【摘要】 针对硬质合金铣刀在侧铣工件过程中出现的刀具磨损问题,采用机器视觉的方法对磨损刀具进行磨损量检测。通过对铣刀底面ROI区域的提取以及刀刃部分直线的拟合,计算出刀具的旋转角度,从而实现对铣刀侧面磨损区域的定位。在此基础上又采用改进的图像形态学和灰度线性变换相结合的图像增强算法,能够很好地解决硬质合金铣刀磨损检测中出现的反光问题。并采用基于双三次插值的Sobel算子边缘提取算法和最小矩形法对刀具磨损区域进行边缘提取和测量,提高了刀具磨损区域的检测精度,实验结果验证了此方法的可行性。更多还原

【Abstract】 In order to solve the problem of tool wear of carbide milling cutter in the process of side milling, the machine vision method is used to detect the wear of carbide milling cutter. By extracting ROI of the bottom surface of the milling cutter and fitting the straight line of the cutting edge, the rotation Angle of the cutter is calculated, so as to locate the wear area on the side of the milling cutter. Based on this, an improved image enhancement algorithm combining image morphology and gray-scale linear transformation is used to solve the problem of reflection in wear detection of carbide milling cutters. The edge extraction algorithm of Sobel operator based on double cubic interpolation and the minimum rectangle method are used to extract and measure the edge of the tool wear area, which improves the detection accuracy of the tool wear area. Experimental results show that this method is feasible.更多还原