Journal Description
Machines
Machines
is an international, peer-reviewed, open access journal on machinery and engineering published monthly online by MDPI. The IFToMM is affiliated with Machines and its members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Mechanical) / CiteScore - Q1 (Control and Optimization)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.5 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.5 (2024);
5-Year Impact Factor:
2.6 (2024)
Latest Articles
Ultra-Local Model-Based Adaptive Enhanced Model-Free Control for PMSM Speed Regulation
Machines 2025, 13(7), 541; https://doi.org/10.3390/machines13070541 (registering DOI) - 21 Jun 2025
Abstract
Conventional model-free control (MFC) is widely used in motor drives due to its simplicity and model independence, yet its performance suffers from imperfect disturbance estimation and input gain mismatch. To address these issues, this paper proposes an adaptive enhanced model-free speed control (AEMFSC)
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Conventional model-free control (MFC) is widely used in motor drives due to its simplicity and model independence, yet its performance suffers from imperfect disturbance estimation and input gain mismatch. To address these issues, this paper proposes an adaptive enhanced model-free speed control (AEMFSC) scheme based on an ultra-local model for permanent magnet synchronous motor (PMSM) drives. First, by integrating a nonlinear disturbance observer (NDOB) and a PD control law into the generalized model-free controller, an enhanced model-free speed controller (EMFSC) was developed to ensure closed-loop stability. Compared with a conventional MFSC, the proposed method eliminated steady-state errors, reduced the speed overshoot, and achieved faster settling with improved disturbance rejection. Second, to address the performance degradation induced by input gain mismatch during time-varying load conditions, we developed an online parameter identification method for real-time estimation. This adaptive mechanism enabled automatic controller parameter adjustment, which significantly enhanced the transient tracking performance of the PMSM drive. Furthermore, an algebraic-framework-based high-precision identification technique is proposed to optimize the initial selection, which effectively reduces the parameter tuning effort. Simulation and experimental results demonstrated that the proposed AEMFSC significantly enhanced the PMSM’s robustness against load torque variations and parameter uncertainties.
Full article
(This article belongs to the Topic Advanced Electrical Machines and Drives Technologies, 2nd Edition)
Open AccessArticle
Cascaded H-Bridge Multilevel Converter Topology for a PV Connected to a Medium-Voltage Grid
by
Hammad Alnuman, Essam Hussain, Mokhtar Aly, Emad M. Ahmed and Ahmed Alshahir
Machines 2025, 13(7), 540; https://doi.org/10.3390/machines13070540 (registering DOI) - 21 Jun 2025
Abstract
When connecting a renewable energy source to a medium-voltage grid, it has to fulfil grid codes and be able to work in a medium-voltage range (>10 kV). Multilevel converters (MLCs) are recognized for their low total harmonic distortion (THD) and ability to work
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When connecting a renewable energy source to a medium-voltage grid, it has to fulfil grid codes and be able to work in a medium-voltage range (>10 kV). Multilevel converters (MLCs) are recognized for their low total harmonic distortion (THD) and ability to work at high voltage compared to other converter types, making them ideal for applications connected to medium-voltage grids whilst being compliant with grid codes and voltage ratings. Cascaded H-bridge multilevel converters (CHBs-MLC) are a type of MLC topology, and they does not need any capacitors or diodes for clamping like other MLC topologies. One of the problems in these types of converters involves the double-frequency harmonics in the DC linking voltage and power, which can increase the size of the capacitors and converters. The use of line frequency transformers for isolation is another factor that increases the system’s size. This paper proposes an isolated CHBs-MLC topology that effectively overcomes double-line frequency harmonics and offers isolation. In the proposed topology, each DC source (renewable energy source) supplies a three-phase load rather than a single-phase load that is seen in conventional MLCs. This is achieved by employing a multi-winding high-frequency transformer (HFT). The primary winding consists of a winding connected to the DC sources. The secondary windings consist of three windings, each supplying one phase of the load. This configuration reduces the DC voltage link ripples, thus improving the power quality. Photovoltaic (PV) renewable energy sources are considered as the DC sources. A case study of a 1.0 MW and 13.8 kV photovoltaic (PV) system is presented, considering two scenarios: variations in solar irradiation and 25% partial panel shedding. The simulations and design results show the benefits of the proposed topology, including a seven-fold reduction in capacitor volume, a 2.7-fold reduction in transformer core volume, a 50% decrease in the current THD, and a 30% reduction in the voltage THD compared to conventional MLCs. The main challenge of the proposed topology is the use of more switches compared to conventional MLCs. However, with advancing technology, the cost is expected to decrease over time.
