Predicting the polyethylene wear rate in pin-on-disc experiments in the context of prosthetic hip implants: deriving a data-driven model using machine learning methods.
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ABSTRACT:
SUBMITTER: Borjali A
PROVIDER: S-EPMC7584286 | biostudies-literature | 2019 May
REPOSITORIES: biostudies-literature
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Predicting the polyethylene wear rate in pin-on-disc experiments in the context of prosthetic hip implants: deriving a data-driven model using machine learning methods.
Borjali A A Monson K K Raeymaekers B B
Tribology international 20190108
Pin-on-disc (PoD) experiments are widely used to quantify and rank wear of different material couples for prosthetic hip implant bearings. However, polyethylene wear results obtained from different PoD experiments are sometimes difficult to compare, which potentially leaves information inaccessible. We use machine learning methods to implement several data-driven models, and subsequently validate them by quantifying the prediction error with respect to published experimental data. A data-driven ...[more]
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