A Comparative Study of the RSM and ANN Models for Predicting Surface Roughness in Roller Burnishing. (2016)

• Record Type: Journal Article
• Authors: Patel, Kiran A.; Brahmbhatt, Pragnesh K.
• Is Part Of: Procedia technology. Volume 23(2016)
• Page Start: 391
• Page End: 397
• Publication Date: 2016
• Subjects: Response surface methodology -- Artificial neural networks -- Burnishing -- Design of experiments -- Surface roughness; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2016.03.042
• Languages: English
• ISSNs: 2212-0173

Abstract

Abstract: In this paper the comparison of the surface roughness prediction models based on response surface methodology (RSM) and artificial neural networks (ANN) is described. The models were developed based on five-level design of experiments conducted on Aluminum alloy 6061 work material with spindle speed, interference, feed, and number of tool pass as the roller burnishing process parameters. The ANN predictive models of surface roughness was developed using a multilayer feed forward neural network and trained with the help of an error back propagation learning algorithm based on the generalized delta rule. Mathematical models of second order RSM and developed ANN models were compared for surface roughness. The comparison evidently indicates that the prediction capabilities of ANN models are far better as compared to the RSM models. The minutiae of experimentation, development of model, testing, and performance comparison are presented in the paper.