Research on automatic alignment and focusing of tool damage visual detection system

Under the condition of no disassembly of CNC machine tools, to address the challenges of time-consuming alignment and focusing adjustment in the tool damage visual detection system based on robotic arm, along with poor robustness of the calculation analysis method, an automatic alignment and focusing method for a robot vision system that integrates the YOLOv5 network for intelligent region of interest (ROI) fusion is proposed. Firstly, the ROI model is utilized to detect and locate the center of the tool, and the coordinates for the end effector of the robotic arm are calculated using the nine-point calibration method. Subsequently, an adaptive selection of the ROI focusing window is performed, and an improved Laplacian function is employed to compute the sharpness evaluation value for determining the best tool image. Experimental results conducted on actual equipment demonstrate that the proposed method enhances sensitivity by at least 1.63 times compared to conventional methods, with an average center point error of 3.76 pixels, effectively improving the accuracy and flexibility of the tool damage visual detection system.