exploration in this field, especially in enhancing object localization techniques for the future drone-based maintenance of operational wind turbine blades (WTBs). Full article

(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)

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29 pages, 4785 KiB

Open AccessEditor’s ChoiceReview

A Review of the Applications and Challenges of Dielectric Elastomer Actuators in Soft Robotics

by Qinghai Zhang, Wei Yu, Jianghua Zhao, Chuizhou Meng and Shijie Guo

Machines 2025, 13(2), 101; https://doi.org/10.3390/machines13020101 - 27 Jan 2025

Viewed by 2387

Abstract

As an electrically driven artificial muscle, dielectric elastomer actuators (DEAs) are notable for their large deformation, fast response speed, and high energy density, showing significant potential in soft robots. The paper discusses the working principles of DEAs, focusing on their reversible deformation under [...] Read more.

As an electrically driven artificial muscle, dielectric elastomer actuators (DEAs) are notable for their large deformation, fast response speed, and high energy density, showing significant potential in soft robots. The paper discusses the working principles of DEAs, focusing on their reversible deformation under electric fields and performance optimization through material and structural innovations. Key applications include soft grippers, locomotion robots (e.g., multilegged, crawling, swimming, and jumping/flying), humanoid robots, and wearable devices. The challenges associated with DEAs are also examined, including the actuation properties of DE material, material fatigue, viscoelastic effects, and environmental adaptability. Finally, modeling and control strategies to enhance DEA performance are introduced, with a perspective on future technological advancements in the field. Full article

(This article belongs to the Special Issue Dielectric Elastomer Actuators: Theory, Modeling and Application)

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21 pages, 39765 KiB

Open AccessEditor’s ChoiceArticle

Numerical Simulation and Experimental Study of Piston Rebound Energy Storage Characteristics for Nitrogen-Hydraulic Combined Impact Hammer

by Hu Chen, Boqiang Shi and Hui Guo

Machines 2025, 13(2), 97; https://doi.org/10.3390/machines13020097 - 26 Jan 2025

Viewed by 660

Abstract

The objective of this study is to analyze the piston rebound energy storage characteristics of the nitrogen-hydraulic combined impact hammer and to investigate the manner in which the piston rebound energy is converted and utilized. The kinetic equation of the impact hammer system [...] Read more.

The objective of this study is to analyze the piston rebound energy storage characteristics of the nitrogen-hydraulic combined impact hammer and to investigate the manner in which the piston rebound energy is converted and utilized. The kinetic equation of the impact hammer system is established. A numerical calculation model is constructed based on AMEsim, which incorporates the piston, cylinders, reversing valve, accumulator, power source, drill rod, and impacted device. The performance experiment system is designed, the oil pressure experiment and the piston motion experiment are constructed, and the accuracy of the numerical calculation model is verified by comparing the numerical calculation results with the experimental results. This paper investigates the fundamental principles of the piston rebound energy storage and analyzes the relationship between the opening percentage of the reversing valve high-pressure port and the piston rebound energy storage at the outset of the rebound stage. Furthermore, the influence of the length of the piston middle section and the number of high-pressure grooves in the signal chamber on the piston rebound energy storage is investigated. Finally, the experimental comparison allows for an analysis of the influence of the piston rebound energy storage on the performance of the nitrogen-hydraulic combined impact hammer. Full article

(This article belongs to the Section Machine Design and Theory)

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18 pages, 3333 KiB

Open AccessEditor’s ChoiceReview

A Short Review: Tribology in Machining to Understand Conventional and Latest Modeling Methods with Machine Learning

by Seisuke Kano

Machines 2025, 13(2), 81; https://doi.org/10.3390/machines13020081 - 23 Jan 2025

Viewed by 1212

Abstract

Tribology plays a critical role in machining technologies. Friction is an essential factor in processes such as composite material machining and bonding. This short review highlights the recent advancements in controlling and leveraging tribological phenomena in machining. For instance, high-precision machining is increasingly [...] Read more.

Tribology plays a critical role in machining technologies. Friction is an essential factor in processes such as composite material machining and bonding. This short review highlights the recent advancements in controlling and leveraging tribological phenomena in machining. For instance, high-precision machining is increasingly relying on the in situ observation and real-time measurement of tools, test specimens, and machining equipment for effective process control. Modern engineering materials often incorporate functional materials in metastable states, such as composites of dissimilar materials, rather than conventional stable-phase materials. In these cases, tribological effects during machining can impede precision. On the other hand, the friction in additive manufacturing demonstrates a constructive application of tribology. Traditionally, understanding and mitigating these tribological phenomena have involved developing physical and chemical models for individual factors and using simulations to inform decisions. However, accurately predicting system behavior has remained challenging due to the complex interactions between machine components and the variations between initial and operational (or deteriorated) states. Recent innovations have introduced data-driven approaches that predict system behavior without the need for detailed models. By integrating advanced monitoring technologies and machine learning, these methods enable real-time predictions within controllable parameters using live data. This shift opens new possibilities for achieving more precise and adaptive machining control. Full article

(This article belongs to the Special Issue Tribology in Manufacturing: Bottlenecks and Advances)

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20 pages, 11567 KiB

Open AccessEditor’s ChoiceArticle

Experimental Acoustic Noise and Sound Quality Characterization of a Switched Reluctance Motor Drive with Hysteresis and PWM Current Control

by Moien Masoumi and Berker Bilgin

Machines 2025, 13(2), 82; https://doi.org/10.3390/machines13020082 - 23 Jan 2025

Cited by 1 | Viewed by 740

Abstract

This paper presents an experimental characterization of acoustic noise and sound quality in a 12/8 Switched Reluctance Motor (SRM) using hysteresis and Pulse Width Modulation (PWM) current control techniques. To overcome the limitations of traditional sound power measurements and enhance the accuracy of [...] Read more.

This paper presents an experimental characterization of acoustic noise and sound quality in a 12/8 Switched Reluctance Motor (SRM) using hysteresis and Pulse Width Modulation (PWM) current control techniques. To overcome the limitations of traditional sound power measurements and enhance the accuracy of acoustic noise evaluation, a setup is applied for calculating sound power based on sound intensity measurements. The study provides a detailed description of the intensity probe-holding fixture, the hardware configuration for acoustic noise experiments, and the software setup tailored to specific measurement requirements. The acoustic noise characteristics of the motor are assessed at various operating points using two distinct current control methods: hysteresis current control with a variable switching frequency of up to 20 kHz and PWM current control with a fixed switching frequency of 12.5 kHz. Measurements of sound pressure and sound intensity enable the calculation of sound power and sound quality metrics under different operating conditions. Furthermore, the study investigates the influence of various factors on the motor’s sound power levels and sound quality. The findings provide valuable insights into the contributions of these factors to acoustic noise characteristics and offer a foundation for improving the motor’s acoustic behavior during the design and control stages. Full article

(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)

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27 pages, 18817 KiB

Open AccessEditor’s ChoiceArticle

Research on Bolt Loosening Mechanism Under Sine-on-Random Coupling Vibration Excitation

by Jiangong Du, Yuanying Qiu and Jing Li

Machines 2025, 13(2), 80; https://doi.org/10.3390/machines13020080 - 23 Jan 2025

Viewed by 825

Abstract

This paper primarily investigates the mechanism of bolt loosening under the Sine-on-Random (SOR) vibration excitation. Firstly, a theoretical model of bolt loosening response under the SOR synthesized excitation is established by a time–frequency conversion method, which converts the sine excitation into Power Spectrum [...] Read more.

