Skip to main content
SpringerLink
Log in

Swarm Intelligence-based Modeling and Multi-objective Optimization of Welding Defect in Electron Beam Welding

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The process parameters involved in electron beam welding have major influences on spiking and penetration efficiency of the joint. In addition to this, the input–output relationships of electron beam welding are nonlinear and complex in nature. Therefore, adaptive neuro-fuzzy inference system (ANFIS)-based input–output modeling had been attempted to predict the spiking severity in electron beam welded joints. Swarm-based optimization algorithms, like grey wolf optimizer, particle swarm optimization, and bonobo optimizer (BO), were used for optimizing the ANFIS architecture and predicting the response precisely. Multi-objective bonobo optimization (MOBO), Multi-objective grey wolf optimization, and Multi-objective particle swarm optimization algorithms had been used for solving the conflicting multi-objective criteria problems associated with the study. The input process parameters were accelerating voltage, beam current, scan speed, focusing distance, and beam oscillation parameters, whereas mean weld-bead penetration and its standard deviation were considered as the responses of the system. The irregular penetration, that is, spiking was expressed in terms of the standard deviation of weld penetration. The accuracy level of optimization and modeling had been tested with some test cases obtained through the real experiments. MOBO had shown the better accuracy in predicting the optimized set of input parameters, which could satisfy both the spiking and penetration criteria, while BO-ANFIS had shown the superior efficiency in predicting the response with minimum error.

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
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Alonso, V.; Dacal-Nieto, A.; Barreto, L.; Amaral, A.; Rivero, E.: Industry 4.0 implications in machine vision metrology: an overview. Procedia Manuf. 41, 359–66 (2019). https://doi.org/10.1016/j.promfg.2019.09.020

    Article  Google Scholar 

  2. Sun, Z.; Karppi, R.: The application of electron beam welding for the joining of dissimilar metals: an overview. J. Mater. Process. Technol. 59, 257–267 (1996). https://doi.org/10.1016/0924-0136(95)02150-7

    Article  Google Scholar 

  3. Jaypuria, S.; Doshi, N.; Pratihar, D.K.: Effects of welding parameters on mechanical properties in electron beam welded CuCrZr alloy plates effects of welding parameters on mechanical properties in electron beam welded CuCrZr alloy plates. Mater. Sci. Eng. 338, 012013 (2018). https://doi.org/10.1088/1757-899X/338/1/012013

    Article  Google Scholar 

  4. Schultz, H.: Electron beam welding. Woodhead Publishing Ltd, Sawston (1994)

    Book  Google Scholar 

  5. Jaypuria, S.; Gupta, S.K.; Pratihar, D.K.: Comparative study of feed-forward and recurrent neural networks in modeling of electron beam welding. In: Advances in Additive Manufacturing and Joining, pp. 521–531. Springer, Singapore (2020). https://doi.org/10.1007/978-981-32-9433-2_45

    Chapter  Google Scholar 

  6. Jaypuria, S.; Chakrabarti, D.; Pratihar, D. K.: Effect of beam oscillations on formation of defects in electron beam welding of copper plate. ASME 2019 14th Int. Manuf. Sci. Eng. Conf. MSEC 2019, vol. 2, American Society of Mechanical Engineers (ASME); 2019. https://doi.org/10.1115/MSEC2019-2943.

  7. Liu, C.; He, J.: Numerical analysis of fluid transport phenomena and spiking defect formation during vacuum electron beam welding of 2219 aluminium alloy plate. Vacuum 132, 70–81 (2016). https://doi.org/10.1016/j.vacuum.2016.07.033

    Article  Google Scholar 

  8. Bardin, F.; Cobo, A.; Lopez-Higuera, J.M.; Collin, O.; Aubry, P.; Dubois, T., et al.: Optical techniques for real-time penetration monitoring for laser welding. Appl. Opt. 44, 3869–3876 (2005). https://doi.org/10.1364/AO.44.003869

    Article  Google Scholar 

  9. Jaypuria, S.; Meher, J.; Kanigalpula, P.K.C.; Pratihar, D.K.: Electron beam butt welding of Cu-Cr-Zr alloy plates: experimental investigations, studies on metallurgical and mechanical properties. Fusion Eng. Des. 137, 209–220 (2018). https://doi.org/10.1016/j.fusengdes.2018.10.004

