Crack identification in tungsten carbide using image processing techniques

Laser processing of cutting tool materials particularly cemented carbides can induce many surface defects including porosity, balling, and micro-cracks. When present in the microstructure of cutting tools, micro-cracks can lead to chipping and early failure. The detection and identification of cracks can be used to predict tool performance post laser processing. To develop a method for crack identification scanning electron microscopy (SEM) images were used. The manual review of SEM images is subjective and time consuming. This study presents a method to identify and quantify cracks from an SEM microstructure of tungsten carbide (WC) in MATLAB. Image processing algorithms were used to segment crack regions from other surface defects and the background microstructure; and subsequently to extract crack geometry and information. The results show successful segmentation of cracks from SEM images with an identification accuracy greater than 95 % across a range of different laser processing parameters.