Feature selection for tool condition monitoring in turning processes

被引：0
|
作者：
Rodriguez-Salgado, D.
Cambero-Rivero, I.
Alonso-Sanchez, F. J.
机构：
[1] Univ Extremadura, Ctr Univ Merida, Merida 06800, Venezuela
[2] Univ Extremadura, Escuela Ingenieros Ind, E-06071 Badajoz, Spain
来源：
ADVANCES IN MATERIALS PROCESSING TECHNOLOGIE | 2006年 / 526卷
关键词：
tool condition monitoring; flank wear; turning; neural networks;
D O I：
10.4028/www.scientific.net/MSF.526.97
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The aim of the present work is to develop a tool condition monitoring system (TCMS) using sensor fusion and artificial neural networks. Particular attention is paid to the manner in which the most correlated features with tool wear are selected. Experimental results show that the proposed system can reliably detect tool condition in turning operations and is viable for industrial applications. This study leads to the conclusion that the vibration in the feed direction and the motor current signals are best suited for the development of a TCMS than the sound signal, which should be used as an additional signal.
引用
收藏
页码：97 / 102
页数：6
相关论文
共 50 条
  • [1] Robust Tool Wear Monitoring Using Systematic Feature Selection in Turning Processes With Consideration of Uncertainties
    Zhang, Bin
    Katinas, Christopher
    Shin, Yung C.
    [J]. JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 2018, 140 (08):
  • [2] Feature extraction and selection in tool condition monitoring system
    Jie, S
    Hong, GS
    Rahman, M
    Wong, YS
    [J]. AL 2002: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2002, 2557 : 487 - 497
  • [3] Feature selection and a method to improve the performance of tool condition monitoring
    Zhengyou Xie
    Jianguang Li
    Yong Lu
    [J]. The International Journal of Advanced Manufacturing Technology, 2019, 100 : 3197 - 3206
  • [4] Feature selection and a method to improve the performance of tool condition monitoring
    Xie, Zhengyou
    Li, Jianguang
    Lu, Yong
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2019, 100 (9-12): : 3197 - 3206
  • [5] A DNA algorithm for feature selection in condition monitoring of manufacturing processes
    Wang, QF
    Du, RX
    [J]. ISPACS 2005: PROCEEDINGS OF THE 2005 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS, 2005, : 773 - 776
  • [6] An approach for condition monitoring of a turning tool
    Sharma, V. S.
    Sharma, S. K.
    Sharma, A. K.
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2007, 221 (04) : 635 - 646
  • [7] Tool Condition Monitoring In Turning Using Acoustic Emission
    Chockalingam, P.
    Ervina, E. M. N.
    Prabhu, C. M. R.
    [J]. GREEN DESIGN AND MANUFACTURE: ADVANCED AND EMERGING APPLICATIONS, 2018, 2030
  • [8] Intelligent tool condition monitoring system for turning operations
    Gao, HL
    Xu, MH
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 3, PROCEEDINGS, 2005, 3498 : 883 - 889
  • [9] Application of Machine Learning for Tool Condition Monitoring in Turning
    Patange, A. D.
    Jegadeeshwaran, R.
    Bajaj, N. S.
    Khairnar, A. N.
    Gavade, N. A.
    [J]. SOUND AND VIBRATION, 2022, 56 (02): : 127 - 145
  • [10] Tool Condition Monitoring in Turning by Applying Machine Vision
    Dutta, Samik
    Pal, Surjya K.
    Sen, Ranjan
    [J]. JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 2016, 138 (05):
12345