为了精确检测机械加工过程中刀具的磨损状态, 分析了刀具磨损图像中背景区、磨损区、未磨损区的灰度值变化, 提出了利用图像处理技术自动检测刀具磨损量的方法。首先, 根据Otsu法和B-样条曲线拟合法, 建立了自动确定上限阈值与下限阈值的算法, 准确地增强了磨损区与背景区、未磨损区之间的灰度对比度; 通过分析刀具磨损图像的稳定区和磨损边缘的非稳定区, 提出了边界提取的局部方差阈值算法, 给出了自适应的局部方差阈值, 清晰地将刀具磨损区从图像中分割出来。在此基础上, 利用形态学描述方法对分割部分进行孔洞填充与边界完整化, 从而直接计算出磨损区域的几何参数尺寸。实验结果表明: 超景深三维显微镜放大倍数为50时, 磨损宽度与磨损长度的检测误差分别为1.024%和1.325%; 放大倍数为100时, 磨损宽度与磨损长度的检测误差分别为0.661%和0.995%。该方法检测精度高、抗干扰能力强、提取的边界完整清晰, 可为提高刀具使用率、保证加工质量提供技术支持。

To automatically detect the tool wear state in metal cutting process, an automatic detection method based on image processing was proposed by analysis of the gray levels of a tool wear image in the background area, wear area and the unworn area. Firstly, the automatic determination algorithms of the upper-and lower-thresholds were respectively presented according to the Otsu method and B-spline curve fitting method. Based on the algorithms, the gray-contrasts between wear area and background area as well as wear area and unworn areas were enhanced. By analysis of the stable region within three areas and the non-stable region at two edges in the tool wear image, the local gray-level variance threshold algorithm was presented for the boundary extraction. An adaptive threshold of the local variance was defined, by which the tool wear region was segregated from the tool wear image clearly. On this basis, the morphological method was used to fill out the holes of the segregated part, so that the corresponding geometric parameters of the wear areas were precisely achieved. Experimental results show that the detection errors of the wear width and the wear length are respectively 1.024% and 1.325% when the magnification of 3D microscope is 50, whereas they are 0.661% and 0.995% when the magnification is 100. The proposed method is characterized by strong anti-interference, and higher detection accuracy. It can supply the technology support for improving the tool utilization, guaranteeing the machining quality, and saving the manufacturing cost.