AN INTEGRATED ULTRASONIC SENSOR FOR MONITORING GRADUAL WEAR ONLINE DURING TURNING OPERATIONS

被引:5
|
作者
NAYFEH, TH
EYADA, OK
DUKE, JC
机构
[1] Department of Industrial and Systems Engineering, Virginia Polytechnic Institute, State University, Blacksburg
[2] Department of Engineering Science and Mechanics/Materials Science and Engineering, Virginia Polytechnic Institute, State University, Blacksburg
关键词
D O I
10.1016/0890-6955(94)00126-5
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The condition of the tool and the cutting process are essential inputs to any productivity improvements through process optimization in conventional and unmanned machining. Tool replacement and tool wear compensation strategies, which are based on prior experience and/or tool history are, in general, under performing. Currently, the methods of tool condition monitoring are either time consuming, as in the case of off-line direct measurements of the tool, or are modestly successful, as in the case of the on-line indirect measurements, such as forces or acoustic emissions. This in part is due to the lack of suitable sensors and/or exact dynamic models, which relate the indirect measurements to the actual tool condition. This paper describes a promising ultrasonic method for on-line direct measurement of gradual wear in turning operations. An integrated (transmit and receive) single ultrasonic transducer operating at a frequency of 10 MHz is placed in contact with the tool. The change in the amount of the reflected energy from the nose and the hanks of the tool can be related to the level of gradual wear and the mechanical integrity of the tool. The experimental results show that under laboratory conditions, a correlation exists between the ultrasonic measurement and gradual wear and that it is tool dependent.
引用
收藏
页码:1385 / 1395
页数:11
相关论文
共 50 条
  • [41] A brief review: acoustic emission method for tool wear monitoring during turning
    Li, XL
    INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2002, 42 (02): : 157 - 165
  • [42] Theoretic Modeling and Numerical Simulation of The Electromagnetic Sensor for Online Wear Debris Monitoring
    Li, Baoxi
    Yang, Dingxin
    Hu, Zheng
    Yang, Yongmin
    PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON QUALITY, RELIABILITY, RISK, MAINTENANCE, AND SAFETY ENGINEERING (QR2MSE), VOLS I-IV, 2013, : 1781 - 1784
  • [43] Sensor signals for tool-wear monitoring in metal cutting operations - a review of methods
    Dimla, DE
    INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2000, 40 (08): : 1073 - 1098
  • [44] Advanced Monitoring of Tool Wear and Cutting States in CNC Turning Process by Utilizing Sensor Fusion
    Tangjitsitcharoen, Somkiat
    Rungruang, Channarong
    Pongsathornwiwat, Narongsak
    MANUFACTURING PROCESS TECHNOLOGY, PTS 1-5, 2011, 189-193 : 377 - 384
  • [45] Integrated approach for optimising machining parameters, tool wear and surface quality in multi-pass turning operations
    Mgwatu, M., I
    ADVANCES IN PRODUCTION ENGINEERING & MANAGEMENT, 2013, 8 (04): : 209 - 218
  • [46] Investigation of an integrated fiber laser sensor system in ultrasonic structural health monitoring
    Wu, Qi
    Okabe, Yoji
    SMART MATERIALS AND STRUCTURES, 2016, 25 (03)
  • [47] AN ONLINE FERROMAGNETIC WEAR DEBRIS SENSOR FOR MACHINERY CONDITION MONITORING AND FAILURE-DETECTION
    CHAMBERS, KW
    ARNESON, MC
    WAGGONER, CA
    WEAR, 1988, 128 (03) : 325 - 337
  • [48] Online Monitoring of a Shaft Turning Process based on Vibration Signals from On-Rotor Sensor
    Li, Chun
    Li, Bing
    Gu, Lichang
    Feng, Guojin
    Gu, Fengshou
    Ball, Andrew D.
    2020 3RD WORLD CONFERENCE ON MECHANICAL ENGINEERING AND INTELLIGENT MANUFACTURING (WCMEIM 2020), 2020, : 402 - 407
  • [49] A low-cost ultrasonic sensor for online monitoring of water levels in rivers and channels
    Masoudimoghaddam, Mohammadreza
    Yazdi, Jafar
    Shahsavandi, Mohammad
    FLOW MEASUREMENT AND INSTRUMENTATION, 2025, 102
  • [50] Online monitoring of shape memory polymers with a material integrated flexible interdigital sensor
    Huebner, Martina
    Schaefer, Hannes
    Koschek, Katharina
    Lang, Walter
    2019 IEEE SENSORS, 2019,