{"title":"Predicting static friction coefficients under heavy loads using machine learning algorithms","authors":"Minhu Jeong, Jinho Kang, Sang-Shin Park","doi":"10.26599/frict.2025.9441114","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":12442,"journal":{"name":"Friction","volume":"53 1","pages":""},"PeriodicalIF":6.3000,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Friction","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.26599/frict.2025.9441114","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
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.
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.