Skip to main content

Advertisement

SpringerLink
Log in

A modified multi-gene genetic programming approach for modelling true stress of dynamic strain aging regime of austenitic stainless steel 304

  • Published:
Meccanica Aims and scope Submit manuscript

Abstract

AISI steel 304 is used in nuclear reactors for the cladding of fuel rods. In the literature, various mathematical modelling methods such as support vector regression (SVR), artificial neural network (ANN) and multi-gene genetic programming (MGGP) have been applied to study the properties of this steel. Among these methods, MGGP possesses the ability to evolve the model structure and its coefficients. The model participating in the evolutionary stage of the MGGP algorithm is a weighted linear sum of several genes, with the weights determined by selecting the genes randomly and combining them using the least squares method to form a MGGP model. As a result, there is a possibility that a gene of lower performance can degrade the performance of the model. To counter this, a modified MGGP (M-MGGP) method is proposed and which introduces a new technique of stepwise regression for the selective combination of genes of only higher performance. The M-MGGP method is applied to the true stress value data obtained from tensile tests conducted on austenitic stainless steel 304 subjected to different strain rates and temperatures. The results show that the M-MGGP model is able to extrapolate the values of true stress more satisfactorily than those of the standardized MGGP, ANN and SVR models. The M-MGGP models are also smaller in size than those from MGGP. The results suggest that the M-MGGP method provides compact and accurate models that can be deployed by experts for efficiently studying the properties of the steel at elevated temperatures.

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

Similar content being viewed by others

References

  1. Armas A, Bettin O, Alvarez-Armas I, Rubiolo G (1988) Strain aging effects on the cyclic behavior of austenitic stainless steels. J Nucl Mater 155:644–649

    Article  ADS  Google Scholar 

  2. Armas A, Hereñú S, Alvarez-Armas I, Degallaix S, Condó A, Lovey F (2008) The influence of temperature on the cyclic behavior of aged and unaged super duplex stainless steels. Mater Sci Eng A 491:434–439

    Article  Google Scholar 

  3. Gupta AK, Singh SK, Reddy S, Hariharan G (2012) Prediction of flow stress in dynamic strain aging regime of austenitic stainless steel 316 using artificial neural network. Mater Des 35:589–595

    Article  Google Scholar 

  4. Hong X, Mitchell R, Chen S, Harris CJ, Li K, Irwin G (2008) Model selection approaches for nonlinear system identification: a review. Int J Syst Sci 39:925–946

    Article  MATH  MathSciNet  Google Scholar 

  5. Llanes L, Mateo A, Iturgoyen L, Anglada M (1996) Aging effects on the cyclic deformation mechanisms of a duplex stainless steel. Acta Mater 44:3967–3978

    Article  Google Scholar 

  6. Peng K, Qian K, Chen W (2004) Effect of dynamic strain aging on high temperature properties of austenitic stainless steel. Mater Sci Eng A 379:372–377

    Article  Google Scholar 

  7. Wang X, Li D (2003) Mechanical, electrochemical and tribological properties of nano-crystalline surface of 304 stainless steel. Wear 255:836–845

    Article  Google Scholar 

  8. Lin Y, Chen X-M (2011) A critical review of experimental results and constitutive descriptions for metals and alloys in hot working. Mater Des 32:1733–1759

    Article  Google Scholar 

  9. Johnson GR, Cook WH (1983) A constitutive model and data for metals subjected to large strains, high strain rates and high temperatures. In: Proceedings of the 7th international symposium on ballistics, 1983. International Ballistics Committee, The Hague, pp 541–547

  10. Samantaray D, Mandal S, Bhaduri A (2009) A comparative study on Johnson Cook, modified Zerilli–Armstrong and Arrhenius-type constitutive models to predict elevated temperature flow behaviour in modified 9Cr–1Mo steel. Comput Mater Sci 47:568–576

    Article  Google Scholar 

  11. Samantaray D, Mandal S, Bhaduri A (2011) A critical comparison of various data processing methods in simple uni-axial compression testing. Mater Des 32:2797–2802

    Article  Google Scholar 

  12. Samantaray D, Mandal S, Borah U, Bhaduri A, Sivaprasad P (2009) A thermo-viscoplastic constitutive model to predict elevated-temperature flow behaviour in a titanium-modified austenitic stainless steel. Mater Sci Eng A 526:1–6

