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Machines
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Recent Developments in Machine Design, Automation and Robotics
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Recent Developments in Machine Design, Automation and Robotics

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 38466

Special Issue Editor

Dr. Raul D. S. G. Campilho

E-Mail Website
Guest Editor
1. ISEP—School of Engineering, Polytechnic of Porto, R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal
2. INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, Pólo FEUP, Rua Dr. Roberto Frias, 400, 4200-465 Porto, Portugal
Interests: composite materials; joining processes; automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The competitiveness of companies in the global market highly depends on the efficiency of industrial processes, which rely on technologically advanced machines and equipment. Moreover, through the extensive use of automation and robotics, it is possible to attain the required product quality, production flexibility to adapt to new product references, production rate, and low fabrication costs. Throughout time, automation and robotics became the best way to achieve the goals of the market. Therefore, these technologies are subject to continuous evolution, constantly presenting new solutions. Major advances and developments were recently experienced, both academically and industrially, with emphasis on the following:

  • Collaborative robotics (cobots): human–robot collaboration in industrial settings, safety protocols and advancements in cobot technology, and cobots in small and medium-sized enterprises.
  • Advanced control systems in automation: adaptive and predictive control algorithms, real-time control strategies for industrial processes, and integration of AI and machine learning in control systems.
  • Additive manufacturing for machine design: 3D printing applications in machine parts and design, and optimization and material advancements in additive manufacturing.
  • Smart factory and Industry 4.0: Internet of Things (IoT) applications in manufacturing, cyber–physical systems and their role in modern factories, and digital twins for predictive maintenance and optimization.
  • Sustainable manufacturing and green design: energy-efficient design and automation, recycling and eco-friendly materials in machine design, and sustainable practices in industrial robotics and automation.
  • Machine learning in robotics: reinforcement learning for robotic applications, vision-based learning and object recognition in robotics, and autonomous decision-making in robotic systems.

This Special Issue intends to bring together a significant number of contributions in this area through the publication of high-quality original works in the field, subsequently promoting its dissemination through the Open Access system.

Dr. Raul D. S. G. Campilho
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine design
  • industrial automation
  • industrial robotics
  • collaborative robotics
  • advanced control systems
  • additive manufacturing
  • smart factory
  • Industry 4.0
  • Internet of Things (IoT)
  • sustainable manufacturing
  • green design
  • machine learning in robotics
  • automation technologies
  • robotics integration
  • adaptive control systems

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Related Special Issue

Published Papers (30 papers)

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23 pages, 5700 KiB  
Article
Near-Zero Parasitic Shift Rectilinear Flexure Stages Based on Coupled n-RRR Planar Parallel Mechanisms
by Loïc Tissot-Daguette, Célestin Vallat, Marijn Nijenhuis, Florent Cosandier and Simon Henein
Machines 2025, 13(6), 530; https://doi.org/10.3390/machines13060530 - 18 Jun 2025
Abstract
Flexure-based linear stages have become prevalent in precision engineering; however, most designs suffer from parasitic shifts that degrade positioning accuracy. Conventional solutions to mitigate these parasitic motions often compromise support stiffness, reduce motion range, and increase structural complexity. This study presents a novel [...] Read more.
Flexure-based linear stages have become prevalent in precision engineering; however, most designs suffer from parasitic shifts that degrade positioning accuracy. Conventional solutions to mitigate these parasitic motions often compromise support stiffness, reduce motion range, and increase structural complexity. This study presents a novel family of flexure-based rectilinear-motion stages using coupled n-RRR planar parallel mechanisms, achieving extremely low parasitic shifts while addressing the forementioned limitations. Four design variants are selected and analyzed via Finite Element Method (FEM) simulations, evaluating parasitic shifts, stroke, and support stiffness. The most precise configuration, a 4-RRR rectilinear stage having kinematic chains coupled via two Watt linkages, exhibits a lateral shift smaller than 0.258 µm and an in-plane parasitic rotation smaller than 12.6 µrad over a 12 mm stroke. Experimental validation using a POM prototype confirms the high positioning precision and support stiffness properties. In addition, a silicon prototype incorporating thermally preloaded buckling beams is investigated to reduce its translational stiffness. Experimental results show a translational stiffness reduction of 98% in the monostable configuration and 112% in the bistable configuration (i.e., negative stiffness), without support stiffness reduction. These results highlight the potential of the proposed mechanisms for a wide range of precision applications, offering a scalable and high-accuracy solution for micro- and nano-positioning systems. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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22 pages, 5111 KiB  
Article
Multibody Simulation of 1U CubeSat Passive Attitude Stabilisation Using a Robotic Arm
by Filippo Foiani, Giulia Morettini, Massimiliano Palmieri, Stefano Carletta, Filippo Cianetti and Marco Dionigi
Machines 2025, 13(6), 509; https://doi.org/10.3390/machines13060509 - 11 Jun 2025
Viewed by 286
Abstract
Robotics plays a pivotal role in contemporary space missions, particularly in the development of robotic manipulators for operations in environments that are inaccessible to humans. In accordance with the trend of integrating multiple functionalities into a single system, this study evaluates the feasibility [...] Read more.
Robotics plays a pivotal role in contemporary space missions, particularly in the development of robotic manipulators for operations in environments that are inaccessible to humans. In accordance with the trend of integrating multiple functionalities into a single system, this study evaluates the feasibility of using a robotic manipulator, termed a C-arm, for passive attitude control of a 1U CubeSat. A simplified multibody model of the CubeSat system was employed to assess the robotic arm’s functionality as a gravity gradient boom and subsequently as a passive magnetic control mechanism by utilising a permanent magnet at its extremity. The effectiveness of the C-arm as a gravitational boom is constrained by size and weight, as evidenced by the simulations; the pitch angle oscillated around ±40°, while roll and yaw angles varied up to 30° and 35°, respectively. Subsequent evaluations sought to enhance pointing accuracy through the utilisation of permanent magnets. However, the absence of dissipative forces resulted in attitude instabilities. In conclusion, the integration of a robotic arm into a 1U CubeSat for passive attitude control shows potential, especially for missions where pointing accuracy can tolerate a certain range, as is typical of CubeSat nanosatellite missions. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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28 pages, 2380 KiB  
Article
A Unified Framework for Automated Testing of Robotic Process Automation Workflows Using Symbolic and Concolic Analysis
by Ciprian Paduraru, Marina Cernat and Adelina-Nicoleta Staicu
Machines 2025, 13(6), 504; https://doi.org/10.3390/machines13060504 - 9 Jun 2025
Viewed by 282
Abstract
Robotic Process Automation is a technology that replicates human interactions with user interfaces across various applications. However, testing Robotic Process Automation implementations remains challenging due to the dynamic nature of workflows. This paper presents a novel testing framework that first integrates symbolic execution [...] Read more.
