Machines

19 pages, 7892 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Viewed by 3146

Abstract

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

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

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

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16 pages, 3622 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 2 | Viewed by 1113

Abstract

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

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

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

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16 pages, 8804 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Viewed by 883

Abstract

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

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

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

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18 pages, 3422 KiB

Open AccessEditor’s ChoiceArticle

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

by Markus Grebe, Alexander Baral and Dominik Martin

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

Viewed by 861

Abstract

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

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

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

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41 pages, 7143 KiB

Open AccessEditor’s ChoiceReview

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

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

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

Cited by 4 | Viewed by 2578

Abstract

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

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

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

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62 pages, 17046 KiB

Open AccessEditor’s ChoiceReview

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

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

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

Cited by 6 | Viewed by 4306

Abstract

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

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

(This article belongs to the Section Advanced Manufacturing)

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15 pages, 3119 KiB

Open AccessEditor’s ChoiceArticle

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

by Nan-Kai Hsieh and Tsung-Yu Yu

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

Viewed by 1432

Abstract

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

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

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

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20 pages, 5509 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Viewed by 964

Abstract

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

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

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

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13 pages, 6682 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 1 | Viewed by 1326

Abstract

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

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

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

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16 pages, 7033 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Viewed by 670

Abstract

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

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

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

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21 pages, 5379 KiB

Open AccessEditor’s ChoiceArticle

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

by Frezgi Tesfom Kebede, Jawad Zaghal and Csaba Felho

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

Cited by 1 | Viewed by 1947

Abstract

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

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

(This article belongs to the Section Advanced Manufacturing)

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21 pages, 16720 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Viewed by 742

Abstract

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

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

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

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29 pages, 53780 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 3 | Viewed by 3063

Abstract

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

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

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

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19 pages, 3670 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 1 | Viewed by 1166

Abstract

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

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

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

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20 pages, 3602 KiB

Open AccessEditor’s ChoiceArticle

Effective Machine Learning Solution for State Classification and Productivity Identification: Case of Pneumatic Pressing Machine

by Alexandros Kolokas, Panagiotis Mallioris, Michalis Koutsiantzis, Christos Bialas, Dimitrios Bechtsis and Evangelos Diamantis

Machines 2024, 12(11), 762; https://doi.org/10.3390/machines12110762 - 30 Oct 2024

Cited by 3 | Viewed by 1288

Abstract

The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency [...] Read more.

The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency and safety of machinery, the reduction of production costs, and the enhancement of product quality. Predictive maintenance (PdM) utilizes historical data and AI models to diagnose equipment’s health and predict the remaining useful life (RUL), providing critical insights for machinery effectiveness and product manufacturing. This prediction is a critical strategy to maximize the useful life of equipment, especially in large-scale and important infostructures. This study focuses on developing an unsupervised machine state-classification solution utilizing real-world industrial measurements collected from a pneumatic pressing machine. Unsupervised machine learning (ML) models were tested to diagnose and output the working state of the pressing machine at each given point (offline, idle, pressing, defective). Our research contributes to extracting valuable insights regarding real-world industrial settings for PdM and production efficiency using unsupervised ML, promoting operation safety, cost reduction, and productivity enhancement in modern industries. Full article

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

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20 pages, 9472 KiB

Open AccessEditor’s ChoiceArticle

A Novel RUL-Centric Data Augmentation Method for Predicting the Remaining Useful Life of Bearings

by Miao He, Zhonghua Li and Fangchao Hu

Machines 2024, 12(11), 766; https://doi.org/10.3390/machines12110766 - 30 Oct 2024

Cited by 3 | Viewed by 993

Abstract

Maintaining the reliability of rotating machinery in industrial environments entails significant challenges. The objective of this paper is to develop a methodology that can accurately predict the condition of rotating machinery in order to facilitate the implementation of effective preventive maintenance strategies. This [...] Read more.

Maintaining the reliability of rotating machinery in industrial environments entails significant challenges. The objective of this paper is to develop a methodology that can accurately predict the condition of rotating machinery in order to facilitate the implementation of effective preventive maintenance strategies. This article proposed a novel RUL-centric data augmentation method, designated as DF-MDAGRU, for the purpose of predicting the remaining useful life (RUL) of bearings. This model is based on an encoder–decoder framework that integrates time–frequency domain feature enhancement with multidimensional dynamic attention gated recurrent units for feature extraction. This method enhances time–frequency domain features through the Discrete Wavelet Downsampling module (DWD) and Convolutional Fourier Residual Block (CFRB). This method employs a Multiscale Channel Attention Module (MS-CAM) and a Multiscale Convolutional Spatial Attention Mechanism (MSSAM) to extract channel and spatial feature information. Finally, the output predictions are processed through linear regression to achieve the final RUL estimation. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches on the FEMETO-ST and XJTU datasets. Full article

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21 pages, 7354 KiB

Open AccessEditor’s ChoiceArticle

Visual-Inertial Fusion-Based Five-Degree-of-Freedom Motion Measurement System for Vessel-Mounted Cranes

by Boyang Yu, Yuansheng Cheng, Xiangjun Xia, Pengfei Liu, Donghong Ning and Zhixiong Li

Machines 2024, 12(11), 748; https://doi.org/10.3390/machines12110748 - 23 Oct 2024

Viewed by 1370

Abstract

Vessel-mounted cranes operate in complex marine environments, where precise measurement of cargo positions and attitudes is a key technological challenge to ensure operational stability and safety. This study introduces an integrated measurement system that combines vision and inertial sensing technologies, utilizing a stereo [...] Read more.

Vessel-mounted cranes operate in complex marine environments, where precise measurement of cargo positions and attitudes is a key technological challenge to ensure operational stability and safety. This study introduces an integrated measurement system that combines vision and inertial sensing technologies, utilizing a stereo camera and two inertial measurement units (IMUs) to capture cargo motion in five degrees of freedom (DOF). By merging data from the stereo camera and IMUs, the system accurately determines the cargo’s position and attitude relative to the camera. The specific methodology is introduced as follows: First, the YOLO model is adopted to identify targets in the image and generate bounding boxes. Then, using the principle of binocular disparity, the depth within the bounding box is calculated to determine the target’s three-dimensional position in the camera coordinate system. Simultaneously, the IMU measures the attitude of the cargo, and a Kalman filter is applied to fuse the data from the two sensors. Experimental results indicate that the system’s measurement errors in the x, y, and z directions are less than 2.58%, 3.35%, and 3.37%, respectively, while errors in the roll and pitch directions are 3.87% and 5.02%. These results demonstrate that the designed measurement system effectively provides the necessary motion information in 5-DOF for vessel-mounted crane control, offering new approaches for pose detection of marine cranes and cargoes. Full article

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

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15 pages, 2222 KiB

Open AccessEditor’s ChoiceArticle

LSTM-Inversion-Based Feedforward–Feedback Nanopositioning Control

by Ruocheng Yin and Juan Ren

Machines 2024, 12(11), 747; https://doi.org/10.3390/machines12110747 - 22 Oct 2024

Cited by 1 | Viewed by 958

Abstract

This work proposes a two-degree of freedom (2DOF) controller for motion tracking of nanopositioning devices, such as piezoelectric actuators (PEAs), with a broad bandwidth and high precision. The proposed 2DOF controller consists of an inversion feedforward controller and a real-time feedback controller. The [...] Read more.

