采用局部阈值分割的刀具损伤视觉检测方法

叶祖坤 1; 李恒 1; 查文彬 1; 何彦 2; 王禹林 1

doi:10.7652/xjtuxb202104006

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采用局部阈值分割的刀具损伤视觉检测方法

更新时间：2025-07-09

- 采用局部阈值分割的刀具损伤视觉检测方法
- A Visual Detection Method of Tool Damage Using Local Threshold Segmentation
- 西安交通大学学报 2021年55卷第4期页码：52-60
- 作者机构：
  
  南京理工大学机械工程学院,南京,210094
- 作者简介：
- 基金信息：
  
  : 2020-10-17。作者简介: 叶祖坤(1994—),男,硕士生;王禹林(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(52075267);重庆市自然科学基金资助项目(cstc2020jcyj-msxm2526);中央高校基本科研业务费专项资金资助项目(30920010005)
- DOI：10.7652/xjtuxb202104006
  中图分类号： TP186
- 网络首发：2021-04-10，
  
  纸质出版：2021
- 稿件说明：
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叶祖坤 1, 李恒 1, 查文彬 1, 等. 采用局部阈值分割的刀具损伤视觉检测方法[J]. 西安交通大学学报, 2021,55(4):52-60.

A Visual Detection Method of Tool Damage Using Local Threshold Segmentation[J]. 2021, 55(4): 52-60.
叶祖坤 1, 李恒 1, 查文彬 1, 等. 采用局部阈值分割的刀具损伤视觉检测方法[J]. 西安交通大学学报, 2021,55(4):52-60. DOI： 10.7652/xjtuxb202104006.

A Visual Detection Method of Tool Damage Using Local Threshold Segmentation[J]. 2021, 55(4): 52-60. DOI： 10.7652/xjtuxb202104006.

摘要

针对目前刀具损伤检测系统难以从采集的机床刀具损伤图像中自动识别到刀具损伤位置并精准测量刀具损伤量的难题

提出了一种采用局部阈值分割的刀具损伤视觉检测方法。该方法对采集的刀具图像进行灰度化、滤波降噪、旋转定位校正后

将刀具图像均分为多个小像素块

对每个像素块进行图像分割

获取每个像素块的分割阈值

即局部阈值

以最大的局部阈值为基准

对刀具图像进行整体像素扫描

同时结合形态学操作

实现刀具损伤位置识别

并基于识别到的损伤信息精准测量刀具损伤几何特征。搭建了离线检测试验平台

验证所提方法的有效性。试验结果表明:所提方法能够解决目前难以从刀具损伤图像中自动识别到刀具损伤位置并精准测量刀具损伤量的难题

与现有的局部方差法、自适应阈值法等方法相比

刀具损伤几何特征测量的平均准确率至少提升19%以上

具有较大优势。

Abstract

A tool damage visual detection method using local threshold segmentation is proposed to solve the problem that the current tool damage detection systems are difficult to automatically identify the location of tool damage and accurately measure the amount of tool damage from the collected machine tool damage images. Firstly

the tool image is divided into several small pixel blocks after graying

filtering

noise reduction

rotating and positioning correction. Each pixel block is segmented to obtain its segmentation threshold

that is

the local threshold. Then based on the largest local threshold

the tool image is scanned for global pixels. At the same time

combined with morphological operation

tool damage location recognition is realized

and the geometric characteristics of tool damage are accurately measured based on the identified damage information. An off-line detection experiment platform is built to verify the effectiveness of the proposed method. Experimental results show that the proposed tool damage visual detection method using local threshold segmentation solves the difficulty in automatically identifying the tool damage location and accurately measuring the amount of tool damage from the tool damage image. Comparisons with the existing local variance method

the adaptive threshold method and other methods show that the proposed method improves the average accuracy of tool damage geometric feature measurement by at least 19%

and has a great advantage.

关键词

Keywords

references

朱爱斌, 胡浩强, 何大勇, 等. 采用频域融合方法的砂轮刀具磨损三维重构技术 [J]. 西安交通大学学报, 2015, 49(5): 82-86, 133.

ZHU Aibin, HU Haoqiang, HE Dayong. et al. Three-dimensional reconstruction of tool wear area for grinding wheel using frequency-domain fusion method [J]. Journal of Xi'an Jiaotong University, 2015, 49(5): 82-86, 133.

CHEN Ni, HAO Bijun, GUO Yuelong, et al. Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach [J]. International Journal of Advanced Manufacturing Technology, 2020, 108(7/8): 2091-2101.

