Sharing Information Between Machine Tools to Improve Surface Finish Forecasting

Daniel R. Clarkson; Lawrence A. Bull; Tina A. Dardeno; Chandula T. Wickramarachchi; Elizabeth J. Cross; Timothy J. Rogers; Keith Worden; Nikolaos Dervilis; Aidan J. Hughes

arXiv · 2310.05807

Sharing Information Between Machine Tools to Improve Surface Finish Forecasting

Daniel R. Clarkson·Lawrence A. Bull·Tina A. Dardeno·Chandula T. Wickramarachchi·Elizabeth J. Cross·Timothy J. Rogers·Keith Worden·Nikolaos Dervilis·Aidan J. Hughes

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Abstract

At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental costs. To counter this, the authors propose a Bayesian hierarchical model to predict surface-roughness measurements for a turning machining process. The hierarchical model is compared to multiple independent Bayesian linear regression models to showcase the benefits of partial pooling in a machining setting with respect to prediction accuracy and uncertainty quantification.

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Daniel R. Clarkson, Lawrence A. Bull, Tina A. Dardeno, Chandula T. Wickramarachchi, Elizabeth J. Cross, Timothy J. Rogers, Keith Worden, Nikolaos Dervilis, Aidan J. Hughes. 2023-10-09. Sharing Information Between Machine Tools to Improve Surface Finish Forecasting. https://arxiv.org/abs/2310.05807

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