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Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models
Date Issued
2017
Date Available
2018-03-23T02:00:12Z
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.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Conference Publication
Publisher
Springer
Journal
Journal of Intelligent Manufacturing
Copyright (Published Version)
2017 Springer
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
File(s)
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Name
insight_publication.pdf
Size
2.25 MB
Format
Adobe PDF
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