Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays
This work presents a study on the applicability of radial base function (RBF) neural networks for prediction of Roughness Average (R-a) in the turning process of SAE 52100 hardened steel, with the use of Taguchi'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) algorithm for selection of radial centers and (iii) algorithm for selection of the spread factor of the radial function. 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 significant 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 roughness prediction than the most common trial and error approach. (C) 2012 Elsevier Ltd. All rights reserved.
surface roughness hard turning rbf neural networks taguchi methods