Optimisation method for NC machining parameters of mechanical mould based on artificial neural network

被引:0
|
作者
Wen R. [1 ]
机构
[1] Department of Mechanical Engineering, Sichuan Vocational College of Chemical Technology, Luzhou
关键词
artificial neural network; data processing; mechanical mould; objective function; parameter optimisation;
D O I
10.1504/IJMTM.2022.123662
中图分类号
学科分类号
摘要
In order to overcome the problems of low production profit and high processing cost existing in traditional methods, an optimisation method for NC machining parameters of mechanical mould based on artificial neural network is proposed. Considering the cutting speed, feed rate, cutting depth, machine power and spindle speed in the process of NC machining of mechanical mould, the maximum profit, minimum processing cost and maximum productivity are taken as the optimisation objectives, and the objective function of NC machining parameters optimisation of mechanical mould is constructed. The NC machining parameters of mechanical mould are taken as the input of parameter optimisation model, and the artificial neural network is used to solve the model. The experimental results show that the proposed method has high production profit, low processing cost, high productivity and good practical application effect. Copyright © 2022 Inderscience Enterprises Ltd.
引用
收藏
页码:168 / 182
页数:14
相关论文
共 50 条
  • [41] Parametric optimisation of wire electrical discharge machining of γ titanium aluminide alloy through an artificial neural network model
    S. Sarkar
    S. Mitra
    B. Bhattacharyya
    The International Journal of Advanced Manufacturing Technology, 2006, 27 : 501 - 508
  • [42] Online learning method based on artificial neural network to optimize magnetic shielding characteristic parameters
    Peng Xiang-Kai
    Ji Jing-Wei
    Li Lin
    Ren Wei
    Xiang Jing-Feng
    Liu Kang-Kang
    Cheng He-Nan
    Zhang Zhen
    Qu Qiu-Zhi
    Li Tang
    Liu Liang
    Lu De-Sheng
    ACTA PHYSICA SINICA, 2019, 68 (13)
  • [43] Parametric optimisation of wire electrical discharge machining of γ titanium aluminide alloy through an artificial neural network model
    Sarkar, S
    Mitra, S
    Bhattacharyya, B
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2006, 27 (5-6): : 501 - 508
  • [44] Surface roughness modeling of high speed machining TC4 based on artificial neural network method
    Liu, Zhixin
    Zhang, Dawei
    Qi, Houjun
    ISSCAA 2006: 1ST INTERNATIONAL SYMPOSIUM ON SYSTEMS AND CONTROL IN AEROSPACE AND ASTRONAUTICS, VOLS 1AND 2, 2006, : 920 - +
  • [45] Laser cutting parameters optimization based on artificial neural network
    Guo Dixin
    Chen Jimin
    Cheng Yuhong
    2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10, 2006, : 1106 - +
  • [46] Unbalance Rotor Parameters Detection Based on Artificial Neural Network
    Gohari, Mohammad
    Kord, Ahmad
    INTERNATIONAL JOURNAL OF ACOUSTICS AND VIBRATION, 2019, 24 (01): : 113 - 118
  • [47] Artificial neural network based on genetic learning for machining of polyetheretherketone composite materials
    C. A. Conceição António
    J. Paulo Davim
    Vítor Lapa
    The International Journal of Advanced Manufacturing Technology, 2008, 39 : 1101 - 1110
  • [48] Artificial neural network based on genetic learning for machining of polyetheretherketone composite materials
    Conceicao Antonio, C. A.
    Davim, J. Paulo
    Lapa, Vitor
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2008, 39 (11-12): : 1101 - 1110
  • [49] Particle swarm optimisation for evolving artificial neural network
    Zhang, CL
    Shao, HH
    Li, Y
    SMC 2000 CONFERENCE PROCEEDINGS: 2000 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOL 1-5, 2000, : 2487 - 2490
  • [50] Optimisation of machining parameters using Hopfield-type neural networks
    Department of Industrial Engineering, Dokuz Eylül University, Buca-Izmir 35160, Turkey
    Int. J. Ind. Syst. Eng., 2013, 4 (462-479):