铝型材挤压模工作带优化

基于MATLAB平台，将BP神经网络、遗传算法和数值模拟技术应用于铝型材挤压模具参数优化设计。采用三层BP神经网络建立型材挤压模具的数学模型，由正交实验法安排模拟实验组合，采用有限元软件进行挤压过程的数值模拟，并以具有不同工作带尺寸的挤压模具中金属流出模口平面上的Z向质点流速均方差作为模型目标值，将模拟结果作为神经网络的输人样本对训练网络并建立网络知识源，通过遗传算法求得模型的全局优化解；最后通过有限元数值模拟技术验证并比较优化所得工作带与经验法确定的工作带对金属流动均匀性的影响。数值模拟结果表明，本研究对挤压模具工作带的优化是有效的。

Optimization of die bearing for aluminum profile extrusion die

BP artificial neural network, genetic algorithm and FEM simulation were applied to optimize the design of profile extrusion die on MATLAB foundation. A three-layer neural network was used to set up mathematical model for profile extrusion dies with different bearing lengths. Orthogonal test was arranged for numerical simulation to get Z-velocity at the die land exit which was used as the target value of the model. The neural network is trained by the above Z-velocity values to form knowledge source, and the general optimized solution was attained through genetic algorithm. At last, the optimized bearing of the extrusion die was analyzed by FEM and compared to the design with experiential way. The simulation results show that the optimization of die bearing is effective.