Machines

19 pages, 1572 KB

Open AccessArticle

Exploring the Impact of Cooling Environments on the Machinability of AM-AlSi10Mg: Optimizing Cooling Techniques and Predictive Modelling

by Zhenhua Dou, Kai Guo, Jie Sun and Xiaoming Huang

Machines 2025, 13(11), 984; https://doi.org/10.3390/machines13110984 (registering DOI) - 24 Oct 2025

Abstract

Additively manufactured (AM) aluminum (Al) alloys are very useful in sectors like automotive, manufacturing, and aerospace because they have unique mechanical properties, such as their light weight, etc. AlSi10Mg made by laser powder bed fusion (LPBF) is one of the most promising materials [...] Read more.

Additively manufactured (AM) aluminum (Al) alloys are very useful in sectors like automotive, manufacturing, and aerospace because they have unique mechanical properties, such as their light weight, etc. AlSi10Mg made by laser powder bed fusion (LPBF) is one of the most promising materials because it has a high strength-to-weight ratio, good thermal resistance, and good corrosion resistance. But machining AlSi10Mg parts is still hard because they have unique microstructural properties from the way they were produced. This research investigates the machining efficacy of the AM-AlSi10Mg alloy in distinct cutting conditions (dry, flood, chilled air, and minimal quantity lubrication with castor oil). The study assesses how different cooling conditions affect important performance metrics such as cutting temperature, surface roughness, and tool wear. Due to castor oil’s superior lubricating and film-forming properties, MQL (Minimal Quantity Lubrication) reduces heat generation between 80 °C and 98 °C for the distinct speed–feed combinations. The Multi-Objective Optimization by Ratio Analysis (MOORA) approach is used to determine the ideal cooling and machining conditions (MQL, Vc of 90 m/min, and fr of 0.05 mm/rev). The relative closeness values derived from the MOORA approach were used to predict machining results using machine learning (ML) models (MLP, GPR, and RF). The MLP showed the strongest relationship between the measured and predicted values, with R values of 0.9995 in training and 0.9993 in testing. Full article

(This article belongs to the Special Issue Neural Networks Applied in Manufacturing and Design)

18 pages, 5537 KB

Open AccessArticle

Prior-Guided Residual Reinforcement Learning for Active Suspension Control

by Jiansen Yang, Shengkun Wang, Fan Bai, Min Wei, Xuan Sun and Yan Wang

Machines 2025, 13(11), 983; https://doi.org/10.3390/machines13110983 (registering DOI) - 24 Oct 2025

Abstract

Active suspension systems have gained significant attention for their capability to improve vehicle dynamics and energy efficiency. However, achieving consistent control performance under diverse and uncertain road conditions remains challenging. This paper proposes a prior-guided residual reinforcement learning framework for active suspension control. [...] Read more.

Active suspension systems have gained significant attention for their capability to improve vehicle dynamics and energy efficiency. However, achieving consistent control performance under diverse and uncertain road conditions remains challenging. This paper proposes a prior-guided residual reinforcement learning framework for active suspension control. The approach integrates a Linear Quadratic Regulator (LQR) as a prior controller to ensure baseline stability, while an enhanced Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm learns the residual control policy to improve adaptability and robustness. Moreover, residual connections and Long Short-Term Memory (LSTM) layers are incorporated into the TD3 structure to enhance dynamic modeling and training stability. The simulation results demonstrate that the proposed method achieves better control performance than passive suspension, a standalone LQR, and conventional TD3 algorithms. Full article

(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)

18 pages, 3445 KB

Open AccessArticle

Underwater Objective Detection Algorithm Based on YOLOv8-Improved Multimodality Image Fusion Technology

by Yage Qie, Chao Fang, Jinghua Huang, Donghao Wu and Jian Jiang

Machines 2025, 13(11), 982; https://doi.org/10.3390/machines13110982 (registering DOI) - 24 Oct 2025

Abstract

The field of underwater robotics is experiencing rapid growth, wherein accurate object detection constitutes a fundamental component. Given the prevalence of false alarms and omission errors caused by intricate subaquatic conditions and substantial image noise, this study introduces an enhanced detection framework that [...] Read more.