This paper primarily investigates the mechanism of bolt loosening under the Sine-on-Random (SOR) vibration excitation. Firstly, a theoretical model of bolt loosening response under the SOR synthesized excitation is established by a time–frequency conversion method, which converts the sine excitation into Power Spectrum Density (PSD) expression in the frequency domain and superimposes it with random vibration excitation to obtain the SOR synthesized excitation spectrum. Then, by means of a four-bolt fastened structure, the bolt loosening mechanisms under both the sine and random vibration excitation are deeply studied, respectively. Ultimately, based on the time–frequency conversion method of SOR synthesized excitation, the bolt loosening responses of the structure under SOR excitation with different tightening torques are analyzed. Furthermore, a three-stage criterion including the Steady Stage, Transition Stage, and Loosen Stage for bolt loosening under SOR excitation is revealed, and the relationship among the SOR synthesized vibration responses and the two forms of single vibration responses is explored based on a corrective energy superposition method by introducing the weight factors of the two single vibration responses under different tightening torques. Finally, test verifications for the four-bolt fastened structure are conducted and good consistencies with the results of the Finite Element Analysis (FEA) are shown. This study provides valuable insights into the detection and prevention of loosening in bolted connection structures under multi-source vibration environments and has important engineering reference significance. Full article

(This article belongs to the Special Issue Design and Experimental Activity of Testing Machines and Mechanical Test Rigs)

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27 pages, 14486 KiB

Open AccessEditor’s ChoiceArticle

Hollow Direct Air-Cooled Rotor Windings: Conjugate Heat Transfer Analysis

by Avo Reinap, Samuel Estenlund and Conny Högmark

Machines 2025, 13(2), 89; https://doi.org/10.3390/machines13020089 - 23 Jan 2025

Viewed by 684

Abstract

This article focuses on the analysis of a direct air-cooled rotor winding of a wound field synchronous machine, the innovation of which lies in the increase in the internal cooling surface, the cooling of the winding compared to the conventional inter-pole cooling, and [...] Read more.

This article focuses on the analysis of a direct air-cooled rotor winding of a wound field synchronous machine, the innovation of which lies in the increase in the internal cooling surface, the cooling of the winding compared to the conventional inter-pole cooling, and the development of a CHT evaluation model accordingly. Conjugate heat transfer (CHT) analysis is used to explore the cooling efficacy of a parallel-cooled hollow-conductor winding of a salient-pole rotor and to identify a cooling performance map. The use of high current densities of 15–20 Arms/mm² in directly cooled windings requires high cooling intensity, which in the case of air cooling results not only in flow velocities above 15 m/s to ensure permissible operating temperatures, but also the need for coolant distribution and heat transfer studies. The experiments and calculations are based on a non-rotating machine and a wind tunnel using the same rotor coil(s). CHT-based thermal calculations provide not only reliable results compared to experimental work and lumped parameter thermal circuits with adjusted aggregate parameters, but also insight related to pressure and cooling flow distribution, thermal loads, and cooling integration issues that are necessary for the development of high power density and reliable electrical machines. The results of the air-cooling integration show that the desired high current density is achievable at the expense of high cooling intensity, where the air velocity ranges from 15 to 30 m/s and 30 to 55 m/s, distinguishing the air velocity of the hollow conductor and bypass channel, compared to the same coil in an electric machine and a wind tunnel at the similar thermal load and limit. Since the hot spot location depends on cooling integration and cooling intensity, modeling and estimating the cooling flow is essential in the development of wound-field synchronous machines. Full article

(This article belongs to the Section Electrical Machines and Drives)

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14 pages, 3703 KiB

Open AccessEditor’s ChoiceArticle

Artificial Neural Network-Based Structural Analysis of 3D-Printed Polyethylene Terephthalate Glycol Tensile Specimens

by Athanasios Manavis, Anastasios Tzotzis, Lazaros Firtikiadis and Panagiotis Kyratsis

Machines 2025, 13(2), 86; https://doi.org/10.3390/machines13020086 - 23 Jan 2025

Viewed by 795

Abstract

Materials are a mainstay of both industry and everyday life. The manufacturing and processing of materials is a very important sector as it affects both the mechanical properties and the usage of the final products. In recent years, the increased use of 3D [...] Read more.

Materials are a mainstay of both industry and everyday life. The manufacturing and processing of materials is a very important sector as it affects both the mechanical properties and the usage of the final products. In recent years, the increased use of 3D printing and, by extension, its materials have caused the creation of gaps in terms of strength that require further scientific study. In this study, the influence of various printing parameters on 3D-printed specimens made of polyethylene terephthalate glycol (PETG) polymer was tested. More specifically, three printing parameters were selected—infill, speed, and type—with three different values each (50%, 70%, and 90%), (5 mm/s, 20 mm/s, and 35 mm/s) and (Grid, Rectilinear, and Wiggle). From the combinations of the three parameters and the three values, 27 different specimens were obtained and thus, 27 equivalent experiments were designed. The measurements were evaluated, and the process was modeled with the Artificial Neural Network (ANN) method, revealing a strong and robust prediction model for the tensile test, with the relative error being below 10%. Both infill density and infill pattern were identified as the most influential parameters, with the Wiggle type being the strongest pattern of all. Additionally, it was found that the infill density acts increasingly on the strength, whereas the printing speed acts decreasingly. Full article

(This article belongs to the Section Advanced Manufacturing)

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25 pages, 24599 KiB

Open AccessEditor’s ChoiceArticle

MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions

by Wenhan Huang, Xiangfeng Zhang, Hong Jiang, Zhenfa Shao and Yu Bai

Machines 2025, 13(1), 71; https://doi.org/10.3390/machines13010071 - 20 Jan 2025

Viewed by 803

Abstract

In complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxiliary classifier generation adversarial network based on [...] Read more.

In complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxiliary classifier generation adversarial network based on a multi-scale convolutional block attention mechanism. Firstly, the multi-scale convolutional block attention mechanism is designed to extract multi-scale information and perform weighted fusion to enhance the ability of the model to capture effective features. Secondly, the minimum variance term is designed to minimize the variance of sample distribution, so that the generated samples are distributed more evenly in the feature space, avoiding the problem of pattern collapse. Finally, the objective function is reconstructed by independent classification loss to improve the ability of model data generation. Experimental results on CWRU and gearbox datasets validate the effectiveness and reliability of the proposed method. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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31 pages, 21587 KiB

Open AccessEditor’s ChoiceArticle

Bearing Fault Feature Extraction Method Based on Adaptive Time-Varying Filtering Empirical Mode Decomposition and Singular Value Decomposition Denoising

by Xuezhuang E, Wenbo Wang and Hao Yuan

Machines 2025, 13(1), 50; https://doi.org/10.3390/machines13010050 - 13 Jan 2025

Viewed by 682

Abstract

Aiming to address the difficulty in extracting the early weak fault features of bearings under complex operating conditions, a fault diagnosis method is proposed based on the adaptive fusion of time-varying filtering empirical mode decomposition (TVF-EMD) modal components and singular value decomposition (SVD) [...] Read more.

Aiming to address the difficulty in extracting the early weak fault features of bearings under complex operating conditions, a fault diagnosis method is proposed based on the adaptive fusion of time-varying filtering empirical mode decomposition (TVF-EMD) modal components and singular value decomposition (SVD) noise reduction. First, the snake optimization (SO) technique is used to optimize the TVF-EMD algorithm in order to determine the optimal parameters that match the input signal. Then, the bearing signal is divided into a number of intrinsic mode functions (IMFs) using TVF-EMD in order to reduce the nonlinearity and non-stationary characteristics of the fault signal. An index for the envelope fault information energy ratio (EFIER) is created to overcome the drawback of there being too many IMF components after TVF-EMD decomposition. The IMF components are ranked in descending order according to the EFIER, and they are fused according to the maximum principle of the energy ratio of envelope fault information until the optimal fusion component is determined. Finally, the fault feature is extracted when the optimal fusion component is denoised using SVD. Two measured bearing fault signals and simulation signals are used to validate the performance of the proposed method. The experimental findings demonstrate that the approach has good sensitive feature screening, fusion, and noise reduction capabilities. The proposed method can more precisely extract the early fault features of bearings and accurately identify fault types. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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19 pages, 9423 KiB

Open AccessEditor’s ChoiceArticle

A Common DC Bus Circulating Current Suppression Method for Motor Emulators of New Energy Vehicles

by Haonan Sun, Dafang Wang, Qi Li and Yingkang Qin

Machines 2025, 13(1), 51; https://doi.org/10.3390/machines13010051 - 13 Jan 2025

Viewed by 735

Abstract

In contrast to the conventional topology, wherein the Device Under Test (DUT) controller and the electric motor emulator (EME) are powered by the DC (Direct Current) voltage source independently, the common DC bus topology necessitates a single power supply. This reduces the cost [...] Read more.