Full article
(This article belongs to the Special Issue Power Converters: Topology, Control, Reliability, and Applications)
Open AccessArticle
New Experimental Single-Axis Excitation Set-Up for Multi-Axial Random Fatigue Assessments
by
Luca Campello, Vivien Denis, Raffaella Sesana, Cristiana Delprete and Roger Serra
Machines 2025, 13(7), 539; https://doi.org/10.3390/machines13070539 - 20 Jun 2025
Abstract
Fatigue failure, generated by local multi-axial random state stress, frequently occurs in many engineering fields. Therefore, it is customary to perform experimental vibration tests for a structural durability assessment. Over the years, a number of testing methodologies, which differ in terms of the
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Fatigue failure, generated by local multi-axial random state stress, frequently occurs in many engineering fields. Therefore, it is customary to perform experimental vibration tests for a structural durability assessment. Over the years, a number of testing methodologies, which differ in terms of the testing machines, specimen geometry, and type of excitation, have been proposed. The aim of this paper is to describe a new testing procedure for random multi-axial fatigue testing. In particular, the paper presents the experimental set-up, the testing procedure, and the data analysis procedure to obtain the multi-axial random fatigue life estimation. The originality of the proposed methodology consists in the experimental set-up, which allows performing multi-axial fatigue tests with different normal-to-shear stress ratios, by choosing the proper frequency range, using a single-axis exciter. The system is composed of a special designed specimen, clamped on a uni-axial shaker. On the specimen tip, a T-shaped mass is placed, which generates a tunable multi-axial stress state. Furthermore, by means of a finite element model, the system dynamic response and the stress on the notched specimen section are estimated. The model is validated through a harmonic acceleration base test. The experimental tests validate the numerical simulations and confirm the presence of bending–torsion coupled loading.
Full article
(This article belongs to the Section Machines Testing and Maintenance)
Open AccessSystematic Review
Security by Design for Industrial Control Systems from a Cyber–Physical System Perspective: A Systematic Mapping Study
by
Ahmed Elmarkez, Soraya Mesli-Kesraoui, Pascal Berruet and Flavio Oquendo
Machines 2025, 13(7), 538; https://doi.org/10.3390/machines13070538 - 20 Jun 2025
Abstract
Industrial Control Systems (ICSs), a specialized type of Cyber–Physical System, have shifted from isolated and obscured environments to ones exposed to diverse Information Technology (IT) security threats, which are now highly interconnected. Their adoption of IT introduces vulnerabilities which they were not originally
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Industrial Control Systems (ICSs), a specialized type of Cyber–Physical System, have shifted from isolated and obscured environments to ones exposed to diverse Information Technology (IT) security threats, which are now highly interconnected. Their adoption of IT introduces vulnerabilities which they were not originally designed to handle, posing critical risks. Thus, it’s imperative to integrate security measures early in CPS development, particularly during the design and implementation phases, to mitigate these vulnerabilities effectively. This study aims to identify, classify, and analyze existing research on the security-by-design paradigm for CPSs, exploring trends and defining the characteristics, advantages, limitations, and open issues of current methodologies. A systematic mapping study was conducted, selecting 55 primary studies through a rigorous protocol. The findings indicate that the majority of methodologies concentrate on the design phase, frequently overlooking other stages of development. Moreover, while there is a notable emphasis on security analysis across most primary studies, there is a notable gap in considering the integration of mitigation measures. This oversight raises concerns about the efficacy of security measures in real-world deployment scenarios. Additionally, there is a significant reliance on human intervention, highlighting the need for further development in automated security solutions. Conflicts between security requirements and other system needs are also inadequately addressed, potentially compromising overall system effectiveness. This work provides a comprehensive overview of CPS security-by-design methodologies and identifies several open issues that require further investigation, emphasizing the need for a holistic approach that includes vulnerability handling, clear security objectives, and effective conflict management, along with improved standard integration, advanced validation methods, and automated tools.
Full article
(This article belongs to the Special Issue Emerging Approaches to Intelligent and Autonomous Systems)
Open AccessArticle
Research on the Equal Probability Grouping Method for Automatic Fitting of Deep Groove Ball Bearings
by
Peiqi Yang, Haoyi Wang, Xuejun Li and Linli Jiang
Machines 2025, 13(7), 537; https://doi.org/10.3390/machines13070537 - 20 Jun 2025
Abstract
At present, the fitting process of deep groove ball bearings has the problems of low manual production efficiency and poor performance of fitted bearings. For the automatic bearing fitting production line, there are some problems, such as a low success rate of fitting
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At present, the fitting process of deep groove ball bearings has the problems of low manual production efficiency and poor performance of fitted bearings. For the automatic bearing fitting production line, there are some problems, such as a low success rate of fitting and easy interruption of the production process. In this article, two grouping methods, the equidistant grouping method and the equal probability grouping method, are proposed. We establish a dimensional deviation distribution model by measuring the dimensional deviation of deep groove ball bearing components. Using the bearing component dimensional deviation distribution model, we carry out the equidistant grouping method and the equal probability grouping method to fit the bearing component. And the influence of the traditional bearing fitting method and the two grouping methods on the success rate of deep groove ball bearing fitting is compared and analyzed. This research found that the traditional bearing fitting method is easy to fall into local optimization, and too many unmatched components which have a larger dimensional deviation lead to the interruption of the fitting process. The success rate of the traditional fitting method is lower than grouping methods. For the two grouping methods, the equal probability grouping method can ensure that the probability of each group of components entering the automatic production line is the same. Compared with the equidistant grouping method, it is easier to make it possible to fit the bearing component. The equal probability grouping method is recommended.