This work proposes a two-degree of freedom (2DOF) controller for motion tracking of nanopositioning devices, such as piezoelectric actuators (PEAs), with a broad bandwidth and high precision. The proposed 2DOF controller consists of an inversion feedforward controller and a real-time feedback controller. The feedforward controller, a sequence-to-sequence LSTM-based inversion model (invLSTMs2s), is used to compensate for the nonlinearity of the PEA, especially at high frequencies, and is collaboratively integrated with a linear MPC feedback controller, which ensures the PEA position tracking performance at low frequencies. Therefore, the proposed 2DOF controller, namely, invLSTMs2s+MPC, is able to achieve high precision over a broad bandwidth. To validate the proposed controller, the uncertainty of invLSTMs2s is checked such that the integration of an inversion model-based feedforward controller has a positive impact on the trajectory tracking performance compared to feedback control only. Experimental validation on a commercial PEA and comparison with existing approaches demonstrate that high tracking accuracies can be achieved by invLSTMs2s+MPC for various reference trajectories. Moreover, invLSTMs2s+MPC is further demonstrated on a multi-dimensional PEA platform for simultaneous multi-direction positioning control. Full article

(This article belongs to the Special Issue Advances in Applied Mechatronics, Volume II)

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23 pages, 10799 KiB

Open AccessEditor’s ChoiceArticle

The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal Deformation

by Jingqi Zhang, Jianxing Zhou, Quanwei Cui, Ning Dong, Hong Jiang and Zhong Fang

Machines 2024, 12(10), 734; https://doi.org/10.3390/machines12100734 - 17 Oct 2024

Viewed by 1267

Abstract

Errors affect the actual meshing process of gears, alter the actual wear pattern of the tooth profile, and may even impact the overall service life of machinery. While existing research predominantly focuses on individual errors or a narrow set of factors, this study [...] Read more.

Errors affect the actual meshing process of gears, alter the actual wear pattern of the tooth profile, and may even impact the overall service life of machinery. While existing research predominantly focuses on individual errors or a narrow set of factors, this study explores the combined effects of multiple errors on tooth profile wear. A comprehensive gear wear prediction model was developed, integrating the slice method, lumped mass method, Hertz contact model, and Archard’s wear theory. This model accounts for initial tooth surface topography, thermal deformation, dynamic effects, and wear, establishing strong correlations between gear wear prediction and key factors such as tooth surface morphology, temperature, and vibration. Experimental validation demonstrated the model’s high accuracy, with relatively small deviations from the observed wear. Initial profile errors (IPEs) at different positions along the tooth width result in varying relative sliding distances, leading to differences in wear depth despite a consistent overall trend. Notably, large IPEs at the dedendum and addendum can influence wear progression, either accelerating or decelerating the wear process over time. Full article

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

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17 pages, 11316 KiB

Open AccessEditor’s ChoiceArticle

Thermal Error Transfer Prediction Modeling of Machine Tool Spindle with Self-Attention Mechanism-Based Feature Fusion

by Yue Zheng, Guoqiang Fu, Sen Mu, Caijiang Lu, Xi Wang and Tao Wang

Machines 2024, 12(10), 728; https://doi.org/10.3390/machines12100728 - 15 Oct 2024

Viewed by 1436

Abstract

Thermal errors affect machining accuracy in high-speed precision machining. The variability of machine tool operating conditions poses a challenge to the modeling of thermal errors. In this paper, a thermal error model based on transfer temperature feature fusion is proposed. Firstly, the temperature [...] Read more.

Thermal errors affect machining accuracy in high-speed precision machining. The variability of machine tool operating conditions poses a challenge to the modeling of thermal errors. In this paper, a thermal error model based on transfer temperature feature fusion is proposed. Firstly, the temperature information fusion features are built as inputs to the model, which is based on a self-attention mechanism to assign weights to the temperature information and fuse the features. Secondly, an improved direct normalization-based adaptive matrix approach is proposed, updating the background matrix using an autoencoder and reconstructing the adaptive matrix to realize domain self-adaptation. In addition, for the improved adaptive matrix, a criterion for determining whether the working conditions are transferrable to each other is proposed. The proposed method shows high prediction accuracy while ensuring training efficiency. Finally, thermal error experiments are performed on a VCM850 CNC machine tool. Full article

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

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22 pages, 7423 KiB

Open AccessEditor’s ChoiceArticle

Advancing UAV Sensor Fault Diagnosis Based on Prior Knowledge and Graph Convolutional Network

by Hui Li, Chaoyin Chen, Tiancai Wan, Shaoshan Sun, Yongbo Li and Zichen Deng

Machines 2024, 12(10), 716; https://doi.org/10.3390/machines12100716 - 10 Oct 2024

Cited by 2 | Viewed by 1249

Abstract

Unmanned aerial vehicles (UAVs) are equipped with various sensors to facilitate control and navigation. However, UAV sensors are highly susceptible to damage under complex flight environments, leading to severe accidents and economic losses. Although fault diagnosis methods based on deep neural networks have [...] Read more.

Unmanned aerial vehicles (UAVs) are equipped with various sensors to facilitate control and navigation. However, UAV sensors are highly susceptible to damage under complex flight environments, leading to severe accidents and economic losses. Although fault diagnosis methods based on deep neural networks have been widely applied in the mechanical field, these methods often fail to integrate multi-source information and overlook the importance of system prior knowledge. As a result, this study employs a spatial-temporal difference graph convolutional network (STDGCN) for the fault diagnosis of UAV sensors, where the graph structure naturally organizes the diverse sensors. Specifically, a difference layer enhances the feature extraction capability of the graph nodes, and the spatial-temporal graph convolutional modules are designed to extract spatial-temporal dependencies from sensor data. Moreover, to ensure the accuracy of the association graph, this research introduces the UAV’s dynamic model as prior knowledge for constructing the association graph. Finally, diagnostic accuracies of 94.93%, 98.71%, and 92.97% were achieved on three self-constructed datasets. In addition, compared to commonly used data-driven approaches, the proposed method demonstrates superior feature extraction capabilities and achieves the highest diagnostic accuracy. Full article

(This article belongs to the Special Issue Data-Driven Approaches regarding Dynamic Modelling, Diagnostics and Prognostics of Complex Mechanical Structures)

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19 pages, 2951 KiB

Open AccessEditor’s ChoiceArticle

Finite-Time Adaptive Control for Electro-Hydraulic Braking Gear Transmission Mechanism with Unilateral Dead Zone Nonlinearity

by Qinghua Cao, Jian Wu, Fuxing Xu, Xinhong Miao, Mingjie Guo and Yuan Chu

Machines 2024, 12(10), 698; https://doi.org/10.3390/machines12100698 - 2 Oct 2024

Viewed by 1046

Abstract

Autonomous vehicles require more precise and reliable braking control, and electro-hydraulic braking (EHB) systems are better adapted to the development of autonomous driving. However, EHB systems inevitably suffer from unilateral dead zone nonlinearity, which adversely affects the position tracking control. Therefore, a finite-time [...] Read more.

Autonomous vehicles require more precise and reliable braking control, and electro-hydraulic braking (EHB) systems are better adapted to the development of autonomous driving. However, EHB systems inevitably suffer from unilateral dead zone nonlinearity, which adversely affects the position tracking control. Therefore, a finite-time adaptive control strategy was designed for unilateral dead zone nonlinearity. Initially, the unilateral dead zone nonlinearity was reformulated into a matched disturbance term and an unmatched disturbance term to reduce the adverse effects of disturbances, thereby enhancing system controllability. Then, the “complexity explosion” in the design of the control strategy was avoided by command filtering, and the design process of the controller was simplified. Furthermore, the finite-time control theory was employed to boost the system’s convergence speed, thereby enhancing control performance. In order to ensure the stability of the system under the dead zone disturbance, the unknown disturbance terms were estimated. The stability of the control strategy was validated through the finite-time stability theorem and the Lyapunov function. Eventually, simulations and hardware-in-the-loop (HIL) experiments validated the feasibility and availability of the finite-time adaptive control strategy. Full article

(This article belongs to the Special Issue Modeling, Estimation, Control, and Decision for Intelligent Vehicles)

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17 pages, 13481 KiB

Open AccessEditor’s ChoiceArticle

Detection of Broken Bars in Induction Motors Operating with Closed-Loop Speed Control

by Francesca Muzio, Lorenzo Mantione, Tomas Garcia-Calva, Lucia Frosini and Daniel Morinigo-Sotelo

Machines 2024, 12(9), 662; https://doi.org/10.3390/machines12090662 - 21 Sep 2024

Cited by 3 | Viewed by 1307

Abstract

Rotor bar breakage in induction motors is often detected by analysing the signatures in the stator current. However, due to the alteration of the current spectrum, traditional methods may fail when inverter-fed motors operate with closed-loop control using a cascade structure to regulate [...] Read more.

