Tool wear estimation for different workpiece materials using the same monitoring system

被引:19
|
作者
Salgado, D. R. [1 ]
Cambero, I. [1 ]
Herrera Olivenza, J. M. [1 ]
Garcia Sanz-Calcedo, J. [1 ]
Nunez Lopez, P. J. [2 ]
Garcia Plaza, E. [2 ]
机构
[1] Univ Extremadura, Dept Mech Energet & Mat Engn, Avda Elvas S-N, Badajoz 06006, Spain
[2] Univ Castilla La Mancha, Higher Tech Sch Ind Engn, E-13071 Ciudad Real, Spain
关键词
Tool wear; Flank wear; monitoring system; steel alloy; aluminium alloy; ARTIFICIAL NEURAL-NETWORK; SIGNALS;
D O I
10.1016/j.proeng.2013.08.246
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper presents a tool wear monitoring system that uses the same signals and prediction strategy for monitoring the machining process of different materials, i.e., a steel and an aluminium alloy. It is an important requirement for a monitoring system to he applied in real applications. Experiments have been performed on a lathe over a range of different cutting conditions, and TiN coated tools were used. The monitoring signals used are the AC feed drive motor current and the cutting vibrations. The geometry tool parameters used as inputs are the tool angle and the radius. The performance of the proposed system was validated against different experiments. In particular, different tests were performed using different numbers of experiments obtaining a rmse for tool wear estimation of 17.63 um and 13.45 um for steel and aluminium alloys respectively.
引用
收藏
页码:608 / 615
页数:8
相关论文
共 50 条
  • [31] APPLICATION OF AUDIBLE SOUND SIGNALS FOR TOOL WEAR MONITORING AND WORKPIECE HARDNESS IDENTIFICATION IN GEAR MILLING USING MACHINE LEARNING TECHNIQUES
    Kothuru, Achyuth
    Nooka, Sai Prasad
    Victoria, Patricia Iglesias
    Liu, Rui
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2017, VOL 10, 2017,
  • [32] Machine tool condition monitoring using workpiece surface texture analysis
    Ashraf A. Kassim
    M.A. Mannan
    Ma Jing
    Machine Vision and Applications, 2000, 11 : 257 - 263
  • [33] An industrial tool wear monitoring system for interrupted turning
    Scheffer, C
    Heyns, PS
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2004, 18 (05) : 1219 - 1242
  • [34] APPLYING SIGNALS OF CONTROL SYSTEM IN TOOL WEAR MONITORING
    Toma, Udiljak
    Mulc, Tihomir
    Ciglar, Damir
    ANNALS OF DAAAM FOR 2008 & PROCEEDINGS OF THE 19TH INTERNATIONAL DAAAM SYMPOSIUM, 2008, : 1415 - 1416
  • [35] Tool wear monitoring using naive Bayes classifiers
    Karandikar, Jaydeep
    McLeay, Tom
    Turner, Sam
    Schmitz, Tony
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2015, 77 (9-12): : 1613 - 1626
  • [36] The milling tool wear monitoring using the acoustic spectrum
    C. S. Ai
    Y. J. Sun
    G. W. He
    X. B. Ze
    W. Li
    K. Mao
    The International Journal of Advanced Manufacturing Technology, 2012, 61 : 457 - 463
  • [37] Study of Tool Wear Monitoring Using Machine Vision
    Peng, Ruitao
    Pang, Haolin
    Jiang, Haojian
    Hu, Yunbo
    AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2020, 54 (03) : 259 - 270
  • [38] Tool wear estimation using resource allocation network
    Pai, PS
    Nagabhushana, TN
    Rao, PKR
    INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2001, 41 (05): : 673 - 685
  • [39] TOOL WEAR MONITORING IN DRILLING USING FORCE SIGNALS
    LIN, SC
    TING, CJ
    WEAR, 1995, 180 (1-2) : 53 - 60
  • [40] Study of Tool Wear Monitoring Using Machine Vision
    Haolin Ruitao Peng
    Haojian Pang
    Yunbo Jiang
    Automatic Control and Computer Sciences, 2020, 54 : 259 - 270