Full article
(This article belongs to the Section Machine Design and Theory)
Open AccessReview
Optimization of Composite Sandwich Structures: A Review
by
Muhammad Ali Sadiq and György Kovács
Machines 2025, 13(7), 536; https://doi.org/10.3390/machines13070536 - 20 Jun 2025
Abstract
Composite sandwich structures play a significant role in various engineering applications due to their excellent strength-to-weight ratio, durability, fatigue life, acoustic performance, damping characteristics, stealth performance, and energy absorption capabilities. The optimization of these structures results in enhancing their mechanical performance, weight reduction,
[...] Read more.
Composite sandwich structures play a significant role in various engineering applications due to their excellent strength-to-weight ratio, durability, fatigue life, acoustic performance, damping characteristics, stealth performance, and energy absorption capabilities. The optimization of these structures results in enhancing their mechanical performance, weight reduction, cost-effectiveness, and sustainability. This review provides a comprehensive analysis of recent advancements in the optimization techniques applied in the case of composite sandwich structures, focusing on structural configuration, facesheets, and innovative cores design, loading conditions, analysis methodologies, and practical applications. Various optimization procedures, single- and multi-objective algorithms, Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Machine Learning (ML)-based optimization frameworks, as well as Finite Element (FE)-based numerical simulations, are discussed in detail. It highlights the role of core material and geometry, face sheet material selection, and manufacturing limitations in achieving optimal performance under static, dynamic, thermal, and impact loads under various boundary conditions. Furthermore, challenges such as computational efficiency, experimental validation, and trade-offs between structural weight and performance are examined. The findings of this review offer insights into the recent and future research directions of optimizing sandwich constructions, emphasizing the integration of advanced numerical techniques for analysis and efficient structural optimization.
Full article
(This article belongs to the Special Issue Design and Manufacturing for Lightweight Components and Structures)
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Open AccessArticle
A Novel Rapid Design Framework for Tooth Profile of Double-Circular-Arc Common-Tangent Flexspline in Harmonic Reducers
by
Xueao Liu, Jianghao Zhang, Hui Wang, Xuecong Wang and Jianzhong Ding
Machines 2025, 13(7), 535; https://doi.org/10.3390/machines13070535 - 20 Jun 2025
Abstract
Due to its small size, high transmission ratio and precision, the harmonic reducer is widely used. The design of the flexspline tooth profile is crucial for the transmission accuracy and service life of harmonic reducers. However, the numerous design parameters and the lack
[...] Read more.
Due to its small size, high transmission ratio and precision, the harmonic reducer is widely used. The design of the flexspline tooth profile is crucial for the transmission accuracy and service life of harmonic reducers. However, the numerous design parameters and the lack of a unified design standard for the flexspline tooth profile make it challenging to accurately determine these parameters. This can lead to issues such as tooth profile interference and excessive stress on the gear teeth during transmission. To address these issues, we propose a novel rapid design framework for the tooth profile of a double-circular-arc common-tangent flexspline in harmonic reducers. Firstly, the mathematical formula for the flexspline tooth profile with a double-circular-arc common-tangent and its conjugate circular spline tooth profile is derived. Then, two-dimensional and three-dimensional parametric finite element models of the harmonic reducer are established, and radial and axial profile modifications of the flexspline are carried out. Based on the parametric two-dimensional finite element model of the harmonic reducer, the optimized Latin hypercube experimental design method is employed to determine the flexspline tooth profile parameters. The method proposed can be implemented using Python language code and integrated into the Abaqus 2019 software, offering the advantage of meeting the requirements for rapid engineering development. Finally, a case study is presented to verify the effectiveness of the proposed design method.
Full article
(This article belongs to the Section Machine Design and Theory)
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Open AccessArticle
Studies on Vibration and Synchronization Characteristics of an Anti-Resonance System Driven by Triple-Frequency Excitation
by
Duyu Hou, Zheng Liang, Zhuozhuang Zhang and Zihan Wang
Machines 2025, 13(7), 534; https://doi.org/10.3390/machines13070534 - 20 Jun 2025
Abstract
In the continuous drilling process of oil wells, to achieve the efficient screening of drilling fluids by the vibrating screen while ensuring the safety of the screening operation, an anti-resonance system driven by two exciters with triple-frequency (denoted as 3:1 frequency ratio) is
[...] Read more.
