Apparent defect recognition of gas metal arc welding based on infrared vision
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摘要: 焊接过程可视化监控与成形缺陷智能识别是实现焊接智能制造的重要途径之一. 采用红外CCD在线采样熔化极气体保护焊(gas metal arc welding,GMAW)熔池红外图像,结合改进滤波算法和图像增强算法对图像进行预处理,通过热电偶进行温度标定,建立红外图像中灰度值与温度值的对应关系,进而获取焊接熔池的温度分布信息,然后采用改进边缘提取算法提取熔池的特征参数,据此建立焊接外观缺陷的特征识别算法. 结果表明,所设计的算法对焊接形状缺陷、烧穿及未熔透等在线识别具有良好的实用性和准确性.Abstract: The visual monitoring of welding process and welding defect identification are vital to the intelligent control of welding processes. In this paper, a mid-wave infrared CCD was used to acquire the infrared images of the welding pool on-line during the welding process. The images captured are preprocessed by the improved filtering algorithm and image enhancement algorithm. To obtain the temperature distribution information of the welding pool, the relationship between the gray value in infrared image and the temperature is established based on temperature calibration of the used thermocouple. The improved edge extraction algorithm is used to extract the characteristic parameters of the welding pool. Then the identification algorithm of welding defect is developed. The results of verified experiments show that the proposed algorithm has good practicability and accuracy in the on-line identification of welding shape, burn-through and unmelted defects.
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Keywords:
- infrared vision /
- gas metal arc welding /
- image processing /
- apparent defect /
- pattern recognition
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表 1 红外传感器参数
Table 1 Parameters of the IR sensor
FPA分辨率 像素尺寸(μm × μm) 响应波长l/μm 积分时间t/µs 最大帧数f/Hz 通讯接口 80 × 80 130 × 130 1 ~ 5 100 ~ 1 000 1 000 USB 2.0 -
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