Authors: J.V. Abellan-Nebot
Addresses: Department of Industrial Systems Engineering and Design, School of Technology and Experimental Sciences, Universitat Jaume I, 12071 Castellon, Spain
Abstract: Nowadays, despite the large volume of worldwide academic research on various aspects of metal cutting the control of workpiece precision still relies on machine-tool operator|s experience and trial and error runs. In order to increase the efficiency of machining systems, many empirical models based on artificial intelligent (AI) approaches have been proposed in the past, where important process improvements were reported. This paper overviews the AI approaches applied in machining operations to predict part accuracy in terms of dimensional deviations and surface roughness. Successful techniques applied in this field such as artificial neural networks, fuzzy logic, adaptive-network-based fuzzy inference systems and Bayesian networks are briefly reviewed and compared to facilitate its use. For each AI approach, the most relevant research works are described and based on those works some guidelines are proposed for its implementation. In addition, advantages and drawbacks of each approach are summarised and a generic guideline for AI approaches selection is proposed.
Keywords: artificial intelligence; AI selection; part accuracy; surface roughness; dimensional deviations; artificial neural networks; ANNs; fuzzy logic; adaptive network based fuzzy inference systems; ANFIS; neuro-fuzzy systems; Bayesian networks; guidelines; accuracy prediction; intelligent manufacturing.
International Journal of Machining and Machinability of Materials, 2010 Vol.8 No.1/2, pp.6 - 37
Published online: 05 Aug 2010 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article