Intelligent die design optimization using enhanced differential evolution and response surface methodology

Abstract

Die design process is one of the most complex production design phases in the automotive manufacturing sector and it is the primary and important factor that affects the product development performance. The goal of this research is to describe how to use intelligent die design based on shape and topology optimization using a new improved differential evolution algorithm and response surface methodology. In the simulation process, not only die deflection, but also press table deflection is taken into account in order to achieve more realistic results. The validation of the present approach is evaluated by a comparison of experimental test and simulation results. The optimal shape parameters for the die structure were obtained using response surface methodology and new improved optimization algorithm. In the optimization phase differential evolution was handled and improved with a new mutation strategy which uses the best vectors in the population as differential vectors was developed and used in the new developed algorithm (DEBVs). With the developed DEBVs algorithm better results with less function evaluation numbers were handled. By using this intelligent methodology in the design stage of die, significant results were obtained: the mass was reduced approximately 24 % and the current maximum stress decreased approximately 72 %.