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
  • [31] Analytical Modeling of Tool Rake Wear in Titanium Alloy Milling Process
    Yue C.
    Du Y.
    Li X.
    Chen Z.
    Liu X.
    Liang S.Y.
    Wang L.
    Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2021, 57 (23): : 232 - 240
  • [32] PREDICTION OF TOOL WEAR BASED ON CUTTING FORCES WHEN END MILLING TITANIUM ALLOY TI-6AL-4V
    Stanley, Cynthia
    Ulutan, Durul
    Mears, Laine
    PROCEEDINGS OF THE ASME 9TH INTERNATIONAL MANUFACTURING SCIENCE AND ENGINEERING CONFERENCE, 2014, VOL 1, 2014,
  • [33] Singularity Analysis of Cutting Force and Vibration for Tool Condition Monitoring in Milling
    Zhou, Chang'an
    Guo, Kai
    Yang, Bin
    Wang, Haijin
    Sun, Jie
    Lu, Laixiao
    IEEE ACCESS, 2019, 7 : 134113 - 134124
  • [34] Method of controlling cutting tool wear based on signal analysis of acoustic emission for milling
    Pechenin, Vadim A.
    Khaimovich, Alexander I.
    Kondratiev, Alexsandr I.
    Bolotov, Michael A.
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON DYNAMICS AND VIBROACOUSTICS OF MACHINES (DVM2016), 2017, 176 : 246 - 252
  • [35] Cutting force denoising in micro-milling tool condition monitoring
    Zhu, K.
    Hong, G. S.
    Wong, Y. S.
    Wang, W.
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2008, 46 (16) : 4391 - 4408
  • [36] Tool wear condition monitoring method based on relevance vector machine
    Jia, Ruhong
    Yue, Caixu
    Liu, Qiang
    Xia, Wei
    Qin, Yiyuan
    Zhao, Mingwei
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2023, 128 (11-12): : 4721 - 4734
  • [37] Tool wear condition monitoring method based on relevance vector machine
    Ruhong Jia
    Caixu Yue
    Qiang Liu
    Wei Xia
    Yiyuan Qin
    Mingwei Zhao
    The International Journal of Advanced Manufacturing Technology, 2023, 128 : 4721 - 4734
  • [38] Fractal analysis of vibration signals for monitoring the condition of milling tool wear
    Xu Chuangwen
    Hualing, C.
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY, 2009, 223 (J6) : 909 - 918
  • [39] A Multisensor Fusion Method for Tool Condition Monitoring in Milling
    Zhou, Yuqing
    Xue, Wei
    SENSORS, 2018, 18 (11)
  • [40] Effects of Cutting Parameters on Tool Insert Wear in End Milling of Titanium Alloy Ti6A14V
    LUO Ming
    WANG Jing
    WU Baohai
    ZHANG Dinghua
    Chinese Journal of Mechanical Engineering, 2017, 30 (01) : 53 - 59