    Article  Google Scholar 

  10. Luo, M.; Hu, R.; Liu, T.; Wu, B.; Pang, S.: Optimization possibility of beam scanning for electron beam welding: Physics understanding and parameters selection criteria. Int. J. Heat Mass Transf. 127, 1313–1326 (2018). https://doi.org/10.1016/j.ijheatmasstransfer.2018.07.014

    Article  Google Scholar 

  11. Trushnikov, D.N.; Koleva, E.G.; Mladenov, G.M.; Belenkiy, V.Y.: Effect of beam deflection oscillations on the weld geometry. J. Mater. Process. Technol. 213, 1623–1634 (2013). https://doi.org/10.1016/j.jmatprotec.2013.03.028

    Article  Google Scholar 

  12. Fetzer, F.; Hu, H.; Berger, P.; Weber, R.; Eberhard, P.; Graf, T.: Fundamental investigations on the spiking mechanism by means of laser beam welding of ice. J. Laser Appl. 30, 012009 (2018). https://doi.org/10.2351/1.4986641

    Article  Google Scholar 

  13. Kanigalpula, P.K.C.; Jaypuria, S.; Pratihar, D.K.; Jha, M.N.: Experimental investigations, input-output modeling, and optimization of spiking phenomenon in electron beam welding of ETP copper plates. Meas. J. Int. Meas. Confed. 129, 302–318 (2018). https://doi.org/10.1016/j.measurement.2018.07.040

    Article  Google Scholar 

  14. Zhang, M.; Chen, G.; Zhou, Y.; Liao, S.: Optimization of deep penetration laser welding of thick stainless steel with a 10kW fiber laser. Mater. Des. 53, 568–576 (2014). https://doi.org/10.1016/j.matdes.2013.06.066

    Article  Google Scholar 

  15. Fu, P.; Mao, Z.; Zuo, C.; Wang, Y.; Wang, C.: Microstructures and fatigue properties of electron beam welds with beam oscillation for heavy section TC4-DT alloy. Chin. J. Aeronaut. 27, 1015–1021 (2014). https://doi.org/10.1016/j.cja.2014.03.020

    Article  Google Scholar 

  16. Jaypuria, S.; Gupta, S.K.; Pratihar, D.K.; Chakrabarti, D.; Jha, M.N.: Effect of amplitude oscillation on spiking in electron beam welding of copper plate. In: Advances in Materials and Manufacturing Engineering, pp. 405–411. Springer, Singapore (2020)

    Chapter  Google Scholar 

  17. Schweier, M.; Heins, J.F.; Haubold, M.W.; Zaeh, M.F.: Spatter formation in laser welding with beam oscillation. Phys. Procedia 41, 20–30 (2013). https://doi.org/10.1016/j.phpro.2013.03.047

    Article  Google Scholar 

  18. Yan, W.; Zhang, H.; Jiang, Z.G.; Hon, K.K.B.: Multi-objective optimization of arc welding parameters: the trade-offs between energy and thermal efficiency. J. Clean. Prod. 140, 1842–1849 (2017). https://doi.org/10.1016/j.jclepro.2016.03.171

    Article  Google Scholar 

  19. Yang, Y.; Cao, L.; Zhou, Q.; Wang, C.; Wu, Q.; Jiang, P.: Multi-objective process parameters optimization of Laser-magnetic hybrid welding combining Kriging and NSGA-II. Robot. Comput. Integr. Manuf. 49, 253–262 (2018). https://doi.org/10.1016/j.rcim.2017.07.003

    Article  Google Scholar 

  20. Das, A.K.; Das, D.; Jaypuria, S.; Pratihar, D.K.; Roy, G.G.: Input-output modeling and multi-objective optimization of weld attributes in EBW. Arab. J. Sci. Eng. 46, 4087–4101 (2021). https://doi.org/10.1007/S13369-020-05248-1/TABLES/10

    Article  Google Scholar 

  21. Wang, X.; Yan, Y.; Gu, X.: Spot welding robot path planning using intelligent algorithm. J Manuf Process 42, 1–10 (2019). https://doi.org/10.1016/j.jmapro.2019.04.014

    Article  Google Scholar 

  22. Das, A.K.; Das, D.; Pratihar, D.K.: Multi-objective optimization and cluster-wise regression analysis to establish input-output relationships of a process. In: Multi-Objective Optimization, pp. 299–318. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-1471-1_14