In the continuous drilling process of oil wells, to achieve the efficient screening of drilling fluids by the vibrating screen while ensuring the safety of the screening operation, an anti-resonance system driven by two exciters with triple-frequency (denoted as 3:1 frequency ratio) is proposed. Initially, differential motion equations are formulated utilizing Lagrange’s equation, followed by the definition of vibration isolation coefficients adopting ratios. Triple-frequency synchronization and stability criterion between two eccentric blocks are subsequently elucidated via the asymptotic method and Routh–Hurwitz criterion. Concurrently, the effects of structural parameters on vibration isolation capacity, steady-state trajectory, and the triple-frequency synchronization phase are investigated through numerical computation. Ultimately, the reliability of the theoretical study is corroborated by simulation analysis. Results indicate that under the allowable system parameters for the practical project, the amplitude of the vibration body can exceed three times that of the isolation body; the two solutions of the stable phase difference (SPD) are different by π, one of which is stable and the other is unstable, and the stability of phase difference is determined by the sign of the stability coefficient. This work is useful for developing new vibrating screens and other multi-frequency vibration machines.
Full article
(This article belongs to the Section Machine Design and Theory)
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Open AccessArticle
A Novel Open Circuit Fault Diagnosis for a Modular Multilevel Converter with Modal Time-Frequency Diagram and FFT-CNN-BIGRU Attention
by
Ziyuan Zhai, Ning Wang, Siran Lu, Bo Zhou and Lei Guo
Machines 2025, 13(6), 533; https://doi.org/10.3390/machines13060533 - 19 Jun 2025
Abstract
Fault diagnosis is one of the most important issues for a modular multilevel converter (MMC). However, conventional solutions are deficient in two aspects. Firstly, they lack the necessary feature information. Secondly, they are incapable of performing open-circuit fault diagnosis of the modular multilevel
[...] Read more.
Fault diagnosis is one of the most important issues for a modular multilevel converter (MMC). However, conventional solutions are deficient in two aspects. Firstly, they lack the necessary feature information. Secondly, they are incapable of performing open-circuit fault diagnosis of the modular multilevel converter with the requisite degree of accuracy. To solve this problem, an intelligent diagnosis method is proposed to integrate the modal time–frequency diagram and FFT-CNN-BiGRU-Attention. By selecting the phase current and bridge arm voltage as the core fault parameters, the particle swarm algorithm is used to optimize the Variational Modal Decomposition parameters, and the fault signal is decomposed and reconstructed into sensitive feature components. The reconstructed signals are further transformed into modal time–frequency diagrams via continuous wavelet transform to fully retain the time–frequency domain features. In the model construction stage, the frequency–domain features are first extracted using the fast Fourier transform (FFT), and the local patterns are captured through a combination with a convolutional neural network; subsequently, the timing correlations are analyzed using bidirectional gated loop cells, and the Attention Mechanism is introduced to strengthen the key features. Simulations show that the proposed method achieves 98.63% accuracy in locating faulty insulated gate bipolar transistors (IGBTs) in the sub-module, with second-level real-time response capability. Compared with the recently published scheme, it maintains stable performance under complex working conditions such as noise interference and data imbalances, showing stronger robustness and practical value. This study provides a new idea for the intelligent operation and maintenance of power electronic devices, which can be extended to the fault diagnosis of other power equipment in the future.
Full article
(This article belongs to the Section Electromechanical Energy Conversion Systems)
Open AccessArticle
A Data-Driven Approach for Energy Consumption Modeling and Optimization of Welding Robot Systems
by
Minling Pan, Bingqi Jia, Lei Zhang, Haihong Pan and Lin Chen
Machines 2025, 13(6), 532; https://doi.org/10.3390/machines13060532 - 18 Jun 2025
Abstract
Welding robots play a crucial role in manufacturing industries, where minimizing energy consumption (EC) is increasingly important for enhancing efficiency and reducing operational costs. This study presents a data-driven approach to model and optimize EC in welding robot systems, utilizing a dataset generated
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Welding robots play a crucial role in manufacturing industries, where minimizing energy consumption (EC) is increasingly important for enhancing efficiency and reducing operational costs. This study presents a data-driven approach to model and optimize EC in welding robot systems, utilizing a dataset generated from real-world measurements of robot EC during various motions and integrated with trajectory data. A predictive model was developed using an extreme gradient boosting (XGBoost) regression technique focused on joint torque data, which achieved a mean absolute percentage error (MAPE) of 1.86%. Furthermore, trajectory optimization was achieved by adjusting the spatial position of the workpiece, effectively reducing EC. To solve the optimization problem, an improved whale optimization algorithm (IWOA) was employed. Experimental validations with a welding robot demonstrate that the proposed method not only accurately predicted EC with a MAPE of 2.66% but also reduced the robot system’s EC by 6.72%, outperforming the traditional method focused solely on joint motor EC, which achieved a 4.08% reduction. These results confirm the efficacy of the proposed approach, underscoring its potential for broad application in robotic systems to achieve significant energy savings.