The field of underwater robotics is experiencing rapid growth, wherein accurate object detection constitutes a fundamental component. Given the prevalence of false alarms and omission errors caused by intricate subaquatic conditions and substantial image noise, this study introduces an enhanced detection framework that combines the YOLOv8 architecture with multimodal visual fusion methodology. To solve the problem of degraded detection performance of the model in complex environments like those with low illumination, features from Visible Light Image are fused with the Thermal Distribution Features exhibited by Infrared Image, thereby yielding more comprehensive image information. Furthermore, to precisely focus on crucial target regions and information, a Multi-Scale Cross-Axis Attention Mechanism (MSCA) is introduced, which significantly enhances Detection Accuracy. Finally, to meet the lightweight requirement of the model, an Efficient Shared Convolution Head (ESC_Head) is designed. The experimental findings reveal that the YOLOv8-FUSED framework attains a mean average precision (mAP) of 82.1%, marking an 8.7% enhancement compared to the baseline YOLOv8 architecture. The proposed approach also exhibits superior detection capabilities relative to existing techniques while simultaneously satisfying the critical requirement for real-time underwater object detection. Moreover, the proposed system successfully meets the essential criteria for real-time detection of underwater objects. Full article

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

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15 pages, 4026 KB

Open AccessArticle

Reducing Pressure Pulsation and Noise in Micro-Hydraulic Systems of Machine Equipment

by Michał Stosiak, Krzysztof Towarnicki, Paulius Skačkauskas and Mykola Karpenko

Machines 2025, 13(11), 981; https://doi.org/10.3390/machines13110981 (registering DOI) - 24 Oct 2025

Abstract

The paper highlights that hydraulic systems are widely used in various machine applications. Among the evaluation criteria for these systems, the noise-related criterion is also considered. This criterion also applies to micro-hydraulic systems as the permissible level of noise emitted into the environment [...] Read more.

The paper highlights that hydraulic systems are widely used in various machine applications. Among the evaluation criteria for these systems, the noise-related criterion is also considered. This criterion also applies to micro-hydraulic systems as the permissible level of noise emitted into the environment is linked to the installed power, which in micro-hydraulic systems is at least an order of magnitude lower than in conventional hydraulic systems. Failure to comply with EU ambient noise emission standards may result in the machine not being approved for use. It is therefore important to identify noise sources and minimize them. It has been noted that, in hydraulic systems, the primary source of noise is pressure pulsation across a wide frequency range. Moreover, it has been pointed out that low-frequency noise and vibrations are particularly harmful to humans. Thus, pressure pulsation dampers are proposed that are effective both at specific forcing frequencies and across a wide frequency range. Experimental results of a micro-hydraulic system are presented. Full article

(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)

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21 pages, 2979 KB

Open AccessArticle

On the Use of the Detectivity Parameter for the Condition Monitoring of Wind Turbines

by Pasquale Grosso, Gianluca D’Elia, Matteo Strozzi, Riccardo Rubini and Marco Cocconcelli

Machines 2025, 13(11), 980; https://doi.org/10.3390/machines13110980 - 24 Oct 2025

Abstract

This study investigates the application of Detectivity, a composite metric derived from Hjorth’s parameters, for the condition monitoring of wind turbines. These parameters were originally introduced to describe the morphology of biomedical signals, and they consist of three scalar descriptors: Activity, Mobility, and [...] Read more.

