Next Article in Journal
Development of an Open-Source Thermal Image Processing Software for Improving Irrigation Management in Potato Crops (Solanum tuberosum L.)
Previous Article in Journal
Fused-Deposition-Material 3D-Printing Procedure and Algorithm Avoiding Use of Any Supports
Open AccessArticle

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

Shenzhen Key Laboratory of Electromagnetic Control, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 471; https://doi.org/10.3390/s20020471
Received: 18 November 2019 / Revised: 28 December 2019 / Accepted: 6 January 2020 / Published: 14 January 2020
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: weld joint type identification; image feature extraction; visual sensor; support vector machine (SVM) weld joint type identification; image feature extraction; visual sensor; support vector machine (SVM)
Show Figures

Figure 1

MDPI and ACS Style

Zeng, J.; Cao, G.-Z.; Peng, Y.-P.; Huang, S.-D. A Weld Joint Type Identification Method for Visual Sensor Based on Image Features and SVM. Sensors 2020, 20, 471.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop