Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 36, Issue 3, Pages 7270-7279Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.09.024
Keywords
Bayesian networks; Artificial neural networks; Surface roughness; High-speed milling; Supervised classification
Categories
Funding
- MOCAVE Project [DPI2006-12736-C02-01]
- Spanish Ministry of Education and Science [TIN2007-62626]
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Machine tool automation is an important aspect for manufacturing companies facing the growing demand of profitability and high quality products as a key for competitiveness. The purpose of supervising machining processes is to detect interferences that would have a negative effect on the process but mainly on the product quality and production time. In a manufacturing environment, the prediction of surface roughness is of significant importance to achieve this objective. This paper shows the efficacy of two different machine learning classification methods, Bayesian networks and artificial neural networks, for predicting surface roughness in high-speed machining. Experimental tests are conducted using the same data set collected in our own milling process for each classifier. Various measures of merit of the models and statistical tests demonstrate the superiority of Bayesian networks in this field. Bayesian networks are also easier to interpret that artificial neural networks. (C) 2008 Elsevier Ltd. All rights reserved.
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