Forecasting process parameters for GMAW-based rapid manufacturing using closed-loop iteration based on neural network

  • Jun Xiong
  • Guangjun ZhangEmail author
  • Jianwen Hu
  • Yongzhe Li


During the layered deposition of forming metallic parts with robotic gas metal arc welding, the geometry of a single weld bead plays an important role in determining the layer thickness and dimensional precision of the deposited layer. This paper addresses prediction of welding process parameters for the expected bead geometry with accordance to the adaptive slicing process in rapid manufacturing. The correlation between the process parameters and the bead geometry was established by applying an artificial neural network. A central composite design was employed to conduct experiments for collecting input–output data. A closed-loop iteration system, consisting of a forward model for predicting bead size and a reverse model for forecasting process parameters, was proposed to predict the optimal welding parameters for the desired bead geometry. The results show that the prediction of process parameters obtained from the designed closed-loop iteration system confirms the feasibility of this system in terms of applicability and automation in the additive manufacturing process.


Rapid manufacturing GMAW Neural network Process parameters Bead geometry 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Santors EC, Shiomi M, Osakada K, Laoui T (2006) Rapid manufacturing of metal components by laser forming. Int J Mach Tools Manuf 46(12–13):1459–1468CrossRefGoogle Scholar
  2. 2.
    Yang JL, Ouyang HW, Wang Y (2010) Direct metal laser fabrication: machine development and experimental work. Int J Adv Manuf Technol 46:1133–1143CrossRefGoogle Scholar
  3. 3.
    Miranda RM, Lopes G, Quintino L, Rodrigues JP, Williams S (2008) Rapid prototyping with high power fiber lasers. Mater Des 29:2072–2075CrossRefGoogle Scholar
  4. 4.
    Alimardani M, Toyserkani E (2008) Prediction of laser solid freeform fabrication using neuro-fuzzy method. Appl Soft Comput 8(1):316–323CrossRefGoogle Scholar
  5. 5.
    Mozaffari A, Fathia A, Khajepourb A, Toyserkanic E (2012) Optimal design of laser solid freeform fabrication system and real-time prediction of melt pool geometry using intelligent evolutionary algorithms. Appl Soft Comput. doi: 10.1016/j.asoc.2012.05.031 zbMATHGoogle Scholar
  6. 6.
    Zhang YM, Chen YW, Li PJ, Male AT (2003) Weld deposition-based rapid prototyping: a preliminary study. J Mater Process Technol 135(2–3):347–357CrossRefGoogle Scholar
  7. 7.
    Suryakumar S, Karunakaran KP, Bernard A, Chandrasekhar U, Raghavender N, Sharma D (2011) Weld bead modeling and process optimization in hybrid layered manufacturing. Comput Aided Des 43:331–344CrossRefGoogle Scholar
  8. 8.
    Mughal MP, Fawad H, Mufti RA (2006) Three-dimensional finite-element modelling of deformation in weld-based rapid prototyping. Proc IME C J Mech Eng Sci 220(6):875–885CrossRefGoogle Scholar
  9. 9.
    Spencer JD, Dickens PM, Wykes CM (1998) Rapid prototyping of metal parts by three-dimensional welding. Proc IME B J Eng Manufact 212(3):175–182CrossRefGoogle Scholar
  10. 10.
    Song YA, Park SY (2006) Experimental investigations into rapid prototyping of composites by novel hybrid deposition process. J Mater Process Technol 171(1):35–40CrossRefGoogle Scholar
  11. 11.
    Song YA, Parka S, Chae SW (2005) 3D welding and milling: part II—optimization of the 3D welding process using an experimental design approach. J Mach Tools Manuf 45:1063–1069CrossRefGoogle Scholar
  12. 12.
    Karunakaran KP, Suryakumar S, Pushpa V, Akula S (2010) Low cost integration of additive and subtractive processes for hybrid layered manufacturing. Robot Comput Integr Manuf 26:490–499CrossRefGoogle Scholar
  13. 13.
    Suryakumar S, Karunakaran KP, Bernard A, Chandrasekhar U, Raghavender N, Sharma D (2011) Weld bead modeling and process optimization in hybrid layered manufacturing. Comput Aided Des 43:331–344CrossRefGoogle Scholar
  14. 14.
    Karunakaran KP, Suryakumar S, Pushpa V, Akula S (2009) Retrofitment of a CNC machine for hybrid layered manufacturing. J Adv Manuf Technol 45:690–703CrossRefGoogle Scholar
  15. 15.
    Leea JI, Um KW (2000) A prediction of welding process parameters by prediction of back-bead geometry. J Mater Process Technol 108:106–113CrossRefGoogle Scholar
  16. 16.
    Rao PS, Gupta OP, Murty SSN, Rao ABK (2009) Effect of process parameters and mathematical model for the prediction of bead geometry in pulsed GMA welding. Int J Adv Manuf Technol 45:496–505CrossRefGoogle Scholar
  17. 17.
    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 123:303–312CrossRefGoogle Scholar
  18. 18.
    Huang W, Kovacevic R (2011) A neural network and multiple regression method for the characterization of the depth of weld penetration in laser welding based on acoustic signatures. J Intell Manuf 22:131–143CrossRefGoogle Scholar
  19. 19.
    Manikya Kanti K, Srinivasa Rao P (2008) Prediction of bead geometry in pulsed GMA welding using back propagation neural network. J Mater Process Technol 200:300–305CrossRefGoogle Scholar
  20. 20.
    Xiong J, Zhang GJ, Hu JW, Wu L (2012) Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J Intell Manuf. doi: 10.1007/s10845-012-0682-1 Google Scholar
  21. 21.
    Montgomery DC (2003) Design and analysis of experiments. Singapore, Wiley (Asia)Google Scholar
  22. 22.
    Werbos PJ (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences, PhD thesis, Harvard University, CambridgeGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Jun Xiong
    • 1
  • Guangjun Zhang
    • 1
    Email author
  • Jianwen Hu
    • 1
  • Yongzhe Li
    • 1
  1. 1.State Key Laboratory of Advanced Welding and JoiningHarbin Institute of TechnologyHarbinPeople’s Republic of China

Personalised recommendations