STUDY OF RESIDUAL STRESSES AND SURFACE ROUGHNESS IN CNC MILLING OF AISI 202 STEEL

Abstract

In today’s manufacturing applications, it is seen that quality and productivity plays an essential role. The basic aim for manufacturing field which is directly under the influence of quality, whereas the profit of the manufacturing field relies subsequently on the productivity of the process, is a Customer fulfillment. The process of optimization is the basic point to control the machining parameters simultaneously for every manufacturing sector to fight in this competitive world. The essential parameter of milling process is surface roughness. By improving surface roughness, the quality of injected product such as cell phones, laptops, aerospace and automotive parts which depend on finishing, is improved. With the decrease in feed rate, surface roughness slightly improved and the effect of milling speed on the surface roughness is negligible. In the case of residual stress on the machined surface, axial depth of cut has major influence followed by feed rate and milling speed. The measurement of Residual stress in feed direction by x- ray diffraction (XRD). The aim of this project is to optimize the surface roughness and residual stress by RSM. RSM was applied to establish a quadratic relationship between input and output parameters and then analysis of variance (ANOVA) was applied for the evaluation of formula. RSM is also used for optimization of cutting parameters of minimum surface roughness. The major objective in CNC milling operation, while the response parameters selected are surface roughness and residual stress. The main objective for every customer is to measure the Surface quality. Thus surface finish is kept as a main objective for CNC Milling performance. Different cutting parameter which influence Residual stress and surface finish, measured for the analysis on AISI 202 stainless steel with the use of Response Surface method (RSM). For analyzing the parameters, ANOVA is used in milling operation. In ANOVA, The p value model is significant when cutting parameters below 0.05. The F value is differentdifferent observed for different cutting parameter. In this way analyze and optimize the Response parameters.