Full article
(This article belongs to the Section Advanced Manufacturing)
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Open AccessArticle
Optimization Design and Dynamic Characteristics Analysis of Self-Responsive Anti-Falling Device for Inclined Shaft TBMs
by
Han Peng, Can Yang, Linjian Shangguan, Lianhui Jia, Bing Li, Chuang Xu and Wenjuan Yang
Machines 2025, 13(6), 531; https://doi.org/10.3390/machines13060531 - 18 Jun 2025
Abstract
To address the frequent failure of anti-falling devices in inclined shaft tunnel boring machines caused by cyclic loading and fatigue during construction, this study proposes an optimized self-responsive anti-falling device design. Based on the operational conditions of the “Tianyue” tunnel boring machine, a
[...] Read more.
To address the frequent failure of anti-falling devices in inclined shaft tunnel boring machines caused by cyclic loading and fatigue during construction, this study proposes an optimized self-responsive anti-falling device design. Based on the operational conditions of the “Tianyue” tunnel boring machine, a three-dimensional model was constructed using SolidWorks. Finite element static analysis was employed to validate structural integrity, revealing a maximum stress of 461.19 MPa with a safety factor of 1.71. Explicit dynamic simulations further demonstrated the dynamic penetration process of propellant-driven telescopic columns through concrete lining walls, achieving a penetration depth exceeding 500 mm. The results demonstrate that the device can respond to falling signals within 12 ms and activate mechanical locking. The Q690D steel structure exhibits a deformation of 5.543 mm with favorable stress distribution, meeting engineering safety requirements. The energy release characteristics of trinitrotoluene propellant and material compatibility were systematically verified. Compared to conventional hydraulic support systems, this design offers significant improvements in response speed, maintenance cost reduction, and environmental adaptability, providing an innovative solution for fall protection in complex geological environments.
Full article
(This article belongs to the Section Machine Design and Theory)
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Open AccessArticle
Near-Zero Parasitic Shift Rectilinear Flexure Stages Based on Coupled n-RRR Planar Parallel Mechanisms
by
Loïc Tissot-Daguette, Célestin Vallat, Marijn Nijenhuis, Florent Cosandier and Simon Henein
Machines 2025, 13(6), 530; https://doi.org/10.3390/machines13060530 - 18 Jun 2025
Abstract
Flexure-based linear stages have become prevalent in precision engineering; however, most designs suffer from parasitic shifts that degrade positioning accuracy. Conventional solutions to mitigate these parasitic motions often compromise support stiffness, reduce motion range, and increase structural complexity. This study presents a novel
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Flexure-based linear stages have become prevalent in precision engineering; however, most designs suffer from parasitic shifts that degrade positioning accuracy. Conventional solutions to mitigate these parasitic motions often compromise support stiffness, reduce motion range, and increase structural complexity. This study presents a novel family of flexure-based rectilinear-motion stages using coupled n-RRR planar parallel mechanisms, achieving extremely low parasitic shifts while addressing the forementioned limitations. Four design variants are selected and analyzed via Finite Element Method (FEM) simulations, evaluating parasitic shifts, stroke, and support stiffness. The most precise configuration, a 4-RRR rectilinear stage having kinematic chains coupled via two Watt linkages, exhibits a lateral shift smaller than 0.258 µm and an in-plane parasitic rotation smaller than 12.6 µrad over a 12 mm stroke. Experimental validation using a POM prototype confirms the high positioning precision and support stiffness properties. In addition, a silicon prototype incorporating thermally preloaded buckling beams is investigated to reduce its translational stiffness. Experimental results show a translational stiffness reduction of 98% in the monostable configuration and 112% in the bistable configuration (i.e., negative stiffness), without support stiffness reduction. These results highlight the potential of the proposed mechanisms for a wide range of precision applications, offering a scalable and high-accuracy solution for micro- and nano-positioning systems.
Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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Open AccessArticle
Factors Affecting Mechanical Properties of Impulse Friction Stir Welded AA2024-T351 Under Static and Cyclic Loads
by
Iuliia Morozova, Aleksei Obrosov, Anton Naumov, Vesselin Michailov and Nikolay Doynov
Machines 2025, 13(6), 529; https://doi.org/10.3390/machines13060529 - 17 Jun 2025
Abstract
This study investigates the factors affecting the mechanical performance of conventional and impulse friction stir welded (FSW and IFSW) AA2024-T351 joints under static and cyclic loading. Emphasis is placed on the influence of fracture-inducing features such as oxide inclusions, constituent particle distributions, crystallographic
[...] Read more.
