Use of digital image processing techniques for evaluating wear of cemented carbide bits in rotary drilling

This paper presents the use of image processing techniques in monitoring of bit wear, especially, WC/Co cemented carbide bits that are commonly used in rotary drilling in mining, civil and petroleum engineering. Image of the bits was acquired using a CCD camera. The background was subtracted from the image to reduce noise effects. A Laplacian filter has been used to enhance edge contrasts. Structural elements have been applied to dilate, erode and close boundary edges. Edge detecting was conducted using a canny edge detector. Image processing approaches; first order surface metrics, gray level co-occurrence matrix (GLCM) based texture analysis and minimum distance based classifiers have been used to estimate wear of tricone drill bits in rock drilling. A digital balance was used to obtain weight loss of the bits and also wear of their heel row and gage row (dimension loss) was measured using a micrometer in different directions. Results showed that, of the surface metrics, bit area and major length axis could be good measures for bit wear estimation. The entropy and contrast features of the GLCM method showed good correlations with bit weight loss. Of the minimum distance based classifiers just Euclidean, City block and Chebychev distances had reliable correlations with weight loss and heel row wear rather than other features.