【摘要】 钢/碳纤维激光直接连接界面的粘接效果与碳纤维的熔融宽度密切相关，而激光热输入是影响其熔融宽度的主要因素.文中进行了DP780钢/PA66碳纤维激光直接连接试验，基于多层感知机和支持向量机探索了碳纤维熔化宽度的BP神经网络预测模型，并利用AOA算法优化了BP神经网络.同时研究了激光参数中光束扫描速率、离焦量和激光功率对熔化宽度的影响，并计算了激光的能量密度.结果表明，通过AOA算法优化的BP神经网络，其训练集、测试集和验证集的相关系数R分别为0.962 96,0.970 09和0.988 28，表明AOA-BP预测模型具有良好的预测精度和泛化能力.此外，响应曲面的分析结果指出，激光参数对碳纤维熔融宽度的影响权重从大到小依次为光束扫描速率、离焦量和激光功率.离焦量正向的增大可以使激光能量分布更加平缓，扩大光束能量的覆盖范围.更多还原

【Abstract】 The steel/CFRP interface bonding is determined by the melting width of CFRP in the direct steel/CFRP laser joining,while laser heat input is the major factor affecting the melting width.In this paper,the experiment of steel/CFRP direct laser joining was conducted and a BP neural network prediction model of the CFRP melting width was explored based on MLP and SVM,and the BP neural network was optimized by the AOA algorithm.Meanwhile,based on the RSM analysis,the weights of beam scanning rate,defocusing and laser power on the melting width were studied,and laser energy density distribution was calculated.The results indicate that the related coefficients R in the training,test and validation set of AOA-BP neural network are 0.962 96,0.970 09,0.988 28,respectively,which means that the established AOA-BP prediction model exhibits well prediction accuracy and generalization capability.Furthermore,the results of RSM reveal that the descending order of the weights of laser parameters influencing the melting width is as follows:beam scanning rate,defocusing,and laser power.The increase in defocusing serves to flatten the laser energy density distribution,expanding the coverage area of beam energy.更多还原