Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process

Authors:
M. Correa;C. Bielza;J. Pamies-Teixeira
Affiliations:
Instituto de Automática Industrial - Spanish National Research Council, Ctra. Campo Real km. 0.200, 28500 Arganda del Rey, Madrid, Spain;Universidad Politécnica de Madrid, Departamento de Inteligencia Artificial, 28660 Boadilla del Monte, Madrid, Spain;Universidade Nova de Lisboa, Faculdade de Ciencias e Tecnologia, Quinta da Torre 2829-516 Caparica, Portugal
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

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.