Abstract
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.
Highlights
Since the mid-20th century, the resistance spot weld (RSW) is widely used in automobile and machine industries [1]
Knowing that it is possible to check the size of the welding using image-processing techniques and computer vision system to measure the weld’s sizes, the following works introduced fully intelligent automatic quality inspection systems of resistance spot welds using vision techniques
The results showed that weld strength could be investigated by using image processing-based electrode displacement and velocity measurement
Summary
Since the mid-20th century, the resistance spot weld (RSW) is widely used in automobile and machine industries [1]. Knowing that it is possible to check the size of the welding using image-processing techniques and computer vision system to measure the weld’s sizes, the following works introduced fully intelligent automatic quality inspection systems of resistance spot welds using vision techniques. Authors [23,24] proposed an online real-time non-destructive evaluation system based on vision algorithms to inspect spot welding quality. Chen et al [28,29] developed an IR camera based non-destructive system to inspect the quality of the spot welding (nugget size, thickness and shape) in vehicles underbodies. The results showed that the measurement accuracy of both spot size and thickness is very good These last few works included non-contact and non-destructive inspection methods of the RSW.
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