    Article  Google Scholar 

  13. Zener C, Hollomon J (1944) Effect of strain rate upon plastic flow of steel. J Appl Phys 15:22–32

    Article  ADS  Google Scholar 

  14. Zerilli FJ, Armstrong RW (1987) Dislocation-mechanics-based constitutive relations for material dynamics calculations. J Appl Phys 61:1816–1825

    Article  ADS  Google Scholar 

  15. Xiao Y-H, Guo C (2011) Constitutive modelling for high temperature behavior of 1Cr12Ni3Mo2VNbN martensitic steel. Mater Sci Eng A 528:5081–5087

    Article  Google Scholar 

  16. Chiou S-T, Cheng W-C, Lee W-S (2005) Strain rate effects on the mechanical properties of a Fe–Mn–Al alloy under dynamic impact deformations. Mater Sci Eng A 392:156–162

    Article  Google Scholar 

  17. He X, Yu Z, Lai X (2008) A method to predict flow stress considering dynamic recrystallization during hot deformation. Comput Mater Sci 44:760–764

    Article  Google Scholar 

  18. Çaydaş U, Ekici S (2012) Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. J Intell Manuf 23:639–650

    Article  Google Scholar 

  19. Gupta AK (2010) Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression. Int J Prod Res 48:763–778

    Article  Google Scholar 

  20. Xu J, Zhang M, Wang Y (2010) Neural networks modelling and generalised predictive control for an autonomous underwater vehicle. Int J Model Identif Control 11:79–86

    Article  Google Scholar 

  21. Yildiz AR (2013) Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Inf Sci Int J 220:399–407

    MathSciNet  Google Scholar 

  22. Yildiz AR (2013) Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Appl Soft Comput 13(3):1433–1439

    Article  Google Scholar 

  23. Garg A, Vijayaraghavan V, Mahapatra SS, Tai K, Wong CH (2014) Performance evaluation of microbial fuel cell by artificial intelligence methods. Expert Syst Appl 41(4):1389–1399

    Article  Google Scholar 

  24. Mohammed AA et al (2013) Crack detection in a rotating shaft using artificial neural networks and PSD characterisation. Meccanica 1–12. doi:10.1007/s11012-013-9790-z

  25. Zapico-Valle JL et al (2013) Rotor crack identification based on neural networks and modal data. Meccanica 1–20. doi:10.1007/s11012-013-9795-7

  26. Raeisi E, Ziaei-Rad S (2013) The worst response of mistuned bladed disk system using neural network and genetic algorithm. Meccanica 48(2):367–379

    Article  Google Scholar 

  27. Fernandez A et al (2012) Regrasping objects during manipulation tasks by combining genetic algorithms and finger gaiting. Meccanica 47(4):939–950

    Article  MATH  MathSciNet  Google Scholar 

  28. Litak G, Rusinek R (2012) Dynamics of a stainless steel turning process by statistical and recurrence analyses. Meccanica 47(6):1517–1526

    Article  Google Scholar 

  29. Kovacic M, Brezocnik M (2003) Genetic programming approach for surface quality prediction. Teh Vjesn 10:19–24

    Google Scholar 

  30. Zhang Y, Bhattacharyya S (2004) Genetic programming in classifying large-scale data: an ensemble method. Inf Sci 163:85–101

    Article  Google Scholar 

  31. Hiden HG (1998) Data-based modelling using genetic programming. PhD Thesis, Department of Chemical and Process Engineering, University of Newcastle

  32. Hinchliffe M, Hiden H, Mckay B, Willis M, Tham M, Barton G (1996) Modelling chemical process systems using a multi-gene genetic programming algorithm. Late breaking papers at the genetic programming 1996 conference, Stanford University, July 28–31, pp 56–65

  33. Garg A, Tai K (2012) Comparison of regression analysis, Artificial Neural Network and genetic programming in handling the multicollinearity problem. In: Proceedings of 2012 international conference on modelling, identification and control (ICMIC2012), Wuhan, China, 24–26 June 2012. IEEE, Piscataway, NJ, pp 353–358

  34. Garg A, Tai K (2013) Comparison of statistical and machine learning methods in modelling of data with multicollinearity. Int J Model Identif Control 18(4):295–312

