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

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Title: Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models
Authors: McParland, Damien
Baron, Szymon
O'Rourke, Sarah
Dowling, Denis P.
Ahearne, Eamonn
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 learningStatisticsCobalt chromium alloysOrthogonal cuttingForces in cuttingGaussian processTool life optimisation
DOI: 10.1007/s10845-017-1317-3
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mechanical & Materials Engineering Research Collection
Insight Research Collection

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