Indian Journal of Science and Technology

Abstract

Background/Objectives: Last decades have witnessed a rapid growth in the development of harder, difficult and complexity to machine metals and alloys. AWJM is one of the most freshly built up nontraditional machining processes in processing various types of hard-to-cut materials nowadays. It is an economical method for heat sensitive materials that cannot be machined by processes that produce heat while machining. Machining parameters play a lead role in determining the economics of machine and machining quality. This paper investigates the prediction of MRR and Surface roughness on Lead Tin Alloy using the Artificial Neural Network (ANN). Methods/Statistical analysis: In this work, the influence of five AWJM parameters of the process on SR and MRR of an American element referred as Lead Tin Alloy which is machined by AWJM was experimentally performed and analyzed. According to RSM design, different experiments have been performed with the combination of input parameters on this American element. Findings: Outcome depicts the minimum error attained for data belonging to test is 1.063814%% for MRR and 0.208967018% for SR. Also the maximum error obtained is about 9.475104% for MRR and 9.070886429% for SR. By training the network deviations may occur but error is reduced because this technique is heuristic.

Keywords: Artificial Neural Network, Material Removal Rate, Response Surface Methodology, Surface Roughness