Prediction of surface roughness in end milling using swarm intelligence

被引：17
|
作者：
El-Mounayri, H [1 ]
Dugla, Z [1 ]
Deng, HY [1 ]
机构：
[1] Purdue Sch Engn & Technol, Dept Mech Engn, Indianapolis, IN USA
来源：
PROCEEDINGS OF THE 2003 IEEE SWARM INTELLIGENCE SYMPOSIUM (SIS 03) | 2003年
关键词：
D O I：
10.1109/SIS.2003.1202272
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A new technique from EC (Evolutionary Is Computation), PSO (Particle Swarm Optimization), : implemented to model the end milling process and predict the resulting surface roughness. Data collected from CNC cutting experiments using DOE approach. Data used for model calibration and validation. The inputs to the model consist of Feed, Speed and Depth of cut while the output from the model is surface roughness. The model is validated through a comparison of the experimental values with their predicted counterparts. A good agreement is found. The proved technique opens the door for a new, simple and efficient approach that could be applied to the calibration of other empirical models of machining.
引用
收藏
页码：220 / 227
页数：8
相关论文
共 50 条
  • [1] Surface roughness prediction in end milling process using intelligent systems
    Sharkawy, Abdel Badie
    El-Sharief, Mahmoud A.
    Soliman, M-Emad S.
    [J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2014, 5 (01) : 135 - 150
  • [2] Prediction of surface roughness of end milling operation using genetic algorithm
    Mahesh, G.
    Muthu, S.
    Devadasan, S. R.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2015, 77 (1-4): : 369 - 381
  • [3] Surface roughness prediction in end milling process using intelligent systems
    Abdel Badie Sharkawy
    Mahmoud A. El-Sharief
    M-Emad S. Soliman
    [J]. International Journal of Machine Learning and Cybernetics, 2014, 5 : 135 - 150
  • [4] Prediction of surface roughness of end milling operation using genetic algorithm
    G. Mahesh
    S. Muthu
    S. R. Devadasan
    [J]. The International Journal of Advanced Manufacturing Technology, 2015, 77 : 369 - 381
  • [5] Prediction of surface roughness for end milling titanium alloy using modified particle swarm optimization LS-SVM
    Liu, Chunjing
    Tang, Dunbing
    He, Hua
    Chen, Xingqiang
    [J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2013, 30 (01) : 53 - 61
  • [6] PREDICTION OF SURFACE ROUGHNESS FOR END MILLING TITANIUM ALLOY USING MODIFIED PARTICLE SWARM OPTIMIZATION LS-SVM
    刘春景
    唐敦兵
    何华
    陈兴强
    [J]. Transactions of Nanjing University of Aeronautics & Astronautics, 2013, 30 (01) : 53 - 61
  • [7] Vision based prediction of surface roughness for end milling
    Patel, Dhiren R.
    Kiran, M. B.
    [J]. MATERIALS TODAY-PROCEEDINGS, 2021, 44 : 792 - 796
  • [8] Prediction and Analysis of the Surface Roughness in CNC End Milling Using Neural Networks
    Chen, Cheng-Hung
    Jeng, Shiou-Yun
    Lin, Cheng-Jian
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (01):
  • [9] Prediction of surface roughness in the end milling machining using Artificial Neural Network
    Zain, Azlan Mohd
    Haron, Habibollah
    Sharif, Safian
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (02) : 1755 - 1768
  • [10] Prediction of Surface Topomorphy and Roughness in Ball-End Milling
    [J]. Antoniadis, A. (antoniadis@chania.teicrete.gr), 1600, Springer-Verlag London Ltd (21):
12345