Laser sharpening evaluation of diamond wheels based on 3D recognition

Abstract

A modified U-Net neural network was used to evaluate the laser sharpening quality of diamond grinding wheels, and the laser sharpening parameters were optimized. The three-dimensional (3D) detection algorithm is researched, and a 3D detection algorithm for the diamond wheel surface matching the two-dimensional (2D) image and the 3D point cloud was proposed. The recognized 2D grain image is filtered to remove edge grains and connected grains, correct the 3D point cloud by eliminating the effect of curvature, and match grain pixels. The laser sharpening experiment of the bronze-bonded diamond wheel was carried out by the orthogonal experiment method, and the quality evaluation of the laser sharpening pictures of the wheel obtained by the experiment was carried out. The embedding depth of the abrasive grains was obtained from the 2D area of the abrasive grains, and the evaluation index of abrasive grain height-depth distribution was proposed. The laser sharpening experiments was carried out to obtain grinding wheels with different sharpening qualities, and the grinding tests were carried out. The effectiveness of the sharpening evaluation index was verified by the amount of grinding force when grinding the workpiece and the surface roughness of the workpiece after grinding. The optimal dressing process parameters were obtained as the average power of 35 W, the repetition frequency of 100 kHz, the rotational speed of 300 r/min, and the scanning speed of 3.6 mm/min.