中文摘要：

针对人工免疫算法搜索时间长、效率低等缺点,对其进行了改进,使其在保持种群多样性的同时,提高了收敛速度。为了减少板料成形工艺参数试错时间,运用数值模拟建立近似模型。以方盒件为例,利用Dynaform软件仿真获得训练数据,通过人工免疫算法优化RBF神经网络,获得隐层中心位置和数量,并采用伪逆法确定输出层的权值。利用改进后的人工免疫算法对该模型进行优化,获得变压边力加载曲线。研究结果表明,采用优化后的变压变力控制曲线能有效地提高板料成形质量。

英文摘要：

According to the disadvantages of artificial immune algorithm to search for long time and low efficiency,it was made some improvement,so as to keep the population diversity,at the same time improve the convergence speed. To reduce the trial and error time of sheet metal forming process parameters,numerical simulation was used to establish the approximate model. To box as an example,software Dynaform was used to obtain the training datas to establish the RBF neural network approximation model. RBF neural network was optimized by the artificial immune algorithm to get the position and the number of hidden layer centrals,and the output layer weights were determined by the pseudo inverse method. The model was optimized by using artificial immune algorithm improved to obtain the load curve of variable blank holding force. Research results show that the optimized variable pressure curve can effectively improve the quality of sheet metal forming.