This study investigates the application of Detectivity, a composite metric derived from Hjorth’s parameters, for the condition monitoring of wind turbines. These parameters were originally introduced to describe the morphology of biomedical signals, and they consist of three scalar descriptors: Activity, Mobility, and Complexity, capturing, respectively, signal variance, frequency content, and waveform shape. Detectivity, proposed in a previous work by the authors as a condensation of Hjorth’s parameters, can be interpreted as the total gain in these parameters with respect to a reference condition corresponding to a healthy component. The analysis is conducted on two distinct datasets. The first, publicly available from the Luleå University website, contains vibration data from six wind turbines in a Swedish wind farm, one of which is affected by a bearing fault. A robust methodology was developed to manage the strong variability in rotational speed. The second dataset includes vibration signals from a 2 MW commercial turbine, acquired over 50 consecutive days during which an inner race fault progressively developed. The use of the Detectivity cumulant proved particularly effective: in the first case, it clearly identified the faulty machine; in the second, it enabled the detection of the time at which the probable onset of the fault occurred. Full article

(This article belongs to the Special Issue Advances in Mechanism and Machine Science Within the IFIT 2024 Conference)

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20 pages, 7813 KB

Open AccessArticle

Integrated Error Compensation for Robotic Arm Polishing of Cylindrical Aspheric Optical Components

by Yao Liu, Ruiliang Li, Jingjing Xie, Yiming Wang and Lin Sun

Machines 2025, 13(11), 979; https://doi.org/10.3390/machines13110979 - 24 Oct 2025

Abstract

This research tackles the intricate machining properties of cylindrical aspheric surfaces with a versatile adaption approach utilizing a robotic arm and a compact tool head, incorporating trajectory optimization. A three-step integrated error compensation framework was established as the core to address spatial inaccuracies [...] Read more.

This research tackles the intricate machining properties of cylindrical aspheric surfaces with a versatile adaption approach utilizing a robotic arm and a compact tool head, incorporating trajectory optimization. A three-step integrated error compensation framework was established as the core to address spatial inaccuracies in robotic systems, incorporating coordinate measuring machine (CMM)-based cylindrical generatrix offset correction, laser tracker-assisted progressive coordinate calibration, and contour profiler-driven feedback compensation. Complemented by a curvature-driven trajectory design, the method ensures uniform polishing coverage for non-uniform curvature surfaces. Experimental validation on S-TiH53 glass cylindrical aspheric components demonstrated a surface profile accuracy of peak-to-valley (PV) value ≤ 2 μm, meeting stringent requirements for high-power laser applications. This systematic approach enhances both efficiency and accuracy in robotic polishing, offering a viable solution for high-end optical manufacturing. Full article

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

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20 pages, 1558 KB

Open AccessArticle

An Approach to Multicriteria Optimization of the Three-Stage Planetary Gear Train

by Jelena Stefanović-Marinović, Marko Perić, Aleksandar Miltenović, Dragan Marinković and Žarko Ćojbašić

Machines 2025, 13(11), 978; https://doi.org/10.3390/machines13110978 - 23 Oct 2025

Abstract

Planetary gear trains offer numerous advantages over traditional gear systems, including high efficiency, the ability to handle large torque loads, and significant reductions in mass and size for the same torque capacity. However, their relatively complex design necessitates the use of optimization techniques [...] Read more.

Planetary gear trains offer numerous advantages over traditional gear systems, including high efficiency, the ability to handle large torque loads, and significant reductions in mass and size for the same torque capacity. However, their relatively complex design necessitates the use of optimization techniques to identify the most suitable configurations for specific applications. A key requirement for effective optimization is a mathematical model that accurately captures the essential operational characteristics of the system. Moreover, the optimization process must account for multiple, often conflicting, objectives. This paper focuses on the multicriteria optimization of a three-stage planetary gear train intended for use in a road vehicle winch. The development of the optimization model involves defining the objective functions, decision variables, and constraints. Optimization criteria were based on the following characteristics: overall volume, mass, transmission efficiency, and the production costs of the gear pairs. In addition to identifying the group of solutions that are Pareto optimal, the model employs the weighted coefficient method to select a single optimal solution from this set. The selected solution is then analyzed through simulation to assess potential gear failure scenarios. By combining optimization techniques with simulation and contact analysis, this study contributes to improving the reliability of planetary gear transmissions. Full article

