On-machine detection of geometric and state parameters of end mills based on machine vision

被引：0
|
作者：
Liu Z. [1 ]
Zhang J. [1 ]
Yin J. [1 ]
Zhao W. [1 ]
机构：
[1] State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2022年 / 43卷 / 07期
关键词：
CNC machining; end mill; geometric and state parameters; image processinghttp; machine vision;
D O I：
10.7527/S1000-6893.2021.25593
中图分类号：
学科分类号：
摘要：
To prevent the tool installation errors caused by frequent changes of tools during the aeronautical components machining, a detection system based on machine vision is proposed to measure geometric and state parameters of milling cutter. The end mill is taken as the research object, and its dynamic image contour under the state of spindle rotation is extracted. Besides, the measurement algorithm for cutting edge radius, tool diameter and overhang length are also developed. To demonstrate the visual deviation caused by the camera's large view during the measurement of overhang length, the deviation correction algorithm is further studied to improve the measurement accuracy. Finally, the proposed method is verified on the CNC machine tool. The results show that the maximum error is 0.51% and the device achieves good repeatability and high precision, which can realize the on-machine detection of tool geometric and state parameters. © 2022 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.
引用
收藏
相关论文
共 22 条
  • [1] ZHANG C, ZHANG J L., On-line tool wear measurement for ball-end milling cutter based on ma-chine vision[J], Computers in Industry, 64, 6, pp. 708-719, (2013)
  • [2] DAI Y Q, ZHU K P., A machine vision system for micro-milling tool condition monitoring[J], Precision Engineering, 52, pp. 183-191, (2018)
  • [3] ABUBAKR M, HASSAN M A, KROLCZYK G M, Et al., Sensors selection for tool failure detection during machining processes:A simple accurate classification model[J], CIRP Journal of Manufacturing Science and Technology, 32, pp. 108-119, (2021)
  • [4] WAN P, LI Y G, LIU C Q, Et al., An accurate tool wear prediction method under varying cutting conditions based on domain adversarial gating neural network[JL], Acta Aeronautica et Astronautica Sinica, 42, 10, (2021)
  • [5] ZHU A B, HE D Y, ZHAO J W, Et al., Online tool wear condition monitoring using binocular vision[J], Insight, 59, 4, pp. 203-210, (2017)
  • [6] LI Y G, LIU C Q, HUA J Q, Et al., A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning[J], CIRP Annals-Manufacturing Technology, 68, 1, pp. 487-490, (2019)
  • [7] NOURI M, FUSSELL B K, ZINITI B L, Et al., Real-time tool wear monitoring in milling using a cutting condition independent method, International Journal of Machine Tools&Manufacture, 89, pp. 1-13, (2015)
  • [8] LI W, LIU T., Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling, Mechanical Systems and Signal Processing, 131, pp. 689-702, (2019)
  • [9] SHAN Z D, ZHANG F, REN Y X, Et al., On line detection technology of the hardness of cast iron parts based on machine vision[J], Journal of Mechanical Engineering, 53, 1, pp. 157-164, (2017)
  • [10] KASSIM A A, MANNAN M A, MIAN Z., Texture analysis methods for tool condition monitoring[J], Image and Vision Computing, 25, 7, pp. 1080-1090, (2007)
123