A Bayesian network model for surface roughness prediction in the machining process

Abstract

The literature reports many scientific works on the use of artificial intelligence techniques such as neural networks or fuzzy logic to predict surface roughness. This article aims at introducing Bayesian network-based classifiers to predict surface roughness (Ra) in high-speed machining. These models are appropriate as prediction techniques because the non-linearity of the machining process demands robust and reliable algorithms to deal with all the invisible trends present when a work piece is machining. The experimental test obtained from a high-speed milling contouring process analysed the indicator of goodness using the Naïve Bayes and the Tree-Augmented Network algorithms. Up to 81.2% accuracy was achieved in the Ra classification results. Therefore, we envisage that Bayesian network-based classifiers may become a powerful and flexible tool in high-speed machining.

Keywords:

• Bayesian networks
• supervised classification
• probabilistic graphical models
• surface roughness
• high-speed milling

Acknowledgements

We are grateful to the referees for their useful comments. This work was partially supported by the AFAVE Project under Grant DPI2003-07798-C04-01 and by the Spanish Ministry of Education and Science under Grant TIN2004-21428-E.