    Chapter  MATH  Google Scholar 

  23. Norouzi, A.; Hamedi, M.; Adineh, V.R.: Strength modeling and optimizing ultrasonic welded parts of ABS-PMMA using artificial intelligence methods. Int. J. Adv. Manuf. Technol. 61, 135–147 (2012). https://doi.org/10.1007/s00170-011-3699-2

    Article  Google Scholar 

  24. Jaypuria, S.; Pratihar, D.K.: Fuzzy Inference System-Based Neuro-Fuzzy Modeling of Electron-Beam Welding, p. 839–50. Springer, Singapore (2019) https://doi.org/10.1007/978-981-32-9072-3_70

    Book  Google Scholar 

  25. Babajanzade Roshan, S.; Behboodi Jooibari, M.; Teimouri, R.; Asgharzadeh-Ahmadi, G.; Falahati-Naghibi, M.; Sohrabpoor, H.: Optimization of friction stir welding process of AA7075 aluminum alloy to achieve desirable mechanical properties using ANFIS models and simulated annealing algorithm. Int. J. Adv. Manuf. Technol. 69, 1803–1818 (2013). https://doi.org/10.1007/s00170-013-5131-6

    Article  Google Scholar 

  26. Vijayan, D.; Seshagiri, R.V.: Parametric optimization of friction stir welding process of age hardenable aluminum alloys−ANFIS modeling. J. Cent. South Univ. 23, 1847–1857 (2016). https://doi.org/10.1007/s11771-016-3239-1

    Article  Google Scholar 

  27. Dhas, J.E.R.; Kumanan, S.: Modeling of residual stress in butt welding. Mater. Manuf. Process. 26, 942–947 (2011). https://doi.org/10.1080/10426914.2011.560232

    Article  Google Scholar 

  28. Wu, D.; Chen, H.; Huang, Y.; He, Y.; Hu, M.; Chen, S.: Monitoring of weld joint penetration during variable polarity plasma arc welding based on the keyhole characteristics and PSO-ANFIS. J. Mater. Process. Technol. 239, 113–124 (2017). https://doi.org/10.1016/j.jmatprotec.2016.07.021

    Article  Google Scholar 

  29. Jaypuria, S.; Mahapatra, T.R.; Jaypuria, O.: Metaheuristic tuned ANFIS model for input-output modeling of friction stir welding. Mater. Today Proc. 18, 3922–30 (2019). https://doi.org/10.1016/j.matpr.2019.07.332

    Article  Google Scholar 

  30. Maroufpoor, S.; Maroufpoor, E.; Bozorg-Haddad, O.; Shiri, J.; Mundher, Y.Z.: Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. J. Hydrol. 575, 544–556 (2019). https://doi.org/10.1016/j.jhydrol.2019.05.045

    Article  Google Scholar 

  31. Das, A.K.; Pratihar, D.K.: Optimal preventive maintenance interval for a Crankshaft balancing machine under reliability constraint using Bonobo Optimizer. In: IFToMM World Congress on Mechanism and Machine Science, pp. 1659–68. Springer, Cham (2019)

    Chapter  Google Scholar 

  32. Jaypuria, S.; Das, A.K.; Pratihar, D.K.: Swarm-Intelligence-Based Computation for Parametric Optimization of Electron Beam Fabrication, p. 153–63. Springer, Singapore (2019) https://doi.org/10.1007/978-981-32-9072-3_14

    Book  Google Scholar 

  33. Dewan, M.W.; Huggett, D.J.; Warren Liao, T.; Wahab, M.A.; Okeil, A.M.: Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network. Mater. Des. 92, 288–299 (2016). https://doi.org/10.1016/j.matdes.2015.12.005

    Article  Google Scholar 

  34. Pratihar, D.K.: Soft computing: fundamentals and applications. Alpha Science International Ltd; 2015

  35. Kennedy, J.; Eberhart, R.: Particle swarm optimization. Proc. ICNN’95 Int. Conf. Neural Netw. 4, 1942–8 (1995). https://doi.org/10.1109/ICNN.1995.488968

    Article  Google Scholar 

  36. Shamshirband, S.; Hadipoor, M.; Baghban, A.; Mosavi, A.; Bukor, J.; Várkonyi-Kóczy, A.R.: Developing an ANFIS-PSO model to predict mercury emissions in combustion flue gases. Mathematics 7, 965 (2019). https://doi.org/10.3390/math7100965

