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Open AccessArticle

A Novel Method of Using Vision System and Fuzzy Logic for Quality Estimation of Resistance Spot Welding

School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430000, China
Author to whom correspondence should be addressed.
Symmetry 2019, 11(8), 990;
Received: 28 June 2019 / Revised: 24 July 2019 / Accepted: 26 July 2019 / Published: 2 August 2019
PDF [38199 KB, uploaded 14 August 2019]


Finding a reliable quality inspection system of resistance spot welding (RSW) has become a very important issue in the automobile industry. In this study, improvement in the quality estimation of the weld nugget’s surface on the car underbody is introduced using image processing methods and training a fuzzy inference system. Image segmentation, mathematical morphology (dilation and erosion), flood fill operation, least-squares fitting curve and some other new techniques such as location and value based selection of pixels are used to extract new geometrical characteristics from the weld nugget’s surface such as size and location, shape, and the numbers and areas of all side expulsions, peaks and troughs inside and outside the fusion zone. Topography of the weld nugget’s surface is created and shown as a 3D model based on the extracted geometrical characteristics from each spot. Extracted data is used to define input fuzzy functions for training a fuzzy logic inference system. Fuzzy logic rules are adopted based on knowledge database. The experiments are conducted on a 6 degree of freedom (DOF) robotic arm with a charge-coupled device (CCD) camera to collect pictures of various RSW locations on car underbodies. The results conclude that the estimation of the 3D model of the weld’s surface and weld’s quality can reach higher accuracy based on our proposed methods. View Full-Text
Keywords: resistance spot welding; vision system; image processing; fuzzy logic resistance spot welding; vision system; image processing; fuzzy logic

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Alghannam, E.; Lu, H.; Ma, M.; Cheng, Q.; Gonzalez, A.A.; Zang, Y.; Li, S. A Novel Method of Using Vision System and Fuzzy Logic for Quality Estimation of Resistance Spot Welding. Symmetry 2019, 11, 990.

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