Research on adaptive grinding of curved optical parts based on neural network control strategy

被引：0
作者：
Xie Mingli [1 ,2 ]
Pan Yipeng [1 ,2 ]
Li Zhipeng [1 ]
Zheng Xuhang [1 ]
An Zijun [1 ]
Dong Min [1 ,2 ]
机构：
[1] Yanshan Univ, Coll Mech Engn, Qinhuangdao 066004, Hebei, Peoples R China
[2] Hebei Innovat Ctr Equipment Lightweight Design &, Qinhuangdao, Hebei, Peoples R China
来源：
SEVENTH ASIA PACIFIC CONFERENCE ON OPTICS MANUFACTURE (APCOM 2021) | 2022年 / 12166卷
关键词：
adaptive grinding; curved optical parts; neural network; control strategy; CONTROL OPTIMIZATION; PREDICTION; QUALITY; FORCE;
D O I：
10.1117/12.2605339
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Taking the grinding process of curved optical parts as the research object, a neural network based the adaptive control method is proposed. MATALAB is used to design the controller and simulate the grinding process. The results show that the method can effectively solve the fluctuation of the grinding force in the grinding process, and the controller has good dynamic characteristics, The purpose of the optimal material cutting rate in the grinding process is realized, and the surface processing quality of optical parts is improved.
引用
收藏
页数：6
相关论文
共 12 条
  • [1] Adaptive control optimization in micro-milling of hardened steels-evaluation of optimization approaches
    Coppel, Ricardo
    Abellan-Nebot, Jose V.
    Siller, Hector R.
    Rodriguez, Ciro A.
    Guedea, Federico
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2016, 84 (9-12) : 2219 - 2238
  • [2] Erkorkmaz K, 2015, CONTROL MACHINING PR, P106
  • [3] Surface grinding machine with a linear-motor-driven table system: Development and performance test
    Inasaki, I
    [J]. CIRP ANNALS 1999 - MANUFACTURING TECHNOLOGY, 1999, : 243 - 246
  • [4] Adaptive Control of Drillstring Torsional Oscillations
    Kabzinski, Jacek
    [J]. IFAC PAPERSONLINE, 2017, 50 (01): : 13360 - 13365
  • [5] Fuzzy-logic control of cutting forces in CNC milling processes using motor currents as indirect force sensors
    Kim, Dohyun
    Jeon, Doyoung
    [J]. PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY, 2011, 35 (01): : 143 - 152
  • [6] Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method
    Klancnik, Simon
    Begic-Hajdarevic, Derzija
    Paulic, Matej
    Ficko, Mirko
    Cekic, Ahmet
    Husic, Maida Cohodar
    [J]. STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING, 2015, 61 (12): : 714 - 720
  • [7] Intelligent machine agent architecture for adaptive control optimization of manufacturing processes
    Kruger, Grant H.
    Shih, Albert J.
    Hattingh, Danie G.
    van Niekerk, Theo I.
    [J]. ADVANCED ENGINEERING INFORMATICS, 2011, 25 (04) : 783 - 796
  • [8] Self-adaptive control of shearer based on cutting resistance recognition
    Liu, Yonggang
    Hou, Liliang
    Qin, Datong
    Zhang, Yi
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2018, 94 (9-12) : 3553 - 3561
  • [9] Prediction model and simulation of cutting force in turning hard-brittle materials
    Ma, Lianjie
    Li, Chen
    Chen, Jie
    Li, Wei
    Tan, Yanqing
    Wang, Chao
    Zhou, Yunguang
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2017, 91 (1-4) : 165 - 174
  • [10] Vrabel' M, 2014, APPL MECH MATER, V474, P212, DOI 10.4028/www.scientific.net/AMM.474.212
12