基于主成分分析-支持向量机（PCA-SVM）模型，提出一种利用近红外辐射信号预测接头形貌的方法，研究了信号的变化规律与焊缝形貌之间的相关性，实现了工艺参数的优化。提取信号的6种时域特征参数并进行主成分分析，获得了接头形貌综合评定指标。根据信号的输入特征，利用支持向量机进行了分类预测。结果表明，近红外辐射信号能够反映焊接过程中焊缝状态的变化，不同缺陷的特征变化具有较大差异，且存在清晰的识别度。该预测模型能够准确识别焊缝成形形貌，准确率高达96.6%。

Based on the principal component analysis-support vector machine (PCA-SVM) model, one method is proposed to predict the joint morphology with the near infrared radiation signal. The correlation between the change laws of signals and the weld formation morphology is investigated and the optimization of process parameters is realized. Six kinds of characteristic parameters of signals in time domain are extracted and the principal component analysis is carried out to obtain the comprehensive evaluation index of joint morphology. Based on the input characteristics of signals， the classification prediction is done by using the support vector machine. The results show that, the near infrared radiation signals can reflect the change of weld state during the welding process, the characteristic changes of different defects have great difference, and the clear recognition exists. The proposed prediction model can accurately identify weld appearance with accuracy up to 96.6%.