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


    McParland, Damien and Baron, Szymon and O'Rourke, Sarah and Dowling, Denis and Ahearn, Eamonn and Parnell, Andrew (2017) Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models. Journal of Intelligent Manufacturing. pp. 1-12. ISSN 0956-5515

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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. 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.

    Item Type: Article
    Additional Information: This is the postprint version of the published article, which is available at: McParland, D., Baron, S., O’Rourke, S. et al. J Intell Manuf (2017). https://doi.org/10.1007/s10845-017-1317-3
    Keywords: Cobalt chromium alloys; Orthogonal cutting; Forces in cutting; Gaussian process; Tool life optimisation;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 10259
    Identification Number: https://doi.org/10.1007/s10845-017-1317-3
    Depositing User: Andrew Parnell
    Date Deposited: 03 Dec 2018 14:46
    Journal or Publication Title: Journal of Intelligent Manufacturing
    Publisher: Springer
    Refereed: Yes
    Funders: Enterprise Ireland (EI)
    URI:

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