Rotor bar breakage in induction motors is often detected by analysing the signatures in the stator current. However, due to the alteration of the current spectrum, traditional methods may fail when inverter-fed motors operate with closed-loop control using a cascade structure to regulate the speed. In this paper, the potential of zero-sequence voltage analysis to detect this fault is investigated, and a new index to quantify the severity of the fault based on this signal is proposed. Signals from motors operating under different control strategies and signals from motors powered from the mains are considered to verify the robustness of the proposed fault severity index. As a result, in all the analysed conditions the value of the proposed index for the healthy motor is found to be approximately 0.010, while for the faulty machine it is between 0.110 and 0.252. Full article

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

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23 pages, 8375 KiB

Open AccessEditor’s ChoiceArticle

Artificial-Intelligence-Based Condition Monitoring of Industrial Collaborative Robots: Detecting Anomalies and Adapting to Trajectory Changes

by Samuel Ayankoso, Fengshou Gu, Hassna Louadah, Hamidreza Fahham and Andrew Ball

Machines 2024, 12(9), 630; https://doi.org/10.3390/machines12090630 - 7 Sep 2024

Cited by 4 | Viewed by 2747

Abstract

The increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data [...] Read more.

The increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data from a universal robot to detect anomalies or early-stage faults using test data from designed anomalous conditions and artificial-intelligence-based anomaly detection techniques called autoencoders. The performance of three autoencoders, namely, a multi-layer-perceptron-based autoencoder, convolutional-neural-network-based autoencoder, and sparse autoencoder, was compared in detecting anomalies. The results indicate that the autoencoders effectively detected anomalies in the examined complex and noisy datasets with more than 93% overall accuracy and an F1 score exceeding 96% for the considered anomalous cases. Moreover, the integration of trajectory change detection and anomaly detection algorithms (i.e., the dynamic time warping algorithm and sparse autoencoder, respectively) was proposed for the local implementation of online condition monitoring. This integrated approach to anomaly detection and trajectory change provides a practical, adaptive, and economical solution for enhancing the reliability and safety of collaborative robots in smart manufacturing environments. Full article

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

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13 pages, 9787 KiB

Open AccessEditor’s ChoiceArticle

A Study on the Static and Dynamic Characteristics of the Spindle System of a Spiral Bevel Gear Grinding Machine

by Shuai Huang, Juxin Wang, Kaifeng Huang and Jianwu Yu

Machines 2024, 12(9), 619; https://doi.org/10.3390/machines12090619 - 4 Sep 2024

Viewed by 1340

Abstract

To enhance the static and dynamic performance of the grinding wheel spindle system, with the gear grinding machine (YKF2060) as the research object, a static mechanics model of the spindle system was established based on Castigliano’s theorem, taking into account the equivalent effect [...] Read more.

To enhance the static and dynamic performance of the grinding wheel spindle system, with the gear grinding machine (YKF2060) as the research object, a static mechanics model of the spindle system was established based on Castigliano’s theorem, taking into account the equivalent effect of the triple-point contact ball bearing at the front end of the spindle. Meanwhile, based on the overall transfer matrix method, a dynamic model of the main spindle–eccentric shaft dual-rotor system was established, taking into account the effects of shear deformation and gyroscopic moments. On this basis, the effect of the spindle span, the front and rear overhang of the eccentric shaft, and the bearing stiffness on the static stiffness and first-order critical speed of the system was analyzed. Finally, static stiffness experiments, modal tests, and finite element simulation models were conducted to verify the static and dynamic models. The results show that the stiffness of the front outer bearing has the greatest influence on the static and dynamic performance of the system, while the stiffness of the rear inner bearing has the least influence. The relative errors of the static stiffness and the first two natural frequencies between static stiffness experiments, modal tests, and finite element simulation models are less than 10%, and the mode shapes match well. The established static and dynamic model can effectively reflect both the static and dynamic characteristics of the spindle system. Full article

(This article belongs to the Section Advanced Manufacturing)

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22 pages, 6340 KiB

Open AccessEditor’s ChoiceArticle

Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD

by Xukang Yang, Anqi Jiang, Wanlu Jiang, Yonghui Zhao, Enyu Tang and Zhiqian Qi

Machines 2024, 12(9), 599; https://doi.org/10.3390/machines12090599 - 28 Aug 2024

Viewed by 886

Abstract

A high-pressure hydraulically adjusted servomotor is an electromechanical–hydraulic integrated system centered on a servo valve that plays a crucial role in ensuring the safe and stable operation of steam turbines. To address the issues of difficult fault diagnoses and the low maintenance efficiency [...] Read more.

A high-pressure hydraulically adjusted servomotor is an electromechanical–hydraulic integrated system centered on a servo valve that plays a crucial role in ensuring the safe and stable operation of steam turbines. To address the issues of difficult fault diagnoses and the low maintenance efficiency of adjusted hydraulic servomotors, this study proposes a model for detecting abnormalities of hydraulically adjusted servomotors. This model uses a multi-scale one-dimensional residual neural network (M1D_ResNet) for feature extraction and a genetic algorithm (GA)-optimized support vector data description (SVDD). Firstly, the multi-scale features of the vibration signals of the hydraulically adjusted servomotor were extracted and fused using one-dimensional convolutional blocks with three different scales to construct a multi-scale one-dimensional residual neural network binary classification model capable of recognizing normal and abnormal states. Then, this model was used as a feature extractor to create a feature set of normal data. Finally, an abnormal detection model for the hydraulically adjusted servomotor was constructed by optimizing the support vector data domain based on this feature set using a genetic algorithm. The proposed method was experimentally validated on a hydraulically adjusted servomotor dataset. The results showed that, compared with the traditional single-scale one-dimensional residual neural network, the multi-scale feature vectors fused by the multi-scale one-dimensional convolutional neural network contained richer state-sensitive information, effectively improving the performance of detecting abnormalities in the hydraulically adjusted servomotor. Full article

(This article belongs to the Special Issue Advanced Control and Fault Detection Techniques in Hydraulic Machines and Systems)

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24 pages, 11901 KiB

Open AccessEditor’s ChoiceArticle

The Workspace Analysis of the Delta Robot Using a Cross-Section Diagram Based on Zero Platform

by Jun-Ho Hong, Ji-Ho Lim, Euntaek Lee and Dongwon Shin

Machines 2024, 12(8), 583; https://doi.org/10.3390/machines12080583 - 22 Aug 2024

Cited by 1 | Viewed by 2270

Abstract

This paper introduces a new concept of a zero-platform delta robot with three key parameters affecting the shape and size of the workspace. This concept is applied to directly bring the torus configuration into the links of the robot and shows its usefulness [...] Read more.

This paper introduces a new concept of a zero-platform delta robot with three key parameters affecting the shape and size of the workspace. This concept is applied to directly bring the torus configuration into the links of the robot and shows its usefulness in configuring and generating the workspace conveniently. Analyzing the workspace of parallel robots, such as delta robots, requires extensive computation due to the constraints between the links, typically requiring complex equations or numerical methods. This paper proposes a new method for quickly estimating the shape and size of the workspace using a cross-section diagram based on a geometrical analysis of the zero-platform delta robot. The shape and size of the workspace can be rapidly estimated because the intersection of three cross-section diagrams needs only the torus’s 2D operation. Comparing the workspace between the cross-section diagram and the 3D CAD software, this paper shows that the cross-section diagram can analyze the shape and size of the workspace quickly and give a more geometrical understanding of the workspace. Full article

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

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18 pages, 3470 KiB

Open AccessEditor’s ChoiceArticle

Soft Robots: Computational Design, Fabrication, and Position Control of a Novel 3-DOF Soft Robot

by Martin Garcia, Andrea-Contreras Esquen, Mark Sabbagh, Devin Grace, Ethan Schneider, Turaj Ashuri, Razvan Cristian Voicu, Ayse Tekes and Amir Ali Amiri Moghadam

Machines 2024, 12(8), 539; https://doi.org/10.3390/machines12080539 - 7 Aug 2024

Cited by 1 | Viewed by 2809

Abstract

This paper presents the computational design, fabrication, and control of a novel 3-degrees-of-freedom (DOF) soft parallel robot. The design is inspired by a delta robot structure. It is engineered to overcome the limitations of traditional soft serial robot arms, which are typically low [...] Read more.

