Sharing Information Between Machine Tools to Improve Surface Finish Forecasting

TL;DR

This paper introduces a Bayesian hierarchical model for predicting surface roughness in machining, leveraging shared information across tasks to improve accuracy and uncertainty estimation, reducing experimental costs.

Contribution

The paper presents a novel hierarchical Bayesian approach for multi-task surface quality prediction, outperforming independent models in accuracy and uncertainty quantification.

Findings

Hierarchical model improves prediction accuracy over independent models.

Partial pooling enhances uncertainty estimation.

Method reduces experimental costs in surface quality prediction.

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.