Prediction and control of weld bead geometry and shape relationships in submerged arc welding of pipes

被引:128
|
作者
Murugan, N [1 ]
Gunaraj, V
机构
[1] Coimbatore Inst Technol, Coimbatore 641014, Tamil Nadu, India
[2] Kumaraguru Coll Technol, Coimbatore 641006, Tamil Nadu, India
关键词
weld bead geometry; arc welding; fabrication;
D O I
10.1016/j.jmatprotec.2005.03.001
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
To automate a welding process, which is the present trend in fabrication industry, it is essential that mathematical models have to be developed to relate the process variables to the weld bead parameters. Because of its high reliability, deep penetration, smooth finish and high productivity, submerged arc welding (SAW) has become a natural choice in industries for fabrication, especially for welding of pipes. Mathematical models have been developed for SAW of pipes using five level factorial techniques to predict three critical dimensions of the weld bead geometry and shape relationships. The models developed have been checked for their adequacy and significance by using the F-test and the t-test, respectively. Main and interaction effects of the process variables on bead geometry and shape factors are presented in graphical form and using which not only the prediction of important weld bead dimensions and shape relationships but also the controlling of the weld bead quality by selecting appropriate process parameter values are possible. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码:478 / 487
页数:10
相关论文
共 50 条
  • [21] Prediction of weld bead geometry of MAG welding based on XGBoost algorithm
    Kai Chen
    Huabin Chen
    Liang Liu
    Shanben Chen
    The International Journal of Advanced Manufacturing Technology, 2019, 101 : 2283 - 2295
  • [22] Neuro hybrid model to predict weld bead width in submerged arc welding process
    Dhas, J. Edwin Raja
    Kumanan, Somasundaram
    JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH, 2010, 69 (05): : 350 - 355
  • [23] Prediction of weld bead geometry of MAG welding based on XGBoost algorithm
    Chen, Kai
    Chen, Huabin
    Liu, Liang
    Chen, Shanben
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2019, 101 (9-12): : 2283 - 2295
  • [24] RELATIONSHIP BETWEEN SUBMERGED-ARC WELDING PARAMETERS AND WELD BEAD SIZE.
    Chandel, Roop S.
    Bala, Satish R.
    Schweissen und Schneiden/Welding and Cutting, 1988, 40 (02): : 28 - 31
  • [25] Prediction and optimization of weld bead volume for the submerged arc process - Part 2
    Gunaraj, V
    Murugan, N
    WELDING JOURNAL, 2000, 79 (11) : 331S - 338S
  • [26] Prediction and optimization of weld bead volume for the submerged arc process - Part 2
    Gunaraj, V., 2000, American Welding Soc, Miami, FL, United States (79):
  • [27] Prediction and optimization of weld bead geometry in oscillating arc narrow gap all-position GMA welding
    W. H. Xu
    S. B. Lin
    C. L. Fan
    C. L. Yang
    The International Journal of Advanced Manufacturing Technology, 2015, 79 : 183 - 196
  • [28] Prediction and optimization of weld bead volume for the submerged arc process - Part 1
    Gunaraj, V
    Murugan, N
    WELDING JOURNAL, 2000, 79 (10) : 286S - 294S
  • [29] RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)
    Ali N. Ahmed
    C. W. Mohd Noor
    Mohammed Falah Allawi
    Ahmed El-Shafie
    Neural Computing and Applications, 2018, 29 : 889 - 899
  • [30] Prediction and optimization of weld bead geometry in oscillating arc narrow gap all-position GMA welding
    Xu, W. H.
    Lin, S. B.
    Fan, C. L.
    Yang, C. L.
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2015, 79 (1-4): : 183 - 196