Paper ID: 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

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.

Submitted: Oct 9, 2023