Robotic Process Automation is a technology that replicates human interactions with user interfaces across various applications. However, testing Robotic Process Automation implementations remains challenging due to the dynamic nature of workflows. This paper presents a novel testing framework that first integrates symbolic execution and concolic testing strategies to enhance Robotic Process Automation workflow validation. Building on insights from these methods, we introduce a hybrid approach that optimizes test coverage and efficiency in specific cases. Our open-source implementation demonstrates that automated testing in the Robotic Process Automation domain significantly improves coverage, reduces manual effort, and enhances reliability. Furthermore, the proposed solution supports multiple Robotic Process Automation platforms and aligns with industry best practices for user interface automation testing. Experimental evaluation, conducted in collaboration with industry, validates the effectiveness of our approach. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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16 pages, 2907 KiB  
Article
Balancing Productivity and Sustainability in EDM: A Comprehensive Analysis of Energy Consumption and Electrode Degradation
by Sunil Kumar Maurya, Gianni Campatelli, Massimo Veracini, Massimo Arcioni and Dario Clori
Machines 2025, 13(6), 469; https://doi.org/10.3390/machines13060469 - 29 May 2025
Viewed by 245
Abstract
Die-sinking Electrical Discharge Machining (EDM) is a manufacturing process for fabricating complex geometries in challenging applications. However, its energy-intensive nature and complex parameter interactions pose challenges in balancing productivity, sustainability, and electrode wear. This study presents a comprehensive analysis of energy consumption and [...] Read more.
Die-sinking Electrical Discharge Machining (EDM) is a manufacturing process for fabricating complex geometries in challenging applications. However, its energy-intensive nature and complex parameter interactions pose challenges in balancing productivity, sustainability, and electrode wear. This study presents a comprehensive analysis of energy consumption and electrode degradation in EDM. Utilizing an advanced experimental setup with real-time energy monitoring, this study investigated the trade-off between machining parameters, energy efficiency, and electrode wear. The study employed a simple and standardized electrode geometry and varied EDM parameters, such as discharge current and pulse duration. The obtained results clearly demonstrated that optimizing EDM machining parameters, particularly discharge current, significantly influenced machining efficiency and electrode wear. Specifically, employing high-current settings of 140 A substantially reduced the total machining time from approximately 33 h (at conservative settings of 40 A) down to around 3.5 h, achieving nearly a tenfold improvement. Moreover, it also led to a reduction in specific energy consumption (SEC), decreasing from 0.81 Wh/mm3 at the low current (40 A) to 0.19 Wh/mm3 at the higher current (140 A), underscoring a definitive inverse relationship between discharge current and energy consumption. The study outcomes provide practical guidelines for enhancing the operational efficiency and sustainability of EDM in advanced manufacturing sectors. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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21 pages, 16801 KiB  
Article
Efficient Transformer-Based Road Scene Segmentation Approach with Attention-Guided Decoding for Memory-Constrained Systems
by Bartas Lisauskas and Rytis Maskeliunas
Machines 2025, 13(6), 466; https://doi.org/10.3390/machines13060466 - 28 May 2025
Viewed by 333
Abstract
Accurate object detection and an understanding of the surroundings are key requirements when applying computer vision systems in the automotive or robotics industries, namely with autonomous vehicles or self-driving robots. A precise understanding of road users or obstacles is essential to avoid potential [...] Read more.
Accurate object detection and an understanding of the surroundings are key requirements when applying computer vision systems in the automotive or robotics industries, namely with autonomous vehicles or self-driving robots. A precise understanding of road users or obstacles is essential to avoid potential accidents. Due to the presence of many objects and the diversity of the environment, the segmentation of the road scene remains a challenging task. In our approach, a Transformer-based backbone is employed for robust feature extraction in the encoder module. In addition, we have developed a custom decoder module in which we implement attention-based fusion mechanisms to effectively combine features. The decoder modification is specifically designed to maintain fine spatial details and enhance the global context understanding, setting our method apart from conventional approaches that typically use simple projection layers or standard query-based decoders. The implemented model consists of 17.2 million parameters and achieves competitive performance, with a mean intersection over union (mIoU) of 76.41% on the Cityscapes validation set. The results gathered indicate the ability of the model to capture both the global context and fine spatial details that are critical to the accurate segmentation of urban scenes. Furthermore, the lightweight design makes the approach suitable for deployment on memory-limited devices. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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22 pages, 7270 KiB  
Article
Type-3 Fuzzy Dynamic Adaptation of the Crossover Parameter in Differential Evolution for the Optimal Design of Type-3 Fuzzy Controllers for the Inverted Pendulum
by Patricia Ochoa, Cinthia Peraza, Patricia Melin, Oscar Castillo and Zong Woo Geem
Machines 2025, 13(6), 450; https://doi.org/10.3390/machines13060450 - 24 May 2025
Viewed by 635
Abstract
This paper proposes a dynamic adaptation of the crossover parameter (CR) in Differential Evolution (DE) using Type-3 fuzzy logic. The strategy involves adjusting the CR value according to the progress of the algorithm by employing a Type-3 fuzzy system (T3FS) to handle the [...] Read more.
This paper proposes a dynamic adaptation of the crossover parameter (CR) in Differential Evolution (DE) using Type-3 fuzzy logic. The strategy involves adjusting the CR value according to the progress of the algorithm by employing a Type-3 fuzzy system (T3FS) to handle the uncertainty and variability inherent in this parameter. To assess the effectiveness of the proposed approach, an inverted pendulum system controlled by a T3FS is employed. The controller parameters are optimized using an enhanced DE algorithm. The results demonstrate that dynamically adapting the crossover rate parameter significantly enhances both the efficiency and accuracy of the DE algorithm, compared to traditional static adjustments. Specifically, the proposed approach achieved an RMSE of 0.0127, outperforming traditional static adjustment methods, which resulted in an RMSE of 0.732. In addition, the results exhibit an improvement in the consistency of the outcomes, with a significantly lower standard deviation (0.0111) compared to conventional methods. These findings underscore the potential of Type-3 fuzzy systems (T3FSs) in enhancing evolutionary algorithms for optimizing fuzzy controllers in highly uncertain nonlinear systems. Furthermore, this emphasizes the capability of T3FSs to enhance evolutionary algorithms in optimizing fuzzy controllers for nonlinear systems subject to significant uncertainty. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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18 pages, 4153 KiB  
Article
Analysis of Electromagnetic Characteristics of Outer Rotor Type BLDC Motor Based on Analytical Method and Optimal Design Using NSGA-II
by Tae-Seong Kim, Jun-Won Yang, Kyung-Hun Shin, Gang-Hyeon Jang, Cheol Han and Jang-Young Choi
Machines 2025, 13(6), 440; https://doi.org/10.3390/machines13060440 - 22 May 2025
Viewed by 257
Abstract
This study investigates the electromagnetic analysis and optimal design of outer rotor type brushless DC (BLDC) motors for fan filter applications. The primary objective is to develop a method that integrates three-dimensional (3D) structural effects with efficient two-dimensional (2D) equivalent analysis. This study [...] Read more.
