Improve the Automatic Detection of Welding Defects in Steel Structures Using Fuzzy Clustering Algorithm

The metal-to-metal connection uses a special welding process, the quality control of which is so important that today the visual control method of destructive analysis has almost replaced the automatic non-destructive detection of welding defects. among non-destructive methods, this particular method is more comprehensive due to the cheapness of radiography compared to other nondestructive methods and the simplicity of radiographic image analysis. however, the diagnosis of human welding defects may be related to errors due to the low quality of radiographic images. therefore, today's radiographic images are analyzed by computer and automated image processing methods. in this work, a clustering method has been used to detect welding defects. the process is to first remove the negative effects such as noise in the image through preprocessing, improve the image quality, and then implement the fuzzy c-means (fcm) algorithm to identify welding defects. in this work, the independent variable is the number of clusters in the fuzzy clustering, and it has been shown that the accuracy of detecting defects in welding images increases as the number of clusters increases. furthermore, based on the obtained results, the average accuracy of the method for small cracks, large cracks, and void defects is 92.01%, 94.67%, and 99.92%, respectively.