Infrared and Laser Engineering, Volume. 48, Issue 3, 317005(2019)

An improved material removal model for robot polishing based on neural networks

Yu Yi1... Kong Lingbao1, Zhang Haitao2, Xu Min1 and Wang Liping2 |Show fewer author(s)
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    References(19)

    [1] [1] Cully A, Clune J, Tarapore D, et al. Robots that can adapt like animals[J]. Nature, 2014, 521(7553): 503-507.

    [2] [2] Peng Lu, Man Chen. Analysis of the application of industrial robot in intelligent manufacturing[J]. Metallurgical Automation, 2018(S1): 134.

    [3] [3] Seok J, Sukam C P, Kim A T, et al. Material removal model for chemical-mechanical polishing considering wafer flexibility and edge effects[J]. Wear, 2004, 257(5-6): 496-508.

    [4] [4] Eder S J, Cihak-Bayr U, Pauschitz A. Nanotribological simulations of multi-grit polishing and grinding[J]. Wear, 2015, 340-341: 25-30.

    [5] [5] Xu H, Komvopoulos K. A quasi-static mechanics analysis of three-dimensional nanoscale surface polishing[J]. Journal of Manufacturing Science & Engineering, 2010, 132(3): 321-333.

    [6] [6] Cao Z C, Chi F C. Theoretical modelling and analysis of the material removal characteristics in fluid jet polishing[J]. International Journal of Mechanical Sciences, 2014, 89: 158-166.

    [7] [7] Tichy J. Contact Mechanics and lubrication hydrodynamics of chemical mechanical polishing[J]. Tree Physiology, 1999, 25(10): 1243-1251.

    [8] [8] Preston F W. The theory and design of plate glass polishing machines[J]. J Soc Glass Tech, 1927, 11: 214.

    [9] [9] Buijs M, Houten K V. A model for lapping of glass[J]. Journal of Materials Science, 1993, 28(11):3014-3020.

    [10] [10] Matsuo H, Ishikawa A, Kikkawa T. Role of frictional force on the polishing rate of Cu chemical mechanical polishing[J]. Japanese Journal of Applied Physics, 2004, 43(4):1813-1819.

    [11] [11] Shorey A B. Mechanisms of material removal in magnetorh-eological finishing (MRF) of glass[D]. Rochester: University of Rochester, 2000.

    [12] [12] Wang C C, Lin S C, Hong H. A material removal model for polishing glass-ceramic and aluminum magnesium storage disks[J]. International Journal of Machine Tools & Manufacture, 2002, 42(8): 979-984.

    [13] [13] Jordan M I, Mitchell T M. Machine learning: Trends, perspectives, and prospects[J]. Science, 2015, 349(6245): 255-260.

    [14] [14] Ghahramani Z. Probabilistic machine learning and artificial intelligence[J]. Nature, 2015, 521(7553): 452-459.

    [15] [15] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.

    [16] [16] Reza K S, Masoud G, Mohammad G, et al. Deep networks can resemble human feed-forward vision in invariant object recognition[J]. Scientific Reports, 2016, 6: 32672.

    [17] [17] Yang Nan, Nan Lin, Zhang Dingyi, et al. Research on image interpretation based on deep learning[J]. Infrared and Laser Engineering, 2018, 47(2): 0203002. (in Chinese)

    [18] [18] Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning[M]. New York: Springer, 2009.

    [19] [19] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision, 2016: 770-778.

    Tools

    Get Citation

    Copy Citation Text

    Yu Yi, Kong Lingbao, Zhang Haitao, Xu Min, Wang Liping. An improved material removal model for robot polishing based on neural networks[J]. Infrared and Laser Engineering, 2019, 48(3): 317005

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: 光电测量

    Received: Nov. 5, 2018

    Accepted: Dec. 15, 2018

    Published Online: Apr. 6, 2019

    The Author Email:

    DOI:10.3788/irla201948.0317005

    Topics