基于改进阈值分割算法的刀具损伤检测方法  

Tool Damage Detection Method Based on Improved Threshold Segmentation Algorithm

作　　者：潘盛湖[1,2] 彭伦文 吕彭杰 PAN Shenghu;PENG Lunwen;LYU Pengjie(School of Mechatronics Engineering,Southwest Petroleum University,Chengdu 610500,China;Oil and Gas Equipment Technology Sharing and Service Platform of Sichuan Province,Southwest Petroleum University,Chengdu 610500,China)

机构地区：[1]西南石油大学机电工程学院 [2]西南石油大学石油天然气装备技术四川省科技资源共享服务平台,成都610500

出　　处：《组合机床与自动化加工技术》2024年第9期142-146,152,共6页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：四川省自然科学基金项目(2022NSFSC2002)。

摘　　要：为了解决刀具损伤缺陷难以被视觉检测系统收集的问题,提出了一种基于粒子群算法(PSO)的Otsu阈值分割法对刀具磨损量进行检测。算法改进了PSO算法惯性系数的更新策略,有效扩大了算法的搜索范围,缩短了算法的运行时间,通过对粒子群添加扰动,解决了传统粒子群算法容易陷入局部最优的问题,搭建了实验平台,验证所用检测方法的有效性。实验结果表明,该检验方法能够实现刀具损伤区域识别和刀具损伤量的测量,而且相较于Otsu、Canny算法,局部阈值分割法等算法具有识别精度高,运行速度快等优点。研究结果对于实际刀具缺陷检测系统具有一定的参考价值。In order to solve the problem that the current tool wear defects are difficult to be collected by the visual inspection system,an Otsu threshold segmentation algorithm based on particle swarm optimization is proposed.to detect tool wear The algorithm improved update strategy for inertia coefficients which effectively expanding the search scope of the algorithm and shortens the running time of the algorithm.By adding a perturbation equation to the particle swarm,solved the problem of traditional particle swarm optimization algorithms easily falling into local optima.Finally,an experimental platform is built to verify the effectiveness of the algorithm.This inspection method can achieve the identification of tool damage areas and the measurement of tool damage amount,and has advantages such as high recognition accuracy and fast running speed compared to traditional Otsu algorithm,Canny algorithm,local threshold segmentation and so on.The research results have certain reference value for the actual tool defect detection system.

关 键 词：刀具磨损 视觉检测 OTSU 粒子群算法 

分 类 号：TH165[机械工程—机械制造及自动化] TG71[金属学及工艺—刀具与模具]