Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool

被引:61
|
作者
Ghanty, P. [1 ]
Vasudevan, M. [2 ]
Mukherjee, D. P. [1 ]
Pal, N. R. [1 ]
Chandrasekhar, N. [2 ]
Maduraimuthu, V. [2 ]
Bhaduri, A. K. [2 ]
Barat, P. [3 ]
Raj, B. [2 ]
机构
[1] Indian Stat Inst, Elect & Commun Sci Unit, Kolkata 700108, W Bengal, India
[2] Indira Gandhi Ctr Atom Res, Kalpakkam 603102, Tamil Nadu, India
[3] Ctr Variable Energy Cyclotron, Kolkata 700064, W Bengal, India
关键词
infrared thermal image; weld bead geometry; artificial neural network; multilayer perceptron; radial basis function; online feature selection;
D O I
10.1179/174329308X300118
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this article an artificial neural network based system to predict weld bead geometry using features derived from the infrared thermal video of a welding process is proposed. The multilayer perceptron and radial basis function networks are used in the prediction model and an online feature selection technique prioritises the features used in the prediction model. The efficacy of the system is demonstrated with a number of welding experiments and using the leave one out cross-validation experiments.
引用
收藏
页码:395 / 401
页数:7
相关论文
共 44 条
  • [1] Predicting weld bead width and depth of penetration from infrared thermal image of weld pool using artificial neural network
    Chokkalingham, S.
    Vasudevan, M.
    Sudarsan, S.
    Chandrasekhar, N.
    INSIGHT, 2012, 54 (05) : 272 - 277
  • [2] Predicting the depth of penetration and weld bead width from the infra red thermal image of the weld pool using artificial neural network modeling
    S. Chokkalingham
    N. Chandrasekhar
    M. Vasudevan
    Journal of Intelligent Manufacturing, 2012, 23 : 1995 - 2001
  • [3] Predicting the depth of penetration and weld bead width from the infra red thermal image of the weld pool using artificial neural network modeling
    Chokkalingham, S.
    Chandrasekhar, N.
    Vasudevan, M.
    JOURNAL OF INTELLIGENT MANUFACTURING, 2012, 23 (05) : 1995 - 2001
  • [4] Artificial Neural Network Modeling for Estimating the Depth of Penetration and Weld Bead Width from the Infra Red Thermal Image of the Weld Pool during A-TIG Welding
    Chokkalingham, S.
    Chandrasekhar, N.
    Vasudevan, M.
    SIMULATED EVOLUTION AND LEARNING, 2010, 6457 : 270 - +
  • [5] Intelligent modeling for estimating weld bead width and depth of penetration from infra-red thermal images of the weld pool
    Chandrasekhar, N.
    Vasudevan, M.
    Bhaduri, A. K.
    Jayakumar, T.
    JOURNAL OF INTELLIGENT MANUFACTURING, 2015, 26 (01) : 59 - 71
  • [6] Intelligent modeling for estimating weld bead width and depth of penetration from infra-red thermal images of the weld pool
    N. Chandrasekhar
    M. Vasudevan
    A. K. Bhaduri
    T. Jayakumar
    Journal of Intelligent Manufacturing, 2015, 26 : 59 - 71
  • [7] Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Models for Predicting the Weld Bead Width and Depth of Penetration from the Infrared Thermal Image of the Weld Pool
    L. Subashini
    M. Vasudevan
    Metallurgical and Materials Transactions B, 2012, 43 : 145 - 154
  • [8] Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Models for Predicting the Weld Bead Width and Depth of Penetration from the Infrared Thermal Image of the Weld Pool
    Subashini, L.
    Vasudevan, M.
    METALLURGICAL AND MATERIALS TRANSACTIONS B-PROCESS METALLURGY AND MATERIALS PROCESSING SCIENCE, 2012, 43 (01): : 145 - 154
  • [9] weld pool weld width prediction based on artificial neural network
    Liu Xiaogang
    Liu Leting
    PROGRESS IN MECHATRONICS AND INFORMATION TECHNOLOGY, PTS 1 AND 2, 2014, 462-463 : 171 - +
  • [10] Determining penetration from topside weld bead and weld pool geometry in PGMAW
    Yan, ZH
    Zhang, GJ
    Gao, HM
    Wu, L
    SCIENCE AND TECHNOLOGY OF WELDING AND JOINING, 2005, 10 (06) : 744 - 749