The increase of welding speed can improve the productivity of laser welding. The behavior of vapor plume is an important indicator reflecting the state of the laser welding process. However, the behavior of vapor plume in high-speed laser welding (whose welding speed is higher than 10 m/min) has not been studied adequately. In this research, images of the vapor plume in high-speed laser welding of SUS304 austenitic stainless steel are captured through high-speed imaging for the monitoring of the welding process. Characteristics of the vapor plume are extracted with image processing, and the influences of laser power and welding speed on these characteristics are analyzed. The relationships between the behavior of vapor plume and melt pool are discussed, and three ejection regimes of the vapor plume are proposed. In order to predict the occurrence of humping, which is a typical defect in high-speed laser welding, samples are built by applying a time sliding window and then a classification model is built with the random forest. In addition, the importance of features in the classification is analyzed. The test results show that the proposed method can predict the humping defect in high-speed laser welding accurately.

焊接速度的提高可以提高激光焊接的生产率。蒸汽羽流的行为是反映激光焊接过程状态的重要指标。然而，在高速激光焊接（焊接速度高于 10 m/min）中蒸汽羽流的行为尚未得到充分研究。在本研究中，通过高速成像捕捉 SUS304 奥氏体不锈钢高速激光焊接中的蒸汽羽流图像，用于焊接过程的监控。通过图像处理提取蒸汽羽流特征，分析激光功率和焊接速度对这些特征的影响。讨论了蒸汽羽流行为与熔池之间的关系，并提出了蒸汽羽流的三种喷射方式。为了预测高速激光焊接中的典型缺陷驼峰的发生，通过应用时间滑动窗口构建样本，然后使用随机森林构建分类模型。此外，还分析了特征在分类中的重要性。试验结果表明，该方法能够准确预测高速激光焊接中的隆起缺陷。