钛合金薄板激光焊接参数的多目标优化

摘要

Depth of weld penetration and heat affected zone width are very important to laser welding quality.Laser welding is a complicated process,and quantization analysis of this process is quite difficult.In this work,a set of TC4 titanium alloy thin plate specimens were used as laboratory samples.Two radial basis function neural network (RBFNN)models were used to predict weld penetration depth and heat affected zone width.In order to minimize the heat affected zone width and maximize the depth of weld penetration,the above two neural networks were used as ob-jective functions of multi-objective optimization algorithm.A simulated annealing algorithm is used to find the optimal solution within non-inferior solutions of the multi-objective optimization algorithm.The results show that the heat af-fected zone width and the depth of weld penetration are well balanced by this method.%激光焊接过程产生的焊斑熔深和热影响区宽度直接影响焊接质量。激光焊接过程复杂，影响因素众多，许多参数难以量化。本文以TC4钛合金薄板为实验样品进行脉冲激光焊接实验。利用两个径向基函数神经网络分别预测焊斑熔深和热影响区宽度。将上述两个径向基函数神经网络作为多目标优化算法的目标函数，以提高焊接熔深并减小热影响区宽度。通过模拟退火算法寻求多目标优化所得的非劣解集中的最优解。实验证明，该方法可有效平衡激光焊接过程的焊斑熔深和热影响区宽度。