Evaluation of grinding wheel loading phenomena by using acoustic emission signals

被引:14
|
作者
Liu, Chien-Sheng [1 ]
Li, Yu-An [2 ,3 ]
机构
[1] Natl Cheng Kung Univ, Dept Mech Engn, 1 Univ Rd, Tainan 70101, Taiwan
[2] Natl Chung Cheng Univ, Dept Mech Engn, 168 Univ Rd, Minhsiung Township 62102, Chiayi, Taiwan
[3] Natl Chung Cheng Univ, Adv Inst Mfg High Tech Innovat, 168 Univ Rd, Minhsiung Township 62102, Chiayi, Taiwan
关键词
Acoustic emission (AE); Surface grinding; Process monitoring; Grinding; Wheel loading; Surface roughness; EXHIBITING HIGH ADHESION; WEAR; FORCE;
D O I
10.1007/s00170-018-2513-9
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the industrial manufacturing field, machining is a major process. Machining operations involve grinding, drilling, milling, turning, pressing, molding, and so on. Among these operations, grinding is the most precise and complicated process. The surface condition of the grinding wheel plays an important role in grinding performance, and the identification of grinding wheel loading phenomena during the grinding process is critical. Accordingly, this present study describes a measurement method based on the acoustic emission (AE) technique to characterize the loading phenomena of a Si2O3 grinding wheel for the grinding mass production process. The proposed measurement method combines the process-integrated measurement of AE signals, offline digital image processing, and surface roughness measurement of the ground workpieces for the evaluation of grinding wheel loading phenomena. The experimental results show that the proposed measurement method provides a quantitative index from the AE signals to evaluate the grinding wheel loading phenomena online for the grinding mass production process, and this quantitative index is determined via some experiments in advance in the same grinding environment to help the monitoring and controlling of the grinding process.
引用
收藏
页码:1109 / 1117
页数:9
相关论文
共 50 条
  • [21] Acoustic Emission-Based Grinding Wheel Condition Monitoring Using Decision Tree Machine Learning Classifiers
    Mouli, D. S. B.
    Rameshkumar, K.
    ADVANCES IN MATERIALS AND MANUFACTURING ENGINEERING, ICAMME 2019, 2020, : 353 - 359
  • [22] Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks
    D. F. G. Moia
    I. H. Thomazella
    P. R. Aguiar
    E. C. Bianchi
    C. H. R. Martins
    Marcelo Marchi
    Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2015, 37 : 627 - 640
  • [23] The relationship between acoustic emission signals and cutting phenomena in turning process
    Alan Hase
    Masaki Wada
    Toshihiko Koga
    Hiroshi Mishina
    The International Journal of Advanced Manufacturing Technology, 2014, 70 : 947 - 955
  • [24] The relationship between acoustic emission signals and cutting phenomena in turning process
    Hase, Alan
    Wada, Masaki
    Koga, Toshihiko
    Mishina, Hiroshi
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2014, 70 (5-8) : 947 - 955
  • [25] Acoustic emission based grinding wheel wear monitoring: Signal processing and feature extraction
    Shen, Chia-Hsuan
    APPLIED ACOUSTICS, 2022, 196
  • [26] Process monitoring of centerless grinding using acoustic emission
    Kim, HY
    Kim, SR
    Ahn, JH
    Kim, SH
    JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2001, 111 (1-3) : 273 - 278
  • [27] Optimisation of grinding parameters for wheel loading and dressing
    Ragavanantham, S.
    Sampathkumar, S.
    Kumar, S. Santhosh
    2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), 2016, : 2341 - 2343
  • [28] Global strategy of grinding wheel performance evaluation applied to grinding of superalloys
    Souza, Adriel Magalhses
    da Silva, Eraldo Jannone
    PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY, 2019, 57 : 113 - 126
  • [29] Detection of Grinding Temperatures Using Laser Irradiation and Acoustic Emission Sensing Technique
    Mohammed, Arif
    Folkes, Janet
    Chen, Xun
    MATERIALS AND MANUFACTURING PROCESSES, 2012, 27 (04) : 395 - 400
  • [30] Estimation of cBN grinding wheel condition using image sensor
    Lee, Eddie Taewan
    Fan, Zhaoyan
    Sencer, Burak
    49TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE (NAMRC 49, 2021), 2021, 53 : 286 - 292