Feature Extraction of Laser Welding Pool Image and Application in Welding Quality Identification

Abstract

Most of the existing laser welding quality identification methods are post-weld identification or low-speed identification (Welding speed below 120 m/min). Efficiently online monitoring of laser welding can take the advantages of laser welding for high-speed and deep-penetration welding. How to eliminate interference information (such as metal vapor, plasma splash, etc.) in the laser welding process, accurately and quickly extract the feature information of welding quality evaluation, and identify defects is a major problem that laser welding online monitoring technology needs to solve urgently. In this paper, the optimized dark channel prior anti-interference processing algorithm can remove the interference of image. The feature information extraction algorithm based on contour and OTSU threshold segmentation are used to extract the features of the welding image that collected by the image acquisition system. Then, the image is classified as a specific defect by the trained BP neural network classification algorithm. Experiments with 304 stainless steels have proved that this method can effectively remove the interference of metal vapor and plasma splash on the feature information, and achieves 97.18% accuracy rate of the binary classification test and 91.29% accuracy rate of the six-classification test. The processing time of the entire algorithm is about 0.3 ms and it can meet the real-time requirements of high-speed laser welding.

Keywords

• Anti-interference processing
• online quality evaluation
• laser welding
• machine learning