This paper presents the computational design, fabrication, and control of a novel 3-degrees-of-freedom (DOF) soft parallel robot. The design is inspired by a delta robot structure. It is engineered to overcome the limitations of traditional soft serial robot arms, which are typically low in structural stiffness and blocking force. Soft robotic systems are becoming increasingly popular due to their inherent compliance match to that of human body, making them an efficient solution for applications requiring direct contact with humans. The proposed soft robot consists of three soft closed-loop kinematic chains, each of which includes a soft actuator and a compliant four-bar arm. The complex nonlinear dynamics of the soft robot are numerically modeled, and the model is validated experimentally using a 6-DOF electromagnetic position sensor. This research contributes to the growing body of literature in the field of soft robotics, providing insights into the computational design, fabrication, and control of soft parallel robots for use in a variety of complex applications. Full article

(This article belongs to the Special Issue Mechanical Design, Mechatronics and Control of 3D-Printed Robots)

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19 pages, 5503 KiB

Open AccessEditor’s ChoiceArticle

Adaptive Neuro-Fuzzy Inference System-Based Predictive Modeling of Mechanical Properties in Additive Manufacturing

by Vasileios D. Sagias, Paraskevi Zacharia, Athanasios Tempeloudis and Constantinos Stergiou

Machines 2024, 12(8), 523; https://doi.org/10.3390/machines12080523 - 31 Jul 2024

Cited by 8 | Viewed by 2049

Abstract

Predicting the mechanical properties of Additive Manufacturing (AM) parts is a complex task due to the intricate nature of the manufacturing processes. This study presents a novel application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the mechanical properties of PLA specimens [...] Read more.

Predicting the mechanical properties of Additive Manufacturing (AM) parts is a complex task due to the intricate nature of the manufacturing processes. This study presents a novel application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the mechanical properties of PLA specimens produced using Fused Filament Fabrication (FFF). The ANFIS model integrates the strengths of neural networks and fuzzy logic to establish a mapping between the inputs and the output mechanical properties, specifically maximum stress, strain, and Young’s modulus. Experimental data were collected from three-point bending tests conducted on FFF samples fabricated from PLA material with different manufacturing parameters, such as infill pattern, infill, layer thickness, printing speed, extruder and bed temperature, printing orientation (along each axis and twist angle), and raster angle. These data were used to train, check, and validate the ANFIS model. The results reveal that the proposed predictive model can effectively predict the mechanical properties of FFF-printed PLA samples, demonstrating its potential for broader applications across various AM technologies and materials, ultimately enhancing the efficiency and effectiveness of the AM fabrication process. Full article

(This article belongs to the Section Advanced Manufacturing)

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17 pages, 7127 KiB

Open AccessEditor’s ChoiceArticle

Computational Fluid Dynamics-Based Optimisation of High-Speed and High-Performance Bearingless Cross-Flow Fan Designs

by Ivana Bagaric, Daniel Steinert, Thomas Nussbaumer and Johann Walter Kolar

Machines 2024, 12(8), 513; https://doi.org/10.3390/machines12080513 - 29 Jul 2024

Viewed by 1251

Abstract

To enhance the fluid dynamic performance of bearingless cross-flow fans (CFFs), this paper presents a CFD-based optimisation of both rotor and static casing wall modifications. High-performance CFFs are essential in industrial applications such as highly specialised laser modules in the semiconductor industry. The [...] Read more.

To enhance the fluid dynamic performance of bearingless cross-flow fans (CFFs), this paper presents a CFD-based optimisation of both rotor and static casing wall modifications. High-performance CFFs are essential in industrial applications such as highly specialised laser modules in the semiconductor industry. The goal for the investigated rotor modifications is to enhance the CFF’s mechanical stiffness by integrating reinforcing shafts, which is expected to increase the limiting bending resonance frequency, thereby permitting higher rotational speeds. Additionally, the effects of these rotor modifications on the fluid dynamic performance are evaluated. For the casing wall modifications, the goal is to optimise design parameters to reduce losses. Optimised bearingless CFFs benefit semiconductor manufacturing by improving the gas circulation system within the laser module. Higher CFF performance is a key enabler for enhancing laser performance, increasing the scanning speed of lithography machines, and ultimately improving chip throughput. Several numerical simulations are conducted and validated using various commissioned prototypes, each measuring

600 mm

in length and

60 mm

in outer diameter. The results reveal that integrating a central shaft increases the rotational speed by up to 42%, from

5000 rpm

to

7100 rpm

, due to enhanced CFF stiffness. However, the loss in fluid flow amounts to 61% and outweighs the gain in rotational speed. Optimising the casing walls results in a 22% increase in maximum fluid flow reaching

1800 m^{3} / h

at

5000 rpm

. It is demonstrated that the performance of bearingless CFFs can be enhanced by modifying the geometry of the casing walls, without requiring changes to the CFF rotor or bearingless motors. Full article

(This article belongs to the Section Turbomachinery)

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16 pages, 2579 KiB

Open AccessEditor’s ChoiceArticle

Experimental Evaluation of Acoustical Materials for Noise Reduction in an Induction Motor Drive

by Ashish Kumar Sahu, Abeka Selliah, Alaa Hassan, Moien Masoumi and Berker Bilgin

Machines 2024, 12(8), 499; https://doi.org/10.3390/machines12080499 - 23 Jul 2024

Cited by 4 | Viewed by 1666

Abstract

Electric propulsion motors are more efficient than internal combustion engines, but they generate high-frequency tonal noise, which can be perceived as annoying. Acoustical materials are typically suitable for high-frequency noise, making them ideal for acoustic noise mitigation. This paper investigates the effectiveness of [...] Read more.

Electric propulsion motors are more efficient than internal combustion engines, but they generate high-frequency tonal noise, which can be perceived as annoying. Acoustical materials are typically suitable for high-frequency noise, making them ideal for acoustic noise mitigation. This paper investigates the effectiveness of three acoustical materials, namely, 2″ Polyurethane foam, 2″ Vinyl-faced quilted glass fiber, and 2″ Studiofoam, in mitigating the acoustic noise from an induction motor and a variable frequency inverter. Acoustic noise rates at multiple motor speeds, with and without the application of acoustical materials, are compared to determine the effectiveness of acoustical materials in mitigating acoustic noise at the transmission stage. Acoustical materials reduce acoustic noise from the induction motor by 5–14 dB(A) at around 500 Hz and by 22–31 dB(A) at around 10,000 Hz. Among the tested materials, Studiofoam demonstrates superior noise absorption capacity across the entire frequency range. Polyurethane foam is a cost-effective and lightweight alternative, and it is equally as effective as Studifoam in mitigating high-frequency acoustic noise above 5000 Hz. Full article

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

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16 pages, 14910 KiB

Open AccessEditor’s ChoiceArticle

A Comparative Study of Pole–Slot Combination with Fractional Slot Concentrated Winding in Outer Rotor Permanent Magnet Synchronous Generator for Hybrid Drone System

by Jeongwon Kim, Ju Lee and Hyunwoo Kim

Machines 2024, 12(7), 464; https://doi.org/10.3390/machines12070464 - 10 Jul 2024

Cited by 1 | Viewed by 2148

Abstract

This paper is a comparative study of pole–slot combinations with fractional slot concentrated windings in an outer rotor permanent magnet synchronous generator (ORPMSG) for a hybrid drone system. Fractional slot machines have been studied for automotive applications because of their high performance and [...] Read more.

