Precision measurement of circular saw blade matrix size parameters based on machine vision

Abstract The precise measurement of the circular saw blade matrix’s dimensions is critical for optimising cutting performance in industrial applications. To overcome the limitations of existing contact and non-contact methods, this paper presents a machine vision-based approach that integrates sophisticated image preprocessing, geometric compensation, and robust feature extraction. A novel thickness-compensated perspective correction technique is employed to rectify image distortions between the top and bottom surfaces of the central aperture, reducing measurement error to 0.014 mm. For the positioning holes, a dual-constraint contour screening approach based on area and distance is implemented to isolate valid edge points, followed by nonlinear least-squares circle fitting. To address inaccuracies at indistinct or intricate tooth edges, an indirect feature extraction method utilising connected-component analysis is proposed, which effectively identifies and quantifies both functional and protective teeth. Comparative investigations with a high-precision coordinate measuring machine show maximum dimensional deviations of 0.02 mm and a discrete-feature recognition accuracy of 100%. The repeatability was maintained within ±0.1 mm across six sample batches. The results confirm that the proposed system meets the stringent accuracy and stability requirements for industrial inspection, providing a scalable and adaptable solution for quality control in circular saw blade production.