In contrast to the conventional topology, wherein the Device Under Test (DUT) controller and the electric motor emulator (EME) are powered by the DC (Direct Current) voltage source independently, the common DC bus topology necessitates a single power supply. This reduces the cost and complexity of the motor emulator system, making it more favorable for large-scale industrial applications. However, this topology introduces significant circulating current issues in the system. A common DC bus circulating current suppression method is proposed in this paper for the motor emulator. First, the mechanism of zero-sequence circulating current generation in the common DC bus topology is analyzed and the expression for the system’s zero-sequence voltage difference is derived. Then, a control method based on a Hybrid PWM (Pulse Width Modulation) strategy that unifies SPWM (SIN Pulse Width Modulation) and SVPWM (Space Vector Pulse Width Modulation) is proposed, which has been shown to be effective in suppressing the zero-sequence circulating current in a motor emulator system with a common DC bus topology. The proposed control method has been experimentally validated using a motor emulator system. Full article

(This article belongs to the Special Issue New Trends in Reliability and Lifetime Improvement in Power-Electronic-Controlled Machines and Devices)

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15 pages, 6899 KiB

Open AccessEditor’s ChoiceArticle

Influence of Potting Radius on the Structural Performance and Failure Mechanism of Inserts in Sandwich Structures

by Filippos Filippou and Alexis Τ. Kermanidis

Machines 2025, 13(1), 34; https://doi.org/10.3390/machines13010034 - 7 Jan 2025

Viewed by 854

Abstract

In this study, the mechanical performance and failure modes of cold-potted inserts within sandwich structures were examined, focusing on the influence of the potting radius, while maintaining constant insert radius and specimen characteristics. In this research, destructive testing was used to evaluate the [...] Read more.

In this study, the mechanical performance and failure modes of cold-potted inserts within sandwich structures were examined, focusing on the influence of the potting radius, while maintaining constant insert radius and specimen characteristics. In this research, destructive testing was used to evaluate the pull out, load-carrying capacity, and failure mechanisms of the inserts. The methods of stiffness degradation and acoustic emissions (AE) were employed for structural health monitoring to capture real-time data on failure progression, including core buckling, core rupture, and skin delamination. The results indicated that increasing the potting radius significantly altered the failure modes and critical failure load of the insert system. A critical potting radius was identified where maximum stiffness was achieved. Beyond this point, insert fracture became the dominant failure mode, with minimal damage to the surrounding core and CFRP skins. Larger potting radii also led to reduced displacement at failure, increased ultimate loads, and elevated stiffness, which were maintained until sudden structural failure. Through detailed isolation and observation of each failure event and with the use of AE data, precise identification of system damage in real time was allowed, offering insights into the progression and causes of failure. Full article

(This article belongs to the Special Issue State of the Art in the Field of Machine and System Testing to Assist in the Diagnosis and Prognosis of Failures)

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18 pages, 4724 KiB

Open AccessEditor’s ChoiceArticle

A Wearable Stiffness-Rendering Haptic Device with a Honeycomb Jamming Mechanism for Bilateral Teleoperation

by Thomas M. Kwok, Bohan Zhang and Wai Tuck Chow

Machines 2025, 13(1), 27; https://doi.org/10.3390/machines13010027 - 6 Jan 2025

Viewed by 1176

Abstract

This paper addresses the challenge of providing kinesthetic feedback in bilateral teleoperation by designing a wearable, lightweight (20 g), and compact haptic device, the HJ-Haptic, utilizing a honeycomb jamming mechanism for object stiffness rendering. The HJ-Haptic device can vary its stiffness, from 1.15 [...] Read more.

This paper addresses the challenge of providing kinesthetic feedback in bilateral teleoperation by designing a wearable, lightweight (20 g), and compact haptic device, the HJ-Haptic, utilizing a honeycomb jamming mechanism for object stiffness rendering. The HJ-Haptic device can vary its stiffness, from 1.15 N/mm to 2.64 N/mm, using a 30 kPa vacuum pressure. We demonstrate its implementation in a teleoperation framework, enabling operators to adjust grip force based on a reliable haptic feedback on object stiffness. A three-point flexural test on the honeycomb jamming mechanism and teleoperated object-grasping tasks were conducted to evaluate the device’s functionality. Our experiments demonstrated a small RMSE and strong correlations in teleoperated motion, stiffness rendering, and interaction force feedback. The HJ-Haptic effectively adjusts its stiffness in response to real-time gripper feedback, mimicking the sensation of direct object grasping with hands. The device’s use of vacuum pressure ensures operator safety by preventing dangerous outcomes in case of gas leakage or material failure. Incorporating the HJ-Haptic into the teleoperation framework provided the reliable perception of object stiffness and stable teleoperation. This study highlights the potential of the honeycomb jamming mechanism for enhancing haptic feedback in various applications, including teleoperation scenarios, as well as interactions with extended-reality environments. Full article

(This article belongs to the Special Issue Advances and Challenges in Wearable Robotics)

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29 pages, 6463 KiB

Open AccessEditor’s ChoiceArticle

A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing

by Tae-yong Kim, Jieun Lee, Seokhyun Gong, Jaehoon Lim, Dowan Kim and Jongpil Jeong

Machines 2025, 13(1), 21; https://doi.org/10.3390/machines13010021 - 31 Dec 2024

Cited by 1 | Viewed by 1405

Abstract

In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. Manufacturing processes often encounter malfunctions [...] Read more.

In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. Manufacturing processes often encounter malfunctions or defective parts that disrupt production and compromise product quality. However, collecting and labeling sufficient data to detect anomalies is time-intensive, and abnormal data are rare, leading to data imbalances. The FS-GAN model leverages few-shot learning to enable accurate predictions with minimal data and uses the generative capabilities of AnoGAN to mitigate the scarcity of abnormal data by generating synthetic normal data. Experimental results demonstrate that FS-GAN outperforms existing models in terms of accuracy and learning speed, even with limited datasets, effectively addressing the data imbalance problem inherent in manufacturing. The model reduces dependency on extensive data collection and labeling efforts, making it suitable for real-world applications. Through reliable and efficient anomaly detection, FS-GAN contributes to production reliability, product quality, and operational efficiency in smart factories. This study highlights the potential of FS-GAN to provide a cost-effective and high-performance solution to the challenges of anomaly detection in the manufacturing industry. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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20 pages, 816 KiB

Open AccessEditor’s ChoiceArticle

Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model

by Shuxia Ye, Bin Da, Liang Qi, Han Xiao and Shankai Li

Machines 2025, 13(1), 7; https://doi.org/10.3390/machines13010007 - 25 Dec 2024

Viewed by 840

Abstract

As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of marine diesel engines. Unlike traditional methods, [...] Read more.

