Mathematical and prediction modeling of material removal rate for evaluating the effects of process parameters

Abstract

In modernized manufacturing industries, a major concentration is given to dimensional accuracy, cost, time, and MRR, etc. MRR is the dominating factor to access the development in productivity and conserving human energy of machining industry and machined tools. In this research, a model is presented to analyze the process parameters and their effect on response factor, i.e., MRR on workpiece of AA6082 material in operation of WEDM. In this research, the factors such as TON, TOFF, SV, and WF are evaluated through the models designed by a 2-level FFD. In order to establish DA model using Buckingham’s π theorem, artificial intelligence model has been used to examine the effects of machining field factors. To learn the importance of input process control factor in MRR, ANOVA was used. Our focus in this consideration is that the inaccuracy/error among the investigated value and predicted value is minimum which acquired from dimensional analysis and artificial intelligence model. The results acknowledge that the cutting conditions and the machine factors have significant effects on the MRR. Finally, the developed DA and ANN models are compared, then selecting the best set of input factors improves the MRR.