Prediction Model of Milling Surface Roughness Based on Genetic Algorithms

被引：3
|
作者：
Chen, Ying [1 ]
Sun, Yanhong [1 ]
Lin, Han [1 ]
Zhang, Bing [1 ]
机构：
[1] Jilin Teachers Inst Engn & Technol, Changchun 130000, Jilin, Peoples R China
来源：
CYBER SECURITY INTELLIGENCE AND ANALYTICS | 2020年 / 928卷
关键词：
Surface roughness; High speed milling; Genetic algorithm; Prediction model;
D O I：
10.1007/978-3-030-15235-2_179
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
According to the orthogonal test results, the surface roughness prediction model based on BP artificial neural network algorithm combined with genetic algorithm and considering material removal rate, a multi-objective optimization mathematical model for high-speed milling process parameters optimization was established, and the optimal combination of parameters satisfying the requirements was found within the given parameters range. The method is validated by comparing the surface roughness and processing efficiency with the optimization parameters determined by range analysis method.
引用
收藏
页码：1315 / 1320
页数：6
相关论文
共 50 条
  • [21] Research on the prediction model of micro-milling surface roughness of Inconel718 based on SVM
    Lu, Xiaohong
    Hu, Xiaochen
    Wang, Hua
    Si, Likun
    Liu, Yongyun
    Gao, Lusi
    [J]. INDUSTRIAL LUBRICATION AND TRIBOLOGY, 2016, 68 (02) : 206 - 211
  • [22] Prediction model of the surface roughness of micro-milling single crystal copper
    Lu, Xiaohong
    Xue, Liang
    Ruan, Feixiang
    Yang, Kun
    Liang, Steven Y.
    [J]. JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2019, 33 (11) : 5369 - 5374
  • [23] A genetic algorithmic approach for optimization of surface roughness prediction model
    Suresh, PVS
    Rao, PV
    Deshmukh, SG
    [J]. INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2002, 42 (06): : 675 - 680
  • [24] Surface roughness prediction method of titanium alloy milling based on CDH platform
    Liu, Xianli
    Sun, Yanming
    Yue, Caixu
    Wei, Xudong
    Sun, Qingzhen
    Liang, Steven Y.
    Wang, Lihui
    Qin, Yiyuan
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2022, 119 (11-12): : 7145 - 7157
  • [25] Prediction of surface roughness of end milling for cycloidal gears based on orthogonal tests
    Luo S.-M.
    Liao L.-X.
    Mo J.-Y.
    [J]. Luo, Shan-Ming (smluo@xmut.edu.cn), 2018, Polska Akademia Nauk (66): : 339 - 352
  • [26] Study on the Prediction Method of Milling Surface Roughness Based on Cutting Kinematics Analysis
    Guo, Guoqiang
    Yang, Boyu
    Li, Jianhua
    Cheng, Qunlin
    Wang, Dazhong
    Lin, Lifang
    Shen, Bin
    [J]. Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2023, 59 (13): : 314 - 324
  • [27] Surface roughness prediction method of titanium alloy milling based on CDH platform
    Xianli Liu
    Yanming Sun
    Caixu Yue
    Xudong Wei
    Qingzhen Sun
    Steven Y. Liang
    Lihui Wang
    Yiyuan Qin
    [J]. The International Journal of Advanced Manufacturing Technology, 2022, 119 : 7145 - 7157
  • [28] Surface roughness prediction of end milling process based on IPSO-LSSVM
    Duan, Chunzheng
    Hao, Qinglong
    [J]. JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING, 2014, 8 (03):
  • [29] Neural-Network Prediction of the Surface Roughness in Milling
    Erygin E.V.
    Duyun T.A.
    Korop A.D.
    [J]. Russian Engineering Research, 2023, 43 (01) : 84 - 87
  • [30] Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning
    Zeng, Shi
    Pi, Dechang
    [J]. SENSORS, 2023, 23 (10)
12345