朱爱斌, 何仁杰, 吴玥璇, 等. 考虑变换域融合方法的刀具磨损区域三维重构 [J]. 西安交通大学学报, 2017, 51(12): 76-83.

ZHU Aibin, HE Renjie, WU Yuexuan, et al. Three-dimensional reconstruction of tool wear area considering transform-domain fusion [J]. Journal of Xi'an Jiaotong University, 2017, 51(12): 76-83.

ZHANG Jilin, ZHANG Chen, GUO Song, et al. Research on tool wear detection based on machine vision in end milling process [J]. Production Engineering: Research and Development, 2012, 6(4/5): 431-437.

SHI Xuechun, WANG Xibin, JIAO Li, et al. A real-time tool failure monitoring system based on cutting force analysis [J]. International Journal of Advanced Manufacturing Technology, 2018, 95(5/6/7/8): 2567-2583.

JAMSHIDI M, RIMPAULT X, BALAZINSKI M, et al. Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining [J]. International Journal of Advanced Manufacturing Technology, 2020, 106(9/10): 3859-3868.

SHANBHAG V, ROLFE B, ARUNACHALAM N, et al. Investigation of stamping tool wear initiation at microscopic level using acoustic emission sensors [J]. Key Engineering Materials, 2019, 794(285): 285-294.

LENG S, WANG Z, MIN T, et al. Detection of tool wear in drilling CFRP/TC4 stacks by acoustic emission [J]. Journal of Vibration Engineering Technologies, 2020, 8(3): 463-470.

UEKITA M, TAKAYA Y. Tool condition monitoring for form milling of large parts by combining spindle motor current and acoustic emission signals [J]. International Journal of Advanced Manufacturing Technology, 2017, 89(1/2/3/4): 65-75.

DUO A, BASAGOITI R, ARRAZOLA P, et al. The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process [J]. International Journal of Advanced Manufacturing Technology, 2019, 102(5/6/7/8): 2133-2146.

陈保家, 陈雪峰, 何正嘉, 等. 利用运行状态信息的机床刀具可靠性预测方法 [J]. 西安交通大学学报, 2010, 44(9): 74-77, 121.

CHEN Baojia, CHEN Xuefeng, HE Zhengjia, et al. Operating condition information-based reliability prediction of cutting tool [J]. Journal of Xi'an Jiaotong University, 2010, 44(9): 74-77, 121.

KJELD B. Wear measurement of cutting tools by computer vision [J]. International Journal of Machine Tools Manufacturing, 1990, 30(l): 131-139.

朱爱斌, 何大勇, 邹超, 等. 刀具磨损图像视差图的非标定方法 [J]. 西安交通大学学报, 2016, 50(3): 8-15.

ZHU Aibin, HE Dayong, ZOU Chao, et al. Uncalibrated method for disparity map of tool wear images [J]. Journal of Xi'an Jiaotong University, 2016, 50(3): 8-15.

PAULINE O, WOON K, RAYMOND J. Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision [J]. International Journal of Advanced Manufacturing Technology, 2019, 104(1/2/3/4): 1369-1379.

HOU Qiulin, SUN Jie, HUANG Panling. A novel algorithm for tool wear online inspection based on machine vision [J]. International Journal of Advanced Manufacturing Technology, 2019, 101(9/10/11/12): 24152423.

JANSI S, SUBASHINI P. Optimized adaptive thresholding based edge detection method for MRI brain images [J]. International Journal of Computer Applications, 2013, 51(20): 1-8.

PENG Ruitao, PANG Haolin, JIANG Haojian, et al. Study of tool wear monitoring using machine vision [J]. Automatic Control and Computer Ences, 2020, 54(3): 259-270.

DAI Y, ZHU K. A machine vision system for micro-milling tool condition monitoring [J]. Precision Engineering, 2017, 52: 183-191.

秦国华, 易鑫, 李怡冉, 等. 刀具磨损的自动检测及检测系统 [J]. 光学精密工程, 2014, 22(12): 3332-3341.

QIN Guohua, YI Xin, LI Yiran, et al. Automatic detection technology and system for tool wear [J]. Optics and Precision Engineering, 2014, 22(12): 3332-3341.

LAURA F, CHARRO N, LIDIA S, et al. Tool wear estimation and visualization using image sensors in micro milling manufacturing [EB/OL]. [2020-06-20]. https:∥dio.org/10.1007/978-3-319-92639-1_33.

DUPLÁK J, HATALA M, ZAJAC J, et al. The comprehensive comparison of the selected cutting materials with standard ISO 3685 in machining process of steel C60 [J]. Applied Mechanics and Materials, 2015, 718(93): 93-98.

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