This study investigates the factors affecting the mechanical performance of conventional and impulse friction stir welded (FSW and IFSW) AA2024-T351 joints under static and cyclic loading. Emphasis is placed on the influence of fracture-inducing features such as oxide inclusions, constituent particle distributions, crystallographic texture, and precipitation state. A series of IFSW welds produced at varying impulse parameters were compared to conventional FSW welds in terms of microhardness, tensile strength, fatigue life, and Taylor factor distribution. IFSW joints demonstrated a significant improvement in tensile strength and elongation, particularly at higher impulse frequencies. Enhanced material mixing due to the reciprocating tool motion in IFSW resulted in finer particle distribution, more favorable crystallographic texture, and reduced weld pitch, all contributing to increased ductility and strength. Fractographic analyses revealed that fatigue failures primarily initiated in the stir zone, typically at unplasticized metallic inclusions. However, IFSW joints displayed longer fatigue lives, particularly when impulse parameters were optimized. These findings underline the complex interplay of microstructural and textural factors in determining weld performance, highlighting IFSW as a promising technique for enhancing the durability of high-strength aluminum welds.
Full article
(This article belongs to the Section Advanced Manufacturing)
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Open AccessArticle
Digital Control of an Inverted Pendulum Using a Velocity-Controlled Robot
by
Marco Costanzo, Raffaele Mazza and Ciro Natale
Machines 2025, 13(6), 528; https://doi.org/10.3390/machines13060528 - 17 Jun 2025
Abstract
This research article tackles the control problem of an inverted pendulum, also known as the Furuta pendulum, mounted on a velocity-controlled robot manipulator in two configurations: the rotary pendulum and the translational pendulum. Differently from most of the existing control architectures where the
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This research article tackles the control problem of an inverted pendulum, also known as the Furuta pendulum, mounted on a velocity-controlled robot manipulator in two configurations: the rotary pendulum and the translational pendulum. Differently from most of the existing control architectures where the motor actuating the pendulum motion is torque-controlled, the proposed control architecture exploits the inner velocity loop usually available on industrial robots, thus easing the implementation of an inverted pendulum. Another aspect investigated in this paper and mostly overlooked in the literature is the digital implementation of the control and, specifically, the latency introduced by the digital controller. The proposed control solution explicitly models such effects in the control design phase, improving the closed-loop performance. The additional novelty introduced by this paper is the friction compensation that is essential in the swing-up phase of the inverted pendulum, whereas classical control strategies for the nonlinear swing-up usually neglect this effect, and their solutions lead to control failures in practical systems. This paper presents detailed modeling and experimental identification phases followed by the control design of both the nonlinear swing-up algorithm and the linear stabilization controller, both experimentally validated on a Meca500 robotic arm controlled via an EtherCAT communication protocol by a mini PC featuring a Xenomai real-time operating system. The overall system showcases the potential of high-performance digital control systems in industrial robotic applications.
Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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Open AccessArticle
The Prediction of Sound Insulation for the Front Wall of Pure Electric Vehicles Based on AFWL-CNN
by
Yan Ma, Jie Yan, Jianjiao Deng, Xiaona Liu, Dianlong Pan, Jingjing Wang and Ping Liu
Machines 2025, 13(6), 527; https://doi.org/10.3390/machines13060527 - 17 Jun 2025
Abstract
The front wall acoustic package system plays a crucial role in automotive design, and its performance directly affects the quality and comfort of the interior noise. In response to the limitations of traditional experimental and simulation methods in terms of accuracy and efficiency,
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The front wall acoustic package system plays a crucial role in automotive design, and its performance directly affects the quality and comfort of the interior noise. In response to the limitations of traditional experimental and simulation methods in terms of accuracy and efficiency, this paper proposes a convolutional neural network (AFWL-CNN) based on adaptive weighted feature learning. Using a data-driven method, the sound insulation performance of the entire vehicle’s front wall acoustic package system was predicted and analyzed based on the original parameters of the front wall acoustic package components, thereby effectively avoiding the shortcomings of traditional TPA and CAE methods. Compared to the traditional CNN model (RMSE = 0.042, MAE = 3.89 dB, I-TIME = 13.67 s), the RMSE of the proposed AFWL-CNN model was optimized to 0.031 (approximately 26.19% improvement), the mean absolute error (MAE) was reduced to 2.84 dB (approximately 26.99% improvement), and the inference time (I-TIME) increased to 17.16 s (approximately 25.53% increase). Although the inference time of the AFWL-CNN model increased by 25.53% compared to the CNN model, it achieved a more significant improvement in prediction accuracy, demonstrating a reasonable trade-off between efficiency and accuracy. Compared to AFWL-LSTM (RMSE = 0.039, MAE = 3.35 dB, I-TIME = 19.81 s), LSTM (RMSE = 0.044, MAE = 4.07 dB, I-TIME = 16.71 s), and CNN–Transformer (RMSE = 0.040, MAE = 3.74 dB, I-TIME = 19.55 s) models, the AFWL-CNN model demonstrated the highest prediction accuracy among the five models. Furthermore, the proposed method was verified using the front wall acoustic package data of a new car model, and the results showed the effectiveness and reliability of this method in predicting the acoustic package performance of the front wall system. This study provides a powerful tool for fast and accurate performance prediction of automotive front acoustic packages, significantly improving design efficiency and providing a data-driven framework that can be used to solve other vehicle noise problems.