This paper is a comparative study of pole–slot combinations with fractional slot concentrated windings in an outer rotor permanent magnet synchronous generator (ORPMSG) for a hybrid drone system. Fractional slot machines have been studied for automotive applications because of their high performance and simple winding manufacturing, compared with those of integer slot machines. In this study, four pole–slot combinations of ORPMSG with fractional slot concentrated windings were selected for comparison with the performance of the hybrid drone system. Based on the results of a finite element analysis (FEA), the machines were analyzed for cogging torque, back electromagnetic force (BEMF), torque, and electromagnetic loss under the same conditions as the machine specifications. Among the four pole–slot combinations of the ORPMSM, a one pole–slot model of the ORPMSG was selected, considering the performances of the machines. The selected pole–slot model of the ORPMSG was manufactured, and experiments were conducted on the manufactured model to verify the FEA results. Finally, the effectiveness of the comparative study of pole–slot combination with fractional slot concentrated winding in ORPMSM was verified by comparing the FEA and experimental results. Full article

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

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18 pages, 3548 KiB

Open AccessEditor’s ChoiceArticle

Numerical and Experimental Study on Dummy Blade with Underplatform Damper

by Di Li, Chenhong Du, Hongguang Li and Guang Meng

Machines 2024, 12(7), 461; https://doi.org/10.3390/machines12070461 - 7 Jul 2024

Cited by 3 | Viewed by 1646

Abstract

To confirm the variation in damping ratio offered by dry friction dampers against structural vibration stress, this study developed a blade vibration response test system for capturing damping characteristic curves through both frequency sweep excitation and damping-freevibration methods. The damping-free vibration method demonstrates [...] Read more.

To confirm the variation in damping ratio offered by dry friction dampers against structural vibration stress, this study developed a blade vibration response test system for capturing damping characteristic curves through both frequency sweep excitation and damping-freevibration methods. The damping-free vibration method demonstrates high efficiency, allowing for the acquisition of a complete damping ratio characteristic curve in a single experiment. Experimental findings indicate that the two contact surfaces on the triangular prism damper produce distinct damping effects, closely aligning with the predicted damping characteristic curves. The peak damping ratio was found to be independent of the centrifugal load of the damper; dampers with varying contact areas produce approximately similar damping characteristics; and the damping effect shows a positive correlation with the root extension length. Full article

(This article belongs to the Special Issue Cutting-Edge Research in Tribology and Its Applications to Rolling Element Bearings)

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22 pages, 5427 KiB

Open AccessEditor’s ChoiceArticle

The Development and Nonlinear Adaptive Robust Control of the Air Chamber Pressure Regulation System of a Slurry Pressure Balance Shield Tunneling Machine

by Shuai Wang, Yakun Zhang, Guofang Gong and Huayong Yang

Machines 2024, 12(7), 457; https://doi.org/10.3390/machines12070457 - 4 Jul 2024

Viewed by 1138

Abstract

The rapid and accurate control of air chamber pressure in slurry pressure balance (SPB) shield tunneling machines is crucial for establishing the balance between slurry pressure and soil and water pressure, ensuring the stability of the support face. A novel air chamber pressure [...] Read more.

The rapid and accurate control of air chamber pressure in slurry pressure balance (SPB) shield tunneling machines is crucial for establishing the balance between slurry pressure and soil and water pressure, ensuring the stability of the support face. A novel air chamber pressure control method based on nonlinear adaptive robust control (ARC) and using a pneumatic proportional three-way pressure-reducing valve is proposed in this paper. Firstly, an electric proportional control system for the air chamber pressure is developed. Secondly, a nonlinear state space model for the air chamber pressure regulation process is established. Utilizing experimental data from the SPB shield tunneling machine test bench, nonlinear adaptive identification is conducted through the nonlinear recursive least square algorithm. The results demonstrate the model’s effectiveness and accuracy. Then, a nonlinear ARC for air chamber pressure is designed based on the backstepping method, and its Lyapunov stability is proved. Finally, the feasibility and effectiveness of the controller designed in this paper is verified through simulation and experiments. The results demonstrate that the developed control system can compensate for the nonlinearity and disturbance in the air chamber pressure regulation process. It can achieve good transient and steady-state performance and has good robustness against uncertainty. Full article

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

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29 pages, 988 KiB

Open AccessEditor’s ChoiceArticle

S-Graph-Based Reactive Scheduling with Unexpected Arrivals of New Orders

by Krisztián Attila Bakon and Tibor Holczinger

Machines 2024, 12(7), 446; https://doi.org/10.3390/machines12070446 - 27 Jun 2024

Cited by 1 | Viewed by 971

Abstract

Manufacturing processes are often disrupted by unexpected events, such as machine breakdowns, raw material shortages, and the arrival of new orders. Effectively managing these uncertainties is crucial for maintaining the feasibility and optimality of the production system. The efficiency of a manufacturing system [...] Read more.

Manufacturing processes are often disrupted by unexpected events, such as machine breakdowns, raw material shortages, and the arrival of new orders. Effectively managing these uncertainties is crucial for maintaining the feasibility and optimality of the production system. The efficiency of a manufacturing system is heavily dependent on the optimality of its scheduling plan. In this study, we present a reactive scheduling approach based on the S-graph framework. The proposed method is specifically designed to handle the arrival of new jobs and generate schedules with the shortest makespan, i.e., the minimum total completion time. Whenever a new order is received, the method dynamically adjusts the production plan through rescheduling. Three distinct scheduling policies are identified that determine which tasks require scheduling or rescheduling and which tasks should remain unchanged in their schedules. To evaluate the effectiveness of the algorithm, we solve several examples from the literature and analyze the results. The findings demonstrate the efficiency and efficacy of the proposed approach. The ability to accommodate new job arrivals and generate schedules with a minimized makespan highlights the practical relevance and benefits of the S-graph-based reactive scheduling method. Full article

(This article belongs to the Special Issue Recent Advances in Manufacturing and Circular Economy)

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14 pages, 5333 KiB

Open AccessEditor’s ChoiceArticle

Response Surface Methodology for Kinematic Design of Soft Pneumatic Joints: An Application to a Bio-Inspired Scorpion-Tail-Actuator

by Michele Gabrio Antonelli, Pierluigi Beomonte Zobel and Nicola Stampone

Machines 2024, 12(7), 439; https://doi.org/10.3390/machines12070439 - 26 Jun 2024

Cited by 4 | Viewed by 1796

Abstract

In soft robotics, the most used actuators are soft pneumatic actuators because of their simplicity, cost-effectiveness, and safety. However, pneumatic actuation is also disadvantageous because of the strong non-linearities associated with using a compressible fluid. The identification of analytical models is often complex, [...] Read more.

In soft robotics, the most used actuators are soft pneumatic actuators because of their simplicity, cost-effectiveness, and safety. However, pneumatic actuation is also disadvantageous because of the strong non-linearities associated with using a compressible fluid. The identification of analytical models is often complex, and finite element analyses are preferred to evaluate deformation and tension states, which are computationally onerous. Alternatively, artificial intelligence algorithms can be used to follow model-free and data-driven approaches to avoid modeling complexity. In this work, however, the response surface methodology was adopted to identify a predictive model of the bending angle for soft pneumatic joints through geometric and functional parameters. The factorial plan was scheduled based on the design of the experiment, minimizing the number of tests needed and saving materials and time. Finally, a bio-inspired application of the identified model is proposed by designing the soft joints and making an actuator that replicates the movements of the scorpion’s tail in the attack position. The model was validated with two external reinforcements to achieve the same final deformation at different feeding pressures. The average absolute errors between predicted and experimental bending angles for I and II reinforcement allowed the identified model to be verified. Full article

(This article belongs to the Special Issue Intelligent Bio-Inspired Robots: New Trends and Future Perspectives)

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17 pages, 8573 KiB

Open AccessEditor’s ChoiceArticle

A Robust Online Diagnostic Strategy of Inverter Open-Circuit Faults for Robotic Joint BLDC Motors

by Mohamed Y. Metwly, Victor M. Logan, Charles L. Clark, Jiangbiao He and Biyun Xie

Machines 2024, 12(7), 430; https://doi.org/10.3390/machines12070430 - 24 Jun 2024

Viewed by 1599

Abstract

As robots are increasingly used in remote, safety-critical, and hazardous applications, the reliability of robots is becoming more important than ever before. Robotic arm joint motor-drive systems are vulnerable to hardware failures due to harsh operating environment in many scenarios, which may yield [...] Read more.

