基于机器视觉的加工刀具磨损监测方法

为了对加工过程中刀具的磨损状态进行监测，针对麻花钻的磨损形式，提出基于机器视觉的加工刀具磨损监测方法. 根据磨损刀具图像的灰度分布特点，提出基于积分图加速和Turky bi-weight核函数的非局部均值去噪方法；采用单、双阈值大津法获取磨损区域的灰度区间，实现对图像的自适应对比度增强；提出基于形态学重构方法的磨损区域局部极值点提取方法，有效完成对磨损区域的检测和边界提取. 该刀具磨损检测方法成功应用于麻花钻头磨损状态的监测过程，实验结果表明，相较于目前已有的机器视觉监测方法，所提出的方法具有更高的检测精度和效率，准确地提取磨损轮廓，从而有效实现对刀具磨损状态的监测和自动化监控加工过程，达到降低人工成本和产品不合格率的目的.

Monitoring method for machining tool wear based on machine vision

A set of tool wear monitoring methods based on machine vision was proposed aiming at the wear form of twist drill, in order to monitor the wear conditon of the tool during the machining process. A non-local mean denoising method was proposed based on integral image and Turky bi-weight kernel function according to the gray distribution of worn tool images. The single and double threshold Otsu methods were proposed to obtain the gray interval of the worn area to adaptively enhance the image. A method of extracting local extreme points of wear regions based on morphological reconstruction was proposed to effectively complete the detection of wear regions and boundary extraction. Experimental results show that the monitoring method of tool wear can be effectively implemented in the monitoring of twist drill wear. And it is proved that the proposed method has higher detection accuracy and efficiency and it can extract tool wear more accurately than other methods. It helps to realize the monitoring of tool wear and automatic monitoring of the processing process, and achieve the purpose of reducing labor costs and product failure rate.