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

Damien McParland (), Szymon Baron, Sarah O’Rourke, Denis Dowling, Eamonn Ahearne and Andrew Parnell
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Damien McParland: University College Dublin
Szymon Baron: University College Dublin
Sarah O’Rourke: Central Statistics Office of Ireland
Denis Dowling: University College Dublin
Eamonn Ahearne: University College Dublin
Andrew Parnell: University College Dublin

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 3, No 18, 1259-1270

Abstract: 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 realtime application of data analytics to machining processes.

Keywords: Cobalt chromium alloys; Orthogonal cutting; Forces in cutting; Gaussian process; Tool life optimisation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10845-017-1317-3

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