Image Processing Algorithm for Quantitative Characterization of Thermal Imaging acquired during On-line Weld Monitoring

Defect in welded structures is a matter of serious concern. The current practice involves interpretation by inspectors and experts, which is time consuming. With greater emphasis on automation during manufacturing process, automated NonDestructive Testing (NDT) Techniques have gained prominence. The key to success of automated NDT lies in the automatic defect recognition and characterization. Dimensional Characterization of defects or discontinuities is essential in order to compare this with the acceptable codes and standards. Greater emphasis is on selecting appropriate mathematical tools so that feature extraction is possible in accurate and reliable manner. Conventionally image-processing algorithms are applied to extract and quantify the features depicting defects. However many image processing algorithms are available for specific applications with its advantages and disadvantages. This paper explores the possibility of image processing algorithms like edge detection and morphological operators for feature extraction and proposes an algorithm for quantitative measurements of defects on thermal images. Lack of Fusion and Tungsten Inclusion are the defects considered. The choice of these particular defects is that these defects are the main causes for rejection and can be detected reliably during thermal imaging.