红外与激光工程, 2019, 48 (3): 0317005, 网络出版: 2019-04-06  

基于神经网络的机器人抛光材料去除提升模型

An improved material removal model for robot polishing based on neural networks
作者单位
1 复旦大学 光科学与工程系 上海超精密光学制造工程技术研究中心, 上海 200433
2 中国科学院长春光学精密机械与物理研究所 应用光学国家重点实验室, 吉林 长春 130033
引用该论文

余熠, 孔令豹, 张海涛, 徐敏, 王丽萍. 基于神经网络的机器人抛光材料去除提升模型[J]. 红外与激光工程, 2019, 48(3): 0317005.

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): 0317005.

参考文献

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

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

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

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

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

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

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

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

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

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

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

[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.

余熠, 孔令豹, 张海涛, 徐敏, 王丽萍. 基于神经网络的机器人抛光材料去除提升模型[J]. 红外与激光工程, 2019, 48(3): 0317005. 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): 0317005.

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!