Skip to main content

Advertisement

SpringerLink
Log in

Developing a visual prediction program for residual stress in girth butt welds using GA-RBF neural network

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Residual stresses are inevitably generated during the welding process of steel structures, which can have a negative impact on the structure’s normal use and fatigue performance. With the continuous advancements in intelligent manufacturing technology and modular construction of steel structures, there is a growing need for accurate prediction of residual stresses. This paper presents a visualization procedure using Python language and ABAQUS commercial software to predict the full-field residual stress of butt welds in cast steel nodes. The procedure takes into account the geometry and welding parameters of the girth butt weld as input data and provides the post-weld residual stress field as output. Instead of using the traditional thermodynamic coupling finite element calculation method, this procedure develops a parameter function form that uniformly describes the residual stress field. A radial basis function network, trained by a genetic algorithm, is employed to establish a nonlinear relationship between the input and output data. The dataset is generated through finite element simulation, and a visualization interface is created within the ABAQUS software. Experimental results validate the speed and accuracy of the proposed method. This procedure can serve as a valuable reference for quickly determining residual stress fields and optimizing welding parameters during the construction process of steel structures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Wu C, Wang C, Kim JW (2022) Welding sequence optimization to reduce welding distortion based on coupled artificial neural network and swarm intelligence algorithm. Eng Appl Artif Intell 114:105142. https://doi.org/10.1016/j.engappai.2022.105142

    Article  Google Scholar 

  2. Tian L, Luo Y (2020) A study on the prediction of inherent deformation in fillet-welded joint using support vector machine and genetic optimization algorithm. J Intell Manuf 31:575–596. https://doi.org/10.1007/s10845-019-01469-w

    Article  Google Scholar 

  3. Ahmed SAA, Shawnim RJ, Mohammedtaher MM (2023) Effect of exfoliation corrosion on the efficient hybrid joint of AA2024-T3 and AA2198-T8 formed by friction stir welding. Heliyon. 9:16577. https://doi.org/10.1016/j.heliyon.2023.e16577

    Article  CAS  Google Scholar 

  4. Ahmadzadeh M, Hoseini Fard A, Saranjam B, Salimi HR (2012) Prediction of residual stresses in gas arc welding by back propagation neural network. NDT & E Int 52:136–143. https://doi.org/10.1016/j.ndteint.2012.07.009

    Article  Google Scholar 

  5. Ahmed SAA, Shawnim RJ, Mohammedtaher MSM (2022) Influence of friction stir welding process on the mechanical characteristics of the hybrid joints AA2198-T8 to AA2024-T3. Adv Mater Sci Eng 2022:1–11. https://doi.org/10.1155/2022/7055446

    Article  CAS  Google Scholar 

  6. Ni J, Zhuang X, Wahab MA (2020) Review on the prediction of residual stress in welded steel components. CMC-Comput Mat Contin:495–523

  7. Coules HE (2013) Contemporary approaches to reducing weld induced residual stress. Mater Sci Technol 29:4–18. https://doi.org/10.1179/1743284712y.0000000106

    Article  ADS  CAS  Google Scholar 

  8. Lopez-Jauregi A, Esnaola JA, Ulacia I, Urrutibeascoa I, Madariaga A (2015) Fatigue analysis of multipass welded joints considering residual stresses. Int J Fatigue 79:75–85. https://doi.org/10.1016/j.ijfatigue.2015.04.013

    Article  CAS  Google Scholar 

  9. Penttilä S, Lund H, Skriko T (2023) Possibilities of artificial intelligence-enabled feedback control system in robotized gas metal arc welding. J Manuf Mater Process 7:102. https://doi.org/10.3390/jmmp7030102

    Article  Google Scholar 

  10. Das D, Jaypuria S, Pratihar DK, Roy GG (2021) Weld optimisation. Sci Technol Weld Join 26:181–195. https://doi.org/10.1080/13621718.2021.1872856

    Article  Google Scholar 

  11. Nele L, Sarno E, Keshari A (2013) Modeling of multiple characteristics of an arc weld joint. Int J Adv Manuf Technol 69:1331–1341. https://doi.org/10.1007/s00170-013-5077-8

    Article  Google Scholar 

  12. Wu C, Wang C, Kim JW (2021) Bending deformation prediction in a welded square thin-walled aluminum alloy tube structure using an artificial neural network. Int J Adv Manuf Technol 117:2791–2805. https://doi.org/10.1007/s00170-021-07884-9

    Article  Google Scholar 

  13. Edwin Raja Dhas J, Kumanan S (2014) Neuro evolutionary model for weld residual stress prediction. Appl Soft Comput 14:461–468. https://doi.org/10.1016/j.asoc.2013.08.019

    Article  Google Scholar 

  14. Eyercioglu O, Ahmed SA, Gov K, Yilmaz NF (2017) The 2D finite element microstructure evaluation of V-shaped arc welding of AISI 1045 steel. Metals. 7:41. https://doi.org/10.3390/met7020041

    Article  CAS  Google Scholar 

  15. Sun YJ, Zang Y, Shi QY (2009) Sensitivity analysis of some high efficiency computational methods for welding process numerical simulation. In: 2009 Second International Conference on Information and Computing Science 92. https://doi.org/10.1109/icic.2009.332

    Chapter  Google Scholar 

  16. Seyyedian Choobi M, Haghpanahi M, Sedighi M (2012) Prediction of welding-induced angular distortions in thin butt-welded plates using artificial neural networks. Comput Mater Sci 62:152–159. https://doi.org/10.1016/j.commatsci.2012.05.032

    Article  CAS  Google Scholar 

  17. Berkay E, Mehmet Ali G, Selcuk M (2021) Artificial intelligence applications for friction stir welding: a review. Mater-Int 27:193–219. https://doi.org/10.1007/s12540-020-00854-y

