Machines

21 pages, 8433 KiB

Open AccessArticle

Development of an Advanced Wear Simulation Model for a Racing Slick Tire Under Dynamic Acceleration Loading

by Alfonse Ly, Christopher Yoon, Joseph Caruana, Omar Ibrahim, Oliver Goy, Moustafa El-Gindy and Zeinab El-Sayegh

Machines 2025, 13(8), 635; https://doi.org/10.3390/machines13080635 (registering DOI) - 22 Jul 2025

Abstract

This study investigates the development of a tire wear model using finite element techniques. Experimental testing was conducted using the Hoosier R25B slick tire mounted onto a Mustang Dynamometer (MD-AWD-500) in the Automotive Center of Excellence, Oshawa, Ontario, Canada. A general acceleration/deceleration procedure was performed until the battery was completely exhausted. A high-fidelity finite element tire model using Virtual Performance Solution by ESI Group, a part of Keysight Technologies, was developed, incorporating highly detailed material testing and constitutive modeling to simulate the tire’s complex mechanical behavior. In conjunction with a finite element model, Archard’s wear theory is implemented algorithmically to determine the wear and volume loss rate of the tire during its acceleration and deceleration procedures. A novel application using a modified wear theory incorporates the temperature dependence of tread hardness to measure tire wear. Experimental tests show that the tire loses 3.10 g of mass within 45 min of testing. The results from the developed finite element model for tire wear suggest a high correlation to experimental values. This study demonstrates the simulated model’s capability to predict wear patterns, ability to quantify tire degradation under dynamic loading conditions and provides valuable insights for optimizing performance and wear estimation. Full article

(This article belongs to the Special Issue Advanced Technologies in Vehicle Interior Noise Control)

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17 pages, 2635 KiB

Open AccessArticle

Effects of Vibration Direction, Feature Selection, and the SVM Kernel on Unbalance Fault Classification

by Mine Ateş and Barış Erkuş

Machines 2025, 13(8), 634; https://doi.org/10.3390/machines13080634 (registering DOI) - 22 Jul 2025

Abstract

In this study, the combined influence of vibration direction, feature selection strategy, and the support vector machine (SVM) kernel on the classification accuracy of unbalance faults was investigated. Experiments were carried out on a Jeffcott rotor test rig at a constant speed and under three operating conditions. The overlapping sliding window method was used for raw sample expansion. Features extracted from time domain signals and from the order and power spectra obtained in the frequency domain were ranked using the Kruskal–Wallis algorithm. Based on the feature-ranking results, the three most discriminative features for each domain–axis combination, as well as all nine most discriminative features for each axis in a hybrid manner, were fed into SVM classifiers with different kernels, and their performance was evaluated using ten-fold cross-validation. Classification using vibration signals in the vertical direction had higher accuracy rates than those using signals in the horizontal direction for the feature sets obtained in the same domains. According to the statistical results, feature set selection had a much greater impact on classification accuracy than SVM kernel choice. Power spectrum-based features allowed higher classification accuracies in all SVM algorithms compared to both the time domain features and the order spectrum-based features for detecting unbalance faults. Increasing the number of features or employing hybrid feature selection did not result in a consistent or significant enhancement in overall classification performance. Selecting the right SVM kernel shapes both the model’s flexibility and its fit to the chosen feature space; when this fit is inadequate, classification accuracy may decrease. Consequently, by selecting the appropriate vibration direction, feature set, and SVM kernel, an improvement of up to 67% in unbalance fault classification accuracy was achieved. Full article

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

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15 pages, 1542 KiB

Open AccessArticle

The Research on Multi-Objective Maintenance Optimization Strategy Based on Stochastic Modeling

by Guixu Xu, Pengwei Jiang, Weibo Ren, Yanfeng Li and Zhongxin Chen

Machines 2025, 13(8), 633; https://doi.org/10.3390/machines13080633 (registering DOI) - 22 Jul 2025

Abstract

The traditional approach that separates remaining useful life prediction from maintenance strategy design often fails to support efficient decision-making. Effective maintenance requires a comprehensive consideration of prediction accuracy, cost control, and equipment safety. To address this issue, this paper proposes a multi-objective maintenance optimization method based on stochastic modeling. First, a multi-sensor data fusion technique is developed, which maps multidimensional degradation signals into a composite degradation state indicator using evaluation metrics such as monotonicity, tendency, and robustness. Then, a linear Wiener process model is established to characterize the degradation trajectory of equipment, and a closed-form analytical solution of its reliability function is derived. On this basis, a multi-objective optimization model is constructed, aiming to maximize equipment safety and minimize maintenance cost. The proposed method is validated using the NASA aircraft engine degradation dataset. The experimental results demonstrate that, while ensuring system reliability, the proposed approach significantly reduces maintenance costs compared to traditional periodic maintenance strategies, confirming its effectiveness and practical value. Full article

(This article belongs to the Special Issue Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems)

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32 pages, 21606 KiB

Open AccessArticle

Calculation Method and Experimental Investigation of Root Bending Stress in Line Contact Spiral Bevel Gear Pairs

by Shiyu Zuo, Yuehai Sun, Liang Chen, Simin Li and Mingyang Wang

Machines 2025, 13(8), 632; https://doi.org/10.3390/machines13080632 (registering DOI) - 22 Jul 2025

Abstract

Compared to spiral bevel gear drives with localized conjugation, line contact spiral bevel gears possess a significantly larger meshing area, theoretically achieving full tooth surface contact and substantially enhancing load capacity. To accurately support the root strength calculation and parameter design of line contact spiral bevel gear drives, this paper presents a theoretical analysis and experimental study of the root bending stress of gear pairs. First, based on the analysis of the meshing characteristics of line contact spiral bevel gear pairs, the load distribution along the contact lines is investigated. Using the slicing method, the load distribution characteristics along the contact line are obtained, and the load sharing among multiple tooth pairs during meshing is further studied. Then, by applying a cantilever beam bending stress model, the root bending stress on such a gear drive is calculated. A root bending moment distribution model is proposed based on the characteristics of the line load distribution previously obtained, from which a formula for calculating root bending stress is derived. Finally, static-condition experiments are conducted to test the root bending stress. The accuracy of the proposed calculation method is verified through experimental testing and finite element analysis. The results of this study provide a foundation for designing lightweight and high-power-density spiral bevel gear drives. Full article

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

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Machines, Volume 13, Issue 8 (August 2025) – 4 articles

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