基于响应曲面和遗传算法-人工神经元网络的热塑性塑料激光透射连接强度的优化  

Optimization of Weld Strength for Laser Transmission Welding of Thermoplastic Based on Response Surface Methodology and Genetic Algorithm-Artificial Neural Network

作　　者：张成[1] 王霄[1] 王凯[1] 张虎[1] 刘江[1] 蒋涛[1] 刘会霞[1] 

机构地区：[1]江苏大学机械工程学院,江苏镇江212013

出　　处：《中国激光》2011年第11期110-116,共7页Chinese Journal of Lasers

摘　　要：采用旋转中心复合设计进行了激光透射焊接热塑性塑料聚碳酸酯的实验设计,分别建立了响应曲面法和基于遗传算法的人工神经元网络法的数学模型,运用这两种数学模型建立激光透射焊接工艺参数(激光功率、扫描速度、夹紧压力、扫描次数)和连接强度的关系模型,然后使用这两种模型分别预测了连接强度,并优化了焊接工艺参数。系统地比较了两种模型的建模能力、泛化能力和优化能力。实验结果表明,两种优化方法的试验结果较接近,但是基于遗传算法的人工神经元网络的建模、泛化和预测的准确性比响应曲面的要好,因此遗传算法-神经元网络是优化焊接质量的一种更有效的方法。A central composite rotatable experimental design （CCRD） is conducted to design experiments of laser transmission welding of thermoplastic-polycarbonate （PC）. The genetic algorithm-artificial neural network （GA- ANN） and response surface methodology （RSM） models which establish the relationships of the laser transmission welding process parameter （laser power, scanning speed, clamping pressure, scan times） and joint strength are established, and then the welding strength is predicted and the welding parameters is optimized by using the developed models respectively. The modeling capabilities, generalization and optimization capabilities of the two models are systematically compared. The result shows that GA-ANN and RSM are not significantly different on the maximum experimental joint strength, but the modeling, generalization and optimization abilities of GA-ANN are better than that of RSM, so GA-ANN is a more effective way to optimize the PC joint strength.

关 键 词：激光技术 激光透射焊接 工艺参数优化 响应曲面 人工神经元网络 遗传算法 

分 类 号：TG456.7[金属学及工艺—焊接]