Neural network modeling for face milling operation

被引：0
|
作者：
Sureshkumar B. [1 ]
Vijayan V. [1 ]
Dinesh S. [1 ]
Rajaguru K. [1 ]
机构：
[1] Dept. of Mech. Engg., K. Ramakrishnan College of Tech, Samayapuram, Tamil Nadu
来源：
International Journal of Vehicle Structures and Systems | 2019年 / 11卷 / 02期
关键词：
Cutting force; Feed force; Neural network; Surface roughness; Temperature; Thrust force;
D O I：
10.4273/ijvss.11.2.20
中图分类号：
学科分类号：
摘要：
Milling operation is one of the important manufacturing processes in production industry. Study and analysis of milling process parameters such as spindle speed, feed rate and depth of cut are important for process planning engineers. The responses are temperature, surface roughness, machining time, feed force, thrust force and cutting force. The main aim of this study is to find out the effects of these parameters in face milling operation on Monel k 400 work piece materials with tungsten carbide insert. The theoretical investigation is carried out with neural network modelling and the 3-1-6 structure neural network models are considered. Developed neural network models show best agreement with experimental values. For same type of operation, result of these experiments shall be useful for future research purpose. © 2019. MechAero Foundation for Technical Research & Education Excellence.
引用
收藏
页码：214 / 219
页数：5
相关论文
共 50 条
  • [11] Specific cutting force coefficients modeling of end milling by neural network
    Lee, SY
    Lee, JM
    KSME INTERNATIONAL JOURNAL, 2000, 14 (06): : 622 - 632
  • [12] Specific cutting force and cutting condition interaction modeling for round insert face milling operation
    Ghorbani, Hamid
    Moetakef-Imani, Behnam
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2016, 84 (5-8): : 1705 - 1715
  • [13] Specific cutting force and cutting condition interaction modeling for round insert face milling operation
    Hamid Ghorbani
    Behnam Moetakef-Imani
    The International Journal of Advanced Manufacturing Technology, 2016, 84 : 1705 - 1715
  • [14] Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process
    Wu, Xuefeng
    Liu, Yahui
    Zhou, Xianliang
    Mou, Aolei
    SENSORS, 2019, 19 (18)
  • [15] NEURAL-NETWORK PROCESS MODELING OF A CONTINUOUS MANUFACTURING OPERATION
    COOK, DF
    WHITTAKER, AD
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 1993, 6 (06) : 559 - 564
  • [16] Simulation and modeling of milling forces for ball-end milling cutter based on BP neural network
    Zhang, Chen
    Zhou, Rurong
    Zhuang, Haijun
    Zhou, Laishui
    Zhongguo Jixie Gongcheng/China Mechanical Engineering, 2005, 16 (20): : 1791 - 1794
  • [17] Roughness profile in high speed face milling operation
    Louati, H.
    Bouzid, W.
    MATERIALS TECHNOLOGY, 2008, 23 (04) : 231 - 237
  • [18] OPTIMIZATION OF TOOL EDGE CONFIGURATION FOR A FACE MILLING OPERATION
    WORTHINGTON, B
    LEE, RR
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 1981, 19 (04) : 371 - 380
  • [19] A new artificial neural network approach to modeling ball-end milling
    El-Mounayri, Hazim
    Briceno, Jorge F.
    Gadallah, Mohamed
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2010, 47 (5-8): : 527 - 534
  • [20] The selection of milling parameters by the PSO-based neural network modeling method
    Farahnakian, Masoud
    Razfar, Mohammad Reza
    Moghri, Mahdi
    Asadnia, Mohsen
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2011, 57 (1-4): : 49 - 60
12345