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.