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

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22 pages, 3921 KB

Open AccessArticle

Tightly Coupled LiDAR-Inertial Odometry for Autonomous Driving via Self-Adaptive Filtering and Factor Graph Optimization

by Weiwei Lyu, Haoting Li, Shuanggen Jin, Haocai Huang, Xiaojuan Tian, Yunlong Zhang, Zheyuan Du and Jinling Wang

Machines 2025, 13(11), 977; https://doi.org/10.3390/machines13110977 - 23 Oct 2025

Abstract

Simultaneous Localization and Mapping (SLAM) has become a critical tool for fully autonomous driving. However, current methods suffer from inefficient data utilization and degraded navigation performance in complex and unknown environments. In this paper, an accurate and tightly coupled method of LiDAR-inertial odometry [...] Read more.

Simultaneous Localization and Mapping (SLAM) has become a critical tool for fully autonomous driving. However, current methods suffer from inefficient data utilization and degraded navigation performance in complex and unknown environments. In this paper, an accurate and tightly coupled method of LiDAR-inertial odometry is proposed. First, a self-adaptive voxel grid filter is developed to dynamically downsample the original point clouds based on environmental feature richness, aiming to balance navigation accuracy and real-time performance. Second, keyframe factors are selected based on thresholds of translation distance, rotation angle, and time interval and then introduced into the factor graph to improve global consistency. Additionally, high-quality Global Navigation Satellite System (GNSS) factors are selected and incorporated into the factor graph through linear interpolation, thereby improving the navigation accuracy in complex and unknown environments. The proposed method is evaluated using KITTI dataset over various scales and environments. Results show that the proposed method has demonstrated very promising better results when compared with the other methods, such as ALOAM, LIO-SAM, and SC-LeGO-LOAM. Especially in urban scenes, the trajectory accuracy of the proposed method has been improved by 33.13%, 57.56%, and 58.4%, respectively, illustrating excellent navigation and positioning capabilities. Full article

(This article belongs to the Section Vehicle Engineering)

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14 pages, 3832 KB

Open AccessArticle

Research on the Error Compensation for the Dynamic Detection of the Starting Torque of Self-Lubricating Spherical Plain Bearings

by Qiang Wang, Ruijie Gu, Ruijie Xie, Bingjing Guo, Zhuangya Zhang, Fenfang Li and Long You

Machines 2025, 13(11), 976; https://doi.org/10.3390/machines13110976 - 23 Oct 2025

Abstract

The starting torque of Self-lubricating Spherical Plain Bearings (SSPBs) has a significant impact on the reliability and service life of aircraft. Due to the low accuracy of the dynamic detection of the starting torque of the bearing, the starting torque cannot be measured [...] Read more.

The starting torque of Self-lubricating Spherical Plain Bearings (SSPBs) has a significant impact on the reliability and service life of aircraft. Due to the low accuracy of the dynamic detection of the starting torque of the bearing, the starting torque cannot be measured accurately under high-frequency swinging conditions. Therefore, the problem of the dynamic detection accuracy of the starting torque of the bearing on a high-frequency swinging friction and wear tester was proposed to be investigated in this paper, and a dynamic simulation model of the swinging system of the tester was constructed. With the combination of the inertia torque test and the least square method, a mathematical model of the inertia torque was developed and the influence of the inertia torque on the results of the dynamic detection of the starting torque was revealed. At the same time, an error compensation procedure for the on-line dynamic detection of the starting torque was written. This research shows that the inertia torque of the swing system of the tester has a great influence on the detection accuracy of the starting torque. As the swing frequency increases, the inertia torque increases, and the dynamic detection accuracy of the starting torque is reduced. The dynamic detection error of the starting torque of the bearing can be efficiently compensated by the error compensation procedure, and then the detection accuracy can be improved. This research provides a good theory for the design of SSPBs and the reasonable control of the starting torque during the use of the bearings, and it is valuable for engineering practice. Full article

