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Journal of Intelligent Manufacturing
Erschienen in: Journal of Intelligent Manufacturing 3/2019

23.03.2017

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

verfasst von: Damien McParland, Szymon Baron, Sarah O’Rourke, Denis Dowling, Eamonn Ahearne, Andrew Parnell

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 3/2019

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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.
Vorheriger Artikel RETRACTED ARTICLE: Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique
Nächster Artikel POLCA system for supply chain management: simulation in the automotive industry

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Metadaten
Titel
Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models
verfasst von
Damien McParland
Szymon Baron
Sarah O’Rourke
Denis Dowling
Eamonn Ahearne
Andrew Parnell
Publikationsdatum
23.03.2017
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 3/2019
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-017-1317-3

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