Skip to main content
SpringerLink
Log in

Meta-Heuristic Algorithms-Tuned Elman vs. Jordan Recurrent Neural Networks for Modeling of Electron Beam Welding Process

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

A boost in the preference of high energy beam, such as electron beam, laser beam etc. has led to the requirement of its automation through accurate input–output modelling. Modeling of electron beam welding is conducted in the present study through Elman and Jordan recurrent neural networks (RNNs), both having a single feed-back loop, to meet the said requirement. The RNNs are trained using some nature-inspired optimization tools, namely cuckoo search, firefly, flower pollination, and crow search utilizing input–output welding data, obtained from a computational fluid dynamics-based heat transfer and fluid flow welding model. RNN predictions are validated through real experiments. Thus, the effect of change in the position of the feed-back loop on the accuracy of prediction of RNNs is investigated. In addition, a few popular statistical tests have been used to evaluate the performances of the RNNs tuned by various optimization algorithms, where flower pollination-tuned Jordan RNN is observed to yield the best results.

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

Similar content being viewed by others

References

  1. Roy GG, Elmer JW, DebRoy T (2006) Mathematical modeling of heat transfer, fluid flow, and solidification during linear welding with a pulsed laser beam. J Appl Phys 100:34903. https://doi.org/10.1063/1.2214392

    Article  Google Scholar 

  2. Rai R, Roy GG, Debroy T (2007) A computationally efficient model of convective heat transfer and solidification characteristics during keyhole mode laser welding. J Appl Phys 101:54909. https://doi.org/10.1063/1.2537587

    Article  Google Scholar 

  3. Das D, Pratihar DK, Roy GG, Pal AR (2018) Phenomenological model-based study on electron beam welding process, and input-output modeling using neural networks trained by back-propagation algorithm, genetic algorithms, particle swarm optimization algorithm and bat algorithm. Appl Intell 48:2698–2718. https://doi.org/10.1007/s10489-017-1101-2

    Article  Google Scholar 

  4. Das D, Pal AR, Das AK et al (2020) Nature-inspired optimization algorithm-tuned feed-forward and recurrent neural networks using CFD-based phenomenological model-generated data to model the EBW process. Arab J Sci Eng 45:2779–2797. https://doi.org/10.1007/s13369-019-04142-9

    Article  Google Scholar 

  5. Rai R, Elmer JW, Palmer TA, DebRoy T (2007) Heat transfer and fluid flow during keyhole mode laser welding of tantalum, Ti–6Al–4V, 304L stainless steel and vanadium. J Phys D Appl Phys 40:5753–5766. https://doi.org/10.1088/0022-3727/40/18/037

    Article  Google Scholar 

  6. Rai R, Palmer TA, Elmer JW, Debroy T (2009) Heat transfer and fluid flow during electron beam welding of 304L stainless steel alloy. Weld J 88:54–61

    Google Scholar 

  7. Das D, Das AK, Pratihar DK, Roy GG (2020) Prediction of Residual Stress in Electron Beam Welding of Stainless Steel from Process Parameters and Natural Frequency of Vibrations Using Machine-Learning Algorithms. Proc Inst Mech Eng Part C J Mech Eng Sci [Accepted]

  8. Das D, Pratihar DK, Roy GG (2020) Establishing a correlation between residual stress and natural frequency of vibration for electron beam buttweld of AISI 304 stainless steel. Arab J Sci Eng 45:5769–5781. https://doi.org/10.1007/s13369-020-04560-0

    Article  Google Scholar 

  9. Pratihar DK (2015) Soft Computing Fundamentals and Applications. Narosa Publishing House Pvt Ltd, New Delhi

    Google Scholar 

  10. Ganjigatti JP, Pratihar DK, Roychoudhury A (2008) Modeling of the MIG welding process using statistical approaches. Int J Adv Manuf Technol 35:1166–1190. https://doi.org/10.1007/s00170-006-0798-6

    Article  Google Scholar 

  11. Datta S, Pratihar DK, Bandyopadhyay PP (2013) Modeling of plasma spray coating process using statistical regression analysis. Int J Adv Manuf Technol 65:967–980. https://doi.org/10.1007/s00170-012-4232-y

