Optimization of stamping process parameters to predict and reduce springback and failure criterion

Abstract

Automotive manufacturers have been struggling with the big challenge of how to produce dimensionally acceptable stamped parts with minimal material cost. The thin nature of the sheet metal has always complicated the process and made the dimensional quality objectives difficult to achieve. The final layout quality is impacted by several fabrication flaws such as springback and failure. A possible approach to circumvent these unwanted process drawbacks consists in optimizing the process parameters with innovative methods. The aim of this paper is to introduce an efficient methodology to deal with complex, computationally expensive multicriteria optimization problems. Our approach is based on the combination of methods to capture Pareto Front, suitable surrogates (to save computational costs) and global optimizers. To illustrate the efficiency, we consider the stamping of an industrial workpiece as test-case. Our approach is applied to springback and failure criteria. To optimize these two criteria, a global approach was chosen. It is the Simulated Annealing algorithm hybridized with the Simultaneous Perturbation Stochastic Approximation in order to gain in time and in precision. The multicriteria concern amounts to the capture of the Pareto Front associated to the two criteria. Indeed, springback and failure are two conflicting criteria. Normal Boundary Intersection and Normalized Normal Constrained Method are considered for generating a set of Pareto-optimal solutions with the characteristic of uniform distribution of front points. The computational results are compared to those obtained with the well-known Non-dominated Sorting Genetic Algorithm II. The results show that our proposed approach is efficient to deal with the multicriteria parametric and shape optimization of highly non-linear mechanical systems.