This study investigates the electromagnetic analysis and optimal design of outer rotor type brushless DC (BLDC) motors for fan filter applications. The primary objective is to develop a method that integrates three-dimensional (3D) structural effects with efficient two-dimensional (2D) equivalent analysis. This study proposes a 2D equivalent analysis method that addresses the unique features of outer rotor type BLDC motors, particularly the permanent magnet (PM) overhang structure. This approach transforms the operating point on the B–H curve to facilitate accurate modeling in a 2D framework, overcoming traditional analysis limitations. An analytical method using spatial harmonics is introduced to derive essential electromagnetic quantities, namely flux linkage and back electromotive force (EMF). The method compensates for slot effects using the Carter coefficient, ensuring precise evaluation of circuit parameters and electromagnetic losses. To optimize motor performance, a multi-objective optimization technique is implemented using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), aiming to maximize both efficiency and power density. The research validates the proposed analytical approach against the finite element analysis method (FEM) results to confirm its accuracy. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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19 pages, 10794 KiB  
Article
The Innovative Design and Performance Testing of a Mobile Robot for the Automated Installation of Spacers on Six-Split Transmission Lines
by Jie Pan, Yongfeng Cheng, Chunhua Hu, Ming Jiang, Yong Ma, Fanhao Meng and Qiang Shi
Machines 2025, 13(5), 432; https://doi.org/10.3390/machines13050432 - 19 May 2025
Viewed by 244
Abstract
The spacer is an important component of a transmission line and can effectively prevent wires from whipping each other and inhibit vibration. Given the complex installation conditions of multi-split lines, the installation of spacers is mainly achieved through manual work, which has the [...] Read more.
The spacer is an important component of a transmission line and can effectively prevent wires from whipping each other and inhibit vibration. Given the complex installation conditions of multi-split lines, the installation of spacers is mainly achieved through manual work, which has the disadvantage of heavy labor intensity and a high risk factor. The robots that install two-split and four-split spacer bars cannot be applied to the complex operating conditions of six-split transmission lines. In order to improve the installation efficiency of spacers and reduce operating costs and risks, a new type of spacer-installing robot was researched based on the six-split transmission lines in this paper. Through the theoretical analysis of the wire’s arc sag, the moving device of the robot was designed. In order to improve the operating efficiency of the robot, the storage and feeding device of the six spacers was designed. A planar arm with the ability to assemble the spacer was designed. The overall design of the robot was completed by integrating the design of each unit. Through the experimental test, the results indicated that the robot was capable of installing six spacers at once, the maximum moving slope was 15 degrees, and the error rate in the spacer installation was 2.33%, which matched the manual installation of the spacers. The robot provided new ideas for the design of new transmission line engineering equipment and expanded the scope of the application of robots in the power industry. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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16 pages, 3243 KiB  
Article
Comparative Analysis of Dry, Minimum Quantity Lubrication, and Nano-Reinforced Minimum Quantity Lubrication Environments on the Machining Performance of AZ91D Magnesium Alloy
by Berat Baris Buldum, Kamil Leksycki and Suleyman Cinar Cagan
Machines 2025, 13(5), 430; https://doi.org/10.3390/machines13050430 - 19 May 2025
Viewed by 370
Abstract
This study investigates the machining performance of AZ91D magnesium alloy under three different cooling environments: dry, minimum quantity lubrication (MQL), and nano-reinforced MQL (NanoMQL) with multi-walled carbon nanotubes. Turning experiments were conducted on a CNC lathe with systematically varied cutting parameters, including cutting [...] Read more.
This study investigates the machining performance of AZ91D magnesium alloy under three different cooling environments: dry, minimum quantity lubrication (MQL), and nano-reinforced MQL (NanoMQL) with multi-walled carbon nanotubes. Turning experiments were conducted on a CNC lathe with systematically varied cutting parameters, including cutting speed (150–450 m/min), feed rate (0.05–0.2 mm/rev), and depth of cut (0.5–2 mm). The machining performance was evaluated through cutting force measurements, surface roughness analysis, and tool wear examination using SEM. The results demonstrate that the NanoMQL environment significantly outperforms both dry and conventional MQL conditions, providing a 42.2% improvement in surface quality compared to dry machining and a 33.6% improvement over conventional MQL. Cutting forces were predominantly influenced by the depth of cut and the feed rate, while cutting speed showed variable effects. SEM analysis revealed that the NanoMQL environment substantially reduced built-up edge formation and flank wear, particularly under aggressive cutting conditions. The superior performance of the NanoMQL environment is attributed to the enhanced thermal conductivity and lubrication properties of carbon nanotubes, which form a protective tribofilm at the tool–workpiece interface. This study provides valuable insights for optimizing the machining parameters of AZ91D magnesium alloy in industrial applications, particularly where high surface quality and tool longevity are required. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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28 pages, 7860 KiB  
Article
Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance Elevator
by Vasileios I. Vlachou and Theoklitos S. Karakatsanis
Machines 2025, 13(5), 427; https://doi.org/10.3390/machines13050427 - 19 May 2025
Viewed by 429
Abstract
Elevators serve as essential vertical transportation systems for both passengers and heavy loads in modern buildings. Electromechanical lifts have become the dominant choice due to their performance advantages over hydraulic systems. A critical component of their drive mechanism is the Permanent Magnet Synchronous [...] Read more.
Elevators serve as essential vertical transportation systems for both passengers and heavy loads in modern buildings. Electromechanical lifts have become the dominant choice due to their performance advantages over hydraulic systems. A critical component of their drive mechanism is the Permanent Magnet Synchronous Motor (PMSM), which is subject to mechanical and electrical stress during continuous operation. This necessitates advanced monitoring techniques to ensure safety, system reliability, and reduced maintenance costs. In this study, a fault-tolerant PMSM is designed and evaluated through 2D Finite Element Analysis (FEA), optimizing key electromagnetic parameters. The design is validated through experimental testing on a real elevator setup, capturing operational data under various loading conditions. These signals are preprocessed and analyzed using advanced machine-learning techniques, specifically a Random Forest classifier, to distinguish between Normal, Marginal, and Critical states of motor health. The model achieved a classification accuracy of 94%, demonstrating high precision in predictive maintenance capabilities. The results confirm that integrating a fault-tolerant PMSM design with real-time data analytics offers a reliable solution for early fault detection, minimizing downtime and enhancing elevator safety. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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19 pages, 8806 KiB  
Article
An Adaptive Control Strategy with Switching Gain and Forgetting Factor for a Robotic Arm Manipulator
by Mohammed Yousri Silaa, Oscar Barambones, Aissa Bencherif and Ilyas Rougab
Machines 2025, 13(5), 424; https://doi.org/10.3390/machines13050424 - 18 May 2025
Viewed by 304
Abstract
This paper presents an adaptive sliding mode controller (ASMC) with the implication of a forgetting factor for a two-degree-of-freedom (2-DOF) arm robot manipulator trajectory tracking. The proposed approach builds upon conventional sliding mode control (SMC), which is well known for its robustness and [...] Read more.
