In-process surface roughness prediction using displacement signals from spindle motion

被引:51
|
作者
Chang, Hun-Keun
Kim, Jin-Hyun
Kim, Il Hae
Jang, Dong Young
Han, Dong Chul
机构
[1] Seoul Natl Univ, Sch Mech & Aerosp Engn, Seoul 151742, South Korea
[2] CAMSYS Inc, Seoul 139743, South Korea
[3] Seoul Natl Univ Technol, Dept Ind & Informat Syst Engn, Seoul 139743, South Korea
关键词
surface roughness; end milling; displacement sensor; spindle displacement;
D O I
10.1016/j.ijmachtools.2006.07.004
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A method to predict surface roughness in real time was proposed and its effectiveness was proved through experiment in this paper. To implement the proposed method in machining process, a sensor system to measure relative displacement caused by the cutting operation was developed. In this research, roughness of machined surface was assumed to be generated by the relative motion between tool and workpiece and the geometric factors of a tool. The relative motion caused by the machining process could be measured in process using a cylindrical capacitive displacement sensor (CCDS). The CCDS was installed at the quill of a spindle and the sensing was not disturbed by the cutting. The workpiece was NAK80 and TiAlN coated carbide end mills were used in the test. Model to predict surface roughness was developed. A simple linear regression model was developed to predict surface roughness using the measured signals of relative motion. Close relation between machined surface roughness and roughness predicted using the measured signals was verified with similarity of about 95%. (C) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1021 / 1026
页数:6
相关论文
共 50 条
  • [41] In-process surface roughness measurement of bulk metallic glass using an adaptive optics system for aberration correction
    Fuh, Yiin-Kuen
    Fan, Jia Ren
    Huang, Chung Yu
    Jang, Shian Ching
    MEASUREMENT, 2013, 46 (10) : 4200 - 4205
  • [42] Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals
    Griffin, James M.
    Diaz, Fernanda
    Geerling, Edgar
    Clasing, Matias
    Ponce, Vicente
    Taylor, Chris
    Turner, Sam
    Michael, Ernest A.
    Mena, F. Patricio
    Bronfman, Leonardo
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2017, 85 : 1020 - 1034
  • [43] An in-process machined surface roughness classification using an ensemble learning algorithm based on extracted automated features from real-time surface images in milling process
    Babu, Mulpur Sarat
    INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM, 2024, 18 (07): : 4499 - 4511
  • [44] In-process surface roughness recognition (ISRR) system in end-milling operations
    Lou, SJ
    Chen, JC
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 1999, 15 (03): : 200 - 209
  • [45] DEVELOPMENT OF A HIGH RESOLUTION IN-PROCESS SENSOR FOR SURFACE ROUGHNESS BY LASER BEAM.
    Mitsuki, Kimiyuki
    Ozawa, Norimitsu
    Kohno, Tsuguo
    1600, (19):
  • [46] The development of an in-process surface roughness adaptive control system in end milling operations
    Julie Z. Zhang
    Joseph C. Chen
    The International Journal of Advanced Manufacturing Technology, 2007, 31 : 877 - 887
  • [47] The development of an in-process surface roughness adaptive control system in end milling operations
    Zhang, Julie Z.
    Chen, Joseph C.
    International Journal of Advanced Manufacturing Technology, 2007, 31 (9-10): : 877 - 887
  • [48] The development of an in-process surface roughness adaptive control system in end milling operations
    Zhang, Julie Z.
    Chen, Joseph C.
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2007, 31 (9-10): : 877 - 887
  • [49] In-Process Surface Roughness Recognition (ISRR) System in End-Milling Operations
    S.-J. Lou
    J. C. Chen
    The International Journal of Advanced Manufacturing Technology, 1999, 15 : 200 - 209
  • [50] A Regression Neural Model for In-process Surface Roughness Monitoring in End Milling Operations
    Huang, PoTsang B.
    Chen, James C.
    Jou, Yuan-Tsan
    ADVANCED DESIGN AND MANUFACTURE II, 2010, 419-420 : 369 - 372