In the field of automotive manufacturing, it is pivotal to obtain a moderate penetration status when joining automotive parts by laser keyhole welding. As a typical characteristic of laser keyhole welding, keyhole behavior can directly reflect the penetration status of the weld bead. In this paper, a coaxial vision monitoring system with a laser auxiliary illuminant is established to collect the keyhole images from the top-face of the weldment during laser welding. The interferences of the bright spot caused by mirror reflection and metal vapors in the collected images are fully considered. Then, a hybrid adaptive keyhole detection algorithm is proposed to accurately segment the keyhole region. Five static image features are extracted from the perspective of the average gray value, area, and perimeter in the keyhole region. The feature selection method of Wrapper, which combines a sequential forward searching algorithm with a random forest classifier, is used to select ten penetration status features (PSFs). The constructed prediction model of weld penetration based on the selected PSFs has excellent performance. The proposed monitoring method in laser keyhole welding irradiated by a laser auxiliary illuminant is contributed to the closed-loop control of weld penetration to obtain a moderate penetration status.

中文翻译：

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激光辅助光源照射激光锁眼焊焊缝熔深监测方法

在汽车制造领域，通过激光锁孔焊接连接汽车零件时获得适度的熔深状态至关重要。作为激光小孔焊接的典型特征，小孔行为可以直接反映焊道的熔深状态。本文建立了一个带有激光辅助光源的同轴视觉监测系统，用于采集激光焊接过程中焊件顶面的锁孔图像。充分考虑了采集图像中镜面反射和金属蒸气引起的亮点的干扰。然后，提出了一种混合自适应锁孔检测算法来准确分割锁孔区域。从锁孔区域的平均灰度值、面积和周长角度提取五个静态图像特征。Wrapper 的特征选择方法结合了顺序前向搜索算法和随机森林分类器，用于选择十个渗透状态特征（PSF）。基于所选 PSF 构建的焊缝熔深预测模型具有优异的性能。所提出的激光辅助光源照射的激光小孔焊接监测方法有助于焊缝熔深的闭环控制，以获得适度的熔深状态。