As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of marine diesel engines. Unlike traditional methods, the proposed approach eliminates the need for explicit modeling and leverages a novel optimization algorithm for data denoising. Additionally, a new noise-resistant monitoring index is introduced to enhance monitoring reliability. The paper is structured into two main sections for validation. The first section addresses advanced data preprocessing, where the Improved Sparrow Search Algorithm (ISSA) is employed to optimize the parameters of Random Singular Value Decomposition (RSVD). This step effectively minimizes noise, reduces manual intervention, and handles high-dimensional data. The second section focuses on analyzing the data characteristics using the Random Matrix Theory (RMT) and establishing novel condition monitoring indicators to achieve more reliable monitoring outcomes. The proposed methodology captures the intricate relationships among key variables within the system, providing a more robust framework for condition monitoring. Applied to a marine diesel engine lubrication system, the method demonstrates significant improvements in noise immunity and monitoring reliability. Comparative analyses of condition monitoring models before and after denoising reveal that the relative error of the proposed monitoring index under varying noise amplitudes is within 1%, substantially lower than that of other indices. Furthermore, the monitoring accuracy is improved by 4.95% when the proposed index is employed for system condition monitoring. Full article

(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)

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12 pages, 6442 KiB

Open AccessEditor’s ChoiceArticle

Design and Validation of an Improved Rotational Variable Stiffness Mechanism

by Carl Nelson, Kasey Moomau and Yucheng Li

Machines 2025, 13(1), 6; https://doi.org/10.3390/machines13010006 - 25 Dec 2024

Cited by 1 | Viewed by 794

Abstract

In various aspects of robotics, including human–robot interaction, the ability to dynamically adjust the apparent stiffness of an interaction (e.g., between the robot and its environment or between the robot and its payload) has become an important capability. Various means have been developed [...] Read more.

In various aspects of robotics, including human–robot interaction, the ability to dynamically adjust the apparent stiffness of an interaction (e.g., between the robot and its environment or between the robot and its payload) has become an important capability. Various means have been developed in recent years to achieve this, notable among them the so-called variable lever devices. In this paper, we present a new variable lever mechanism based on a gear–rack pair. This unique design combines the functionality of the lever itself with that of the stiffness-adjustment transmission. We show through simulations and hardware experiments the relatively large resulting range of achievable stiffness adjustment and efficient operation. Full article

(This article belongs to the Special Issue Selected Papers from MEDER 2024: Advances of Mechanism Design for Robotic Machines)

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15 pages, 8534 KiB

Open AccessEditor’s ChoiceArticle

Development of Digital Flow Valve Applied to Aero-Engine Fuel Control and Research on Performance of Its Flow Characteristics

by Yuesong Li

Machines 2024, 12(12), 936; https://doi.org/10.3390/machines12120936 - 20 Dec 2024

Viewed by 720

Abstract

Digital valves have strong anti-pollution ability and good linearity, so they are more suitable for aero-engine fuel control. However, for high-precision flow control, incremental digital valves require a high-precision, high-dynamic servomotor drive; binary-coded digital valves require many on/off valves; and high-speed switching digital [...] Read more.

Digital valves have strong anti-pollution ability and good linearity, so they are more suitable for aero-engine fuel control. However, for high-precision flow control, incremental digital valves require a high-precision, high-dynamic servomotor drive; binary-coded digital valves require many on/off valves; and high-speed switching digital valves can cause flow shock and pulsation. In this study, an aero-engine fuel control decimal-coded digital flow valve was developed, which not only has the advantages of digital valves but also avoids the above problems. Firstly, the structure and operation principle of the decimal-coded digital flow valve is introduced; then, its model is established based on Simulink/Simcape, and its flow characteristics are simulated and analyzed. Then, experiments on the flow characteristics are presented. The simulation and experiment show that under a supply pressure of 1 MPa, 2 MPa, and 3 MPa, the maximum flow of the decimal-coded digital valve is 11.4457 L/min, 16.3719 L/min, and 19.3733 L/min, and the control accuracy is 0.0775 L/min, 0.1086 L/min, and 0.1294 L/min, respectively. In addition, it has very good linearity, and the settling time is less than 0.09s. Full article

(This article belongs to the Section Automation and Control Systems)

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25 pages, 1305 KiB

Open AccessEditor’s ChoiceArticle

Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data

by Matthew Alberts, Sam St. John, Simon Odie, Anahita Khojandi, Bradley Jared, Tony Schmitz, Jaydeep Karandikar and Jamie B. Coble

Machines 2024, 12(12), 923; https://doi.org/10.3390/machines12120923 - 17 Dec 2024

Cited by 2 | Viewed by 991

Abstract

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due [...] Read more.

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection. Full article

(This article belongs to the Special Issue Application of Sensing Measurement in Machining)

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15 pages, 3412 KiB

Open AccessEditor’s ChoiceArticle

Prediction of Fretting Wear Lifetime of a Coated System

by Kyungmok Kim

Machines 2024, 12(12), 910; https://doi.org/10.3390/machines12120910 - 11 Dec 2024

Viewed by 728

Abstract

This article proposes a model of predicting the fretting wear lifetime of a low-friction coating. The proposed model incorporates multiple factors that influence the fretting wear damage of coatings: the imposed contact load, imposed average velocity, coating hardness, and initial surface roughness of [...] Read more.

This article proposes a model of predicting the fretting wear lifetime of a low-friction coating. The proposed model incorporates multiple factors that influence the fretting wear damage of coatings: the imposed contact load, imposed average velocity, coating hardness, and initial surface roughness of counterparts. The fretting wear lifetime of coatings, defined as the number of cycles critical to friction coefficient evolution, was collected from the literature. For the purpose of identifying parameters in the model, experimental fretting wear lifetime data were analyzed. The results show that the fretting wear lifetime of a coating can be described by an inverse power law regarding the contact load, imposed average velocity, and initial surface roughness of counterparts. In contrast, the fretting wear lifetime of a coating was observed to increase with increased coating hardness. It was observed that the exponents of the inverse power law varied with respect to the type of coating. The proposed fretting wear lifetime model enables the prediction of coating lifetime under various fretting conditions. Full article

(This article belongs to the Special Issue Design and Characterization of Engineered Bearing Surfaces)

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18 pages, 10226 KiB

Open AccessEditor’s ChoiceArticle

Hybrid Deep Learning Model for Fault Diagnosis in Centrifugal Pumps: A Comparative Study of VGG16, ResNet50, and Wavelet Coherence Analysis

by Wasim Zaman, Muhammad Farooq Siddique, Saif Ullah, Faisal Saleem and Jong-Myon Kim

Machines 2024, 12(12), 905; https://doi.org/10.3390/machines12120905 - 10 Dec 2024

Cited by 2 | Viewed by 1215

Abstract

Significant in various industrial applications, centrifugal pumps (CPs) play an important role in ensuring operational efficiency, yet they are susceptible to faults that can disrupt production and increase maintenance costs. This study proposes a robust hybrid model for accurate fault detection and classification [...] Read more.

Significant in various industrial applications, centrifugal pumps (CPs) play an important role in ensuring operational efficiency, yet they are susceptible to faults that can disrupt production and increase maintenance costs. This study proposes a robust hybrid model for accurate fault detection and classification in CPs, integrating Wavelet Coherence Analysis (WCA) with deep learning architectures VGG16 and ResNet50. WCA is initially applied to vibration signals, creating time–frequency representations that capture both temporal and frequency information, essential for identifying subtle fault characteristics. These enhanced signals are processed by VGG16 and ResNet50, each contributing unique and complementary features that enhance feature representation. The hybrid approach fuses the extracted features, resulting in a more discriminative feature set that optimizes class separation. The proposed model achieved a test accuracy of 96.39%, demonstrating minimal class overlap in t-SNE plots and a precise confusion matrix. When compared to the ResNet50-based and VGG16-based models from previous studies, which reached 91.57% and 92.77% accuracy, respectively, the hybrid model displayed better classification performance, particularly in distinguishing closely related fault classes. High F1-scores across all fault categories further validate its effectiveness. This work underscores the value of combining multiple CNN architectures with advanced signal processing for reliable fault diagnosis, improving accuracy in real-world CP applications. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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23 pages, 5495 KiB

Open AccessEditor’s ChoiceArticle

Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining Processes

by Shih-Ming Wang, Li-Jen Hsu, Hariyanto Gunawan and Ren-Qi Tu

Machines 2024, 12(12), 908; https://doi.org/10.3390/machines12120908 - 10 Dec 2024

Viewed by 745

Abstract

Machining thicker workpieces in the process of Wire Electrical Discharge Machining (WEDM) can result in a concave phenomenon known as a “drum shape error” due to the vibration of wires and accumulation of debris, which leads to secondary discharge in the middle of [...] Read more.