    Article  Google Scholar 

  12. Das AK, Pratihar DK (2018) Performance improvement of a genetic algorithm using a novel restart strategy with elitism principle. Int J Hybrid Intell Syst. https://doi.org/10.3233/HIS-180257

    Article  Google Scholar 

  13. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings. of IEEE International Conference on Neural Networks (ICNN’95). pp 1942–1948

  14. Jha MN, Pratihar DK, Dey V et al (2011) Study on electron beam butt welding of austenitic stainless steel 304 plates and its input-output modelling using neural networks. Proc Inst Mech Eng Part B-J Eng Manuf 225:2051–2070. https://doi.org/10.1177/0954405411404856

    Article  Google Scholar 

  15. Elman JL (1990) Finding structure in time. Cogn Sci 14:179–211. https://doi.org/10.1207/s15516709cog1402_1

    Article  Google Scholar 

  16. Pham DT, Karaboga D (1999) Training Elman and Jordan networks for system identification using genetic algorithms. Artif Intell Eng 13:107–117. https://doi.org/10.1016/S0954-1810(98)00013-2

    Article  Google Scholar 

  17. Ge HW, Liang YC, Marchese M (2007) A modified particle swarm optimization-based dynamic recurrent neural network for identifying and controlling nonlinear systems. Comput Struct 85:1611–1622. https://doi.org/10.1016/j.compstruc.2007.03.001

    Article  Google Scholar 

  18. Zhou C, Ding LY, He R (2013) PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze River. Autom Constr 36:208–217. https://doi.org/10.1016/j.autcon.2013.03.001

    Article  Google Scholar 

  19. Yang XS, Deb S (2009) Cuckoo search via Levy flights. In: Abraham A, Carvalho A, Herrera F, Pai V (eds) Nature and Biologically Inspired Computing (NABIC). IEEE, Coimbatore, India, pp 210–214

    Google Scholar 

  20. Swain KB, Solanki SS, Mahakula AK (2014) Bio inspired Cuckoo Search Algorithm based neural network and its application to noise cancellation. In: Signal Processing and Integrated Networks (SPIN). IEEE, pp 632–635

  21. Gotmare A, Patidar R, George NV (2015) Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model. Expert Syst Appl 42:2538–2546. https://doi.org/10.1016/j.eswa.2014.10.040

    Article  Google Scholar 

  22. Goswami D, Chakraborty S (2013) Optimal Process Parameter Selection in Laser Transmission Welding by Cuckoo Search Algorithm. In: Proceedings of the International Conference on Advanced Engineering Optimization Through Intelligent Techniques (AEOTIT). Gujarat, India, pp 40–44

  23. Yang X-S (2010) Firefly algorithm, levy flights and global optimization. In: Bramer M, Ellis R, Petridis M (eds) Research and development in intelligent systems XXVI. Springer, London, pp 209–218

    Chapter  Google Scholar 

  24. Alweshah M, Abdullah S (2015) Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems. Appl Soft Comput J 35:513–524. https://doi.org/10.1016/j.asoc.2015.06.018

    Article  Google Scholar 

  25. Yang X-S (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional Computation and Natural Computation: 11th International Conference, UCNC 2012. Springer, Orléans, France, pp 240–249

    Chapter  Google Scholar 

  26. Chiroma H, Khan A, Abubakar AI et al (2016) A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm. Appl Soft Comput J 48:50–58. https://doi.org/10.1016/j.asoc.2016.06.038

    Article  Google Scholar 

  27. Acherjee B, Maity D, Kuar AS (2017) Parameters optimisation of transmission laser welding of dissimilar plastics using RSM and flower pollination algorithm integrated approach. Int J Math Model Numer Optim 8:1–22. https://doi.org/10.1504/IJMMNO.2017.10004515

    Article  Google Scholar 

  28. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/j.compstruc.2016.03.001

    Article  Google Scholar 

  29. Oliva D, Hinojosa S, Cuevas E et al (2017) Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm. Expert Syst Appl 79:164–180. https://doi.org/10.1016/j.eswa.2017.02.042

    Article  Google Scholar 

  30. Abdelaziz AY, Fathy A (2017) A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks. Eng Sci Technol an Int J 20:391–402. https://doi.org/10.1016/j.jestch.2017.02.004

