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
  • [1] Optimization of Machining Parameters Based on Principal Component Analysis and Artificial Neural Network
    Yuan, Lei
    Zeng, Shasha
    PROCEEDINGS OF 2019 5TH INTERNATIONAL CONFERENCE ON MECHATRONICS AND ROBOTICS ENGINEERING (ICMRE 2019), 2019, : 46 - 49
  • [2] Optimal Selection of Cutting Parameters in Blade NC Machining Based on BP Neural Network and Genetic Algorithm
    Cao, Yan
    Dong, Xuejiao
    Du, Jiang
    FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE IV, PTS 1-5, 2014, 496-500 : 1539 - 1542
  • [3] Optimisation of spark erosion machining process parameters using hybrid grey relational analysis and artificial neural network model
    Manikandan N.
    Raju R.
    Palanisamy D.
    Binoj J.S.
    International Journal of Machining and Machinability of Materials, 2020, 22 (01) : 1 - 23
  • [4] Identification of Mechanical Parameters of Kyeongju Bentonite Based on Artificial Neural Network Technique
    Kim, Minseop
    Lee, Seungrae
    Yoon, Seok
    Jeon, Min-Kyung
    JOURNAL OF NUCLEAR FUEL CYCLE AND WASTE TECHNOLOGY, 2022, 20 (03): : 269 - 278
  • [5] Artificial neural network based model for computation of injection mould complexity
    Rawin Raviwongse
    Venkat Allada
    The International Journal of Advanced Manufacturing Technology, 1997, 13 : 577 - 586
  • [6] Artificial neural network based model for computation of injection mould complexity
    Raviwongse, R
    Allada, V
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 1997, 13 (08) : 577 - 586
  • [7] Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network
    Lin, Yung-Chih
    Wu, Kung-Da
    Shih, Wei-Cheng
    Hsu, Pao-Kai
    Hung, Jui-Pin
    APPLIED SCIENCES-BASEL, 2020, 10 (11):
  • [8] Optimisation of pedotransfer functions using an artificial neural network ensemble method
    Baker, L.
    Ellison, D.
    GEODERMA, 2008, 144 (1-2) : 212 - 224
  • [9] Fast Prediction Method of Combustion Chamber Parameters Based on Artificial Neural Network
    Shao, Chenhuzhe
    Liu, Yue
    Zhang, Zhedian
    Lei, Fulin
    Fu, Jinglun
    Rossello, Josep L.
    ELECTRONICS, 2023, 12 (23)
  • [10] Application Of Artificial Neural Network Modeling For Machining Parameters Optimization In Drilling Operation
    Kannan, T. Deepan Bharathi
    Kannan, G. Rajesh
    Kumar, B. Suresh
    Baskar, N.
    INTERNATIONAL CONFERENCE ON ADVANCES IN MANUFACTURING AND MATERIALS ENGINEERING (ICAMME 2014), 2014, 5 : 2242 - 2249