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

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16 pages, 2360 KB

Open AccessArticle

The Diagnosis and Recovery of Faults in the Workshop Environmental Control System Sensor Network Based on Medium-to-Long-Term Predictions

by Shaohan Xiao, Fangping Ye, Xinyuan Zhang, Mengying Tan and Canwen Zhang

Machines 2025, 13(11), 975; https://doi.org/10.3390/machines13110975 - 22 Oct 2025

Abstract

For the fault issues in the workshop environmental control system sensor network, a fault diagnosis and recovery method based on medium-to-long-term predictions is proposed. Firstly, a temperature observer based on the Informer model is established. Then, the predicted data temporarily replaces the missing [...] Read more.

For the fault issues in the workshop environmental control system sensor network, a fault diagnosis and recovery method based on medium-to-long-term predictions is proposed. Firstly, a temperature observer based on the Informer model is established. Then, the predicted data temporarily replaces the missing real data, and the model predicts the state of the sensor system within the step size. Secondly, the predicted data is combined with the measured temperature series, and residuals are utilized for real-time detection of sensor faults. Finally, the predicted data at the time of the fault replaces the real data, enabling the recovery of fault data; experiments are conducted to verify the effectiveness of the proposed method. The results indicate that when the prediction horizon is 1, 5, 10, 20, and 50, the average fault diagnosis rates under four fault levels are 94.40%, 95.28%, 94.79%, 92.52%, and 93.35%, respectively. The average coefficients of determination for data recovery are 0.999, 0.997, 0.995, 0.985, and 0.915, respectively. This achieves medium-to-long-term predictions in the field of sensor fault diagnosis. Full article

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

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24 pages, 27351 KB

Open AccessArticle

High-Efficiency Milling of Inconel 718 Superalloy: Effects of Cutting Conditions on Tool Life and Surface Roughness

by Kazumasa Kawasaki

Machines 2025, 13(11), 974; https://doi.org/10.3390/machines13110974 - 22 Oct 2025

Abstract

Inconel 718 is a Ni-based superalloy with excellent corrosion resistance, heat resistance, high-temperature strength and high creep resistance. It is also known to be a difficult-to-machine material. Conventional machining methods have not only low machining efficiency, but also high cost and low versatility [...] Read more.

Inconel 718 is a Ni-based superalloy with excellent corrosion resistance, heat resistance, high-temperature strength and high creep resistance. It is also known to be a difficult-to-machine material. Conventional machining methods have not only low machining efficiency, but also high cost and low versatility using CBN and ceramic tools, so cost reduction and highly efficient machining by substituting relatively inexpensive cemented carbide tools are required. Some results on the tool life in milling for intermittent cutting for Inconel 718 superalloy have been reported, and the tool life has been considered a problem. Therefore, there is a need to clarify the basic characteristics of milling, such as tool wear and adhesion conditions, and to identify long tool life and highly efficient cutting conditions in order to achieve highly efficient milling of Inconel 718 superalloy. In this study, the milling of Inconel 718 superalloy was conducted using an end mill with a constant depth of cut, and milling efficiency was defined as the table feed rate of the milling machine in mm/min. The tool wear, welding condition, and surface roughness of the workpiece were evaluated according to the combination of cutting speed and feed rate per edge, with a milling efficiency of 800 mm/min. The experimental results showed that with the combination of a cutting speed of 10.33 m/min and feed rate of 0.4 mm/tooth, and the combination of 20.65 m/min and 0.4 mm/tooth, when there was a lower cutting speed and higher feed rate per edge, less weld detachment occurred, less progression of flank wear, and less chipping occurred, and the tool edge was more stable. It was also confirmed that, by keeping the cutting speed constant and increasing the feed rate per edge, both long tool life and highly efficient milling were possible under the above conditions. Full article

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

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Machines, Volume 13, Issue 11 (November 2025) – 11 articles

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