Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models
|Title:||Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models||Authors:||McParland, Damien
Dowling, Denis P.
Parnell, Andrew C.
|Permanent link:||http://hdl.handle.net/10197/8724||Date:||2017||Abstract:||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 real time application of data analytics to machining processes.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||Springer||Copyright (published version):||2017 Springer||Keywords:||Machine learning;Statistics;Cobalt chromium alloys;Orthogonal cutting;Forces in cutting;Gaussian process;Tool life optimisation||DOI:||10.1007/s10845-017-1317-3||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mechanical & Materials Engineering Research Collection|
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