纺织学报 ›› 2020, Vol. 41 ›› Issue (10): 158-163.doi: 10.13475/j.fzxb.20200306006

• 机械与器材 • 上一篇    下一篇

长条状细薄带钩零件变形自动检测系统

朱世根1,2(), 杨宏贤1,2, 白云峰1,2, 丁浩1,2, 朱巧莲3   

  1. 1.东华大学 机械工程学院, 上海 201620
    2.东华大学 纺织装备教育部工程研究中心, 上海 201620
    3.义乌云溪新材料科技有限公司, 浙江 义乌 322000
  • 收稿日期:2020-03-23 修回日期:2020-07-08 出版日期:2020-10-15 发布日期:2020-10-27
  • 作者简介:朱世根(1963—),男,教授,博士。主要研究方向为成型制造与强化。E-mail:sgzhu@dhu.edu.cn
  • 基金资助:
    中央高校改善基本办学条件专项资助(14X15)

Investigation on automatic deformation inspection system of long and thin parts with hooks

ZHU Shigen1,2(), YANG Hongxian1,2, BAI Yunfeng1,2, DING Hao1,2, ZHU Qiaolian3   

  1. 1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China
    2. Engineering Research Center of Advanced Textile Machinery, Ministry of Education, Donghua University, Shanghai 201620, China
    3. Yiwu Yunxi New Material Technology Company, Yiwu, Zhejiang 322000,China
  • Received:2020-03-23 Revised:2020-07-08 Online:2020-10-15 Published:2020-10-27

摘要:

现有的机器视觉变形自动检测系统主要局限于小尺寸精密零件或大尺寸非精密零件的检测,而对于一些大尺寸、高精密的大批量检测对象,在实际生产中主要依靠人工分拣,难以同时保证变形检测的高精准度与高效率。针对此问题,设计研发了一种基于视觉的变形自动检测方法和系统。通过设计模块化的高速检测硬件系统,利用虚实像联合检测方法量化变形检测指标;利用LabVIEW及IMAQ Vision视觉工具包作为开发环境,根据零件形状特点,设计了基于缝隙值的分区检测算法,避免直接检测整体变形的误差;在此基础上,针对针钩形态,确定最优化的检测方案;针对零件位姿偏差问题,使用基于多基准的改进定位策略。结果表明:该系统可避免弯钩的误检,提高针钩尺寸测量精度和检测结果可重复性,重复检出率可达91%~96%。最后,将机检零件与标准零件进行比较,机检零件一致性完全可满足装机使用需求。

关键词: 机器视觉, 变形检测, 虚拟仪器, LabVIEW, 织针, 零件变形测量算法

Abstract:

The existing machine vision automatic deformation inspection system is mainly limited to the detection of small size precision parts or large size non precision parts. For some large-scale, high-precision and large-quantity inspection objects, manual sorting is mainly used in actual production. It is difficult to guarantee high accuracy and efficiency at the same time. Therefore, an automatic deformation inspection method and system based on vision was proposed. First of all, a modular high-speed detection hardware system was designed, and the detection index was quantified by the virtual real image joint detection method. Secondly, using LabVIEW and IMAQ vision visual toolkit as the development environment, according to the shape characteristics of the parts, a partition detection algorithm based on the gap value was designed to avoid various errors of direct detection of the overall deformation. On this basis, the optimal detection scheme was determined according to the shape of the needle hook, and the improved positioning strategy based on multi-references was used to solve the problem of pose deviation. The results show that the system can avoid the false detection of the hook, improve the measurement accuracy of the hook size and the repeatability of the detection results, and the repeat detection rate can reach 91%-96%. Finally, comparing with standard parts, it is verified that the consistency of machine-checked parts meets the needs of installation.

Key words: machine vision, deformation inspection, virtual instrument, LabVIEW, needle, deformation detection algorithm of parts

中图分类号: 

  • TH71

图1

零件变形方式"

图2

检测系统构成示意图"

图3

零件图像"

图4

软件架构示意图"

图5

搜索范围与零件最小外接矩形"

图6

ROI定位基准"

图7

检测方案"

表1

不同检测方案检测结果"

方案 机器检测 合格品
误检数
合格品误
检率/%
合格品 不合格品
60 240 7 6.6
52 248 0 0

图8

方案一误检弯钩形态"

图9

零件投影长度的变化"

表2

改进定位前缝隙值检测结果"

次数 1 2 3 4 5
1 0.055 0.055 0.055 0.055 0.252
2 0.055 0.053 0.055 0.055 0.266
3 0.051 0.043 0.055 0.055 0.266
4 0.047 0.055 0.055 0.055 0.252
5 0.055 0.055 0.055 0.054 0.262
6 0.042 0.048 0.055 0.055 0.270
7 0.055 0.054 0.055 0.051 0.277
8 0.055 0.055 0.055 0.044 0.253
9 0.048 0.045 0.057 0.056 0.252
10 0.054 0.043 0.055 0.054 0.269
测量误差 0.013 0.012 0.002 0.012 0.025

表3

改进定位后缝隙值检测结果"

次数 1 2 3 4 5
1 0.048 0.055 0.055 0.052 0.279
2 0.055 0.055 0.055 0.045 0.279
3 0.055 0.055 0.055 0.055 0.266
4 0.047 0.055 0.055 0.054 0.279
5 0.055 0.055 0.068 0.055 0.279
6 0.055 0.055 0.069 0.055 0.273
7 0.046 0.055 0.055 0.054 0.279
8 0.050 0.042 0.055 0.055 0.279
9 0.042 0.042 0.055 0.044 0.273
10 0.055 0.055 0.055 0.055 0.279
测量误差 0.013 0.013 0.014 0.011 0.013

表4

系统重复精度实验结果"

类型 实验次数 初检合
格数
初检合格品再检
个数 重复检出率/%
改进前 1 106 79 75
2 98 78 80
3 100 83 83
4 106 83 78
改进后 1 99 90 91
2 101 97 96
3 96 87 91
4 96 89 93

图10

零件装夹排列示意图"

图11

机检与标准零件一致性对比图"

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