Modeling and multiple response optimization of quality characteristics for the micro machining processes

Abstract

In the present trend, new fabrication methods for producing newlineminiaturized components are gaining popularity due to the recent newlineadvancements in Micro-Electro Mechanical Systems (MEMS). Micro newlinemachining differs from the traditional machining with small size tool, newlineresolution of x-y and z stages also optical microscope to observe the cutting newlinestatus. Micro-machine tool industry has made prominent growth in its newlinemanufacturing capabilities in the last decade but still micro-machine tools are newlinenot utilized to their full potential. This limitation is a result of the failure to newlinerun the machine tools at their optimum operating conditions. Since the microend newlinemilling and micro-WEDG processes are widely used, most important newlineoperations in the electronics, automobile, air-craft, medical, defense and other newlineindustries, these two micro-machining operations are considered in this newlineresearch work. Now-a-days, statistical techniques play a vital role for newlinemodeling, simulation and optimization of micro-machining processes, due to newlineaccuracy and less computation time. In micro end milling and micro-WEDG newlineprocesses, the material removal rate and surface roughness are important aspects, which require attention from industry personnel as well as in newlineResearch and development. Hence, an attempt has been made to predict the newlinebest combination of micro-end milling process and micro-WEDG process newlineparameters for achieving the better output of single and multiple quality newlinecharacteristics using the statistical techniques like Taguchi method, Pareto newlineANOVA, Taguchi s Quality Loss Function (TQLF), Principal Component newlineAnalysis (PCA) and Response surface methodologies (RSM). This research work analyses the effect of micro-end milling newlineparameters such as spindle speed, feed rate and depth of cut on material newlineremoval rate and surface roughness. Initially single response optimization of newlinethe process parameters of micro-end milling process is carried out using newlineTaguchi method and Pareto ANOVA method. newline