计算机视觉
人工智能
机械加工
阈值
螺旋(铁路)
图像处理
计算机科学
机器视觉
刀具磨损
数据采集
面子(社会学概念)
分割
工程类
图像(数学)
机械工程
社会学
操作系统
社会科学
作者
Wenming Wei,Jia Yin,Jun Zhang,Huijie Zhang,Zhuangzhuang Lu
出处
期刊:Materials
[MDPI AG]
日期:2021-09-30
卷期号:14 (19): 5690-5690
被引量:10
摘要
Tool wear and breakage detection technologies are of vital importance for the development of automatic machining systems and improvement in machining quality and efficiency. The monitoring of integral spiral end milling cutters, however, has rarely been investigated due to their complex structures. In this paper, an image acquisition system and image processing methods are developed for the wear and breakage detection of milling cutters based on machine vision. The image acquisition system is composed of three light sources and two cameras mounted on a moving frame, which renders the system applicable in cutters of different dimensions and shapes. The images captured by the acquisition system are then preprocessed with denoising and contrast enhancing operations. The failure regions on the rake face, flank face and tool tip of the cutter are extracted with the Otsu thresholding method and the Markov Random Field image segmentation method afterwards. Eventually, the feasibility of the proposed image acquisition system and image processing methods is demonstrated through an experiment of titanium alloy machining. The proposed image acquisition system and image processing methods not only provide high quality detection of the integral spiral end milling cutter but can also be easily converted to detect other cutting systems with complex structures.
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