A DOE Based Approach for the Design of RBF Artificial Neural Networks Applied to Prediction of Surface Roughness… Fabrício José Pontes fpontes@embraer.com.br Messias Borges Silva messias@dequi.eel.usp.br Faculdade de Engenharia de Guaratinguetá UNESP – Universidade Estadual Paulista Departamento de Mecânica 12516-410 Guaratinguetá, São Paulo, Brazil João Roberto Ferreira jorofe@iem.efei.br Anderson Paulo de Paiva andersonppaiva@unifei.com.br Pedro Paulo Balestrassi pedro@unifei.edu.br Gustavo Bonnard Schönhorst gustavobonnard@gmail.com UNIFEI – Universidade Federal de Itajubá Instituto de Eng. Produção e Gestão Caixa Postal 50 37500-903 Itajubá, Minas Gerais, Brazil A DOE Based Approach for the Design of RBF Artificial Neural Networks Applied to Prediction of Surface Roughness in AISI 52100 Hardened Steel Turning The use of artificial neural networks for prediction in hard turning has received considerable attention in literature. An often quoted drawback of ANNs is the lack of a systematic way for the design of high performance networks. This study presents a DOE based approach for the design of ANNs of Radial Basis Function (RBF) architecture applied to surface roughness prediction in turning of AISI 52100 hardened steel. Experimental factors are the number of radial units on the hidden layer, the algorithm employed to calculate the spread factor of radial units and the algorithm employed to calculate radial function centers. DOE is employed to select levels of factors that benefit network prediction skills. Experiments with data sets of distinct sizes were conducted and network configurations leading to high performance were identified. ANN models obtained proved capable to predict roughness in accurate, precise and affordable way. Results pointed significant factors for network design and revealed that interaction effects between design parameters have significant influence on network performance for the task proposed. The work concludes that the DOE methodology constitutes a better approach to the design of RBF networks for roughness prediction than the most common trial and error approach. Keywords: surface roughness, design of experiments, radial basis function neural networks, hard turning, AISI 52100 hardened steel Introduction 1 Hard turning has become an important process in modern metal industry. It is defined as an operation in which materials in hardened state (50–70 HRC) are machined with single point cutting tools, and which was made possible due to the relatively recent development of new cutting tool materials, such as cubic boron nitride and ceramics (Singh and Rao, 2007). Its main goal is to remove work piece material in a single cut rather than in a lengthy grinding operation. Although presenting potential advantages over traditional machining processes in some applications, hard turning presents unique characteristics, such as segmented chip formation and micro- structural alterations at the machined surfaces, which are fundamentally different from conventional turning (Karpat and Özel, 2007). A better knowledge of this process could ultimately lead to the combination or elimination of one of the operations required, thus reducing product cycle time and increasing productivity, according to Singh and Rao (2007). Many works on hard turning aim to develop models for surface quality. This is an essential consumer requirement in machining processes because of its impact on product performance (Ambrogio et al., 2008). Basheer et al. (2008) affirm that characteristics of surfaces machined have significant influence on the ability of the material to withstand stresses, temperature, friction and corrosion. A widely used surface quality indicator is surface roughness (Özel and Karpat, 2005). The formation of surface roughness is a complex process, affected by many factors as tool variables, work piece variables and cutting parameters (Singh and Rao, 2007). Various authors have obtained good results employing artificial neural networks (ANNs) for surface roughness prediction. As pointed out by Coit, Jackson and Smith (1998), neurocomputing Paper accepted July, 2010. Technical Editor: Anselmo Eduardo Diniz suits modeling of complex manufacturing operations due to its universal function approximation capability, resistance to the noise or missing data, accommodation of multiple non-linear variables for unknown interactions and good generalization capability. Some works, however, report drawbacks in using ANNs for prediction (Ambrogio et al., 2008; Bagci and Isik, 2006). An often reported problem with ANNs is the optimization of network parameters. Zhong, Khoo and Han (2006) affirm that there is no exact solution for the definition of the number of layers and neural nodes required for particular applications. This study proposes the application of the Design of Experiments (DOE) methodology for the design of neural networks of RBF (Radial Basis Function) architecture applied to the prediction of surface roughness (Ra) in the turning process of AISI 52100 hardened steel. The factors considered were the network parameters: number of radial units on the hidden layer, the algorithm employed to calculate the spread factor of radial units and the algorithm employed to calculate center location of the radial functions. The goals of the experimental planning are to identify levels of factors that benefits network prediction skills, to assess the relative importance of each design parameter on network performance and to investigate possible interactions between levels of design factors. Experiments with distinct sizes of training sets were conducted. This made it possible to evaluate the relative importance of each design factor on network performance and the accuracy attainable by RBFs as the amount of examples available for training and selection varies. Pairs of input-output data obtained from turning operations were used to generate examples for network training and for confirmation runs. Cutting speed (V), feed (f), and depth of cut (d) were employed as network inputs. The results pinpoint network configurations that presented the best results in prediction, for each size of training set. J. of the Braz. Soc. of Mech. Sci. & Eng. Copyright © 2010 by ABCM Special Issue 2010, Vol. XXXII, No. 5 / 503