Application of the Extreme Learning Machine for Modeling the Bead Geometry in Gas Metal Arc Welding Process

Document Type : Research Article


1 Department of Mechanical Engineering, Birjand University of Technology

2 Departmentof Mechanical Engineering, Birjand University of Technology, Birjand, Iran

3 Department of Computer Engineering, Birjand University of Technology, Birjand, Iran

4 Department of Mechanical Engineering, University of Birjand, Birjand, Iran


Gas metal arc welding (GMAW) is a widespread process used for rapid prototyping of metallic parts. In this process, in order to obtain a desired welding geometry, it is very important to predict the weld bead geometry based on the input process parameters, which are voltage, wire feed rate, welding speed and welding nozzle angle. For this purpose, a global model of the welding geometry must be defined based on these parameters. Due to the non-linear and coupled multivariable relationship between the process parameters and the weld bead geometry, it is not possible to define this model in form of an explicit mathematical expression, and therefore application of supervised learning algorithms can be investigated as an efficient alternative in this problem. In this paper, application of the extreme learning machine (ELM) and support vector machine (SVM), as two efficient and powerful machine learning algorithms for predictive modelling of this process has been investigated and error analysis of the proposed models suggest that the output parameters of this process can be predicted by the ELM algorithm with higher precision and generalization capability.


dor 20.1001.1.25882953.2019.

Main Subjects

[1] J. Xiong, G. Zhang, J. Hu, L. Wu, Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis, Journal of Intelligent Manufacturing, 25(1) (2014) 157-163.
[2] Y. Zhang, Y. Chen, P. Li, A.T. Male, Weld deposition-based rapid prototyping: a preliminary study, Journal of Materials Processing Technology, 135(2) (2003) 347-357.
[3] Y.-A. Song, S. Park, Experimental investigations into rapid prototyping of composites by novel hybrid deposition process, Journal of Materials Processing Technology, 171(1) (2006) 35-40.
[4] K. Karunakaran, S. Suryakumar, V. Pushpa, S. Akula, Retrofitment of a CNC machine for hybrid layered manufacturing, The International Journal of Advanced Manufacturing Technology, 45(7) (2009) 690-703.
[5] I. Kim, J. Son, C. Park, C. Lee, Y.K. Prasad, A study on prediction of bead height in robotic arc welding using a neural network, Journal of Materials Processing Technology, 130 (2002) 229-234.
[6] F. Kollahan, M. Hydari, Modeling and optimization of Gas Metal Arc Welding process using statistical methods and Simulated Annealing algorithm, Journal of Mechanical Eng., 40(1) (2010) 4.
[7] W. Huang, R. Kovacevic, A neural network and multiple regression method for the characterization of the depth of weld penetration in laser welding based on acoustic signatures, Journal of Intelligent Manufacturing, 22(2) (2011) 131-143.
[8] M.A. Moghaddam, R. Golmezergi, F. Kolahan, Multi-variable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNN–PSO approach, Measurement, 92 (2016) 279-287.
[9] J.S. Panchagnula, S. Simhambhatla, Manufacture of complex thin-walled metallic objects using weld-deposition based additive manufacturing, Robotics and Computer-Integrated Manufacturing, 49 (2018) 194-203.
[10]J. Xiong, Y. Lei, H. Chen, G. Zhang, Fabrication of inclined thin-walled parts in multi-layer single-pass GMAW-based additive manufacturing with flat position deposition, Journal of Materials Processing Technology, 240 (2017) 397-403.
[11]A. Olabi, F. Alsinani, A. Alabdulkarim, A. Ruggiero, L. Tricarico, K. Benyounis, Optimizing the CO2 laser welding process for dissimilar materials, Optics and Lasers in Engineering, 51(7) (2013) 832-839.
[12]V.V. Murugan, V. Gunaraj, Effects of process parameters on angular distortion of gas metal arc welded structural steel plates, Welding journal, 84(11) (2005) 165-171.
[13]K. Li, Y. Zhang, Consumable double-electrode GMAW-Part 1: The process, WELDING JOURNAL-NEW YORK-, 87(1) (2008) 11.
[14]L. Yang, R. Chandel, M. Bibby, An analysis of curvilinear regression equations for modeling the submerged-arc welding process, Journal of Materials Processing Technology, 37(1-4) (1993) 601-611.
[15] S. Kumanan, J. Dhas, K. Gowthaman, Determination of submerged arc welding process parameters using Taguchi method and regression analysis, (2007).
[16]D. Kim, S. Rhee, H. Park, Modelling and optimization of a GMA welding process by genetic algorithm and response surface methodology, International Journal of Production Research, 40(7) (2002) 1699-1711.
[17]M. Sen, M. Mukherjee, T. Pal, Evaluation of correlations between DPGMAW process parameters and bead geometry, Weld. J, 94(8) (2015) 265s-279s.
[18]R.P. Singh, R. Garg, D.K. Shukla, Mathematical modeling of effect of polarity on weld bead geometry in submerged arc welding, Journal of Manufacturing Processes, 21 (2016) 14-22.
[19]A. Babkin, E. Gladkov, Identification of Welding Parameters for Quality Welds in GMAW, Welding Journal, (2016).
[20]R.C. Deo, M. ┼×ahin, Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia, Atmospheric Research, 153 (2015) 512-525.
[21]G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: theory and applications, Neurocomputing, 70(1-3) (2006) 489-501.
[22]G.-B. Huang, X. Ding, H. Zhou, Optimization method based extreme learning machine for classification, Neurocomputing, 74(1-3) (2010) 155-163.
[23]G.-B. Huang, H. Zhou, X. Ding, R. Zhang, Extreme learning machine for regression and multiclass classification, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2) (2012) 513-529.
[24]C. Wan, Z. Xu, P. Pinson, Z.Y. Dong, K.P. Wong, Probabilistic forecasting of wind power generation using extreme learning machine, IEEE Transactions on Power Systems, 29(3) (2014) 1033-1044.
[25]V.N. Vapnik, V. Vapnik, Statistical learning theory, Wiley New York, 1998.
[26]C. Cortes, V. Vapnik, Support-vector networks, Machine learning, 20(3) (1995) 273-297.
[27]D. Meyer, F. Leisch, K. Hornik, The support vector machine under test, Neurocomputing, 55(1) (2003) 169-186.
[28]N. Cristianini, J. Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, Cambridge university press, 2000
[29] F. Parrella, "Online support vector regression," Master's Thesis, Department of Information Science, University of Genoa, Italy, 2007.
[30]MATLAB Codes of ELM Algorithm (for ELM with kernels), in, 2018.
[31]K. Pelckmans, J.A.K. Suykens, T.V. Gestel, J.D. Brabanter, L. Lukas, B. Hamers, B.D. Moor, J. Vandewalle, LS-SVMlab: a matlab/c toolbox for least squares support vector machines, Tutorial. KULeuven-ESAT. Leuven, Belgium, 142 (2002) 1-2.