WITHDRAWN: Experimental investigation and optimization of surface grinding on EN31AM steel using Taguchi and ANN

Abstract

Grinding is the machining processes; it is used to enhance the dimensional accuracy and surface quality of workpiece. The surface grinding process is affected by the different process parameters such as grinding wheel speed, material removal rate (MRR), number of passes, grinding wheel grain size, work piece speed, material hardness and depth of cut respectively. The feed and speed are the important factors because by increasing these factors will directly impacts on surface roughness (SR). Even though, the SR is reduced by improving the MRR. Surface grinding is the major process in metals cutting applications; it is widely used in finishing operations of cutting machining processes. SR and MRR are the significant output responses in the manufacturing relating to quality and quantity. In this article, an experimental investigation of surface grinding of EN31AM steel is studied to minimize the SR and maximize MRR for the optimization of grinding process parameters. In addition, artificial neural network (ANN) and Taguchi also employed for optimizing the input process parameters such as depth of cut, feed, and speed.