Prediction of weld formation in 5083 aluminum alloy by twin-wire CMT welding based on deep learning

  • Limeng YinEmail author
  • Jinzhao Wang
  • Huiqin Hu
  • Shanguo Han
  • Yupeng Zhang
Research Paper


Based on a large amount of experimental data from twin-wire CMT welding of 5083 aluminum alloy, deep neural network technology was adopted to analyze the welding process parameters and the weld dimensions, and a precise prediction model for the weld formation parameters was established. The results show that the key parameters influencing the prediction accuracy of the twin-wire CMT deep neural network model are the number of hidden layer neurons, the number of network training iterations, and the learning rate of the deep network. For a single factor, regardless of the weld width, weld penetration or weld reinforcement, the predicted value curve changes smoothly and without distortion from the measured value curve. The accuracy of the complex nonlinear model can be evaluated by linear regression analyses of the predicted data and the measured data. In addition, the deep neural network has the obvious advantages of high efficiency and precision due to its strong multi-dimensional nonlinear fitting abilities in the quantitative analysis of the arc welding system from the input welding parameters to the output weld dimensions. This model can provide data support and scientific reference for the process designs for 5083 aluminum alloy twin-wire CMT welding or additive manufacturing and the determination of the numerically calculated heat source size. Also, this model provides innovative ideas for the application of deep learning technology in the welding field.


Twin-wire CMT welding 5083 aluminum alloy Deep learning Neural network Formation prediction 


Funding information

This research was supported by the National Natural Science Foundation of China (No. 51674056), the Frontier and Applied Basic Research Projects of Chongqing (No. cstc2018jcyjAX0108), the Opening Project of Materials Corrosion and Protection Key Laboratory of Sichuan Province (No. 2016CL15), and the Science and Technology Planning Project of Guangdong Province (2017A010106007, 2017A070701026, 2017GDASCX-0113, 2018GDASCX-0803).


  1. 1.
    Xiang T, Li H, Wei HL, Gao Y (2016) Effects of filling status of cold wire on the welding process stability in twin-arc integrated cold wire hybrid welding. Int J Adv Manuf Technol 83(9–12):1583–1593CrossRefGoogle Scholar
  2. 2.
    Ahsan MRU, Cheepu M, Kim TH, Jeong C, Park YD (2017) Mechanisms of weld pool flow and slag formation location in cold metal transfer (CMT) gas metal arc welding (GMAW). Weld World 61(2):1–11Google Scholar
  3. 3.
    Shi C, Zou Y, Zou Z, Wu D (2014) Twin-wire indirect arc welding by modeling and experiment. J Mater Process Technol 214(11):2292–2299CrossRefGoogle Scholar
  4. 4.
    Xiong J, Zhang G, Zhang W (2015) Forming appearance analysis in multi-layer single-pass GMAW-based additive manufacturing. Int J Adv Manuf Technol 80(9–12):1767–1776CrossRefGoogle Scholar
  5. 5.
    Ghosh A, Yadav A, Kumar A (2017) Modelling and experimental validation of moving tilted volumetric heat source in gas metal arc welding process. J Mater Process Technol 239:52–65CrossRefGoogle Scholar
  6. 6.
    Kiran DV, Cho DW, Song WH, Na SJ (2014) Arc behavior in two wire tandem submerged arc welding. J Mater Process Technol 214(8):1546–1556CrossRefGoogle Scholar
  7. 7.
    Heaton J (2017) Ian goodfellow, yoshua bengio, and aaron courville: deep learning. Genet Program Evolvable Mach 19(1–2):1–3Google Scholar
  8. 8.
    Karimzadeh F, Ebnonnasir A, Foroughi A (2006) Artificial neural network modeling for evaluating of epitaxial growth of Ti6Al4V weldment. Mater Sci Eng A 432(1):184–190CrossRefGoogle Scholar
  9. 9.
    Günther J, Pilarski PM, Helfrich G, Shen H, Diepold K (2014) First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning. Procedia Technol 15:474–483CrossRefGoogle Scholar
  10. 10.
    Nagesh DS, Datta GL (2002) Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks. J Mater Process Technol 123(2):303–312CrossRefGoogle Scholar
  11. 11.
    Chollet F et al (2015) Keras. GitHub repository. Google Scholar
  12. 12.
    Montes-Atenas G, Seguel F, Valencia A, Bhatti SM, Khan MS, Soto I, Yoma NB (2016) Predicting bubble size and bubble rate data in water and in froth flotation-like slurry from computational fluid dynamics (CFD) by applying deep neural networks (DNN). Int Commun Heat Mass Transfer 76:197–201CrossRefGoogle Scholar
  13. 13.
    Britto ASF, Raj RE, Mabel MC (2017) Prediction of shear and tensile strength of the diffusion bonded AA5083 and AA7075 aluminium alloy using ANN. Mater Sci Eng A 692:1–8CrossRefGoogle Scholar
  14. 14.
    Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958Google Scholar
  15. 15.
    Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures, neural networks: tricks of the trade. In: Montavon G, Orr GB, Müller KR (eds) Lecture notes in computer science. Springer, Berlin, pp 437–478Google Scholar
  16. 16.
    Nielsen MA (2015) Neural networks and deep learning. Determination Press
  17. 17.
    Lee J, Um K (2000) A comparison in a back-bead prediction of gas metal arc welding using multiple regression analysis and artificial neural network. Opt Lasers Eng 34(3):149–158CrossRefGoogle Scholar
  18. 18.
    Fan J, Huang LS (2001) Goodness-of-fit tests for parametric regression models. J Am Stat Assoc 96(454):640–652CrossRefGoogle Scholar

Copyright information

© International Institute of Welding 2019

Authors and Affiliations

  • Limeng Yin
    • 1
    • 2
    Email author
  • Jinzhao Wang
    • 1
    • 2
  • Huiqin Hu
    • 3
  • Shanguo Han
    • 2
  • Yupeng Zhang
    • 2
  1. 1.School of Metallurgy and Materials EngineeringChongqing University of Science and TechnologyChongqingChina
  2. 2.Guangdong Provincial Key Laboratory of Advanced Welding TechnologyGuangdong Welding Institute (China-Ukaine E.O. Paton Institute of Welding)GuangzhouChina
  3. 3.School of Materials Science and EngineeringXi‘an University of TechnologyXi’anChina

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