基于人工智能的覆盖件冲压数值分析及工艺参数优化

Numerical Simulation and Process Optimization of Panel Stamping Based on Artificial Intelligence

将有限元数值模拟技术与人工神经网络及遗传算法等人工智能技术相结合,对典型覆盖件汽车油底壳零件的冲压成形过程进行了有限元数值分析,随后以压边力、拉延筋参数等主要工艺参数作为优化参数,以板坯无缺陷为优化目标,建立了优化参数与目标函数之间的BP神经网络模型,与遗传算法相结合,实现了冲压成形工艺参数的优化,为金属板料成形工艺参数的优化设计提供了一条先进合理的途径,具有一定的推广意义和应用价值。

The stamping process of automobile panel was studied by numerical simulation and artificial intelligence including ANN and GA methods. The prediction model of object function is established by using artificial neural network. In object function, blank holder force, draw bead height and fillet radius are design variables and prevention of cracking is considered as the optimization objective. Process parameters optimization is performed with genetic algorithm. The experimental results indicate that the numerical simulation is effective and the process optimization based on artificial neural network and genetic algorithm is feasible. An effective mean is offered for determining optimum deformation process parameters of sheet metal forming.