Application of Response Surface Analysis and Genetic Algorithm for the Optimization of Single Point Incremental Forming Process

Abstract

Single point incremental forming (SPIF) is a modern method of forming sheet metal, where parts can be formed without the use of dedicated dies. The ability of SPIF to form a part is based on various forming parameters. Previous work was not accomplished with the help of design of experiments (DOE), thus reducing the number of parameters varied at any time. This paper presents a Box-Behnken experimental design, which develops the numerical plan, formalizes the forming parameters critical in SPIF and analyse data. The most critical factors affecting SPIF were found to be wall inclination angle, incremental step size, material thickness and tool size. The main effects of these parameters on the quality of the formed parts were studied in detail. Actually this work aims to “optimize the thinning rate and the maximum force by considering the tool diameter and the vertical pitch as unknown parameters for two different wall angles and thicknesses”. To this purpose, an optimization procedure based on the use of response surface methodology (RSM) and genetic algorithms (GA) have been proposed for application to find the optimum solutions. Finally, it demonstrated that the developed methods can solve high non-linear problems successfully. Associated plots are shown to be very efficient for a quick localization of the region of the search space containing the global optimum values of the SPIF parameters.