Machine learning-based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants  

作　　者：Vipin Kumar Ravi Prakash Tewari Anubhav Rawat 

机构地区：[1]Department of Applied Mechanics,Motilal Nehru National Institute of Technology Allahabad,Prayagraj,Uttar Pradesh,India

出　　处：《Biosurface and Biotribology》2024年第4期143-158,共16页生物表面与生物摩擦学(英文)

摘　　要：The purpose of this research is to develop data-driven machine learning(ML)models capable of estimating the specific wear rate of ultra-high molecular weight polyethylene(UHMWPE)used in hip replacement implants.The results of the data-driven models are demonstrating a high level of consistency with the experimental findings acquired from the pin-on-disk(POD)trials.With a performance evaluation of 0.06 mean absolute error(MAE),0.17 Root Mean Square Error(RMSE),and 0.96 R^(2),the Random Forest Regression is found to be the best model.Another machine learning model,called Gradient Boosting Regression,is also found to possess satisfactory predictive perfor-mance by having an MAE of 0.09,RMSE of 0.24,and R^(2)of 0.96.According to the findings of a parametric analysis that made use of an ML model,the surface texture geometry has a substantial dependence on the wear behaviour of UHMWPE bearings that are used in hip replacement implants.This strategy has the potential to enhance experiment design and lessen the necessity for time-consuming POD trials for the purpose of assessing the wear of hip replacement implants.

关 键 词：artificial joint BIOMATERIALS BIOMECHANICS biomedical application biomedical devices bionic surface biosurface BIOTRIBOLOGY computer simulation human knee 

分 类 号：TQ3[化学工程]