This paper presents an adaptive sliding mode controller (ASMC) with the implication of a forgetting factor for a two-degree-of-freedom (2-DOF) arm robot manipulator trajectory tracking. The proposed approach builds upon conventional sliding mode control (SMC), which is well known for its robustness and low tracking error. The controller dynamically adjusts this parameter by introducing an adaptive mechanism to enhance trajectory tracking, guarantee high robustness, and reduce chattering effects. In order to mitigate gain drift, a forgetting factor is incorporated into the adaptation law, ensuring stable and reliable control performance. Stability is validated using Lyapunov theory, and the effectiveness of the proposed ASMC is evaluated through numerical simulations. The simulations are conducted in MATLAB R2023b using a dynamic model of the 2-DOF robotic manipulator. Comparative results with conventional SMC confirm that the adaptive approach significantly improves tracking accuracy, noise robustness, and chattering suppression. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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21 pages, 4279 KiB  
Article
Development and Evaluation of a Tool for Blind Users Utilizing AI Object Detection and Haptic Feedback
by Georgios Voutsakelis, Ioannis Dimkaros, Nikolaos Tzimos, George Kokkonis and Sotirios Kontogiannis
Machines 2025, 13(5), 398; https://doi.org/10.3390/machines13050398 - 10 May 2025
Cited by 1 | Viewed by 589
Abstract
This paper presents the development and evaluation of a smartphone application designed to improve accessibility for blind users. It uses the lightweight EfficientDet-lite2 model and the comprehensive COCO dataset in order to provide real-time object detection. The novelty of the application is in [...] Read more.
This paper presents the development and evaluation of a smartphone application designed to improve accessibility for blind users. It uses the lightweight EfficientDet-lite2 model and the comprehensive COCO dataset in order to provide real-time object detection. The novelty of the application is in the integration of haptic feedback, which is activated when users touch objects that are detected on the screen, combined with audio notifications that announce the name of the detected object in multiple languages. This multimodal feedback mechanism helps blind users to recognize, explore, and move within their environment more effectively and safely. Extensive usability and user experience evaluation was conducted with blind and blindfolded users. The evaluation assessed the usability, effectiveness, accessibility, and user satisfaction and experience of the application. Additionally, a comparative analysis was performed between the use of haptic feedback and scenarios where haptic feedback was disabled. The results show a higher level of user satisfaction, greater ease of use, and significant potential for improving the independence of blind people when the haptic feedback is enabled. The findings also suggest that the inclusion of haptic feedback significantly enhances the user experience. This study underlines the importance of multimodal feedback systems in assistive technologies and the potential of mobile applications to provide accessible solutions for blind users. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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21 pages, 5195 KiB  
Article
On the Specimen Design, Physical Properties and Geometry Effect on Heat Generation and Thermal Gradient in Ultrasonic Fatigue
by Felipe Klein Fiorentin, Rita Dantas, Jorge Wolfs Gil, Andrea Piga Carboni, Thiago Antonio Fiorentin and Abílio Manuel Pinho de Jesus
Machines 2025, 13(5), 380; https://doi.org/10.3390/machines13050380 - 30 Apr 2025
Viewed by 264
Abstract
Performing fatigue characterisation is often an expensive task, being both time consuming and expensive. Taking that into account, ultrasonic fatigue testing is an interesting solution, since it can be thousands of times faster than traditional experiments. In ultrasonic fatigue testing, excitation frequencies are [...] Read more.
Performing fatigue characterisation is often an expensive task, being both time consuming and expensive. Taking that into account, ultrasonic fatigue testing is an interesting solution, since it can be thousands of times faster than traditional experiments. In ultrasonic fatigue testing, excitation frequencies are in the order of magnitude of 20 kHz, while common fatigue testing frequencies are typically approximately a few hundreds of Hz. Although promising, ultrasonic fatigue testing has some challenges, like high strain rates, heat generation and complex specimen design. Regarding the latter, since the working principle of ultrasonic fatigue tests relies on exciting the specimen in one of its natural frequencies, finding a specimen geometry to resonate at this given frequency might be challenging. Additionally, some materials often present challenges associated with high temperature during tests. The goal of this paper is to provide guidelines for specimen design, encompassing the effects of critical factors and their impact on important test parameters, like temperature and dimensions. The proposed methodology developed a parameter able to quantify the heat generation severity during ultrasonic fatigue testing for several materials based on their physical properties. Moreover, the effects of the geometry and material properties in the temperature during loading cycles, with special focus on thermal gradients were enumerated. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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18 pages, 8206 KiB  
Article
Hybrid Deep Learning for Fault Diagnosis in Photovoltaic Systems
by Mouaad Bougoffa, Samir Benmoussa, Mohand Djeziri and Olivier Palais
Machines 2025, 13(5), 378; https://doi.org/10.3390/machines13050378 - 30 Apr 2025
Viewed by 611
Abstract
Photovoltaic (PV) systems are integral to global renewable energy generation, yet their efficiency and reliability are frequently compromised by undetected faults, leading to significant energy losses, increased maintenance costs, and reduced operational lifespans. To address these challenges, this study proposes a novel hybrid [...] Read more.
Photovoltaic (PV) systems are integral to global renewable energy generation, yet their efficiency and reliability are frequently compromised by undetected faults, leading to significant energy losses, increased maintenance costs, and reduced operational lifespans. To address these challenges, this study proposes a novel hybrid deep learning framework that combines Stacked Sparse Auto-Encoders (SSAE) for autonomous feature extraction with an Optimized-Multi-Layer Perceptron (OMLP) for precise fault classification. The SSAE extracts high-dimensional fault features from raw operational data, while the OMLP leverages these features to classify faults with exceptional accuracy. The model was rigorously validated using real-world PV datasets, encompassing diverse fault types such as partial shading, open circuits, and module degradation under dynamic environmental conditions. Results demonstrate state-of-the-art performance, with the model achieving 99.82% accuracy, 99.7% precision, 99.4% sensitivity, and 100% specificity, outperforming traditional machine learning and deep learning approaches. These findings highlight the framework’s robustness and reliability in real-world applications. By significantly enhancing fault detection accuracy and computational efficiency, the proposed approach optimizes PV system performance, reduces operational costs, and supports sustainable energy production. This study concludes that the hybrid SSAE-Optimized MLP model represents a scalable and efficient solution for improving the reliability and longevity of renewable energy infrastructure, setting a new benchmark for intelligent maintenance strategies in the field. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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19 pages, 11846 KiB  
Article
Roll/Tip-Over Risk Analysis of Agricultural Self-Propelled Machines Using Airborne LiDAR Data: GIS-Based Approach
by Daniele Puri, Leonardo Vita, Davide Gattamelata and Valerio Tulliani
Machines 2025, 13(5), 377; https://doi.org/10.3390/machines13050377 - 30 Apr 2025
Viewed by 222
Abstract
Occupational Health and Safety (OHS) in agriculture is a critical concern worldwide, with self-propelled machinery accidents, particularly tip/roll-overs, being a leading cause of injuries and fatalities. In such a context, while great attention has been paid to machinery safety improvement, a major challenge [...] Read more.