As robots are increasingly used in remote, safety-critical, and hazardous applications, the reliability of robots is becoming more important than ever before. Robotic arm joint motor-drive systems are vulnerable to hardware failures due to harsh operating environment in many scenarios, which may yield various joint failures and result in significant downtime costs. Targeting the most common robotic joint brushless DC (BLDC) motor-drive systems, this paper proposes a robust online diagnostic method for semiconductor faults for BLDC motor drives. The proposed fault diagnostic technique is based on the stator current signature analysis. Specifically, this paper investigates the performance of the BLDC joint motors under open-circuit faults of the inverter switches using finite element co-simulation tools. Furthermore, the proposed methodology is not only capable of detecting any open-circuit faults but also identifying faulty switches based on a knowledge table by considering various fault conditions. The robustness of the proposed technique was verified through extensive simulations under different speed and load conditions. Moreover, simulations have been carried out on a Kinova Gen-3 robot arm to verify the theoretical findings, highlighting the impacts of locked joints on the robot’s end-effector locations. Finally, experimental results are presented to corroborate the performance of the proposed fault diagnostic strategy. Full article

(This article belongs to the Special Issue Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and Fault Diagnosis)

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13 pages, 5419 KiB

Open AccessEditor’s ChoiceArticle

Design and Test of a 2-DOF Compliant Positioning Stage with Antagonistic Piezoelectric Actuation

by Haitao Wu, Hui Tang and Yanding Qin

Machines 2024, 12(6), 420; https://doi.org/10.3390/machines12060420 - 19 Jun 2024

Cited by 4 | Viewed by 1654

Abstract

This paper designs a two-degrees-of-freedom (DOF) compliant positioning stage with antagonistic piezoelectric actuation. Two pairs of PEAs are arranged in an antagonistic configuration to generate reciprocating motions. Flexure mechanisms are intentionally adopted to construct the fixtures for PEAs, whose elastic deformations can help [...] Read more.

This paper designs a two-degrees-of-freedom (DOF) compliant positioning stage with antagonistic piezoelectric actuation. Two pairs of PEAs are arranged in an antagonistic configuration to generate reciprocating motions. Flexure mechanisms are intentionally adopted to construct the fixtures for PEAs, whose elastic deformations can help to reduce the stress concentration on the PEA caused by the extension of the PEA in the other direction. Subsequently, the parameter and performance of the 2-DOF compliant positioning stage is optimized and verified by finite element analysis. Finally, a prototype is fabricated and tested. The experimental results show that the developed positioning stage achieves a working stroke of 28.27 μm × 27.62 μm. Motion resolutions of both axes are 8 nm and natural frequencies in the working directions are up to 2018 Hz, which is promising for high-precision positioning control. Full article

(This article belongs to the Special Issue Optimization and Design of Compliant Mechanisms)

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20 pages, 11034 KiB

Open AccessEditor’s ChoiceArticle

Experimental Evaluation of Mechanical Compression Properties of Aluminum Alloy Lattice Trusses for Anti-Ice System Applications

by Carlo Giovanni Ferro, Sara Varetti and Paolo Maggiore

Machines 2024, 12(6), 404; https://doi.org/10.3390/machines12060404 - 12 Jun 2024

Cited by 2 | Viewed by 1297

Abstract

Lattice structures have emerged as promising materials for aerospace structure applications due to their high strength-to-weight ratios, customizable properties, and efficient use of materials. These properties make them attractive for use in anti-ice systems, where lightweight and heat exchange are essential. This paper [...] Read more.

Lattice structures have emerged as promising materials for aerospace structure applications due to their high strength-to-weight ratios, customizable properties, and efficient use of materials. These properties make them attractive for use in anti-ice systems, where lightweight and heat exchange are essential. This paper presents an extensive experimental investigation into mechanical compression properties of lattice trusses fabricated from AlSi10Mg powder alloy, a material commonly used in casted aerospace parts. The truss structures were manufactured using the additive manufacturing selective laser melting technique and were subjected to uniaxial compressive loading to assess their performance. The results demonstrate that AlSi10Mg lattice trusses exhibit remarkable compressive strength with strong correlations depending upon both topology and cells’ parameters setup. The findings described highlight the potential of AlSi10Mg alloy as a promising material for custom truss fabrication, offering customizable cost-effective and lightweight solutions for the aerospace market. This study also emphasizes the role of additive manufacturing in producing complex structures with pointwise-tailored mechanical properties. Full article

(This article belongs to the Special Issue Recent Advances in 3D Printing in Industry 4.0)

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15 pages, 4434 KiB

Open AccessEditor’s ChoiceArticle

Preliminary Testing of a Passive Exoskeleton Prototype Based on McKibben Muscles

by Maria Paterna, Carlo De Benedictis and Carlo Ferraresi

Machines 2024, 12(6), 388; https://doi.org/10.3390/machines12060388 - 5 Jun 2024

Cited by 1 | Viewed by 1688

Abstract

Upper-limb exoskeletons for industrial applications can enhance the comfort and productivity of workers by reducing muscle activity and intra-articular forces during overhead work. Current devices typically employ a spring-based mechanism to balance the gravitational torque acting on the shoulder. As an alternative, this [...] Read more.

Upper-limb exoskeletons for industrial applications can enhance the comfort and productivity of workers by reducing muscle activity and intra-articular forces during overhead work. Current devices typically employ a spring-based mechanism to balance the gravitational torque acting on the shoulder. As an alternative, this paper presents the design of a passive upper-limb exoskeleton based on McKibben artificial muscles. The interaction forces between the exoskeleton and the user, as well as the mechanical resistance of the exoskeleton structure, were investigated to finalize the design of the device prior to its prototyping. Details are provided about the solutions adopted to assemble, wear, and regulate the exoskeleton’s structure. The first version of the device weighing about 5.5 kg was manufactured and tested by two users in a motion analysis laboratory. The results of this study highlight that the exoskeleton can effectively reduce the activation level of shoulder muscles without affecting the lumbar strain. Full article

(This article belongs to the Special Issue Intelligent Bio-Inspired Robots: New Trends and Future Perspectives)

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24 pages, 14201 KiB

Open AccessEditor’s ChoiceArticle

Research on Collaboration Motion Planning Method for a Dual-Arm Robot Based on Closed-Chain Kinematics

by Yuantian Qin, Kai Zhang, Kuiquan Meng and Zhehang Yin

Machines 2024, 12(6), 387; https://doi.org/10.3390/machines12060387 - 4 Jun 2024

Cited by 1 | Viewed by 2037

Abstract

Aiming to address challenges in the motion coordination of dual-arm robot engineering applications, a comprehensive set of planning methods is devised. This paper takes a dual-arm system composed of two six-degrees-of-freedom industrial robots as the research object. Initially, a transformation model is established [...] Read more.

Aiming to address challenges in the motion coordination of dual-arm robot engineering applications, a comprehensive set of planning methods is devised. This paper takes a dual-arm system composed of two six-degrees-of-freedom industrial robots as the research object. Initially, a transformation model is established for the characteristic trajectories between the workpiece coordinate system and various other coordinate systems. Subsequently, the position and orientation curves of the working trajectory are discretized to facilitate the controller’s execution. Furthermore, an analysis is conducted of the closed-chain kinematics relationship between two arms of the robot and a pose-calibration method based on a reference coordinate system is introduced. Finally, constraints to the collaborative motion of the dual-arm robot are analyzed, leading to the establishment of a motion collaboration planning methodology. Simulations and experiments demonstrate that the proposed approach enables effective and collaborative task planning for dual-arm robots. Moreover, joint angle and angular velocity curves corresponding to the motion trajectory exhibit smoothness, reducing joint impacts. Full article

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

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19 pages, 8284 KiB

Open AccessEditor’s ChoiceArticle

Establishment and Analysis of Load Spectrum for Bogie Frame of High-Speed Train at 400 km/h Speed Level

by Guidong Tao, Zhiming Liu, Chengxiang Ji and Guangxue Yang

Machines 2024, 12(6), 382; https://doi.org/10.3390/machines12060382 - 3 Jun 2024

Cited by 4 | Viewed by 2003

Abstract

The bogie frame, as one of the most critical load-bearing structures of the Electric Multiple Unit (EMU), is responsible for bearing and transmitting various loads from the car body, wheelsets, and its own installation components. With the increasing operating speed of high-speed EMUs, [...] Read more.

