铝合金爬坡TIG焊熔池失稳状态的视觉检测

对焊接过程中的熔池状态进行视觉检测是实现焊缝质量在线监测的重要手段. 针对中厚板铝合金爬坡钨极氦弧焊过程易出现的熔池失稳和成形缺陷问题，提出了一种基于熔池图像特征的钨极惰性气体保护焊(TIG)焊接状态监测方法. 基于构建的被动视觉传感系统，实现强弧光干扰条件下清晰熔池图像的获取. 提出了一种基于Otsu’s阈值分割和视觉显著性特征(VSF)的氦弧焊熔池图像处理算法，用于提取熔池图像的形态特征，并分析了所提取视觉特征与铝合金爬坡TIG焊过程稳定性的关系. 最后建立了支持向量机(SVM)模型实现熔池稳定性状态的在线识别. 结果表明，相对于熔池轮廓几何特征，熔池尾端熔融金属的形态特征能够更有效地反映出铝合金爬坡TIG焊过程中出现的熔池不稳定状态. 所建立的焊接状态分类模型在单一特征输入条件下，最高准确率达到95.94%. 所提出的实时检测方法为大型铝合金构件TIG焊缝成形缺陷的在线智能诊断与工艺优化提供了基础.

Unstable state vision detection of molten pool during aluminum alloy climbing-TIG welding

Visual detection of the state of the molten pool during the welding process is an important means to realize the online monitoring of weld quality. Aiming at the problems of molten pool unstable state and forming defects that are likely to occur during the climbing tungsten helium arc welding process of medium and thick aluminum alloys, this paper proposes a Tungsten Inert Gas Welding(TIG) welding status monitoring method based on the image characteristics of the molten pool. Based on the constructed passive vision sensor system, the acquisition of clear images of the molten pool under the interference of strong arc light is realized. A helium arc welding based on Otsu’s threshold segmentation and visual saliency features(VSF) is proposed. The image processing algorithm of the molten pool is used to extract the morphological features of the molten pool, and the relationship between the extracted visual features and the stability of the aluminum alloy climbing-TIG welding process is analyzed. Finally, a support vector machine (SVM) model is established to identify the welding state. The experimental results show that, compared with the geometric characteristics of the molten pool contour, the morphological characteristics of the molten metal at the end of the molten pool can more effectively reflect the unstable state of the molten pool during the aluminum alloy climbing-TIG welding process. The established welding state classification model has a maximum accuracy of 95.94% under the condition of a single feature input. The proposed real-time detection method provides a basis for online intelligent diagnosis and process optimization of TIG weld forming defects of large aluminum alloy components.