Integration of machine learning prediction and optimization for determination of the coefficient of friction of textured UHMWPE surfaces

Author:

Feng Huihui12ORCID,Liu Jing1,van Ostayen Ron2ORCID,Ji Cuicui1,Xu Haoran1

Affiliation:

1. College of Mechanical and Electrical Engineering, Hohai University, Changzhou, PR China

2. Department of Precision and Microsystems Engineering, Delft University of Technology, Delft, The Netherlands

Abstract

The frictional performance of water-lubricated UHMWPE is influenced by the combination of structural parameters and operating conditions. To improve the efficiency of optimal design of surface texture aimed at improving frictional performance, a novel integration of the Orthogonal Array method (OAM), machine learning (ML) prediction, and Particle Swarm Optimization (PSO) is proposed for predicting and optimizing the coefficient of friction (COF) of copper ball-textured UHMWPE surfaces using a small dataset. In order to reduce manufacturing and testing cost, decrease required training samples for ML algorithm, OAM which could efficiently acquire data set with comprehensive feature information is used to determine the parameters of test samples to generate a small but effective dataset. 25 textured samples based on L16 (44) and L9 (34) are fabricated, with the parameter set determined using OAM. COFs of the samples are tested using RTEC tribo-tester. Trend analysis is conducted to investigate the influence of force, frequency, depth and ellipse axis ratio on COF. Multi-linear Regression (MLR) and Gaussian Process Regression are employed. MLR exhibits better prediction accuracy and is integrated with PSO to minimize COF. The error between the experimental and the theoretical results obtained by the integration method of MLR and PSO is only 1.04%, demonstrating the feasibility of predicting COF and optimizing surface texture using the integrated method with a limited dataset determined by OAM.

Funder

Fundamental Research Funds for the Central Universities

China Research council

National Natural Science Foundation of China

Changzhou Sci&Tech Program

Publisher

SAGE Publications

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3