Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays Fabrício José Pontes b , Anderson Paulo de Paiva a , Pedro Paulo Balestrassi a , João Roberto Ferreira a,⇑ , Messias Borges da Silva b a Institute of Industrial Engineering, Federal University of Itajubá, 37500-903 Itajubá-MG, Brazil b Faculty of Engineering of Guaratinguetá, Sao Paulo State University, 12516-410 Guaratinguetá-SP, Brazil article info Keywords: RBF neural networks Taguchi methods Hard turning Surface roughness abstract This work presents a study on the applicability of radial base function (RBF) neural networks for predic- tion of Roughness Average (R a ) in the turning process of SAE 52100 hardened steel, with the use of Tagu- chi’s orthogonal arrays as a tool to design parameters of the network. Experiments were conducted with training sets of different sizes to make possible to compare the performance of the best network obtained from each experiment. The following design factors were considered: (i) number of radial units, (ii) algo- rithm for selection of radial centers and (iii) algorithm for selection of the spread factor of the radial func- tion. Artificial neural networks (ANN) models obtained proved capable to predict surface roughness in accurate, precise and affordable way. Results pointed significant factors for network design have signif- icant influence on network performance for the task proposed. The work concludes that the design of experiments (DOE) methodology constitutes a better approach to the design of RBF networks for rough- ness prediction than the most common trial and error approach. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction Surface quality is an essential consumer requirement in machining processes because of its impact on product perfor- mance. The characteristics of machined surfaces have significant influence on the ability of the material to withstand stresses, temperature, friction and corrosion (Basheer, Dabade, Suhas, & Bhanuprasad, 2008). The need for the products with high quality surface finish keeps increasing rapidly because of new application in various fields like aerospace, automobile, die and mold manufac- turing and manufacturers are required to increase productivity while maintaining and improving surface quality in order to remain competitive (Karpat & Özel, 2008; Sharma, Dhiman, Sehgal, & Sharma, 2008). A widely used surface quality indicator is surface roughness. High surface roughness values decrease the fatigue life of ma- chined components (Benardos & Vosniakos, 2002; Özel & Karpat, 2005). The formation of surface roughness is a complex process, af- fected by many factors like tool variables, workpiece material and cutting parameters. The complex relationship among the parameters involved makes it difficult to generate explicit analyt- ical models for hard turning processes (Karpat & Özel, 2008). In hard turning, most of process performance characteristics are predictable and, therefore, can be modeled. These models, ob- tained in different ways, may be used as objective functions in optimization, simulation, controlling and prediction algorithms (Tamizharasan, Sevaraj, & Haq, 2006). Al-Ahmari (2007) sustains that machinability models are important for a proper selection of process parameters in planning manufacturing operations. A better knowledge of the process could ultimately lead to the com- bination or elimination of one of the operations required in the process, thus reducing product cycle time and increasing produc- tivity (Singh & Rao, 2007). Among the strategies employed for modeling surface rough- ness, methods based on expert systems are very often employed by researchers (Chen, Lin, Yang, & Tsai, 2010; Zain, Haron, & Sharif, 2010). Benardos and Vosniakos (2003), in a review about surface roughness prediction in machining processes, pointed that models built by means of artificial intelligence (AI) based approaches were more realistic and accurate in the comparison to those based on theoretical approaches. AI techniques, according to the authors, ‘‘take into consideration particularities of the equipment used and the real machining phenomena’’ and are able to include them into the model under construction. Several works make use of ANNs for surface roughness prediction. It can be seen as a ‘sensorless’ ap- proach for estimation of roughness (Sick, 2002), where networks 0957-4174/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2012.01.058 ⇑ Corresponding author. Address: Av BPS 1303, 37500-903 Itajubá/MG, Brazil. Tel.: +55 35 36291150; fax: +55 35 36291148. E-mail addresses: fpontes@embraer.com.br (F.J. Pontes), andersonppaiva@ unifei.com.br (Anderson Paulo de Paiva), pedro@unifei.edu.br (P.P. Balestrassi), jorofe@unifei.edu.br (J.R. Ferreira), messias@dequi.eel.usp.br (Messias Borges da Silva). Expert Systems with Applications 39 (2012) 7776–7787 Contents lists available at SciVerse ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa