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

TL;DR

This paper introduces a Bayesian hierarchical Gaussian Process model to predict tool wear in turning a difficult-to-machine medical alloy, enabling optimization of machining parameters and potential real-time application.

Contribution

The paper presents a novel non-parametric Bayesian approach to predict tool wear in machining of Co-Cr-Mo alloy, allowing for accurate predictions and optimization of cutting parameters.

Findings

Predicted tool wear rates are non-linear.

Model can identify optimal cutting settings.

Potential for real-time data analytics in machining.

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.