The bogie frame, as one of the most critical load-bearing structures of the Electric Multiple Unit (EMU), is responsible for bearing and transmitting various loads from the car body, wheelsets, and its own installation components. With the increasing operating speed of high-speed EMUs, especially when the design and operational speeds exceed 400 km/h, the applicability of current international standards is uncertain. The load spectrum serves as the foundation for structural reliability design and fatigue evaluation. In this paper, the measured loads of the bogie frame of a CR400AF high-speed train on the Beijing–Shanghai high-speed railway are obtained, and the time-domain characteristic of the measured loads is analyzed under different operating conditions. Then, through the Weibull distribution of three parameters, the Weibull parameters at the 450 km/h speed level are fitted, and the maximum load and cumulative frequency under the speed level are derived. Finally, the load spectrum of the bogie frame at the 450 km/h speed level is established, which provides a more realistic load condition for accurately evaluating the fatigue strength of bogie frames at higher speed levels. Full article

(This article belongs to the Section Vehicle Engineering)

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15 pages, 4251 KiB

Open AccessEditor’s ChoiceReview

Nonlinear Passive Observer for Motion Estimation in Multi-Axis Precision Motion Control

by Hector Gutierrez and Dengfeng Li

Machines 2024, 12(6), 376; https://doi.org/10.3390/machines12060376 - 30 May 2024

Viewed by 897

Abstract

A nonlinear passive observer (NPO) for estimating the time-varying velocity vector of a multi-axis high-precision motion control stage is presented. The proposed nonlinear estimation strategy is developed based on a Lyapunov stability analysis, which proves that the NPO is stable. Three test cases [...] Read more.

A nonlinear passive observer (NPO) for estimating the time-varying velocity vector of a multi-axis high-precision motion control stage is presented. The proposed nonlinear estimation strategy is developed based on a Lyapunov stability analysis, which proves that the NPO is stable. Three test cases are used to investigate the performance of the proposed observer. Experimental results are given to demonstrate the performance of the proposed NPO in accurately estimating time-varying velocity during alignment, reciprocating motion, and multi-axis motion in high-precision motion control applications. Full article

(This article belongs to the Special Issue Advances in Applied Mechatronics, Volume II)

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21 pages, 7912 KiB

Open AccessEditor’s ChoiceArticle

Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network

by Xukang Yang, Anqi Jiang, Wanlu Jiang, Yonghui Zhao, Enyu Tang and Shangteng Chang

Machines 2024, 12(6), 368; https://doi.org/10.3390/machines12060368 - 24 May 2024

Cited by 7 | Viewed by 1251

Abstract

Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of [...] Read more.

Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of multiple pressure signals through the Multi-source Information Fusion (MSIF) method. The comprehensive expression of device status information is obtained. After that, this paper proposes a fault detection Algorithm GA-SVDD-neg, which uses Genetic Algorithm (GA) to optimize Support Vector Data Description with negative examples (SVDD-neg). Through joint optimization with the Mutual Information (MI) feature selection algorithm, the features that are most sensitive to the state deterioration of the adjustment hydraulic servomotor are selected. Experiments show that the MI algorithm has a better performance than other feature dimensionality reduction algorithms in the field of the abnormal detection of adjustment hydraulic servomotors, and the GA-SVDD-neg algorithm has a stronger robustness and generality than other anomaly detection algorithms. In addition, to make full use of the advantages of deep learning in automatic feature extraction and classification, this paper realizes the fault diagnosis of the adjustment hydraulic servomotor based on 1D Convolutional Neural Network (1DCNN). The experimental results show that this algorithm has the same superior performance as the traditional algorithm in feature extraction and can accurately diagnose the known faults of the adjustment hydraulic servomotor. This research is of great significance for the intelligent transformation of adjustment hydraulic servomotors and can also provide a reference for the fault warning and diagnosis of the Electro-Hydraulic (EH) system of the same type of steam turbine. Full article

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

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34 pages, 1581 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning Approach for LPRE Bearings Remaining Useful Life Estimation Based on Hidden Markov Models and Fatigue Modelling

by Federica Galli, Philippe Weber, Ghaleb Hoblos, Vincent Sircoulomb, Giuseppe Fiore and Charlotte Rostain

Machines 2024, 12(6), 367; https://doi.org/10.3390/machines12060367 - 24 May 2024

Cited by 1 | Viewed by 1576

Abstract

Ball bearings are one of the most critical components of rotating machines. They ensure shaft support and friction reduction, thus their malfunctioning directly affects the machine’s performance. As a consequence, it is necessary to monitor the health conditions of such a component to [...] Read more.

Ball bearings are one of the most critical components of rotating machines. They ensure shaft support and friction reduction, thus their malfunctioning directly affects the machine’s performance. As a consequence, it is necessary to monitor the health conditions of such a component to avoid major degradations which could permanently damage the entire machine. In this context, HMS (Health Monitoring Systems) and PHM (Prognosis and Health Monitoring) methodologies propose a wide range of algorithms for bearing diagnosis and prognosis. The present article proposes an end-to-end PHM approach for ball bearing RUL (Remaining Useful Life) estimation. The proposed methodology is composed of three main steps: HI (Health Indicator) construction, bearing diagnosis and RUL estimation. The HI is obtained by processing non-stationary vibration data with the MODWPT (Maximum Overlap Discrete Wavelet Packet Transform). After that, a degradation profile is defined and coupled with crack initiation and crack propagation fatigue models. Lastly, a MB-HMM (Hidden Markov Model) is trained to capture the bearing degradation dynamics. This latter model is used to estimate the current degradation state as well as the RUL. The obtained results show good RUL prediction capabilities. In particular, the fatigue models allowed a reduction of the ML (Machine Learning) model size, improving the algorithms training phase. Full article

(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction (2nd Edition))

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21 pages, 6405 KiB

Open AccessEditor’s ChoiceArticle

New Health Indicator Construction and Fault Detection Network for Rolling Bearings via Convolutional Auto-Encoder and Contrast Learning

by Dongdong Wu, Da Chen and Gang Yu

Machines 2024, 12(6), 362; https://doi.org/10.3390/machines12060362 - 23 May 2024

Cited by 5 | Viewed by 1626

Abstract

As one of the most important components in rotating machinery, if bearings fail, serious disasters may occur. Therefore, the remaining useful life (RUL) prediction of bearings is of great significance. Health indicator (HI) construction and early fault detection play a crucial role in [...] Read more.

