Abstract
Most of the existing laser welding quality identification methods are post-weld identification or low-speed identification (Welding speed below 120m/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 0.3ms and it can meet the real-time requirements of high-speed laser welding.
Highlights
As a new type of welding technology, laser welding is one of the most ideal technologies to realize high-speed and deeppenetration welding
Miao et al proposed a weld quality recognition method based on CNN (Convolutional Neural Network) that has the advantage of high accuracy [24], and the time for single identification is 3.2 s, but this 3.2 s processing time is too long for synchronous detection
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
Summary
As a new type of welding technology, laser welding is one of the most ideal technologies to realize high-speed and deeppenetration welding. Miao et al proposed a weld quality recognition method based on CNN (Convolutional Neural Network) that has the advantage of high accuracy [24], and the time for single identification is 3.2 s, but this 3.2 s processing time is too long for synchronous detection. Wen and Gao used a high-speed camera to acquire images of the plume generated during laser welding in the UV and visible wavelengths, and classified the samples by using the feature parameters of the splash images and the k-nearest neighbor classification method. Fekri-Ershad and Tajeripour [35] proposed a surface texture defect detection method based on single dimensional local binary patterns It has high detection rate and low computational complexity. The device has a simple structure and can monitor the quality of weld simultaneously during the welding process
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