Occupational Health and Safety (OHS) in agriculture is a critical concern worldwide, with self-propelled machinery accidents, particularly tip/roll-overs, being a leading cause of injuries and fatalities. In such a context, while great attention has been paid to machinery safety improvement, a major challenge is the lack of studies addressing the analysis of the work environment to provide farmers with precise information on field slope steepness. This information, merged with an awareness of machinery performance, such as tilt angles, can facilitate farmers in making decisions about machinery operations in hilly and mountainous areas. To address this gap, the Italian Compensation Authority (INAIL) launched a research programme to integrate georeferenced slope data with the tilt angle specifications of common self-propelled machinery, following EN ISO 16231-2:2015 standards. This study presents the first results of this research project, which was focused on vineyards in the alpine region of the Autonomous Province of Trento, where terrestrial LiDAR technology was used to analyze slope steepness. The findings aim to provide practical guidelines for safer machinery operation, benefiting farmers, risk assessors, and manufacturers. By enhancing awareness of tip/roll-over risks and promoting informed decision-making, this research aims to contribute to improving OHS in agriculture, particularly in challenging terrains. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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16 pages, 9202 KiB  
Article
Hybrid Brushless Wound-Rotor Synchronous Machine with Dual-Mode Operation for Washing Machine Applications
by Sheeraz Ahmed, Qasim Ali, Ghulam Jawad Sirewal, Kapeel Kumar and Gilsu Choi
Machines 2025, 13(5), 342; https://doi.org/10.3390/machines13050342 - 22 Apr 2025
Viewed by 534
Abstract
This paper proposes a hybrid brushless wound-rotor synchronous machine (HB-WRSM) with an outer rotor topology that can operate as a permanent magnet synchronous machine (PMSM), as well as an HB-WRSM. In the first part, the existing brushless wound-rotor synchronous machine (BL-WRSM) is modified [...] Read more.
This paper proposes a hybrid brushless wound-rotor synchronous machine (HB-WRSM) with an outer rotor topology that can operate as a permanent magnet synchronous machine (PMSM), as well as an HB-WRSM. In the first part, the existing brushless wound-rotor synchronous machine (BL-WRSM) is modified into a hybrid model by introducing permanent magnets (PMs) in the rotor pole faces to improve the magnetic field strength and other performance variables of the machine. In the second part, a centrifugal switch is introduced, which can change the machine operation from HB-WRSM to PMSM. The proposed machine uses an inner stator, outer rotor model with 36 stator slots and 48 poles, making the stator winding a concentrated winding. The HB-WRSM is utilized for dual-speed applications such as washing machines that run at low speed (46 rpm) and high speed (1370 rpm). For high speed, to have a better efficiency and less torque ripple, the machine is switched to PMSM mode using a centrifugal switch. The results are compared with the existing BL-WRSM. A 2D model is simulated using ANSYS Electromagnetics Suite to validate the machine model and performance. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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34 pages, 3804 KiB  
Article
EnsembleXAI-Motor: A Lightweight Framework for Fault Classification in Electric Vehicle Drive Motors Using Feature Selection, Ensemble Learning, and Explainable AI
by Md. Ehsanul Haque, Mahe Zabin and Jia Uddin
Machines 2025, 13(4), 314; https://doi.org/10.3390/machines13040314 - 12 Apr 2025
Viewed by 1300
Abstract
As electric vehicles (EVs) are growing, the fault diagnosis in their drive motor becomes more important to have optimal performance and safety. Traditional fault detection methods suffer mainly from high false positive and false negative rates, computational complexity, and lack of transparency in [...] Read more.
As electric vehicles (EVs) are growing, the fault diagnosis in their drive motor becomes more important to have optimal performance and safety. Traditional fault detection methods suffer mainly from high false positive and false negative rates, computational complexity, and lack of transparency in decision-making methods. In addition, existing models are also heavy and inefficient. A lightweight framework for fault diagnosis in EV drive motors is presented with the aid of Recursive Feature Elimination with Cross-Validation (RFE-CV), parameter optimization, and in-depth preprocessing. We further optimize the models and their combination to a hybrid Soft Voting Classifier. These techniques were applied to a dataset of 40,040 data entries that had been simulated by a Variable Frequency Drive (VFD) model. We evaluated eight machine learning models, and our proposed Soft Voting Classifier has the highest test accuracy of 94.52% and a Kappa score of 0.9210 on diagnostic performance. Also, the model has minimal memory usage and low inference latency. In addition, Local Interpretable Model-Agnostic Explanations (LIME) were used to improve transparency and gain an understanding of decisions made through the Soft Voting Classifier. Also, the framework was validated by an additional real-world dataset, thereby further confirming its robustness and consistency in performance for different conditions, which indicates the generalizability of the framework in real-world applications. RFE-CV is found to be very effective in feature selection and helps to construct a lightweight and cost-effective ensemble voting model for enhancing fault diagnosis for EV Drive Motors, overcoming its unsatisfactory transparency, accuracy, and computational efficiency. Finally, it contributes to the development of safer and more reliable EV systems through the development of models supervised on fewer features to give the computing time that is a little lighter without compromising its diagnostic performance. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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36 pages, 4990 KiB  
Article
Toward Inclusive Smart Cities: Sound-Based Vehicle Diagnostics, Emergency Signal Recognition, and Beyond
by Amr Rashed, Yousry Abdulazeem, Tamer Ahmed Farrag, Amna Bamaqa, Malik Almaliki, Mahmoud Badawy and Mostafa A. Elhosseini
Machines 2025, 13(4), 258; https://doi.org/10.3390/machines13040258 - 21 Mar 2025
Viewed by 739
Abstract
Sound-based early fault detection for vehicles is a critical yet underexplored area, particularly within Intelligent Transportation Systems (ITSs) for smart cities. Despite the clear necessity for sound-based diagnostic systems, the scarcity of specialized publicly available datasets presents a major challenge. This study addresses [...] Read more.
