A Weld Joint Type Identification Method for Visual Sensor Based on Image Features and SVM

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 2; p. 471
• Main Authors: Zeng, Jiang, Cao, Guang-Zhong, Peng, Ye-Ping, Huang, Su-Dan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14-01-2020; MDPI
• Subjects: Accuracy; Calibration; Cameras; Comparative studies; Coordinate transformations; Identification methods; image feature extraction; Image processing; Laser beam welding; Lasers; Methods; Robots; Safety factors; Sensors; support vector machine (svm); Support vector machines; Teaching; Visual fields; visual sensor; weld joint type identification; Welded joints; Welding parameters; Workpieces; visual sensor; image feature extraction; support vector machine (SVM); weld joint type identification
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20020471

In the field of welding robotics, visual sensors, which are mainly composed of a camera and a laser, have proven to be promising devices because of their high precision, good stability, and high safety factor. In real welding environments, there are various kinds of weld joints due to the diversity of the workpieces. The location algorithms for different weld joint types are different, and the welding parameters applied in welding are also different. It is very inefficient to manually change the image processing algorithm and welding parameters according to the weld joint type before each welding task. Therefore, it will greatly improve the efficiency and automation of the welding system if a visual sensor can automatically identify the weld joint before welding. However, there are few studies regarding these problems and the accuracy and applicability of existing methods are not strong. Therefore, a weld joint identification method for visual sensor based on image features and support vector machine (SVM) is proposed in this paper. The deformation of laser around a weld joint is taken as recognition information. Two kinds of features are extracted as feature vectors to enrich the identification information. Subsequently, based on the extracted feature vectors, the optimal SVM model for weld joint type identification is established. A comparative study of proposed and conventional strategies for weld joint identification is carried out via a contrast experiment and a robustness testing experiment. The experimental results show that the identification accuracy rate achieves 98.4%. The validity and robustness of the proposed method are verified.