As one of the most important components in rotating machinery, if bearings fail, serious disasters may occur. Therefore, the remaining useful life (RUL) prediction of bearings is of great significance. Health indicator (HI) construction and early fault detection play a crucial role in data-driven RUL prediction. Unfortunately, most existing HI construction methods require prior knowledge and preset trends, making it difficult to reflect the actual degradation trend of bearings. And the existing early fault detection methods rely on massive historical data, yet manual annotation is time-consuming and laborious. To address the above issues, a novel deep convolutional auto-encoder (CAE) based on envelope spectral feature extraction is developed in this work. A sliding value window is defined in the envelope spectrum to obtain initial health indicators, which are used as preliminary labels for model training. Subsequently, CAE is trained by minimizing the composite loss function. The proposed construction method can reflect the actual degradation trend of bearings. Afterwards, the autoencoder is pre-trained through contrast learning (CL) to improve its discriminative ability. The model that has undergone offline pre-training is more sensitive to early faults. Finally, the HI construction method is combined with the early fault detection method to obtain a comprehensive network for online health assessment and fault detection, thus laying a solid foundation for subsequent RUL prediction. The superiority of the proposed method has been verified through experiments. Full article

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

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13 pages, 4650 KiB

Open AccessEditor’s ChoiceArticle

Mechanism Analysis and Optimization Design of Exoskeleton Robot with Non-Circular Gear–Pentabar Mechanism

by Guibin Wang, Maile Zhou, Hao Sun, Zhaoxiang Wei, Herui Dong, Tingbo Xu and Daqing Yin

Machines 2024, 12(5), 351; https://doi.org/10.3390/machines12050351 - 19 May 2024

Cited by 3 | Viewed by 1586

Abstract

To address the complex structure of existing rod mechanism exoskeleton robots and the difficulty in solving the motion trajectory of multi−rod mechanisms, an exoskeleton knee robot with a differential non−circular gear–pentarod mechanism is designed based on non−circular gears with arbitrary transmission ratios to [...] Read more.

To address the complex structure of existing rod mechanism exoskeleton robots and the difficulty in solving the motion trajectory of multi−rod mechanisms, an exoskeleton knee robot with a differential non−circular gear–pentarod mechanism is designed based on non−circular gears with arbitrary transmission ratios to constrain the degrees of freedom of the R-para-rod mechanism. In this study, the kinematic model of a non-circular gear–five−rod mechanism is established based on motion mapping theory by obtaining the normal motion positions of the human lower limb. An optimization design software for the non-circular gear–five−rod mechanism is developed using the MATLAB 2018b visualization platform, with the non−circular active gear as the sole input variable. A set of ideal parameters is obtained through parameter adjustment and optimal parameter selection, and the corresponding trajectories are compared with human trajectories. The three−dimensional model of the mechanism is established according to the obtained parameters, and the motion simulation of the non−circular gear–five−bar mechanism demonstrates that the mechanism can better reproduce the expected human knee joint motion posture, meeting the working requirements of an exoskeleton knee robot. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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19 pages, 9470 KiB

Open AccessEditor’s ChoiceArticle

Optimizing Machining Efficiency in High-Speed Milling of Super Duplex Stainless Steel with SiAlON Ceramic Inserts

by Monica Guimarães, Victor Saciotto, Qianxi He, Jose M. DePaiva, Anselmo Diniz and Stephen Veldhuis

Machines 2024, 12(5), 349; https://doi.org/10.3390/machines12050349 - 17 May 2024

Cited by 3 | Viewed by 2333

Abstract

Super duplex stainless steels (SDSSs) are widely utilized across industries owing to their remarkable mechanical properties and corrosion resistance. However, machining SDSS presents considerable challenges, particularly at high speeds. This study investigates the machinability of SDSS grade SAF 2507 (UNS S32750) under high-speed [...] Read more.

Super duplex stainless steels (SDSSs) are widely utilized across industries owing to their remarkable mechanical properties and corrosion resistance. However, machining SDSS presents considerable challenges, particularly at high speeds. This study investigates the machinability of SDSS grade SAF 2507 (UNS S32750) under high-speed milling conditions using SiAlON insert tools. Comprehensive analysis of key machinability indicators, including chip compression ratio, chip analysis, shear angle, tool wear, and friction conditions, reveals that lower cutting speeds optimize machining performance, reducing cutting forces and improving chip formation. Finite element analysis (FEA) corroborates the efficacy of lower speeds and moderate feed rates. Furthermore, insights into friction dynamics at the tool–chip interface are offered, alongside strategies for enhancing SDSS machining. This study revealed the critical impact of cutting speed on cutting forces, with a significant reduction in forces at cutting speeds of 950 and 1350 m/min, but a substantial increase at 1750 m/min, particularly when tool wear is severe. Furthermore, the combination of 950 and 1350 m/min cutting speeds with a 0.2 mm/tooth feed rate led to smoother chip surfaces and decreased friction coefficients, thus enhancing machining efficiency. The presence of stick–slip phenomena at 1750 m/min indicated thermoplastic instability. Optimizing machining parameters for super duplex stainless steel necessitates balancing material removal rate and surface integrity, as the latter plays an important role in ensuring long-term performance and reliability in critical applications. Full article

(This article belongs to the Special Issue Recent Advances in Surface Integrity with Machining and Milling)

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24 pages, 6524 KiB

Open AccessEditor’s ChoiceArticle

Systematic Development of a Novel Laser-Sintering Machine with Roving Integration and Sustainability Evaluation

by Michael Baranowski, Johannes Scholz, Florian Kößler and Jürgen Fleischer

Machines 2024, 12(5), 336; https://doi.org/10.3390/machines12050336 - 14 May 2024

Viewed by 1197

Abstract

Incorporating continuous carbon fibre-reinforced polymer (CCFRP) parts within additive manufacturing processes presents a significant advancement in the fabrication of robust lightweight parts, particularly relevant to aerospace, engineering, and various industrial sectors. Nonetheless, prevailing additive manufacturing methodologies for CCFRP parts exhibit notable limitations. Techniques [...] Read more.

Incorporating continuous carbon fibre-reinforced polymer (CCFRP) parts within additive manufacturing processes presents a significant advancement in the fabrication of robust lightweight parts, particularly relevant to aerospace, engineering, and various industrial sectors. Nonetheless, prevailing additive manufacturing methodologies for CCFRP parts exhibit notable limitations. Techniques reliant on resin and extrusion entail extensive and costly post-processing procedures to eliminate support structures, constraining design versatility and complicating small-scale production endeavours. In contrast, laser sintering (LS) emerges as a promising avenue for industrial application. It facilitates the efficient and cost-effective manufacturing of resilient parts without needing support structures. However, the current state of research and technological capabilities has yet to yield an LS machine that integrates the benefits of continuous fibre reinforcement with the inherent advantages of the LS process. This paper describes the systematic development process according to VDI 2221 of a new type of LS machine with automated continuous fibre integration while keeping the advantages of the LS process. The resulting physical prototype of the machine is also presented. Furthermore, this study presents an approach to integrate the cost and Product Carbon Footprint of the process in the product design. For this purpose, a machine state model was developed, and the costs and Product Carbon footprint of a part were analysed based on the model. The promising potential for future lightweight products is demonstrated through the production of CCFRP parts. Full article

(This article belongs to the Special Issue Advances in Composites Manufacturing: Machines, Systems and Processes)

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19 pages, 7648 KiB

Open AccessEditor’s ChoiceReview

State-of-the-Art Lightweight Implementation Methods in Electrical Machines

by Han Zhao, Jing Li, Xiaochen Zhang, Bin Xiong, Chenyi Zhao, Yixiao Ruan, Huanran Wang, Jing Zhang, Zhouwei Lan, Xiaoyan Huang and He Zhang

Machines 2024, 12(5), 339; https://doi.org/10.3390/machines12050339 - 14 May 2024

Cited by 5 | Viewed by 2162

Abstract

The demand for high-power density motors has been increasing due to their remarkable output capability and compact construction. To achieve a significant improvement in motor power density, lightweight design methods have been recognized as an effective enabler. Therefore, extensive investigations have been conducted [...] Read more.

The demand for high-power density motors has been increasing due to their remarkable output capability and compact construction. To achieve a significant improvement in motor power density, lightweight design methods have been recognized as an effective enabler. Therefore, extensive investigations have been conducted to reduce motor mass and achieve lightweight configurations through the exploration of lightweight materials, structures and manufacturing techniques. This article provides a comprehensive review and summary of state-of-the-art lightweight implementation methods for electrical machines, including the utilization of lightweight materials, structural lightweight design, and incorporation of advanced manufacturing technologies, such as additive manufacturing techniques. The advantages and limitations of each approach are also discussed in this paper. Furthermore, some comments and forecasts on potential future methodologies for motor lightweighting are also provided. Full article

(This article belongs to the Special Issue New Trends of Permanent Magnet Machines)

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