Sharing Information Between Machine Tools to Improve Surface Finish Forecasting

Abstract

At present, most surface-quality prediction methods can only perform single-task prediction [1] 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.