International audienceOne of the hottest challenges in automotive industry is related to weight reduction in sheet metal forming processes, in order to produce a high quality metal part with minimal material cost. Stamping is the most widely used sheet metal forming process; but its implementation comes with several fabrication flaws such as springback and failure. A global and simple approach to circumvent these unwanted process drawbacks consists in optimizing the initial blank shape 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 the Pareto Front, approximate criteria (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 the springback and failure criteria. To optimize these two criteria, a global optimization algorithm 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 problems amounts to the capture of the Pareto Front associated to the two criteria. Normal Boundary Intersection and Normalized Normal Constraint 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 shape optimization of highly non-linear mechanical systems
International audienceThe aim of this paper is to present a method to perform evolutionary multi-objective optimization of CPU intensive sheet metal forming applications using kriging surrogates. Two main ingredients are employed to achieve this goal. First of all, given a learning dataset, the kriging surrogate is designed to minimize the leave-one-out error. Secondly, during the optimization, new data points are added to the learning set to update the surrogate locally (by well chosen points on the current Pareto front) and globally (by maximum kriging variance points over the entire design landscape). The ability of the method to capture Pareto fronts with accuracy is demonstrated on the well-known ZDT test functions. The method is then tested on a real-life problem, the simultaneous minimization of springback and failure for a three-dimensional CPU intensive high strength steel stamping industrial use case
International audienceAutomotive 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
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