    Article  Google Scholar 

  37. Mirjalili, S.; Mirjalili, S.M.; Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69(46), 61 (2014). https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  38. Dehghani, M.; Seifi, A.; Riahi-Madvar, H.: Novel forecasting models for immediate-short-term to long-term influent flow prediction by combining ANFIS and grey wolf optimization. J. Hydrol. 576, 698–725 (2019). https://doi.org/10.1016/j.jhydrol.2019.06.065

    Article  Google Scholar 

  39. Esmin, A.A.A.; Coelho, R.A.; Matwin, S.: A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artif. Intell. Rev. 44, 23–45 (2015). https://doi.org/10.1007/s10462-013-9400-4

    Article  Google Scholar 

  40. Das, A. K.; Pratihar, D. K.: A New Bonobo optimizer (BO) for Real-Parameter optimization. Proc. 2019 IEEE Reg. 10 Symp. TENSYMP 2019, Institute of Electrical and Electronics Engineers Inc.; 2019, p. 108–13. https://doi.org/10.1109/TENSYMP46218.2019.8971108.

  41. Coello, C.C.; Lechuga, M.S.: MOPSO A proposal for multiple objective particle swarm optimization. Proc. Congr. Evol. Comput. 2, 1051–1056 (2002). https://doi.org/10.1109/CEC.2002.1004388

    Article  Google Scholar 

  42. Poli, R.; Kennedy, J.; Blackwell, T.: Particle swarm optimization: an overview. Swarm Intell. 1, 33–57 (2007). https://doi.org/10.1007/s11721-007-0002-0

    Article  Google Scholar 

  43. Mirjalili, S.; Saremi, S.; Mirjalili, S.M.; Coelho, L.D.S.: Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst. Appl. 47, 106–119 (2016). https://doi.org/10.1016/j.eswa.2015.10.039

    Article  Google Scholar 

  44. Das, A.K.; Nikum, A.K.; Krishnan, S.V.; Pratihar, D.K.: Multi-objective Bonobo Optimizer (MOBO): an intelligent heuristic for multi-criteria optimization. Knowl. Inf. Syst. 62, 4407–4444 (2020). https://doi.org/10.1007/S10115-020-01503-X/TABLES/8

    Article  Google Scholar 

  45. Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002). https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  46. Salehi, I.; Shirani, M.; Semnani, A.; Hassani, M.; Habibollahi, S.: Comparative study between response surface methodology and artificial neural network for adsorption of crystal violet on magnetic activated carbon. Arab. J. Sci. Eng. 41, 2611–2621 (2016). https://doi.org/10.1007/S13369-016-2109-3

    Article  Google Scholar 

  47. Gupta, S.K.; Jaypuria, S.; Pratihar, D.K.; Saha, P.: Study on mechanical and metallurgical properties of fiber laser welded Nb-1% Zr-0.1% C alloy. Opt. Laser Technol. 127, 106153 (2020). https://doi.org/10.1016/j.optlastec.2020.106153

    Article  Google Scholar 

Download references

Acknowledgements

The authors like to thank Board Research Nuclear Science (BRNS), Department of Atomic Energy, Govt. of India, for providing the financial aid to procure and conduct experiments. We also convey our sincere regards to IIT Kharagpur, India, for providing the technical and administrative supports.

Author information

Authors and Affiliations

  1. Indian Institute of Technology Kharagpur, Kharagpur, India

    Sanjib Jaypuria, Amit Kumar Das & Debasish Das

  2. Department of Mechanical Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    P. K. C. Kanigalpula

  3. Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Dilip Kumar Pratihar

  4. Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Debalay Chakrabarti

  5. Power Beam Equipment Design Section, Bhabha Atomic Research Center, Mumbai, India

    M. N. Jha

Authors
  1. Sanjib Jaypuria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amit Kumar Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. K. C. Kanigalpula
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Debasish Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dilip Kumar Pratihar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Debalay Chakrabarti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. M. N. Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dilip Kumar Pratihar.

Ethics declarations

Conflict of interest

Please note that there is no conflict of interest with anybody.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jaypuria, S., Das, A.K., Kanigalpula, P.K.C. et al. Swarm Intelligence-based Modeling and Multi-objective Optimization of Welding Defect in Electron Beam Welding. Arab J Sci Eng 48, 1807–1827 (2023). https://doi.org/10.1007/s13369-022-07017-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-022-07017-8

Keywords

Search

Navigation