采用自适应区域生长的微型钻铣刀具磨损检测方法

邓晓鹏; 王妍; 洪煜; 胡小锋

doi:10.7652/xjtuxb202112012

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采用自适应区域生长的微型钻铣刀具磨损检测方法

更新时间：2025-07-09

- 采用自适应区域生长的微型钻铣刀具磨损检测方法
- Wear Detection for Micro-Drill and Micro-Milling Tool via Adaptive Region Growth Algorithm
- 西安交通大学学报 2021年55卷第12期页码：98-107
- 作者机构：
  
  上海交通大学机械与动力工程学院,上海,200240
- 作者简介：
- 基金信息：
  
  : 2021-03-24。作者简介: 邓晓鹏(1997—),男,硕士生;胡小锋(通信作者),男,副教授,博士生导师。基金项目: 国家重点研发计划资助项目(2018YFB1700500);上海市科学技术委员会资助项目(19511105302)
- DOI：10.7652/xjtuxb202112012
  中图分类号： TG71;TP391.41
- 网络首发：2021-12-10，
  
  纸质出版：2021
- 稿件说明：
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邓晓鹏, 王妍, 洪煜, 等. 采用自适应区域生长的微型钻铣刀具磨损检测方法[J]. 西安交通大学学报, 2021,55(12):98-107.

Wear Detection for Micro-Drill and Micro-Milling Tool via Adaptive Region Growth Algorithm[J]. 2021, 55(12): 98-107.
邓晓鹏, 王妍, 洪煜, 等. 采用自适应区域生长的微型钻铣刀具磨损检测方法[J]. 西安交通大学学报, 2021,55(12):98-107. DOI： 10.7652/xjtuxb202112012.

Wear Detection for Micro-Drill and Micro-Milling Tool via Adaptive Region Growth Algorithm[J]. 2021, 55(12): 98-107. DOI： 10.7652/xjtuxb202112012.

摘要

针对微型钻头与微型铣刀磨损图像中干扰严重、磨损区域自动识别困难以及磨损量测量不准确等问题

融合最大类间方差法与区域生长算法

提出一种基于自适应区域生长的刀具磨损视觉检测方法。首先通过最大类间方差(Otsu)算法自动裁剪磨损图像

结合图像像素变化规律确定区域生长起点与初始阈值

并以类间方差为依据进行阈值更新

获得磨损区域二值图像。在此基础上

通过最小二乘法对刀具轮廓线进行重构

实现刀具磨损量的自动测量。最后

通过磨损测量数据的一致性分析验证算法的有效性。结果表明:所提方法测量结果与人工测量结果具有较强的一致性; 相较于目前已有的机器视觉检测方法

该方法可以有效避免刀体干扰区域的影响

准确提取微型钻铣刀具的磨损信息

可以精准实现对微型钻铣刀具磨损状态的检测。

Abstract

Aiming at the problems

such as serious interference

difficulty in automatic identification of wear region and inaccurate measurement of wear quantity in micro drill and micro milling tool wear images

a visual detection method for tool wear based on adaptive region growth is proposed by combining the maximum inter-class variance method with the region growing algorithm

where the wear image is automatically trimmed by Otsu algorithm

and the starting point and initial threshold of region growth are determined according to the change rule of image pixels. The threshold is updated according to the inter-class variance to obtain the binary image of the wear area. Then the tool contour is reconstructed by least square method

and automatic measurement of tool wear is realized. The consistency analysis of wear measurement data is performed to verify the effectiveness of the proposed algorithm. It is indicated that the measurement results from the proposed method coincide well with the manual measurement results. Compared with the existing machine vision detection methods

this method can effectively avoid the influence of the interference area

and accurately extract the wear information of the micro drill and micro milling tool

thereby accurately realizing the detection of the wear state of the micro drill and micro milling tool.

关键词

Keywords

references

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