Machining thicker workpieces in the process of Wire Electrical Discharge Machining (WEDM) can result in a concave phenomenon known as a “drum shape error” due to the vibration of wires and accumulation of debris, which leads to secondary discharge in the middle of the workpiece. Reducing the drum shape error typically requires a longer finishing process. Finding a balance between precision and machining time efficiency has become a challenge for modern machining shops. This study employed experimental analysis to investigate the effect of individual parameters on the shape error and machining removal rate (MRR). Key influential parameters, including open voltage (OV), pulse ON time (ON), pulse OFF time (OFF), and servo voltage (SV), were chosen for data collection using full factorial and Taguchi orthogonal arrays. Regression analysis was conducted to establish multiple regression equations. These equations were used to develop optimization rules, and subsequently, a user-friendly human–machine interface was developed using C# based on these optimization rules to create a shape error and MRR optimization system. The system can predict the optimal parameter combinations to minimize the shape error and increase the MRR. The results of the verification experiments showed that the prediction accuracy can reach 94.7% for shape error and 99.2% for MRR. Additionally, the shape error can be minimized by up to 40%. Full article

(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)

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23 pages, 10631 KiB

Open AccessEditor’s ChoiceArticle

Multi-Agent Reinforcement Learning Tracking Control of a Bionic Wheel-Legged Quadruped

by Rezwan Al Islam Khan, Chenyun Zhang, Zhongxiao Deng, Anzheng Zhang, Yuzhen Pan, Xuan Zhao, Huiliang Shang and Ruijiao Li

Machines 2024, 12(12), 902; https://doi.org/10.3390/machines12120902 - 9 Dec 2024

Cited by 1 | Viewed by 2075

Abstract

This paper presents a novel approach to developing control strategies for mobile robots, specifically the Pegasus, a bionic wheel-legged quadruped robot with unique chassis mechanics that enable four-wheel independent steering and diverse gaits. A multi-agent (MA) reinforcement learning (RL) controller is proposed, treating [...] Read more.

This paper presents a novel approach to developing control strategies for mobile robots, specifically the Pegasus, a bionic wheel-legged quadruped robot with unique chassis mechanics that enable four-wheel independent steering and diverse gaits. A multi-agent (MA) reinforcement learning (RL) controller is proposed, treating each leg as an independent agent with the goal of autonomous learning. The framework involves a multi-agent setup to model torso and leg dynamics, incorporating motion guidance optimization signal in the policy training and reward function. By doing so, we address leg schedule patterns for the complex configuration of the Pegasus, the requirement for various gaits, and the design of reward functions for MA-RL agents. Agents were trained using two variations of policy networks based on the framework, and real-world tests show promising results with easy policy transfer from simulation to the actual hardware. The proposed framework models acquired higher rewards and converged faster in training than other variants. Various experiments on the robot deployed framework showed fast response (0.8 s) under disturbance and low linear, angular velocity, and heading error, which was 2.5 cm/s, 0.06 rad/s, and 4°, respectively. Overall, the study demonstrates the feasibility of the proposed MA-RL control framework. Full article

(This article belongs to the Special Issue Design and Application of Bionic Robots)

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22 pages, 8560 KiB

Open AccessEditor’s ChoiceArticle

Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems

by Tingting Wu, Hongliang Song, Hongli Gao, Zongshen Wu and Feifei Han

Machines 2024, 12(12), 895; https://doi.org/10.3390/machines12120895 - 6 Dec 2024

Viewed by 943

Abstract

Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning [...] Read more.

Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning require high-quality data for fault samples. This study leverages the relative advantages of data mining methods and threshold techniques, proposing an adaptive threshold construction method based on dynamic parameter relationship inference. Employing an algorithm for inferring dynamic relationships among multiple parameters of the lubrication system builds an adaptive threshold detection model. Extensive diesel engine tests and actual fault data demonstrate that the proposed method can address the issues of missed faults encountered by static threshold methods and the low detection accuracy of machine learning approaches without the need for fault samples. This significantly enhances fault detection accuracy in marine diesel engine lubrication systems, offering considerable industrial practical value. Full article

(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)

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17 pages, 9200 KiB

Open AccessEditor’s ChoiceArticle

Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network

by Hehua Yan, Jinbiao Tan, Yixiong Luo, Shiyong Wang and Jiafu Wan

Machines 2024, 12(12), 891; https://doi.org/10.3390/machines12120891 - 6 Dec 2024

Cited by 1 | Viewed by 827

Abstract

The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data annotation. A tree-structured [...] Read more.

The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data annotation. A tree-structured labeling scheme is introduced to allow for multi-granularity fault annotation. A hierarchical multi-granularity diagnostic network is designed to automatically learn multi-level fault information from condition data using feature extractors of varying granularity, allowing for the extraction of shared fault information across conditions. Additionally, a multi-granularity fault loss function is developed to help the deep network learn tree-structured labels, improving intra-class compactness and reducing hierarchical similarity between classes. Two experimental cases demonstrate that the proposed method exhibits robust cross-condition domain adaptability and performs better in unseen conditions than state-of-the-art methods. Full article

(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)

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14 pages, 9345 KiB

Open AccessEditor’s ChoiceArticle

Effect of Oil Film Radial Clearances on Dynamic Characteristics of Variable Speed Rotor with Non-Concentric SFD

by Weijian Nie, Xiaoguang Yang, Guang Tang, Qicheng Zhang and Ge Wang

Machines 2024, 12(12), 882; https://doi.org/10.3390/machines12120882 - 5 Dec 2024

Viewed by 709

Abstract

Variable-speed aircraft engines require the power turbine rotor to operate stably within a wide range of output speeds, posing a challenge for rotor vibration reduction design. Non-concentric squeeze film dampers (NCSFDs) have been widely used in rotor vibration reduction design due to their [...] Read more.

Variable-speed aircraft engines require the power turbine rotor to operate stably within a wide range of output speeds, posing a challenge for rotor vibration reduction design. Non-concentric squeeze film dampers (NCSFDs) have been widely used in rotor vibration reduction design due to their simple structure. However, comprehensive research on the matching and applicability of NCSFDs under varying operating speeds is lacking. Therefore, this paper investigates the influence of oil film radial clearances on the dynamic characteristics of a variable-speed rotor system with an NCSFD, examining its suitability across variable speeds. This study introduces the principle of equivalent rotor dynamics similarity design, demonstrating good consistency between simulated and real rotor dynamic characteristics, with a radial clearance of 0.10 mm being deemed optimal. The vibration response variation in the rotor at a fixed speed within the range of 0.51 n to 1.0 n does not exceed 4 μm, and the vibration acceleration variation does not exceed 0.04 g, indicating a wide, stable operating speed range. This study can be helpful for the engineering design and vibration reduction design of variable-speed rotors in aircraft engines. Full article

(This article belongs to the Special Issue Power and Propulsion Engineering)

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38 pages, 14107 KiB

Open AccessEditor’s ChoiceReview

Smart In-Process Inspection in Human–Cyber–Physical Manufacturing Systems: A Research Proposal on Human–Automation Symbiosis and Its Prospects

by Shu Wang and Roger J. Jiao

Machines 2024, 12(12), 873; https://doi.org/10.3390/machines12120873 - 2 Dec 2024

Cited by 2 | Viewed by 1391

Abstract

This positioning paper explores integrating smart in-process inspection and human–automation symbiosis within human–cyber–physical manufacturing systems. As manufacturing environments evolve with increased automation and digitalization, the synergy between human operators and intelligent systems becomes vital for optimizing production performance. Human–automation symbiosis, a vision widely [...] Read more.