Sound-based early fault detection for vehicles is a critical yet underexplored area, particularly within Intelligent Transportation Systems (ITSs) for smart cities. Despite the clear necessity for sound-based diagnostic systems, the scarcity of specialized publicly available datasets presents a major challenge. This study addresses this gap by contributing in multiple dimensions. Firstly, it emphasizes the significance of sound-based diagnostics for real-time detection of faults through analyzing sounds directly generated by vehicles, such as engine or brake noises, and the classification of external emergency sounds, like sirens, relevant to vehicle safety. Secondly, this paper introduces a novel dataset encompassing vehicle fault sounds, emergency sirens, and environmental noises specifically curated to address the absence of such specialized datasets. A comprehensive framework is proposed, combining audio preprocessing, feature extraction (via Mel Spectrograms, MFCCs, and Chromatograms), and classification using 11 models. Evaluations using both compact (52 features) and expanded (126 features) representations show that several classes (e.g., Engine Misfire, Fuel Pump Cartridge Fault, Radiator Fan Failure) achieve near-perfect accuracy, though acoustically similar classes like Universal Joint Failure, Knocking, and Pre-ignition Problem remain challenging. Logistic Regression yielded the highest accuracy of 86.5% for the vehicle fault dataset (DB1) using compact features, while neural networks performed best for datasets DB2 and DB3, achieving 88.4% and 85.5%, respectively. In the second scenario, a Bayesian-Optimized Weighted Soft Voting with Feature Selection (BOWSVFS) approach is proposed, significantly enhancing accuracy to 91.04% for DB1, 88.85% for DB2, and 86.85% for DB3. These results highlight the effectiveness of the proposed methods in addressing key ITS limitations and enhancing accessibility for individuals with disabilities through auditory-based vehicle diagnostics and emergency recognition systems. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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14 pages, 863 KiB  
Article
Surface Classification from Robot Internal Measurement Unit Time-Series Data Using Cascaded and Parallel Deep Learning Fusion Models
by Ghaith Al-refai, Dina Karasneh, Hisham Elmoaqet, Mutaz Ryalat and Natheer Almtireen
Machines 2025, 13(3), 251; https://doi.org/10.3390/machines13030251 - 20 Mar 2025
Viewed by 520
Abstract
Surface classification is critical for ground robots operating in diverse environments, as it improves mobility, stability, and adaptability. This study introduces IMU-based deep learning models for surface classification as a low-cost alternative to computer vision systems. Two feature fusion models were introduced to [...] Read more.
Surface classification is critical for ground robots operating in diverse environments, as it improves mobility, stability, and adaptability. This study introduces IMU-based deep learning models for surface classification as a low-cost alternative to computer vision systems. Two feature fusion models were introduced to classify the surface type using time-series data from an IMU sensor mounted on a ground robot. The first model, a cascaded fusion model, employs a 1-D Convolutional Neural Network (CNN) followed by a Long Short-Term Memory (LSTM) network and then a multi-head attention mechanism. The second model is a parallel fusion model, which processes sensor data through both a CNN and an LSTM simultaneously before concatenating the resulting feature vectors and then passing them to a multi-head attention mechanism. Both models utilize a multi-head attention mechanism to enhance focus on relevant segments of the time-sequence data. The models were trained on a normalized Internal Measurement Unit (IMU) dataset, with hyperparameter tuning achieved via grid search for optimal performance. Results showed that the cascaded model achieved higher accuracy metrics, including a mean Average Precision (mAP) of 0.721 compared to 0.693 for the parallel model. However, the cascaded model incurred a 44.37% increase in processing time, which makes the parallel fusion model more suitable for real-time applications. The multi-head attention mechanism contributed significantly to accuracy improvements, particularly in the cascaded model. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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24 pages, 10737 KiB  
Article
Experimental Design and Simulation of a Fly-Cutting Plant for Academic Environment Practices
by Diego Fernando Ramírez-Jiménez and Cristian Alejandro Torres Valencia
Machines 2025, 13(1), 15; https://doi.org/10.3390/machines13010015 - 29 Dec 2024
Viewed by 3966
Abstract
Test plants or laboratory prototypes are essential for developing training activities in engineering. In the field of automation and control, simulators or high-fidelity equipment models commonly used in industrial processes are necessary. These tools allow engineering trainees to gain experience working with devices [...] Read more.
Test plants or laboratory prototypes are essential for developing training activities in engineering. In the field of automation and control, simulators or high-fidelity equipment models commonly used in industrial processes are necessary. These tools allow engineering trainees to gain experience working with devices similar to those they will encounter in their professional contexts. This paper presents the design and simulation of a fly-cutting plant for academic use. A 3D model was developed in SketchUp, incorporating features typical of industrial plants. The system’s simulation was carried out in MATLAB R2023b using mathematical modeling. The primary contribution of this work is the design of a low-cost, compact industrial prototype that includes a conveyor belt and a continuous cutting mechanism, enabling the understanding and operation of large-scale industrial processes. Performance tests were conducted using MATLAB, Simulink, and Code Composer Studio. Subsequently, operational and cutting tests were performed using classical control techniques. Additionally, the design features of the fly-cutting plant, which can be easily implemented for process control training activities, are detailed. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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32 pages, 3912 KiB  
Article
Proposed Multi-ST Model for Collaborating Multiple Robots in Dynamic Environments
by Hai Van Pham, Huy Quoc Do, Minh Nguyen Quang, Farzin Asadi and Philip Moore
Machines 2024, 12(11), 797; https://doi.org/10.3390/machines12110797 - 11 Nov 2024
Cited by 2 | Viewed by 1259
Abstract
Coverage path planning describes the process of finding an effective path robots can take to traverse a defined dynamic operating environment where there are static (fixed) and dynamic (mobile) obstacles that must be located and avoided in coverage path planning. However, most coverage [...] Read more.
Coverage path planning describes the process of finding an effective path robots can take to traverse a defined dynamic operating environment where there are static (fixed) and dynamic (mobile) obstacles that must be located and avoided in coverage path planning. However, most coverage path planning methods are limited in their ability to effectively manage the coordination of multiple robots operating in concert. In this paper, we propose a novel coverage path planning model (termed Multi-ST) which utilizes the spiral-spanning tree coverage algorithm with intelligent reasoning and knowledge-based methods to achieve optimal coverage, obstacle avoidance, and robot coordination. In experimental testing, we have evaluated the proposed model with a comparative analysis of alternative current approaches under the same conditions. The reported results show that the proposed model enables the avoidance of static and moving obstacles by multiple robots operating in concert in a dynamic operating environment. Moreover, the results demonstrate that the proposed model outperforms existing coverage path planning methods in terms of coverage quality, robustness, scalability, and efficiency. In this paper, the assumptions, limitations, and constraints applicable to this study are set out along with related challenges, open research questions, and proposed directions for future research. We posit that our proposed approach can provide an effective basis upon which multiple robots can operate in concert in a range of ‘real-world’ domains and systems where coverage path planning and the avoidance of static and dynamic obstacles encountered in completing tasks is a systemic requirement. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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20 pages, 10093 KiB  
Article
Digital Twin for Flexible Manufacturing Systems and Optimization Through Simulation: A Case Study
by Adriana Florescu
Machines 2024, 12(11), 785; https://doi.org/10.3390/machines12110785 - 7 Nov 2024
Cited by 4 | Viewed by 4824
Abstract
The research presented in this paper aligns with the advancement of Industry 4.0 by integrating intelligent machine tools and industrial robots within Flexible Manufacturing Systems (FMS). Primarily, a development approach for Digital Twin (DT) is presented, beginning from the design, sizing, and configuration [...] Read more.
