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We present a novel approach to estimating the effect of control parameters ontool wear rates and related changes in the three force components in turning ofmedical grade Co-Cr-Mo (ASTM F75) alloy. Co-Cr-Mo is known to be a difficult tocut material which, due to a combination of mechanical and physical properties,is used for the critical structural components of implantable medicalprosthetics. We run a designed experiment which enables us to estimate toolwear from feed rate and cutting speed, and constrain them using a Bayesianhierarchical Gaussian Process model which enables prediction of tool wear ratesfor untried experimental settings. The predicted tool wear rates are non-linearand, using our models, we can identify experimental settings which optimise thelife of the tool. This approach has potential in the future for realtimeapplication of data analytics to machining processes.

Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models