This positioning paper explores integrating smart in-process inspection and human–automation symbiosis within human–cyber–physical manufacturing systems. As manufacturing environments evolve with increased automation and digitalization, the synergy between human operators and intelligent systems becomes vital for optimizing production performance. Human–automation symbiosis, a vision widely endorsed as the future of human–automation research, emphasizes closer partnership and mutually beneficial collaboration between human and automation agents. In addition, to maintain high product quality and enable the in-time feedback of process issues for advanced manufacturing, in-process inspection is an efficient strategy that manufacturers adopt. In this regard, this paper outlines a research framework combining smart in-process inspection and human–automation symbiosis, enabling real-time defect identification and process optimization with cognitive intelligence. Smart in-process inspection studies the effective automation of real-time inspection and defect mitigation using data-driven technologies and intelligent agents to foster adaptability in complex production environments. Concurrently, human–automation symbiosis focuses on achieving a symbiotic human–automation relationship through cognitive task allocation and behavioral nudges to enhance human–automation collaboration. It promotes a human-centered manufacturing paradigm by integrating the studies in advanced manufacturing systems, cognitive engineering, and human–automation interaction. This paper examines critical technical challenges, including defect inspection and mitigation, human cognition modeling for adaptive task allocation, and manufacturing nudging design and personalization. A research roadmap detailing the technical solutions to these challenges is proposed. Full article

(This article belongs to the Special Issue Cyber-Physical Systems in Intelligent Manufacturing)

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25 pages, 4153 KiB

Open AccessEditor’s ChoiceArticle

Enhanced Fault Detection in Satellite Attitude Control Systems Using LSTM-Based Deep Learning and Redundant Reaction Wheels

by Sajad Saraygord Afshari

Machines 2024, 12(12), 856; https://doi.org/10.3390/machines12120856 - 27 Nov 2024

Cited by 1 | Viewed by 1261

Abstract

Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to [...] Read more.

Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to faults—a factor with the potential to precipitate catastrophic failures such as total satellite loss. In light of this, we introduce a fault detection methodology grounded in deep learning techniques specifically designed for satellite attitude control systems. Our proposed method utilizes a Long Short-Term Memory (LSTM) model adept at learning temporal patterns inherent to both healthy and faulty system behaviors. Incorporated into our model is a torque allocation algorithm designed to circumvent specific velocities known to induce torque disturbances, a factor known to influence LSTM performance adversely. To bolster the robustness of our fault detection technique, we also incorporated denoising autoencoders within the LSTM framework, thereby enabling the model to identify temporal patterns in healthy and faulty system behavior, even amidst the noise. The method was evaluated using cross-validation on simulated satellite data comprising 1000 time series samples and across different fault scenarios, such as stiction and resonance at varying intensities (90%, 50%, and 30%). The results confirm achieving performance metrics such as Mean Squared Error for accurate fault identification. This research underscores a stride in the evolution of fault detection and control strategies for satellite attitude control systems, holding promise to boost the reliability and efficiency of future space missions. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control of Power Machinery: Developments and Challenges)

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16 pages, 3622 KiB

Open AccessEditor’s ChoiceArticle

A Soft Start Method for Doubly Fed Induction Machines Based on Synchronization with the Power System at Standstill Conditions

by José M. Guerrero, Kumar Mahtani, Itxaso Aranzabal, Julen Gómez-Cornejo, José A. Sánchez and Carlos A. Platero

Machines 2024, 12(12), 847; https://doi.org/10.3390/machines12120847 - 25 Nov 2024

Cited by 1 | Viewed by 950

Abstract

Due to their exceptional operational versatility, doubly fed induction machines (DFIM) are widely employed in power systems comprising variable renewable energy-based electrical generation sources, such as wind farms and pumped-storage hydropower plants. However, their starting and grid synchronization methods require numerous maneuvers or [...] Read more.

Due to their exceptional operational versatility, doubly fed induction machines (DFIM) are widely employed in power systems comprising variable renewable energy-based electrical generation sources, such as wind farms and pumped-storage hydropower plants. However, their starting and grid synchronization methods require numerous maneuvers or additional components, making the process challenging. In this paper, a soft start method for DFIM, inspired by the traditional synchronization method of synchronous machines, is proposed. This method involves matching the frequencies, voltages, and phase angles on both sides of the main circuit breaker, by adjusting the excitation through the controlled power converter at standstill conditions. Once synchronization is achieved, the frequency is gradually reduced to the rated operational levels. This straightforward starting method effectively suppresses large inrush currents and voltage sags. The proposed method has been validated through computer simulations and experimental tests, yielding satisfactory results. Full article

(This article belongs to the Section Electrical Machines and Drives)

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19 pages, 7892 KiB

Open AccessEditor’s ChoiceArticle

Detection of Damage on Inner and Outer Races of Ball Bearings Using a Low-Cost Monitoring System and Deep Convolution Neural Networks

by Handeul You, Dongyeon Kim, Juchan Kim, Keunu Park and Sangjin Maeng

Machines 2024, 12(12), 843; https://doi.org/10.3390/machines12120843 - 25 Nov 2024

Viewed by 3064

Abstract

Bearings are vital components in machinery, and their malfunction can result in equipment damage and reduced productivity. As a result, considerable research attention has been directed toward the early detection of bearing faults. With recent rapid advancements in machine learning algorithms, there is [...] Read more.

Bearings are vital components in machinery, and their malfunction can result in equipment damage and reduced productivity. As a result, considerable research attention has been directed toward the early detection of bearing faults. With recent rapid advancements in machine learning algorithms, there is increasing interest in proactively diagnosing bearing faults by analyzing signals obtained from bearings. Although numerous studies have introduced machine learning methods for bearing fault diagnosis, the high costs associated with sensors and data acquisition devices limit their practical application in industrial environments. Additionally, research aimed at identifying the root causes of faults through diagnostic algorithms has progressed relatively slowly. This study proposes a cost-effective monitoring system to improve economic feasibility. Its primary benefits include significant cost savings compared to traditional high-priced equipment, along with versatility and ease of installation, enabling straightforward attachment and removal. The system collects data by measuring the vibrations of both normal and faulty bearings under various operating conditions on a test bed. Using these data, a deep neural network is trained to enable real-time feature extraction and classification of bearing conditions. Furthermore, an explainable AI technique is applied to extract key feature values identified by the fault classification algorithm, providing a method to support the analysis of fault causes. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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16 pages, 8804 KiB

Open AccessEditor’s ChoiceArticle

Research on Unbalanced Vibration Characteristics and Assembly Phase Angle Probability Distribution of Dual-Rotor System

by Hui Li, Changzhi Shi, Xuejun Li, Mingfeng Li and Jie Bian

Machines 2024, 12(12), 842; https://doi.org/10.3390/machines12120842 - 24 Nov 2024

Viewed by 738

Abstract

This paper addresses the complex issue of vibration response characteristics resulting from the unbalanced assembly of the double rotors in the 31F aero-engine. The study investigates the vibration response behavior of the dual-rotor system through the adjustment of rotor assembly phase angle. Initially, [...] Read more.

