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Optimization of an aluminum alloy anti-collision side beam hot stamping process using a multi-objective genetic algorithm

Zhou J., Wang B., Lin J., Fu L.

Archives of Civil and Mechanical Engineering

2013

Vol. 13, no. 3

401--411

The process parameters of aluminum alloy hot stamping are essential for product forming quality. In the case of an anti-collision side beam inside car doors, the finite-element model of aluminum alloy hot stamping is set up, and the forming quality is investigated under an ordinary process condition. The blank hold force (BHF) has a significant impact on the forming quality in hot stamping. Using the Latin hypercube method to sample the simulation data points and the finite-element (FE) model to calculate the forming quality indices of the data points according to the response value of the indices, the quadratic response surfaces between the process parameter inputs and the forming quality indices are initialized. Using the multi-objective genetic algorithm NSGA-II (non-dominated sorting genetic algorithm) to optimize the responses of the process parameters, the Pareto solutions corresponding to combinations of the blank hold force and stamping speed are obtained. Finally, based on the optimal process parameters, stamping tests are carried out. Compared with the results of the stamping trial and numerical simulation, it is demonstrated that the finite-element model can predict forming defects and be consistent with the actual condition and that the optimization procedure proposed in the paper is feasible.

Forging preform shape optimization using surrogate models

Repalle J., Grandhi R. V.

Computer Assisted Mechanics and Engineering Sciences

2007

Vol. 14, No. 1

53-66

Forging of practical products from simple billet shapes is a complex and nonlinear process due to the multi-disciplinary phenomenon of material flow and processing conditions. General forgings are usually produced in a number of stages in order to avoid defects such as underfill, extra flash, voids, and folds. In spite of advancements in analysis techniques, forging process simulations do not provide function sensitivity information. Hence, the research focuses on exploring efficient non-gradient based preform shape optimization methods. In this research, an attempt is made to develop a preform shape design technique based on interpolative surrogate models, namely Kriging. These surrogate models yield insight into the relationship between output responses and input variables and they facilitate the integration of discipline-dependent analysis codes. Furthermore, error analysis and a comparison between Kriging and other approximation models (response surface and multi-point approximations) are presented. A discussion about what the results mean to a designer is provided. A case study of an automotive component preform shape design is presented for demonstration.