Multi objective genetic algorithm to optimize the local heat treatment of a hardenable aluminum alloy
CHALLENGE - The continuous research for lightweight components for transport applications to reduce the harmful emissions drives the attention to the light alloys as in the case of Aluminium (Al) alloys, capable to combine low density with high values of the strength-to-weight ratio. Such advantages are partially counterbalanced by the poor formability at room temperature.
SOLUTION - A viable solution is to adopt a localized heat treatment by laser of the blank before the forming process to obtain a tailored distribution of material properties so that the blank can be formed at room temperature by means of conventional press machines. Such an approach has been extensively investigated for age hardenable alloys, but in the present work the attention is focused on the 5000 series; in particular, the optimization of the deep drawing process of the alloy AA5754 H32 is proposed through a numerical/experimental approach. The design and optimization of the process has been carried out coupling a 2D axisymmetric Finite Element model with the integration platform modeFRONTIER able to automatically manage the FE simulations by means of an improved version of the multi objective genetic algorithm (MOGA-II).
BENEFITS - The proposed model revealed to be robust (a good fitting between experimental and numerical results was obtained). Results suggested that, in order to improve the material drawability, the extension of the annealed region should cover half of the blank and the blankholder should apply a pressure equal to 1.5% of the material yield strength in the annealed conditions. The proposed approach demonstrated that, thanks to a light model (simulation time lower than 10 minutes per single run) and a sufficient number of simulation runs (300 in total), a possible optimization route could be quickly suggested in less than 15 hours of computation.