| Summary: | Fillet welds of highly reflective materials are common in industrial production. It is a great challenge to accurately locate the fillet welds of highly reflective materials. Therefore, this paper proposes a fillet weld identification and location method that can overcome the negative effects of high reflectivity. The proposed method is based on improving the semantic segmentation performance of the DeeplabV3+ network for structural light and reflective noise, and, with MobilnetV2, replaces the main trunk network to improve the detection efficiency of the model. To solve the problem of the irregular and discontinuous shapes of the structural light skeleton extracted by traditional methods, an improved closing operation using dilation in a combined Zhang-suen algorithm was proposed for structural light skeleton extraction. Then, a three-dimensional reconstruction as a mathematical model of the system was established to obtain the coordinates of the weld feature points and the welding-torch angle. Finally, many experiments on highly reflective stainless steel fillet welds were carried out. The experimental results show that the average detection errors of the system in the Y-axis and Z-axis are 0.3347 mm and 0.3135 mm, respectively, and the average detection error of the welding torch angle is 0.1836° in the test of a stainless steel irregular fillet weld. The method is robust, universal, and accurate for highly reflective irregular fillet welds.
|
|---|