焊接缺陷产生原因复杂，影响因素众多，基于人工智能的缺陷成因诊断算法成为焊接智能化的发展方向. 将PSO-BP神经网络应用于焊接缺陷成因诊断，利用神经网络的连接学习机制代替传统专家系统的规则推理机制，并对PSO算法进行自适应调整，引入动态权重因子，搭建自适应PSO-BP神经网络模型. 结果表明，与传统PSO-BP神经网络模型相比，改进的PSO-BP神经网络模型训练所需要的迭代次数减少13.1%，诊断结果准确率从93.3%提高至96.7%，精确率从91.3%提高至98.3%，综合能力指标从91.7%提高至96.9%. 改进算法能够明显提升焊接缺陷成因诊断的效率和精度，具有较好的工程应用价值.
Considering the complex causes and various impact factors for welding defects, diagnosis methods based on artificial intelligence algorithms are regarded as one of the directions for the development of intelligentizing welding. In this study, an improved diagnosis method for welding defect based on PSO-BP neural network is proposed. Connection learning mechanism of neural network is used instead of the rule reasoning mechanism of traditional expert systems. It also makes adaptive adjustments to the PSO algorithm, introduced dynamic weight factors, and builds an adaptive PSO-BP neural network model. Compared with the traditional PSO-BP neural network model, the number of iterations required to train the improved PSO-BP neural network model reduced by 13.1%, the accuracy of diagnostic results increased from 93.3% to 96.7%, the precision increased from 91.3% to 98.3%, and the comprehensive performance index increased from 91.7% to 96.9%. The results show that the improved algorithm can significantly improve the efficiency and accuracy of welding defect diagnosis, and has good engineering application value.
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