Full article
(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)
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Open AccessArticle
Robotic Positioning Accuracy Enhancement via Memory Red Billed Blue Magpie Optimizer and Adaptive Momentum PSO Tuned Graph Neural Network
by
Jian Liu, Xiaona Huang, Yonghong Deng, Canjun Xiao and Zhibin Li
Machines 2025, 13(6), 526; https://doi.org/10.3390/machines13060526 - 16 Jun 2025
Abstract
Robotic positioning accuracy is critically affected by both geometric and non-geometric errors. To address this dual error issue comprehensively, this paper proposes a novel two-stage compensation framework. First, a Memory based red billed blue magpie optimizer (MRBMO) is employed to identify and compensate
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Robotic positioning accuracy is critically affected by both geometric and non-geometric errors. To address this dual error issue comprehensively, this paper proposes a novel two-stage compensation framework. First, a Memory based red billed blue magpie optimizer (MRBMO) is employed to identify and compensate for geometric errors by optimizing the geometric parameters based on end-effector observations. This memory-guided evolutionary mechanism effectively enhances the convergence accuracy and stability of the geometric calibration process. Second, a tuned graph neural network (AMPSO-GNN) is developed to model and compensate for non-geometric errors, such as cable deformation, thermal drift, and control imperfections. The GNN architecture captures the topological structure of the robotic system, while the adaptive momentum PSO dynamically optimizes the network’s hyperparameters for improved generalization. Experimental results on a six-axis industrial robot demonstrate that the proposed method significantly reduces residual positioning errors, achieving higher accuracy compared to conventional calibration and compensation strategies. This dual-compensation approach offers a scalable and robust solution for precision-critical robotic applications.
Full article
(This article belongs to the Special Issue Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision)
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Open AccessArticle
Direct Force Control Technology for Longitudinal Trajectory of Receiver Aircraft Based on Incremental Nonlinear Dynamic Inversion and Active Disturbance Rejection Controller
by
Xin Bao, Yan Li and Zhong Wang
Machines 2025, 13(6), 525; https://doi.org/10.3390/machines13060525 - 16 Jun 2025
Abstract
Aiming at the requirements of rapidity, high precision, and robustness for the longitudinal trajectory control of the receiver aircraft in autonomous aerial refueling, a direct lift control (DLC) strategy that integrates incremental nonlinear dynamic inversion (INDI) and nonlinear extended state observer (NESO) is
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Aiming at the requirements of rapidity, high precision, and robustness for the longitudinal trajectory control of the receiver aircraft in autonomous aerial refueling, a direct lift control (DLC) strategy that integrates incremental nonlinear dynamic inversion (INDI) and nonlinear extended state observer (NESO) is proposed. First, a control strategy for generating direct lift through the coordinated action of the flaperons and elevators is presented, and a longitudinal dynamics model is established. Secondly, based on the INDI and DLC methods, the rapid tracking and control of altitude are achieved. Finally, an NESO is designed. The observer gains are designed through the pole placement method and the robust optimization method to achieve the estimation of states such as airspeed, angle of attack, pitch rate, and pitch angle, as well as unknown force and moment disturbances. The estimated force and moment disturbances are used to implement the active disturbance rejection control. Simulation results show that the strategy has no altitude tracking error under normal operating conditions, and the altitude tracking error is less than 0.2 m under typical disturbance conditions, indicating high control accuracy. Under disturbance conditions, the estimation errors of true airspeed, angle of attack, pitch angle, and pitch angular velocity are less than 0.3 m/s, 0.12°, 0.1°, and 0.2°/s, respectively, demonstrating the high-precision estimation capability of the observer. The NESO exhibits high accuracy in state estimation, the rudder deflection is smooth, and the anti-disturbance capability is significantly better than traditional methods, providing an engineered solution for the longitudinal control of the receiver aircraft.
