• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2016, Vol. 52 ›› Issue (16): 15-21.doi: 10.3901/JME.2016.16.015

• 仪器科学与技术 • 上一篇    下一篇

基于图像清晰度评价的磨削表面粗糙度检测方法*

易怀安1,2, 刘坚1, 路恩会1   

  1. 1. 湖南大学汽车车身先进设计制造国家重点实验室 长沙 410082;
    2. 怀化学院机械与光电物理学院 怀化 418000
  • 出版日期:2016-08-20 发布日期:2016-08-20
  • 作者简介:

    易怀安,男,1973年出生,博士研究生。主要研究方向为机器视觉在机械制造中的应用。

    E-mail:yihuaian@126.com

    刘坚(通信作者),男,1975年出生,博士,教授,博士研究生导师。主要研究方向为质量控制。

    E-mail:liujian@hnu.edu.cn

  • 基金资助:
    * 国家自然科学基金(71271078)和长沙市科技重大专项(K1307006-11-1)资助项目; 20150807收到初稿,20160527收到修改稿;

Detection Method of Grinding Surface Roughness Based on Image Definition Evaluation

YI Huaian1,2, LIU Jian1, LU Enhui1   

  1. 1. State Key Laboratory of Advanced Design and Manufacture for Vehicle Body,Hunan University, Changsha 410082;
    2. College of Mechanical and Photoelectric Physics, Huaihua University, Huaihua 418000
  • Online:2016-08-20 Published:2016-08-20

摘要:

针对当前机器视觉检测粗糙度主要采用图像灰度值信息进行统计分析,没有充分利用色彩信息且忽略了人眼视觉系统主观评判的问题,提出一种基于图像清晰度评价的磨削表面粗糙度检测方法。根据色块在不同等级粗糙度表面上形成的图像清晰度不一样,采用熵函数评价算法和基于色彩相关性的彩色图像清晰度评价算法分别构建清晰度与粗糙度之间的关系模型,论证了基于图像清晰度检测磨削表面粗糙度方法的可行性。试验结果表明,基于图像清晰度检测磨削表面粗糙度是一种可行的粗糙度检测方法,清晰度与粗糙度相关性强,清晰度有随着粗糙度的增大而减小的趋势,且利用色彩相关性的彩色图像清晰度评价算法对磨削表面粗糙度检测具有较好的灵敏性,并且该方法符合人眼视觉系统的主观评价;清晰度算法和主观评价二者结合可快速简易地在线检测工件的整体表面轮廓粗糙度。

关键词: 关系模型, 清晰度评价算法, 图像清晰度, 表面粗糙度

Abstract:

A novel method to detect the grinding surface roughness based on image definition was proposed to solve the problem that the current machine vision detected roughness mainly by adopting image grey value information for statistical analysis, not making full use of color information, and also ignoring the problem of the subjective evaluation of human visual system. According to the phenomenon that the image definition formed on the different grade roughness surface of different color pieces is different, the article built a relational model between resolution and roughness by using two resolution evaluation algorithms, which including the entropy function evaluation algorithm and the color image evaluation algorithm based on color correlation, respectively, to demonstrate the feasibility of the detection method proposed. The experimental results show that the detection method is feasible, the relevance between resolution and roughness is strong, the resolution is decreased while the roughness is increased, and the color image evaluation algorithm based on color correlation is better sensitive. Meanwhile, the idea of the proposed method conforms to the subjective evaluation of human eye vision system, the method combined resolution algorithm with subjective evaluation can detect the whole surface profile roughness of workpiece on-line quickly.

Key words: image definition, relational model, resolution evaluation algorithm, surface roughness