Unsupervised Tool Wear Monitoring in the Corner Milling of a Titanium Alloy Based on a Cutting Condition-Independent Method

被引:4
|
作者
Li, Zhimeng [1 ]
Zhong, Wen [1 ]
Shi, Yonggang [1 ]
Yu, Ming [2 ]
Zhao, Jian [1 ]
Wang, Guofeng [3 ]
机构
[1] Tianjin Chengjian Univ, Sch Control & Mech Engn, Tianjin 300384, Peoples R China
[2] Tianjin Chengjian Univ, Sch Comp & Informat Engn, Tianjin 300384, Peoples R China
[3] Tianjin Univ, Sch Mech Engn, Tianjin 300350, Peoples R China
关键词
tool wear monitoring; corner-milling; unsupervised; FREQUENCY; VIBRATION;
D O I
10.3390/machines10080616
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Real-time tool condition monitoring (TCM) for corner milling often poses significant challenges. On one hand, corner milling requires configuring complex milling paths, leading to the failure of conventional feature extraction methods to characterize tool conditions. On the other hand, it is costly to obtain sufficient test data on corner milling for most of the current pattern recognition methods, which are based on the supervised method. In this work, we propose a time-frequency intrinsic feature extraction strategy of acoustic emission signal (AEs) to construct a cutting condition-independent method for tool wear monitoring. The proposed new feature-extraction strategy is used to obtain the tool wear conditions through the intrinsic information of the time-frequency image of AEs. In addition, an unsupervised tool condition recognition framework, including the unsupervised feature selection, the clustering based on adjacent grids searching (CAGS) and the density factor based on CAGS, is proposed to determine the relationship between tool wear values and AE features. To test the effectiveness of the monitoring system, the experiment is conducted through the corner milling of a titanium alloy workpiece. Five metrics, PUR, CSM, NMI, CluCE and ClaCE, are used to evaluate the effectiveness of the recognition results. Compared with the state-of-the-art supervised methods, our method provides commensurate monitoring effectiveness but requires much fewer test data to build the model, which greatly reduces the operating cost of the TCM system.
引用
收藏
页数:22
相关论文
共 50 条
  • [11] A cutting power model for tool wear monitoring in milling
    Shao, H
    Wang, HL
    Zhao, XM
    INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2004, 44 (14): : 1503 - 1509
  • [12] Tool Wear Condition Monitoring in Milling Process Based on Current Sensors
    Zhou, Yuqing
    Sun, Weifang
    IEEE ACCESS, 2020, 8 (08): : 95491 - 95502
  • [13] Effect of cutting conditions on tool wear and wear mechanism in micro-milling of additively manufactured titanium alloy
    Aslantas, K.
    Hascelik, A.
    Ercetin, A.
    Danish, Mohd
    Alatrushi, Luqman K. H.
    Rubaiee, Saeed
    Bin Mahfouz, Abdullah
    TRIBOLOGY INTERNATIONAL, 2024, 193
  • [14] Tool wear in disk milling grooving of titanium alloy
    Xin, Hongmin
    Shi, Yaoyao
    Ning, Liqun
    ADVANCES IN MECHANICAL ENGINEERING, 2016, 8 (10): : 1 - 11
  • [15] On-line Monitoring for Cutting Tool Wear Condition Based on the Parameters
    Han, Fenghua
    Xie, Feng
    2ND INTERNATIONAL CONFERENCE ON DESIGN, MATERIALS, AND MANUFACTURING, 2017, 220
  • [16] Tool wear monitoring in milling of titanium alloy Ti-6Al-4 V under MQL conditions based on a new tool wear categorization method
    Hu, Meng
    Ming, Weiwei
    An, Qinglong
    Chen, Ming
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2019, 104 (9-12): : 4117 - 4128
  • [17] Tool wear monitoring in milling of titanium alloy Ti–6Al–4 V under MQL conditions based on a new tool wear categorization method
    Meng Hu
    Weiwei Ming
    Qinglong An
    Ming Chen
    The International Journal of Advanced Manufacturing Technology, 2019, 104 : 4117 - 4128
  • [18] Condition Monitoring of Milling Tool Wear Based on Fractal Dimension of Vibration Signals
    Xu, Chuangwen
    Chen, Hualing
    Liu, Zhe
    Cheng, Zhongwen
    STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING, 2009, 55 (01): : 15 - 25
  • [19] A tool wear condition monitoring approach for end milling based on numerical simulation
    Zhu, Qinsong
    Sun, Weifang
    Zhou, Yuqing
    Gao, Chen
    EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY, 2021, 23 (02): : 371 - 380
  • [20] A tool wear condition monitoring approach for end milling based on numerical simulation
    Zhu Q.
    Sun W.
    Zhou Y.
    Gao C.
    Eksploatacja i Niezawodnosc, 2021, 23 (02): : 371 - 380