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(This article belongs to the Section Automation and Control Systems)
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Open AccessArticle
Three-Dimensional Stability Lobe Construction for Face Milling of Thin-Wall Components with Position-Dependent Dynamics and Process Damping
by
Jinjie Jia, Lixue Chen, Wenyuan Song and Mingcong Huang
Machines 2025, 13(6), 524; https://doi.org/10.3390/machines13060524 - 16 Jun 2025
Abstract
Titanium alloy thin-walled components are extensively used in aerospace engineering, yet their milling stability remains a persistent challenge due to vibration-induced surface anomalies. This study develops an advanced dynamic model for the face milling of titanium alloy thin-walled structures, systematically integrating axial cutting
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Titanium alloy thin-walled components are extensively used in aerospace engineering, yet their milling stability remains a persistent challenge due to vibration-induced surface anomalies. This study develops an advanced dynamic model for the face milling of titanium alloy thin-walled structures, systematically integrating axial cutting dynamics with regenerative chatter mechanisms and nonlinear process damping phenomena. The proposed framework crucially accounts for time-varying tool–workpiece interactions and damping characteristics, enabling precise characterization of stability transitions under dynamically varying axial immersion conditions. A novel extension of the semi-discretization method is implemented to resolve multi-parameter stability solutions, establishing a computational paradigm for generating three-dimensional stability lobe diagrams (3D SLDs) that concurrently evaluate spindle speed, cutting position, and the axial depth of a cut. Comprehensive experimental validation through time-domain chatter tests demonstrates remarkable consistency between theoretical predictions and empirical chatter thresholds. The results reveal that process damping significantly suppresses chatter at low spindle speeds, while regenerative effects dominate instability at higher speeds. This work provides a systematic framework for optimizing machining parameters in thin-walled component manufacturing, offering improved accuracy in stability prediction compared to traditional two-dimensional SLD methods. The proposed methodology bridges the gap between theoretical dynamics and industrial applications, facilitating efficient high-precision machining of titanium alloys.
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(This article belongs to the Special Issue Machine Tools for Precision Machining: Design, Control and Prospects)
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Open AccessArticle
Deep Reinforcement Learning-Based Active Disturbance Rejection Control for Trajectory Tracking of Autonomous Ground Electric Vehicles
by
Xianjian Jin, Huaizhen Lv, Yinchen Tao, Jianning Lu, Jianbo Lv and Nonsly Valerienne Opinat Ikiela
Machines 2025, 13(6), 523; https://doi.org/10.3390/machines13060523 - 16 Jun 2025
Abstract
This paper proposes an integrated control framework for improving the trajectory tracking performance of autonomous ground electric vehicles (AGEVs) under complex disturbances, including parameter uncertainties, and environmental changes. The framework integrates active disturbance rejection control (ADRC) for real-time disturbance estimation and compensation with
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This paper proposes an integrated control framework for improving the trajectory tracking performance of autonomous ground electric vehicles (AGEVs) under complex disturbances, including parameter uncertainties, and environmental changes. The framework integrates active disturbance rejection control (ADRC) for real-time disturbance estimation and compensation with a deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) algorithm for dynamic optimization of controller parameters to improve tracking accuracy and robustness. More specifically, it combines the Line of Sight (LOS) guidance rate with ADRC, proves the stability of LOS through the Lyapunov law, and designs a yaw angle controller, using the extended state observer to reduce the impact of disturbances on tracking accuracy. And the approach also addresses the nonlinear vehicle dynamic characteristics of AGEVs while mitigating internal and external disturbances by leveraging the inherent decoupling capability of ADRC and the data-driven parameter adaptation capability of DDPG. Simulations via CarSim/Simulink are carried out to validate the controller performance in serpentine and double-lane-change maneuvers. The simulation results show that the proposed framework outperforms traditional control strategies with significant improvements in lateral tracking accuracy, yaw stability, and sideslip angle suppression.
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(This article belongs to the Special Issue Intelligent Sensing, Planning and Control for Autonomous Ground Vehicles)
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Open AccessArticle
A Generic Modeling Method of Multi-Modal/Multi-Layer Digital Twins for the Remote Monitoring and Intelligent Maintenance of Industrial Equipment
by
Maolin Yang, Yifan Cao, Siwei Shangguan, Xin Chen and Pingyu Jiang
Machines 2025, 13(6), 522; https://doi.org/10.3390/machines13060522 - 16 Jun 2025
Abstract
Digital twin (DT) is a useful tool for the remote monitoring, analyzing, controlling, etc. of industrial equipment in a harsh working environment unfriendly to human workers. Although much research has been devoted to DT modeling methods, there are still limitations. For example, (1)
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Digital twin (DT) is a useful tool for the remote monitoring, analyzing, controlling, etc. of industrial equipment in a harsh working environment unfriendly to human workers. Although much research has been devoted to DT modeling methods, there are still limitations. For example, (1) existing DT modeling methods are usually focused on specific types of equipment rather than being generally applicable to different types of equipment and requirements. (2) Existing DT models usually emphasize working condition monitoring and have relatively limited capability for modeling the operation and maintenance mechanism of the equipment for further decision making. (3) How to integrate artificial intelligence algorithms into DT models still requires further exploration. In this regard, a systematic and general DT modeling method is proposed for the remote monitoring and intelligent maintenance of industrial equipment. The DT model contains a multi-modal digital model, a multi-layer status model, and an intelligent interaction model driven by a kind of human-readable/computer-deployable event-state knowledge graph. Using the model, the dynamic workflows, working mechanisms, working status, workpiece logistics, monitoring data, and intelligent functions, etc., during the remote monitoring and maintenance of industrial equipment can be realized. The model was verified through three different DT modeling scenarios of a robot-based carbon block polishing processing line.
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(This article belongs to the Special Issue Intelligent Fault Detection and Diagnosis in Condition-Based Maintenance)
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