Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network

被引:1
|
作者
Cai, X. J. [1 ]
Liu, Z. Q. [1 ]
Wang, Q. C. [1 ]
Han, S. [1 ]
An, Q. L. [1 ]
Chen, M. [1 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China
来源
HIGH SPEED MACHINING | 2011年 / 188卷
关键词
Surface roughness; Artificial neural network (ANN); Free machining steel;
D O I
10.4028/www.scientific.net/AMR.188.535
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was R-a. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting R-a with a mean squared error 5.46%, was presented.
引用
收藏
页码:535 / 541
页数:7
相关论文
共 50 条
  • [31] Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network
    Boukezzi F.
    Noureddine R.
    Benamar A.
    Noureddine F.
    Boukezzi, Farid (f_boukezzi@yahoo.com), 1600, Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland (26): : 567 - 583
  • [32] Mathematical modeling and optimization of surface roughness in turning of polyamide based on artificial neural network
    Madic, M.
    Marinkovic, V.
    Radovanovic, M.
    MECHANIKA, 2012, (05): : 574 - 581
  • [33] Neural network prediction of surface roughness in milling of AISI 1040 steel
    Topal, E. S.
    Sinanoglu, C.
    Gercekcioglu, E.
    Yildizli, K.
    JOURNAL OF THE BALKAN TRIBOLOGICAL ASSOCIATION, 2007, 13 (01): : 18 - 23
  • [34] Surface Roughness Modeling and Prediction Based on Vibration Signal Analysis and Machining Parameters in Milling of Aluminum by Artificial Neural Network
    Abdelwahab S.A.
    Journal of The Institution of Engineers (India): Series C, 2023, 104 (02) : 345 - 375
  • [35] COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORK AND MULTIPLE REGRESSION FOR THE PREDICTION OF SUPERFICIAL ROUGHNESS IN DRY TURNING
    Morales-Tamayo, Yoandrys
    Zamora-Hernandez, Yusimit
    Vasquez-Carrera, Paco
    Porras-Vasconez, Mario
    Barzaga-Quesada, Joao
    Lopez-Bustamante, Ringo
    INGENIUS-REVISTA DE CIENCIA Y TECNOLOGIA, 2018, (19): : 79 - 88
  • [36] Prediction of Machining Force and Surface Roughness in Ultrasonic Vibration-Assisted Turning Using Neural Networks
    Soleimanimehr, H.
    Nategh, M. J.
    Amini, S.
    ADVANCES IN MATERIALS AND PROCESSING TECHNOLOGIES, PTS 1 AND 2, 2010, 83-86 : 326 - +
  • [37] Modelling and Prediction of Effect of Machining Parameters on Surface Roughness in Turning Operations
    Ozdemir, Mustafa
    TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2020, 27 (03): : 751 - 760
  • [38] Surface roughness prediction of electrical discharge machining ceramics based on evolutionary neural network
    Xu, Xiaoqing
    Luo, Zhigao
    Xu, Dapeng
    Ding, Shengyin
    Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, 2007, 38 (03): : 164 - 167
  • [39] Neural Network Based Quality Increase Of Surface Roughness Results In Free Form Machining
    Korosec, Marjan
    Duhovnik, Joze
    Kopac, Janez
    AUTOMATIKA, 2010, 51 (01) : 71 - 78
  • [40] Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
    Rashid, M. F. F. Ab.
    Lani, M. R. Abdul
    WORLD CONGRESS ON ENGINEERING, WCE 2010, VOL III, 2010, : 2219 - 2224