The research presented in this paper aligns with the advancement of Industry 4.0 by integrating intelligent machine tools and industrial robots within Flexible Manufacturing Systems (FMS). Primarily, a development approach for Digital Twin (DT) is presented, beginning from the design, sizing, and configuration stages of the system and extending through its implementation, commissioning, operation, and simulation-based optimization. The digitization of current industrial processes entails the development of applications based on modern technologies, utilizing state-of-the-art tools and software. The general objective was to create a digital replica of a process to propose optimization solutions through simulation and subsequently achieve virtual commissioning. The practical nature of the research is reflected in the design and implementation of a Digital Twin for a real physical system processing a family of cylindrical parts within an existing experimental FMS. A digital model of the system was created by defining each individual device and piece of equipment from the physical system, so the virtual model operates just like the real one. By implementing the Digital Twin, both time-based and event-based simulations were performed. Through the execution of multiple scenarios, it was possible to identify system errors and collisions, and propose optimization solutions by implementing complex, collaborative-robot equipment where multiple interactions occur simultaneously. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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20 pages, 7994 KiB  
Article
Design of Connector Assembly Equipment for the Automotive Industry
by Pedro M. P. Curralo, Raul D. S. G. Campilho, Joaquim A. P. Pereira and Francisco J. G. Silva
Machines 2024, 12(10), 731; https://doi.org/10.3390/machines12100731 - 16 Oct 2024
Cited by 1 | Viewed by 1592
Abstract
The automotive industry is one of the most demanding sectors of all manufacturing industries due to its competitiveness. It is necessary to innovate through the implementation of automated and robotic equipment, leading to cycle time and labor cost reduction. This work aims to [...] Read more.
The automotive industry is one of the most demanding sectors of all manufacturing industries due to its competitiveness. It is necessary to innovate through the implementation of automated and robotic equipment, leading to cycle time and labor cost reduction. This work aims to design semi-automatic equipment to assemble cabling connectors used in the automotive sector, replacing a manual process currently taking place in an automotive components company. In the proposed equipment, the operator places a connector in the equipment, and the components (pins and seals) are automatically inserted. A vision sensor with artificial intelligence then confirms the correct application. The equipment operation defined as Finite Element Method (FEM) was applied for structural verification; the materials and fabrication processes were detailed; the associated costs were calculated, and the equipment subsets were validated. The design was successfully accomplished, and the imposed requirements were fulfilled, with significant advantages over the current process, providing new knowledge on how semi-automatic systems can be deployed to enhance the productivity and quality of manufacturing processes. The design principles and insights gained from this work can be applied to other automation challenges, particularly where manual processes need to be replaced by more efficient semi-automatic or automatic systems. The modularity of the overall solution and the design concepts of the component inserter, component feeder, and assembly process allow for its use in different assembly scenarios beyond the automotive sector, such as electronics or aerospace, providing a contribution to increased competitiveness and survival in the global market. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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34 pages, 39851 KiB  
Article
Supporting Human–Robot Interaction in Manufacturing with Augmented Reality and Effective Human–Computer Interaction: A Review and Framework
by Karthik Subramanian, Liya Thomas, Melis Sahin and Ferat Sahin
Machines 2024, 12(10), 706; https://doi.org/10.3390/machines12100706 - 4 Oct 2024
Cited by 6 | Viewed by 2732
Abstract
The integration of Augmented Reality (AR) into Human–Robot Interaction (HRI) represents a significant advancement in collaborative technologies. This paper provides a comprehensive review of AR applications within HRI with a focus on manufacturing, emphasizing their role in enhancing collaboration, trust, and safety. By [...] Read more.
The integration of Augmented Reality (AR) into Human–Robot Interaction (HRI) represents a significant advancement in collaborative technologies. This paper provides a comprehensive review of AR applications within HRI with a focus on manufacturing, emphasizing their role in enhancing collaboration, trust, and safety. By aggregating findings from numerous studies, this research highlights key challenges, including the need for improved Situational Awareness, enhanced safety, and more effective communication between humans and robots. A framework developed from the literature is presented, detailing the critical elements of AR necessary for advancing HRI. The framework outlines effective methods for continuously evaluating AR systems for HRI. The framework is supported with the help of two case studies and another ongoing research endeavor presented in this paper. This structured approach focuses on enhancing collaboration and safety, with a strong emphasis on integrating best practices from Human–Computer Interaction (HCI) centered around user experience and design. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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16 pages, 11219 KiB  
Article
Design and Experimental Research of a Non-Destructive Detection Device for High-Precision Cylindrical Roller Dynamic Unbalance
by Zhuangya Zhang, Baorun Yang, Mingde Duan, Ruijie Gu, Shijie Liang and Yang Chen
Machines 2024, 12(10), 684; https://doi.org/10.3390/machines12100684 - 29 Sep 2024
Viewed by 745
Abstract
Due to their small size and light mass, small precision cylindrical rollers present challenges in dynamic unbalance detection, including difficulties in measurement and the risk of surface damage. This paper proposes a non-destructive detection device for assessing the dynamic unbalance of small precision [...] Read more.
Due to their small size and light mass, small precision cylindrical rollers present challenges in dynamic unbalance detection, including difficulties in measurement and the risk of surface damage. This paper proposes a non-destructive detection device for assessing the dynamic unbalance of small precision cylindrical rollers. The device utilizes an air flotation support method combined with resonance amplification to indirectly measure the dynamic unbalance. A dynamic model of the air flotation tooling-cylindrical roller vibration system was developed to explore the relationship between the vibration parameters of the air flotation tooling and the dynamic unbalance of the cylindrical roller. Modal analysis and harmonic response analysis were performed, revealing that the amplitude of the vibration system at resonance could be detected using the sensor. Additionally, modal testing was conducted to determine the natural frequency of the system. A non-destructive detection platform was constructed for testing the dynamic unbalance of cylindrical rollers. Microscopic observation of the roller surface before and after testing confirmed that the device successfully performs non-destructive detection of dynamic unbalance. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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21 pages, 4249 KiB  
Article
Design and Development of a Flexible Manufacturing Cell Controller Using an Open-Source Communication Protocol for Interoperability
by Evangelos Tzimas, George Papazetis, Panorios Benardos and George-Christopher Vosniakos
Machines 2024, 12(8), 519; https://doi.org/10.3390/machines12080519 - 30 Jul 2024
Viewed by 2616
Abstract
Flexible manufacturing cells provide significant advantages in low-volume mass-customization production but also induce added complexity and technical challenges in terms of integration, control, and extensibility. The variety of closed-source industrial protocols, the heterogeneous equipment, and the product’s manufacturing specifications are main points of [...] Read more.
