Universal fillet weld joint recognition and positioning for robot welding using structured light

Welding is a widely used connection method because it is economical materials and strong adaptability to various geometric shapes. For welding assemblies that do not require high rigidity, fillet welding is commonly used due to its economy and efficiency. Robot-based intelligent welding has become popular because the arc light and fume generated during the welding process are harmful to humans. Weld recognition and positioning, as a core in robot-based intelligent welding, have been researched over the last few decades. In the study of fillet weld joint positioning based on structured light vision, it is a commonly used method to position weld by extracting the centerline and feature point of the structured-light stripe. However, the centerline is sometimes inaccurately positioned under strong interference such as highly reflective material and arc light and spatter, resulting in incorrect weld positioning. To solve this shortcoming, this study proposes a universal fillet weld joint recognition and positioning method using structured light. Firstly, the weld likelihood (likelihood comes from inferential statistics and is synonymous with probability) is calculated using the designed convolution kernel. Secondly, the fillet weld joint candidates are preselected using an efficient non-maximum suppression algorithm. Finally, the candidates are reexamined based on local structural feature, and the true fillet weld joints are recognized. The main novelties of the proposed method include: (1) The idea using weld likelihood calculation, preselection and reexamination to position fillet weld joint is proposed for the first time, which can consider both the false positives and false negatives. (2) The proposed method uses convolution, non-maximum suppression and local feature to position fillet weld joint, bypassing the step of calculating the structured-light centerline, thereby reducing the influence of arc light and high reflection. The proposed method is robust, universal and accurate. Moreover, as demonstrated by the following performance indices: the false positives and false negatives are both 0 for normal steel, rusty, highly reflective, and arc light-and-spatter interference welding assemblies, and the false positives are 0 and false negatives are 14.6% for welding assemblies under a multi-line structured light. In addition, the average and maximum biases in the accuracy test are 0.1 mm and 0.52 mm.

Automated summary

Welding is widely used due to its economical materials and adaptability to various shapes. Robot-based intelligent welding is gaining popularity as a safe alternative to manual welding. This study proposes a universal method using structured light for recognizing and positioning fillet weld joints, which is robust, versatile, and accurate.