    Article  CAS  Google Scholar 

  18. Zhu ZL, Liang YL (2020) Prediction of residual stress of carburized steel based on machine learning. Appl Sci-Basel 10:7759. https://doi.org/10.3390/app10217759

    Article  CAS  Google Scholar 

  19. Liu F, Tao C, Dong Z, Jiang K, Zhou S, Zhang Z, Shen C (2021) Prediction of welding residual stress and deformation in electro-gas welding using artificial neural network. Mater Today Commun 29:102786. https://doi.org/10.1016/j.mtcomm.2021.102786

    Article  CAS  Google Scholar 

  20. Lostado R, Martinez RF, Donald BJM, Villanueva PM (2015) Combining soft computing techniques and the finite element method to design and optimize complex welded products. Integr Comput-Aided Eng 22:153–170. https://doi.org/10.3233/ica-150484

    Article  Google Scholar 

  21. Li L, Liu D, Ren S, Zhou H, Zhou J (2021) Prediction of welding deformation and residual stress of a thin plate by improved support vector regression. Scanning 2021:8892128. https://doi.org/10.1155/2021/8892128

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Mathew J, Griffin J, Alamaniotis M, Kanarachos S, Fitzpatrick ME (2018) Prediction of welding residual stresses using machine learning: comparison between neural networks and neuro-fuzzy systems. Appl Soft Comput 70:131–146. https://doi.org/10.1016/j.asoc.2018.05.017

    Article  Google Scholar 

  23. Tian L, Luo Y, Wang Y, Wu X (2014) Prediction of transverse and angular distortions of gas tungsten arc bead-on-plate welding using artificial neural network. Mater Des 54:458–472. https://doi.org/10.1016/j.matdes.2013.08.082

    Article  CAS  Google Scholar 

  24. Flint TF, Francis JA, Smith MC, Balakrishnan J (2017) Extension of the double-ellipsoidal heat source model to narrow-groove and keyhole weld configurations. J Mater Process Technol 246:123–135. https://doi.org/10.1016/j.jmatprotec.2017.02.002

    Article  Google Scholar 

  25. Cristian RR, Daniela FG, José EM, Cíntia Petry M (2021) Prediction of angular distortion due GMAW process of thin-sheets Hardox 450® steel by numerical model and artificial neural network. J Manuf Process 68:1202–1213. https://doi.org/10.1016/j.jmapro.2021.06.045

    Article  Google Scholar 

  26. Dong P, Hong JK (2007) On the residual stress profiles in new API 579/ASME FFS-1 Appendix E. Weld World 51:119–127. https://doi.org/10.1007/bf03266579

    Article  Google Scholar 

  27. Pal S, Pal SK, Samantaray AK (2007) Radial basis function neural network model based prediction of weld plate distortion due to pulsed metal inert gas welding. Sci Technol Weld Join 12:725–731. https://doi.org/10.1179/174329307x249351

    Article  Google Scholar 

  28. Ahmed AN, Noor CWM, Allawi MF, El-Shafie A (2018) RBF-NN-based model for prediction of weld bead geometry in shielded metal arc welding (SMAW). Neural Comput & Applic 29:889–899. https://doi.org/10.1007/s00521-016-2496-0

    Article  Google Scholar 

  29. Lu Y, Xing Y, Li X, Xu S (2020) A new approach of CMT seam welding deformation forecasting based on GA-BPNN. Frat Integrita Strut 14:325–336. https://doi.org/10.3221/igf-esis.53.25

    Article  Google Scholar 

  30. Kumanan S, Kumar RA, Dhas J (2007) Development of a welding residual stress predictor using a function-replacing hybrid system. Int J Adv Manuf Technol 31:1083–1091. https://doi.org/10.1007/s00170-005-0297-1

    Article  Google Scholar 

  31. Chen G, Guo Q, Lu Y (2016) Residual stress analusis of girth butt weld in cast steel joints. Prog Steel Build Structures 18:25–33 (in Chinese)

    CAS  Google Scholar 

  32. ASTM (2008) SL 499-2010: Standard test method for determining residual stresses by the hole-drilling strain-gage method (ASTM E837-08, IDT). Ministry of water resources of the People’s Repubilc of China, Beijing, China

    Google Scholar 

  33. Chipanga T (2009) Determination of the accuracy of non-destructive residual stress measurement methods. Cape Peninsula University of Technology

    Google Scholar 

  34. Rossini NS, Dassisti M, Benyounis KY, Olabi AG (2012) Methods of measuring residual stresses in components. Mater Des 35:572–588. https://doi.org/10.1016/j.matdes.2011.08.022

    Article  Google Scholar 

Download references

Funding

The work was supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (KYCX23_0225).

Author information

Authors and Affiliations

  1. Jiangsu Key Laboratory of Engineering Mechanics, School of Civil Engineering, Southeast University, Nanjing, 211189, China

    Haihan Jiao & Hui Jin

Authors
  1. Haihan Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Haihan Jiao: conceptualization, methodology, software, original draft preparation, and writing. Hui Jin: investigation, supervision, reviewing, and editing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hui Jin.

Ethics declarations

Ethics approval

The authors confirm that they have abided to the publication ethics and state that this work is original and has not been used for publication anywhere & before.

Consent to participate

The authors are willing to participate in journal promotions and updates.

Consent for publication

The authors give consent to the journal regarding the publication of this work.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiao, H., Jin, H. Developing a visual prediction program for residual stress in girth butt welds using GA-RBF neural network. Int J Adv Manuf Technol 131, 1615–1628 (2024). https://doi.org/10.1007/s00170-024-13147-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-024-13147-0

Keywords

Search

Navigation