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