Classification of Progressive Wear on a Multi-Directional Pin-on-Disc Tribometer Simulating Conditions in Human Joints-UHMWPE against CoCrMo Using Acoustic Emission and Machine Learning
Pushkar Deshpande,
Kilian Wasmer,
Thomas Imwinkelried,
Roman Heuberger,
Michael Dreyer,
Bernhard Weisse,
Rowena Crockett,
Vigneashwara Pandiyan
Affiliations
Pushkar Deshpande
Laboratory for Advanced Materials Processing (LAMP), Empa-Swiss Federal Laboratories for Materials Science & Technology, Feuerwerkerstrasse 39, CH-3602 Thun, Switzerland
Kilian Wasmer
Laboratory for Advanced Materials Processing (LAMP), Empa-Swiss Federal Laboratories for Materials Science & Technology, Feuerwerkerstrasse 39, CH-3602 Thun, Switzerland
Thomas Imwinkelried
RMS Foundation, CH-2544 Bettlach, Switzerland
Roman Heuberger
RMS Foundation, CH-2544 Bettlach, Switzerland
Michael Dreyer
Laboratory for Mechanical Systems Engineering, Empa-Swiss Federal Laboratories for Materials Science & Technology, Ueberlandstrasse 129, CH-8600 Dubendorf, Switzerland
Bernhard Weisse
Laboratory for Mechanical Systems Engineering, Empa-Swiss Federal Laboratories for Materials Science & Technology, Ueberlandstrasse 129, CH-8600 Dubendorf, Switzerland
Rowena Crockett
Surface Science & Coating Technologies, Empa-Swiss Federal Laboratories for Materials Science & Technology, Ueberlandstrasse 129, CH-8600 Dubendorf, Switzerland
Vigneashwara Pandiyan
Laboratory for Advanced Materials Processing (LAMP), Empa-Swiss Federal Laboratories for Materials Science & Technology, Feuerwerkerstrasse 39, CH-3602 Thun, Switzerland
Human joint prostheses experience wear failure due to the complex interactions between Ultra-High-Molecular-Weight Polyethylene (UHMWPE) and Cobalt-Chromium-Molybdenum (CoCrMo). This study uses the wear classification to investigate the gradual and progressive abrasive wear mechanisms in UHMWPE. Pin-on-disc tests were conducted under simulated in vivo conditions, monitoring wear using Acoustic Emission (AE). Two Machine Learning (ML) frameworks were employed for wear classification: manual feature extraction with ML classifiers and a contrastive learning-based Convolutional Neural Network (CNN) with ML classifiers. The CNN-based feature extraction approach achieved superior classification performance (94% to 96%) compared to manual feature extraction (81% to 89%). The ML techniques enable accurate wear classification, aiding in understanding surface states and early failure detection. Real-time monitoring using AE sensors shows promise for interventions and improving prosthetic joint design.
