Condition Monitoring in a Machine Tool Spindle using Wireless Sensor

被引:0
|
作者
Ho, Chao-Ching
Kuo, Tzu-Hsin
Tsai, Tsung-Ting
机构
来源
关键词
machine monitoring; wireless sensors; solar energy transmission;
D O I
10.4028/www.scientific.net/AMR.126-128.678
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The development of robust condition monitoring system for a machine tool spindle is an important task because the spindle has a significant effect on the processing quality. This paper presents the architecture of data acquisition system for detecting spindle vibration in turning processes in order to develop an on-line condition monitoring system. In this work, a solar-powered wireless sensor system is installed inside the spindle and is used to monitor the machine tool processing state in real time, thereby improving the processing quality. Accelerometer sensors are employed to estimate tool wear; these sensors monitor the vibration of the spindle. The vibration monitoring data of the high-speed spindle is wirelessly transmitted to an external information device in real time. As an alternative to sensors that employ wired power transmission, a solar energy transmission system has been developed to provide the required electric power to the sensor system. The experimental results show that the proposed system successfully measures the vibration frequency of the rotational machine tool spindle.
引用
收藏
页码:678 / 683
页数:6
相关论文
共 50 条
  • [31] Wireless Sensor Development for Machinery Condition Monitoring
    Antoine, Thomas
    SOUND AND VIBRATION, 2014, 48 (05): : 6 - 7
  • [32] SENSOR INTEGRATION USING NEURAL NETWORKS FOR INTELLIGENT TOOL CONDITION MONITORING
    RANGWALA, S
    DORNFELD, D
    JOURNAL OF ENGINEERING FOR INDUSTRY-TRANSACTIONS OF THE ASME, 1990, 112 (03): : 219 - 228
  • [33] Sensor integration using neural networks for intelligent tool condition monitoring
    Rangwala, S.
    Dornfeld, D.
    Journal of engineering for industry, 1990, 112 (03): : 219 - 228
  • [34] Machine tool condition monitoring using workpiece surface texture analysis
    Kassim, AA
    Mannan, MA
    Jing, M
    MACHINE VISION AND APPLICATIONS, 2000, 11 (05) : 257 - 263
  • [35] Indirect Tool Condition Monitoring Using Ensemble Machine Learning Techniques
    Schueller, Alexandra
    Saldano, Christopher
    JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 2023, 145 (01):
  • [36] On-Machine Tool Condition Monitoring System Using Image Processing
    Kanto, Kenta
    Kubota, Junichi
    Fujishima, Makoto
    Mori, Masahiko
    INTERNATIONAL JOURNAL OF AUTOMATION TECHNOLOGY, 2022, 16 (03) : 280 - 285
  • [37] Machine tool condition monitoring using statistical quality control charts
    Jennings, AD
    Drake, PR
    INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 1997, 37 (09): : 1243 - 1249
  • [38] 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
  • [39] Sensor Stream Mining for Tool Condition Monitoring
    Karacal, Cem
    Cho, Sohyung
    Yu, William
    CIE: 2009 INTERNATIONAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING, VOLS 1-3, 2009, : 1429 - 1433
  • [40] Vibration — A tool for machine diagnostics and condition monitoring
    K N Gupta
    Sadhana, 1997, 22 : 393 - 410