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21 pages, 5195 KiB

Open AccessArticle

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 123

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

Open AccessArticle

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 172

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

Open AccessArticle

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 73

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

Open AccessArticle

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 286

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

Open AccessArticle

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 771

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

Open AccessArticle

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 496

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

Open AccessArticle

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 353

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

Open AccessArticle

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 3826

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

Open AccessArticle

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 1090

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

Open AccessArticle

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 3 | Viewed by 3744

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

Open AccessArticle

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 1407

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

Open AccessArticle

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 3 | Viewed by 2237

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

Open AccessArticle

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 672

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

Open AccessArticle

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 2440

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

Open AccessArticle

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 916

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

Open AccessArticle

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 2 | Viewed by 2275

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

19 pages, 15630 KiB

Open AccessReview

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 1 | Viewed by 2913

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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Recent Developments in Machine Design, Automation and Robotics

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