Predicting static friction coefficients under heavy loads using machine learning algorithms

IF 6.3 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Minhu Jeong, Jinho Kang, Sang-Shin Park
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引用次数: 0

Abstract

Fastening structures in vehicles that endure repetitive shear loads must maintain sufficient clamping forces to prevent loosening caused by joint slippage. The minimum clamping force required for control slippage is calculated using analytical and theoretical methods and is closely related to the static friction coefficient between the joint materials. In this study, we introduce a novel test apparatus designed to measure the static friction coefficient between two materials under high load conditions, with its experimental suitability confirmed through reliability verification. We experimentally analyzed the effects of normal load, surface roughness, and mechanical properties on the static friction coefficient for materials commonly used in vehicle joints, including coated steel, steel, and aluminum alloys. Four machine learning algorithms Gaussian process regression (GPR), ensemble, artificial neural network (ANN), and support vector regression (SVR), were evaluated for developing a prediction model for the static friction coefficient. The prediction performance of each model was assessed using various evaluation metrics, and the results showed that the GPR model achieved higher accuracy in predicting the static friction coefficient compared to the other models.

Abstract Image

使用机器学习算法预测重载下的静摩擦系数
承受重复剪切载荷的车辆中的紧固结构必须保持足够的夹紧力,以防止由关节滑移引起的松动。控制滑移所需的最小夹紧力是用分析和理论方法计算的,它与接头材料之间的静摩擦系数密切相关。在本研究中,我们设计了一种新型的测试装置,用于测量高载荷条件下两种材料之间的静摩擦系数,并通过可靠性验证验证了其实验适用性。我们通过实验分析了法向载荷、表面粗糙度和机械性能对汽车常用连接材料(包括涂层钢、钢和铝合金)静摩擦系数的影响。采用高斯过程回归(GPR)、集成(ensemble)、人工神经网络(ANN)和支持向量回归(SVR)四种机器学习算法建立了静摩擦系数预测模型。采用各种评价指标对各模型的预测性能进行了评价,结果表明,探地雷达模型对静摩擦系数的预测精度高于其他模型。
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来源期刊
Friction
Friction Engineering-Mechanical Engineering
CiteScore
12.90
自引率
13.20%
发文量
324
审稿时长
13 weeks
期刊介绍: Friction is a peer-reviewed international journal for the publication of theoretical and experimental research works related to the friction, lubrication and wear. Original, high quality research papers and review articles on all aspects of tribology are welcome, including, but are not limited to, a variety of topics, such as: Friction: Origin of friction, Friction theories, New phenomena of friction, Nano-friction, Ultra-low friction, Molecular friction, Ultra-high friction, Friction at high speed, Friction at high temperature or low temperature, Friction at solid/liquid interfaces, Bio-friction, Adhesion, etc. Lubrication: Superlubricity, Green lubricants, Nano-lubrication, Boundary lubrication, Thin film lubrication, Elastohydrodynamic lubrication, Mixed lubrication, New lubricants, New additives, Gas lubrication, Solid lubrication, etc. Wear: Wear materials, Wear mechanism, Wear models, Wear in severe conditions, Wear measurement, Wear monitoring, etc. Surface Engineering: Surface texturing, Molecular films, Surface coatings, Surface modification, Bionic surfaces, etc. Basic Sciences: Tribology system, Principles of tribology, Thermodynamics of tribo-systems, Micro-fluidics, Thermal stability of tribo-systems, etc. Friction is an open access journal. It is published quarterly by Tsinghua University Press and Springer, and sponsored by the State Key Laboratory of Tribology (TsinghuaUniversity) and the Tribology Institute of Chinese Mechanical Engineering Society.
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