Journal Article


Andrew Parnell
Eamonn Ahearn
Denis Dowling
Sarah O'Rourke
Szymon Baron
Damien McParland



hierarchical model process model tool wear models designed experiment medical physical and mechanical properties prediction models

Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models (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. However, 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.
Collections Ireland -> Maynooth University -> Type = Article
Ireland -> Maynooth University -> Academic Unit = Faculty of Science and Engineering: Research Institutes: Hamilton Institute
Ireland -> Maynooth University -> Status = Published

Full list of authors on original publication

Andrew Parnell, Eamonn Ahearn, Denis Dowling, Sarah O'Rourke, Szymon Baron, Damien McParland

Experts in our system

Andrew Parnell
Maynooth University
Total Publications: 45
Denis P. Dowling
University College Dublin
Total Publications: 50
Sarah O'Rourke
University College Dublin
Total Publications: 3
Szymon Baron
University College Dublin
Total Publications: 8
Damien McParland
University College Dublin
Total Publications: 9