This paper addresses the complex issue of vibration response characteristics resulting from the unbalanced assembly of the double rotors in the 31F aero-engine. The study investigates the vibration response behavior of the dual-rotor system through the adjustment of rotor assembly phase angle. Initially, a dynamic model of the four-disk, five-pivot dual-rotor system is established, with its natural frequencies and vibration modes verified. The influence of size and the position of the unbalance on the vibration amplitude in the dual-rotor system is analyzed. Additionally, the probability distribution of the assembly phase angles for both the compressor and turbine sections of the low-pressure rotor is examined. The results indicate that for the low-pressure rotor exhibiting excessive vibration, adjusting the assembly phase angle of the rotors’ system’s compressor or the turbine section by 180 degrees leads to a vibration qualification rate of 70.1435%. This finding is consistent with the observations from the field experience method used in the former Soviet Union. Finally, corresponding experimental verification is conducted. Full article

(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)

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18 pages, 3422 KiB

Open AccessEditor’s ChoiceArticle

Use of Image Recognition and Machine Learning for the Automatic and Objective Evaluation of Standstill Marks on Rolling Bearings

by Markus Grebe, Alexander Baral and Dominik Martin

Machines 2024, 12(12), 840; https://doi.org/10.3390/machines12120840 - 23 Nov 2024

Viewed by 805

Abstract

One main research area of the Competence Centre for Tribology is so-called standstill marks (SSMs) at roller bearings that occur if the bearing is exposed to vibrations or performs just micromovements. SSMs obtained from experiments are usually photographed, evaluated and manually categorized into [...] Read more.

One main research area of the Competence Centre for Tribology is so-called standstill marks (SSMs) at roller bearings that occur if the bearing is exposed to vibrations or performs just micromovements. SSMs obtained from experiments are usually photographed, evaluated and manually categorized into six classes. An internal project has now investigated the extent to which this evaluation can be automated and objectified. Images of standstill marks were classified using convolutional neural networks that were implemented with the deep learning library Pytorch. With basic convolutional neural networks, an accuracy of 70.19% for the classification of all six classes and 83.65% for the classification of pairwise classes was achieved. Classification accuracies were improved by image augmentation and transfer learning with pre-trained convolutional neural networks. Overall, an accuracy of 83.65% for the classification of all six standstill mark classes and 91.35% for the classification of pairwise classes was achieved. Since 16 individual marks are generated per test run in a typical quasi standstill test (QSST) of the CCT and the deviation in the prediction of the classification is a maximum of one school grade, the accuracy achieved is already sufficient to carry out a reliable and objective evaluation of the markings. Full article

(This article belongs to the Special Issue Remaining Useful Life Prediction for Rolling Element Bearings)

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41 pages, 7143 KiB

Open AccessEditor’s ChoiceReview

Overview of IoT Security Challenges and Sensors Specifications in PMSM for Elevator Applications

by Eftychios I. Vlachou, Vasileios I. Vlachou, Dimitrios E. Efstathiou and Theoklitos S. Karakatsanis

Machines 2024, 12(12), 839; https://doi.org/10.3390/machines12120839 - 22 Nov 2024

Cited by 1 | Viewed by 2030

Abstract

The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being [...] Read more.

The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being incorporated in various industrial applications, especially in electrical machines as a means of control, monitoring and preventive maintenance. This paper is focused on reviewing the use PMSM in lift systems, the application of various condition monitoring techniques and real-time data collection techniques using IoT technology. In addition, we focus on different categories of industrial sensors, their connectivity and the standards they should meet for PMSMs used in elevator applications. Finally, we analyze various secure ways of transmitting data on different platforms so that the transmission of information takes into account possible unwanted instructions from exogenous factors. Full article

(This article belongs to the Special Issue Advancements in Condition Monitoring of Electric Motors: Integrating Digital Twins, AI, and IoT for Enhanced Operational Efficiency, Fault Diagnosis, and Cybersecurity)

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15 pages, 3119 KiB

Open AccessEditor’s ChoiceArticle

Fault Detection in Harmonic Drive Using Multi-Sensor Data Fusion and Gravitational Search Algorithm

by Nan-Kai Hsieh and Tsung-Yu Yu

Machines 2024, 12(12), 831; https://doi.org/10.3390/machines12120831 - 21 Nov 2024

Viewed by 1197

Abstract

This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, [...] Read more.

This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, which can compromise system stability and production efficiency. To enhance diagnostic accuracy, the research employs wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) to extract multi-scale features from vibration signals. These features are subsequently fused, and GSA is used to optimize the high-dimensional fused features, eliminating redundant data and mitigating overfitting. The optimized features are then input into a support vector machine (SVM) for fault classification, with K-fold cross-validation used to assess the model’s generalization capabilities. Experimental results demonstrate that the proposed diagnosis method, which integrates multi-sensor data fusion with GSA optimization, significantly improves fault diagnosis accuracy compared to methods using single-sensor signals or unoptimized features. This improvement is particularly notable in multi-class fault scenarios. Additionally, GSA’s global search capability effectively addresses overfitting issues caused by high-dimensional data, resulting in a diagnostic model with greater reliability and accuracy across various fault conditions. Full article

(This article belongs to the Special Issue Compensation for Geometric Errors and Improvement in Accuracy Through Innovative Design and Advanced Control Strategies)

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62 pages, 17046 KiB

Open AccessEditor’s ChoiceReview

A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring

by Haoyuan Zhang, Shanglei Jiang, Defeng Gao, Yuwen Sun and Wenxiang Bai

Machines 2024, 12(12), 833; https://doi.org/10.3390/machines12120833 - 21 Nov 2024

Cited by 3 | Viewed by 3476

Abstract

Tool wear is an inevitable phenomenon in the machining process. By monitoring the wear state of a tool, the machining system can give early warning and make advance decisions, which effectively ensures improved machining quality and production efficiency. In the past two decades, [...] Read more.

Tool wear is an inevitable phenomenon in the machining process. By monitoring the wear state of a tool, the machining system can give early warning and make advance decisions, which effectively ensures improved machining quality and production efficiency. In the past two decades, scholars have conducted extensive research on tool wear monitoring (TWM) and obtained a series of remarkable research achievements. However, physics-based models have difficulty predicting tool wear accurately. Meanwhile, the diversity of actual machining environments further limits the application of physical models. Data-driven models can establish the deep mapping relationship between signals and tool wear, but they only fit trained data well. They still have difficulty adapting to complex machining conditions. In this paper, physics-based and data-driven TWM models are first reviewed in detail, including the factors that affect tool wear, typical data-based models, and methods for extracting and selecting features. Then, tracking research hotspots, emerging physics–data fusion models are systematically summarized. Full article

(This article belongs to the Section Advanced Manufacturing)

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20 pages, 5509 KiB

Open AccessEditor’s ChoiceArticle

Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes

by Xiaoping Li, Yujie Sun, Xinyue Liu and Shaoxuan Zhang

Machines 2024, 12(11), 801; https://doi.org/10.3390/machines12110801 - 12 Nov 2024

Viewed by 888

Abstract

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian [...] Read more.

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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13 pages, 6682 KiB

Open AccessEditor’s ChoiceArticle

Design of a Thermal Performance Test Equipment for a High-Temperature and High-Pressure Heat Exchanger in an Aero-Engine

by Wongeun Yun, Manyeong Ha, Kuisoon Kim and Geesoo Lee

Machines 2024, 12(11), 794; https://doi.org/10.3390/machines12110794 - 10 Nov 2024

Viewed by 1117

Abstract

For next-generation power systems, particularly aero-gas turbine engines, ultra-light and highly efficient heat exchangers are considered key enabling technologies for realizing advanced cycles. Consequently, the development of efficient and accurate aero-engine heat exchanger test equipment is essential to support future gas turbine heat [...] Read more.

