Tool wear is usually the most relevant parameter of tool performance detection, which directly affects the product quality and tool life. Online tool condition monitoring can effectively reduce workpiece scrap and machine failure. Chisel edge wear is one of the main wear forms in high-speed steel (HSS) twist drill. A machine vision method for chisel edge wear measurement is proposed in this paper to improve measure accuracy and reduce testing costs. Firstly, Otsu segmentation based on Laplacian edge information is proposed for tool image segmentation. A morphology-based Canny operator is developed to perform edge detection and image registration for rough positioning of the tool wear area. Zernike moment-based sub-pixel edge detection is proposed to improve the measurement accuracy. Finally, principal curve is employed to fit sub-pixel edge points to obtain smooth edge curve of the tool wear. Finally, the chisel edge wear can be detected and measured effectively based on the proposed method. In the real machining process, the test results show that the proposed system shows high response speed and inspecting accuracy, which is more convenient and efficient than manual measurement with an electron microscope. This system can be effectively applied to the real-time monitoring of tool wear in industry.

刀具磨损通常是刀具性能检测中最相关的参数，它直接影响产品质量和刀具寿命。在线工具状态监控可以有效减少工件报废和机器故障。凿子边缘磨损是高速钢（HSS）麻花钻的主要磨损形式之一。本文提出了一种用于切削刃边缘磨损测量的机器视觉方法，以提高测量精度并降低测试成本。首先，提出了基于拉普拉斯边缘信息的大津分割技术，对工具图像进行分割。开发了基于形态的Canny算子，以进行边缘检测和图像配准，以粗略定位刀具磨损区域。为了提高测量精度，提出了基于Zernike矩的子像素边缘检测。最后，主曲线用于拟合子像素边缘点，以获得工具磨损的平滑边缘曲线。最后，基于所提出的方法可以有效地检测和测量凿子的边缘磨损。在实际的加工过程中，测试结果表明，所提出的系统具有较高的响应速度和检测精度，比电子显微镜手动测量更方便，更有效。该系统可以有效地应用于工业中刀具磨损的实时监控。比使用电子显微镜进行手动测量更方便，更高效。该系统可以有效地应用于工业中刀具磨损的实时监控。比使用电子显微镜进行手动测量更方便，更高效。该系统可以有效地应用于工业中刀具磨损的实时监控。