Sharing Information Between Machine Tools to Improve Surface Finish Forecasting

• URL: http://arxiv.org/abs/2310.05807v1
• Date: Mon, 9 Oct 2023 15:44:35 GMT
• Title: Sharing Information Between Machine Tools to Improve Surface Finish Forecasting
• Authors: Daniel R. Clarkson, Lawrence A. Bull, Tina A. Dardeno, Chandula T. Wickramarachchi, Elizabeth J. Cross, Timothy J. Rogers, Keith Worden, Nikolaos Dervilis and Aidan J. Hughes
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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