Abstract
Weld surface quality inspection is an indispensable part in the welding automation engineering. When Laser Triangulation Method (LTM) is used to scan the weld surface, external factors will interfere with the laser center line, resulting in data loss and quality detection failure, and massive data loss is difficult to be repaired in the traditional way. In order to improve the quality of weld surface defect detection, it is necessary to find an effective and strong anti-interference method to fix the lost data. This paper presents a method of fixing the data loss caused by external environmental factors in the process of detecting flatbed lap weld surface defects by LTM. The paper includes the analysis of the causes of data loss, its impact for practical applications and a repair method. The repair method derives a formula which is suitable for expressing three-dimensional points on weld surface by combining Bidirectional Reflectance Distribution Function (BRDF) and weld surface section model. The accuracy of the algorithm is verified by experiments on standard block and single weld cross-section. The accuracy of the method up to 92% and the repair rate improved by 30%. The method was tested on flatbed lap weld to confirm its effectiveness in the field of catching surface defects hidden in the lost data.
Highlights
Welding is widely used in conventional manufacturing techniques, and is the key technique of the manufacturing industry for which strict quality and safety are required
In online real-time weld defect detection, Automated Optical Inspection (AOI) is the most commonly used method, which including AOI based on image and AOI based on point cloud
The repair method of data loss in the process of weld surface defect detection with Laser Triangulation Method (LTM) presented in this paper, based on light intensity and 3D geometry
Summary
Welding is widely used in conventional manufacturing techniques, and is the key technique of the manufacturing industry for which strict quality and safety are required. Weld quality defection can induce undesirable component cracking, corrosion and fracturing. Inspection and monitoring of weld quality are necessary [1], [2]. With the development of intelligent welding, online real-time weld defect detection has come up [3]. In online real-time weld defect detection, Automated Optical Inspection (AOI) is the most commonly used method, which including AOI based on image and AOI based on point cloud. Many scholars have conducted in-depth studies on the stability and efficiency of these two methods
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