Borjali A, Monson K, Raeymaekers B
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USA.
Tribol Int. 2019 May;133:101-110. doi: 10.1016/j.triboint.2019.01.014. Epub 2019 Jan 8.
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 model can supplement results from PoD wear experiments, and enables predicting polyethylene wear of new PoD experiments based on its operating parameters. It also reveals the relative contribution of individual PoD operating parameters to the resulting polyethylene wear, thus informing design of experiments, and potentially reducing the need for time consuming PoD wear measurements.
销盘（PoD）实验被广泛用于量化和排名用于人工髋关节植入物轴承的不同材料配对的磨损情况。然而，从不同的销盘实验中获得的聚乙烯磨损结果有时难以比较，这可能会导致信息无法获取。我们使用机器学习方法来实现几个数据驱动的模型，随后通过相对于已发表的实验数据量化预测误差来对它们进行验证。数据驱动的模型可以补充销盘磨损实验的结果，并能够根据其操作参数预测新的销盘实验中的聚乙烯磨损情况。它还揭示了各个销盘操作参数对最终聚乙烯磨损的相对贡献，从而为实验设计提供信息，并有可能减少对耗时的销盘磨损测量的需求。
