Evolutionary Optimization and Use of Neural Network for Optimum Stamping Process Design for Minimum Springback

Abstract

Sheet metal forming is characterized by various process parameters such as the forming sequence, shapes of products and dies, friction parameters, forming speed etc. A designer is faced with the challenge of identifying optimal process parameters for minimum springback. Currently, a vast majority of such applications in practice are guided by trial and error and user experience. In this paper, we present two useful designer aids; an evolutionary algorithm and a neural network integrated evolutionary algorithm. We have taken a simple springback minimization problem to illustrate the methodology although the evolutionary algorithm is generic and capable of handling both single and multiobjective, unconstrained and constrained optimization problems. The springback minimization problem has been modeled as a discrete variable, unconstrained, single objective optimization problem and solved using both optimization methods. Both the algorithms are capable of generating multiple optimal solutions in a single run unlike most available optimization methods that provide a single solution. The neural network integrated evolutionary algorithm reduces the computational time significantly as the neural network approximates the springback instead of performing an actual springback computation. The results clearly indicate that both the algorithms are useful optimization tools that can be used to solve a variety of parametric optimization problems in the domain of sheet metal forming.