Flexible manufacturing cells provide significant advantages in low-volume mass-customization production but also induce added complexity and technical challenges in terms of integration, control, and extensibility. The variety of closed-source industrial protocols, the heterogeneous equipment, and the product’s manufacturing specifications are main points of consideration in the development of such a system. This study aims to describe the approach, from concept to implementation, for the development of the controller for a flexible manufacturing cell consisting of heterogeneous equipment in terms of functions and communication interfaces. Emphasis is put on the considerations and challenges for effective integration, extensibility, and interoperability. Scheduling and monitoring performed by the developed controller are demonstrated for a manufacturing cell producing microfluidic devices (bioMEMS) that consists of six workstations and a robot-based handling system. Communication between the system controller and the workstations was based on open-source technologies instead of proprietary software and protocols, to support interoperability and, to a considerable extent, code reusability. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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20 pages, 7730 KiB  
Article
Accuracy Analysis of Complex Transmission System with Distributed Tooth Profile Errors
by Min Zhang, Zhijing Zhang, Jian Xiong and Xiao Chen
Machines 2024, 12(7), 459; https://doi.org/10.3390/machines12070459 - 6 Jul 2024
Cited by 2 | Viewed by 1052
Abstract
Tooth profile errors are the internal excitations that cause gear meshing errors, which are critical error factors affecting gear transmission accuracy. In existing studies, it is usually regarded as a constant or random distribution function. However, the actual machined tooth profile error is [...] Read more.
Tooth profile errors are the internal excitations that cause gear meshing errors, which are critical error factors affecting gear transmission accuracy. In existing studies, it is usually regarded as a constant or random distribution function. However, the actual machined tooth profile error is not a constant, so this estimation is inconsistent with the actual situation, resulting in an inaccurate evaluation of transmission accuracy. This paper proposes a method for representing tooth profile errors using distribution errors (including systematic and random errors), and a mathematical model of distributed tooth profile errors is presented. The contact stresses of the complex transmission system were compared with those obtained by formulas, proving that tooth profile errors increase contact stress. A method for calculating gear meshing error is proposed to evaluate the actual output accuracy of the complex transmission system. Compared with the test, the output accuracy is reduced by 13.8% under the temperature environment and distributed tooth profile errors. The proposed methods can accurately predict the transmission accuracy of precision transmission systems at the design stage and provide theoretical support for reducing systematic and random errors at the gear machining stage. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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15 pages, 1589 KiB  
Article
AI-Driven Virtual Sensors for Real-Time Dynamic Analysis of Mechanisms: A Feasibility Study
by Davide Fabiocchi, Nicola Giulietti, Marco Carnevale and Hermes Giberti
Machines 2024, 12(4), 257; https://doi.org/10.3390/machines12040257 - 12 Apr 2024
Cited by 3 | Viewed by 2480
Abstract
The measurement of the ground forces on a real structure or mechanism in operation can be time-consuming and expensive, particularly when production cannot be halted to install sensors. In cases in which disassembling the parts of the system to accommodate sensor installation is [...] Read more.
The measurement of the ground forces on a real structure or mechanism in operation can be time-consuming and expensive, particularly when production cannot be halted to install sensors. In cases in which disassembling the parts of the system to accommodate sensor installation is neither feasible nor desirable, observing the structure or mechanism in operation and quickly deducing its force trends would facilitate monitoring activities in industrial processes. This opportunity is gradually becoming a reality thanks to the coupling of artificial intelligence (AI) with design techniques such as the finite element and multi-body methods. Properly trained inferential models could make it possible to study the dynamic behavior of real systems and mechanisms in operation simply by observing them in real time through a camera, and they could become valuable tools for investigation during the operation of machinery and devices without the use of additional sensors, which are difficult to use and install. In this paper, the idea presented is developed and applied to a simple mechanism for which the reaction forces during operating conditions are to be determined. This paper explores the implementation of an innovative vision-based virtual sensor that, through data-driven training, is able to emulate traditional sensing solutions for the estimation of reaction forces. The virtual sensor and relative inferential model is validated in a scenario as close to the real world as possible, taking into account interfering inputs that add to the measurement uncertainty, as in a real-world measurement scenario. The results indicate that the proposed model has great robustness and accuracy, as evidenced by the low RMSE values in predicting the reaction forces. This demonstrates the model’s effectiveness in reproducing real-world scenarios, highlighting its potential in the real-time estimation of ground reaction forces in industrial settings. The success of this vision-based virtual sensor model opens new avenues for more robust, accurate, and cost-effective solutions for force estimation, addressing the challenges of uncertainty and the limitations of physical sensor deployment. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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Review

Jump to: Research

44 pages, 4373 KiB  
Review
Recent Advances in Multi-Agent Reinforcement Learning for Intelligent Automation and Control of Water Environment Systems
by Lei Jia and Yan Pei
Machines 2025, 13(6), 503; https://doi.org/10.3390/machines13060503 - 9 Jun 2025
Viewed by 866
Abstract
Multi-agent reinforcement learning (MARL) has demonstrated significant application potential in addressing cooperative control, policy optimization, and task allocation problems in complex systems. This paper focuses on its applications and development in water environmental systems, providing a systematic review of the theoretical foundations of [...] Read more.
Multi-agent reinforcement learning (MARL) has demonstrated significant application potential in addressing cooperative control, policy optimization, and task allocation problems in complex systems. This paper focuses on its applications and development in water environmental systems, providing a systematic review of the theoretical foundations of multi-agent systems and reinforcement learning and summarizing three representative categories of mainstream MARL algorithms. Typical control scenarios in water systems are also examined. From the perspective of cooperative control, this paper investigates the modeling mechanisms and policy coordination strategies of MARL in key tasks such as water supply scheduling, hydro-energy co-regulation, and autonomous monitoring. It further analyzes the challenges and solutions for improving global cooperative efficiency under practical constraints such as limited resources, system heterogeneity, and unstable communication. Additionally, recent progress in cross-domain generalization, integrated communication–perception frameworks, and system-level robustness enhancement is summarized. This work aims to provide a theoretical foundation and key insights for advancing research and practical applications of MARL-based intelligent control in water infrastructure systems. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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19 pages, 15630 KiB  
Review
Review of Automated Operations in Drilling and Mining
by Athanasios Kokkinis, Theodore Frantzis, Konstantinos Skordis, George Nikolakopoulos and Panagiotis Koustoumpardis
Machines 2024, 12(12), 845; https://doi.org/10.3390/machines12120845 - 25 Nov 2024
Cited by 2 | Viewed by 3626
Abstract
Current advances and trends in the fields of mechanical, material, and software engineering have allowed mining technology to undergo a significant transformation. Aiming to maximize the efficiency and safety of the mining process, several enabling technologies, such as the recent advances in artificial [...] Read more.
Current advances and trends in the fields of mechanical, material, and software engineering have allowed mining technology to undergo a significant transformation. Aiming to maximize the efficiency and safety of the mining process, several enabling technologies, such as the recent advances in artificial intelligence, IoT, sensor fusion, computational modeling, and advanced robotics, are being progressively adopted in mining machine manufacturing while replacing conventional parts and approaches that used to be the norm in the rock ore extraction industry. This article aims to provide an overview of research trends and state-of-the-art technologies in face exploration and drilling operations in order to define the vision toward the realization of fully autonomous mining exploration machines of the future, capable of operating without any external infrastructure. As the trend of mining at large depths is increasing and as the re-opening of abandoned mines is gaining more interest, near-to-face mining exploration approaches for identifying new ore bodies need to undergo significant revision. This article aims to contribute to future developments in the use of fully autonomous and cooperative smaller mining exploration machines. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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