For next-generation power systems, particularly aero-gas turbine engines, ultra-light and highly efficient heat exchangers are considered key enabling technologies for realizing advanced cycles. Consequently, the development of efficient and accurate aero-engine heat exchanger test equipment is essential to support future gas turbine heat exchanger advancements. This paper presents the development of a high-pressure and high-temperature (HPHT) heat exchanger test facility designed for aero-engine heat exchangers. The maximum temperature and pressure of the test facility were configured to simulate the conditions of the last-stage compressor of a large civil engine, specifically 1000 K and 5.5 MPa. These conditions were achieved using multiple electric heater systems in conjunction with an air compression system consisting of three turbo compressor units and a reciprocating compressor unit. A commissioning test was conducted using a compact tubular heat exchanger, and the results indicate that the test facility operates stably and that the measured data closely align with the predicted performance of the heat exchanger. A commissioning test of the tubular heat exchanger showed a thermal imbalance of 1.02% between the high-pressure (HP) and low-pressure (LP) lines. This level of imbalance is consistent with the ISO standard uncertainty of ±2.3% for heat dissipation. In addition, CFD simulation results indicated an average deviation of approximately 1.4% in the low-pressure outlet temperature. The close alignment between experimental and CFD results confirms the theoretical reliability of the test bench. The HPHT thermal performance test facility will be expected to serve as a critical test bed for evaluating heat exchangers for current and future gas turbine applications. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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16 pages, 7033 KiB

Open AccessEditor’s ChoiceArticle

Influence of Distributor Structure on Through-Sea Valve Vibration Characteristics and Improvement Design

by Qingchao Yang, Zebin Li, Aimin Diao and Zhaozhao Ma

Machines 2024, 12(11), 791; https://doi.org/10.3390/machines12110791 - 8 Nov 2024

Viewed by 624

Abstract

To address the issue of excessive transient noise during the opening and closing of a sea valve, a method for reducing pressure fluctuations during the opening of the electromagnetic hydraulic distributor has been proposed by analyzing the structure and working principle of the [...] Read more.

To address the issue of excessive transient noise during the opening and closing of a sea valve, a method for reducing pressure fluctuations during the opening of the electromagnetic hydraulic distributor has been proposed by analyzing the structure and working principle of the distributor. Based on theoretical calculation and simulation analysis, the size and shape of the buffer slot of the flow hole are determined under the condition that the stable working flow rate remains unchanged. An improved electromagnetic hydraulic distributor is developed and tested. The results indicate that this method can effectively control the opening and closing transient noise of the sea valve. Full article

(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)

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21 pages, 5379 KiB

Open AccessEditor’s ChoiceArticle

Characterization of Surface Integrity of 3D-Printed Stainless Steel by Successive Grinding and Varied Burnishing Parameters

by Frezgi Tesfom Kebede, Jawad Zaghal and Csaba Felho

Machines 2024, 12(11), 790; https://doi.org/10.3390/machines12110790 - 7 Nov 2024

Cited by 1 | Viewed by 1842

Abstract

Additive manufacturing (AM)’s ability to produce customized products with reduced material wastage and other advantages helped the technology to gain popularity in many industries. However, its poor surface integrity is its weak side, and to overcome this, additional post-processes are essential. Slide diamond [...] Read more.

Additive manufacturing (AM)’s ability to produce customized products with reduced material wastage and other advantages helped the technology to gain popularity in many industries. However, its poor surface integrity is its weak side, and to overcome this, additional post-processes are essential. Slide diamond burnishing, known for its enhancement of surface roughness, residual stress, microhardness, and other properties, was combined with grinding in this research after 3D printing of MetcoAdd 17-4PH-A to mitigate the mentioned shortcomings. This study aimed to analyze the effects of each process on surface roughness, residual stress (both on the surface and in-depth), and microhardness. Workpieces were ground with the same parameters and burnished with four levels of force, feed, and number of passes. The L16 Taguchi experimental design was used to optimize the process parameters and to study their effects. For surface roughness, the optimum parameters were found to be 60 N force, 0.02 m/min feed rate, and three passes. The longitudinal surface residual stress has optimal values at 80 N force, 0.02 m/min feed rate, and four passes. In the case of transverse surface residual stress, the optimal values were 60 N force, 0.17 m/min feed rate, and three passes. Microhardness was maximized with 60 N force, 0.02 m/min feed rate, and one pass. Additionally, the in-depth residual stress for selected surfaces was investigated, and 100 N force showed a deep burnishing effect. Further multi-objective optimization using desirability function analysis found that the optimal parameters for all responses were achieved at the fourth burnishing force level (100 N), the first tool feed level (0.02 m/min), and the fourth number of passes level (four passes). Ultimately, both grinding and burnishing processes exhibited significant enhancements in the measured parameters. Full article

(This article belongs to the Section Advanced Manufacturing)

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21 pages, 16720 KiB

Open AccessEditor’s ChoiceArticle

An Enhanced Spectral Amplitude Modulation Method for Fault Diagnosis of Rolling Bearings

by Zongcai Ma, Yongqi Chen, Tao Zhang and Ziyang Liao

Machines 2024, 12(11), 779; https://doi.org/10.3390/machines12110779 - 6 Nov 2024

Viewed by 678

Abstract

As a classic nonlinear filtering method, Spectral Amplitude Modulation (SAM) is widely used in the field of bearing fault characteristic frequency identification. However, when the vibration signal contains high-intensity noise interference, the accuracy of SAM in identifying fault characteristic frequencies is greatly reduced. [...] Read more.

As a classic nonlinear filtering method, Spectral Amplitude Modulation (SAM) is widely used in the field of bearing fault characteristic frequency identification. However, when the vibration signal contains high-intensity noise interference, the accuracy of SAM in identifying fault characteristic frequencies is greatly reduced. To solve the above problems, a Data Enhancement Spectral Amplitude Modulation (DA-SAM) method is proposed. This method further processes the modified signal through improved wavelet transform (IWT), calculates its logarithmic maximum square envelope spectrum to replace the original square envelope spectrum, and finally completes SAM. By highlighting signal characteristics and strengthening feature information, interference information can be minimized, thereby improving the robustness of the SAM method. In this paper, this method is verified through fault data sets. The research results show that this method can effectively reduce the interference of noise on fault diagnosis, and the fault characteristic information obtained is clearer. The superiority of this method compared with the SAM method, Autogram method, and fast spectral kurtosis diagram method is proved. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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29 pages, 53780 KiB

Open AccessEditor’s ChoiceArticle

Comprehensive Analysis of Major Fault-to-Failure Mechanisms in Harmonic Drives

by Roberto Guida, Antonio Carlo Bertolino, Andrea De Martin and Massimo Sorli

Machines 2024, 12(11), 776; https://doi.org/10.3390/machines12110776 - 5 Nov 2024

Cited by 1 | Viewed by 2501

Abstract

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure [...] Read more.

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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19 pages, 3670 KiB

Open AccessEditor’s ChoiceArticle

Modal Parameter Identification of Electric Spindles Based on Covariance-Driven Stochastic Subspace

by Wenhong Zhou, Liuzhou Zhong, Weimin Kang, Yuetong Xu, Congcong Luan and Jianzhong Fu

Machines 2024, 12(11), 774; https://doi.org/10.3390/machines12110774 - 4 Nov 2024

Cited by 1 | Viewed by 1107

Abstract

Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study [...] Read more.

Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study proposes a covariance-driven stochastic subspace identification (SSI-cov) method integrated with a simulated annealing (SA) strategy and fuzzy C-means (FCM) clustering algorithm to achieve the automated identification of modal parameters for electric spindles. Using both finite element simulations and experimental tests conducted at 22 °C, the first five natural frequencies of the electric spindle under free, constrained, and dynamic conditions were extracted. The experimental results demonstrated experiment errors of 0.17% to 0.33%, 1.05% to 3.27%, and 1.29% to 3.31% for the free, constrained, and dynamic states, respectively. Compared to the traditional SSI-cov method, the proposed SA-FCM method improved accuracy by 12.05% to 27.32% in the free state, 17.45% to 47.83% in the constrained state, and 25.45% to 49.12% in the dynamic state. The frequency identification errors were reduced to a range of 2.25 Hz to 20.81 Hz, significantly decreasing errors in higher-order modes and demonstrating the robustness of the algorithm. The proposed method required no manual intervention, and it could be utilized to accurately analyze the modal parameters of electric spindles under free, constrained, and dynamic conditions, providing a precise and reliable solution for the modal analysis of electric spindles in various dynamic states. Full article

(This article belongs to the Special Issue Error Measurement, Analysis, and Compensation Technology for CNC Machine Tools)

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