    Article  Google Scholar 

  31. Wang GG, Deb S, Cui Z (2019) Monarch butterfly optimization. Neural Comput Appl 31:1995–2014. https://doi.org/10.1007/s00521-015-1923-y

    Article  Google Scholar 

  32. Wang GG, Deb S, Coelho LDS (2018) Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. Int J Bio-Inspired Comput 12:1–22. https://doi.org/10.1504/IJBIC.2018.093328

    Article  Google Scholar 

  33. Wang GG, Deb S, Gao XZ, Coelho LDS (2016) A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. Int J Bio-Inspired Comput 8:394–409. https://doi.org/10.1504/IJBIC.2016.081335

    Article  Google Scholar 

  34. Wang GG (2018) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Comput 10:151–164. https://doi.org/10.1007/s12293-016-0212-3

    Article  Google Scholar 

  35. Li S, Chen H, Wang M et al (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.055

    Article  Google Scholar 

  36. Askari Q, Younas I, Saeed M (2020) Political optimizer: a novel socio-inspired meta-heuristic for global optimization. Knowledge-Based Syst 195:105709. https://doi.org/10.1016/j.knosys.2020.105709

    Article  Google Scholar 

  37. Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowledge-Based Syst. https://doi.org/10.1016/j.knosys.2019.105190

    Article  Google Scholar 

  38. Heidari AA, Mirjalili S, Faris H et al (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  39. Askari Q, Saeed M, Younas I (2020) Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Syst Appl 161:113702. https://doi.org/10.1016/j.eswa.2020.113702

    Article  Google Scholar 

  40. Wang GG, Gandomi AH, Alavi AH, Gong D (2019) A comprehensive review of krill herd algorithm: variants, hybrids and applications. Artif Intell Rev 51:119–148. https://doi.org/10.1007/s10462-017-9559-1

    Article  Google Scholar 

  41. Roy GG, Zhang Z, Mishra S, et al (2002) A Computer Program to Calculate Fluid Flow and Heat Transfer during Fusion Welding with Free Surface. Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania, p 16802

  42. Das D, Pratihar DK, Roy GG (2018) Cooling rate predictions and its correlation with grain characteristics during electron beam welding of stainless steel. Int J Adv Manuf Technol 97:2241–2254. https://doi.org/10.1007/s00170-018-2095-6

    Article  Google Scholar 

  43. Das D, Pratihar DK, Roy GG (2016) Electron beam melting of steel plates: temperature measurement using thermocouples and prediction through finite element analysis. In: Mandal DK, Syan CS (eds) CAD/CAM, Robotics and Factories of the Future. Springer, New Delhi, pp 579–588

    Chapter  Google Scholar 

  44. Das D, Pratihar DK, Roy GG (2020) Effects of space charge on weld geometry and cooling rate during electron beam welding of stainless steel. Optik (Stuttg) 206:163722. https://doi.org/10.1016/j.ijleo.2019.163722

    Article  Google Scholar 

  45. Yang X (2015) Recent advances in swarm intelligence and evolutionary computation, vol 585. Springer, London

    Google Scholar 

  46. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18. https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

  47. Chakri A, Khelif R, Benouaret M, Yang XS (2017) New directional bat algorithm for continuous optimization problems. Expert Syst Appl 69:159–175. https://doi.org/10.1016/j.eswa.2016.10.050

    Article  Google Scholar 

Download references

Acknowledgement

The first four authors gratefully acknowledged the financial support of the Ministry of Human Resource Development (MHRD), Government of India, for carrying out this research.

Author information

Authors and Affiliations

  1. Indian Institute of Technology Kharagpur, Kharagpur, India

    Debasish Das, Amit Kumar Das, Abhishek Rudra Pal & Sanjib Jaypuria

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

    Dilip Kumar Pratihar

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

    Gour Gopal Roy

Authors
  1. Debasish Das
    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. Abhishek Rudra Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanjib Jaypuria
    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. Gour Gopal Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dilip Kumar Pratihar.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Das, D., Das, A.K., Pal, A.R. et al. Meta-Heuristic Algorithms-Tuned Elman vs. Jordan Recurrent Neural Networks for Modeling of Electron Beam Welding Process. Neural Process Lett 53, 1647–1663 (2021). https://doi.org/10.1007/s11063-021-10471-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-021-10471-4

Keywords

Search

Navigation