Sharing Information Between Machine Tools to Improve Surface Finish Forecasting
Machine Learning
2023-10-10 v1 Computational Engineering, Finance, and Science
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.
Cite
@article{arxiv.2310.05807,
title = {Sharing Information Between Machine Tools to Improve Surface Finish Forecasting},
author = {Daniel R. Clarkson and Lawrence A. Bull and Tina A. Dardeno and Chandula T. Wickramarachchi and Elizabeth J. Cross and Timothy J. Rogers and Keith Worden and Nikolaos Dervilis and Aidan J. Hughes},
journal= {arXiv preprint arXiv:2310.05807},
year = {2023}
}
Comments
Submitted to International Workshop on Structural Health Monitoring 2023, Stanford University, California, USA