    Google Scholar 

  35. Garg A, Tai K (2011) A hybrid genetic programming-artificial neural network approach for modeling of vibratory finishing process. In: International proceedings of computer science and information technology, ICIIC 2011: international conference on information and intelligent computing, Hong Kong, 25–26 November 2011, vol 18, pp 14–19

  36. Garg A, Tai K, Lee CH, Savalani MM (2013) A hybrid m5′-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process. J Intell Manuf. doi:10.1007/s10845-013-0734-1

    Google Scholar 

  37. Garg A, Sriram S, Tai K (2013) Empirical analysis of model selection criteria for genetic programming in modeling of time series system. In: Proceedings of 2013 IEEE conference on computational intelligence for financial engineering and economics (CIFEr), Singapore, 16–19 April 2013, pp 84–88

  38. Garg A, Tai K (2013) Selection of a robust experimental design for the effective modeling of nonlinear systems using genetic programming. In: Proceedings of 2013 IEEE symposium series on computational intelligence and data mining (CIDM), Singapore, 16–19 April 2013, pp 293–298

  39. Garg A, Rachmawati L, Tai K (2013) Classification-driven model selection approach of genetic programming in modelling of turning process. Int J Adv Manuf Technol 69(5–8):1137–1151

    Article  Google Scholar 

  40. Garg A, Bhalerao Y, Tai K (2013) Review of empirical modeling techniques for modeling of turning process. Int J Model Identif Control 20(2):121–129

    Article  Google Scholar 

  41. Garg A, Savalani MM, Tai K (2014) State-of-the-art in empirical modelling of rapid prototyping processes. Rapid Prototyp J (in press)

  42. Gupta AK, Krishnamurthy HN, Singh Y, Prasad KM, Singh SK (2012) Development of constitutive models for dynamic strain aging regime in austenitic stainless steel 304. Mater Des 45:616–627

    Article  Google Scholar 

  43. Koza JR (1994) Genetic programming II: automatic discovery of reusable programs. MIT, Cambridge

    MATH  Google Scholar 

  44. Garg A, Tai K (2012) Review of genetic programming in modeling of machining processes. In: Proceedings of 2012 international conference on modelling, identification and control (ICMIC2012), Wuhan, China, 24–26 June 2012. IEEE, pp 653–658

  45. Searson DP, Leahy DE, Willis MJ (2010) GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. In: International multiconference of engineers and computer scientists 2010, vol 1, pp 77–80

  46. Hearst MA, Dumais S, Osman E, Platt J, Scholkopf B (1998) Support vector machines. Intell Syst Appl IEEE 13:18–28

    Article  Google Scholar 

  47. Hua S, Sun Z (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17:721–728

    Article  Google Scholar 

  48. Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT Press, Cambridge

    Google Scholar 

  49. Byvatov E, Schneider G (2003) Support vector machine applications in bioinformatics. Appl Bioinform 2:67

    Google Scholar 

  50. Pelckmans K, Suykens JAK, Vangestel T, De Brabanter J, Lukas L, Hamers B et al (2002) LS-SVMlab: a MATLAB/C toolbox for least squares support vector machines. Tutorial. KULeuven-ESA, Leuven

    Google Scholar 

  51. Salgado DR, Alonso FJ (2007) An approach based on current and sound signals for in-process tool wear monitoring. Int J Mach Tools Manuf 47:2140–2152

    Article  Google Scholar 

  52. Salgado DR, Alonso FJ, Cambero I, Marcelo A (2009) In-process surface roughness prediction system using cutting vibrations in turning. Int J Adv Manuf Technol 43:40–51

    Article  Google Scholar 

  53. Gou Z, Fyfe C (2004) A canonical correlation neural network for multicollinearity and functional data. Neural Netw 17:285–293

    Article  MATH  Google Scholar 

  54. Lucignano C, Montanari R, Tagliaferri V, Ucciardello N (2010) Artificial neural networks to optimize the extrusion of an aluminium alloy. J Intell Manuf 21:569–574

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the Singapore Ministry of Education Academic Research Fund through research Grant RG30/10 and Department of Atomic Energy (DAE), Government of India, through Young Scientist Research Award 2009/36/45-BRNS/1751, which the authors gratefully acknowledge.

Author information

Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore

    A. Garg & K. Tai

  2. Department of Mechanical Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, AP, India

    A. K. Gupta

Authors
  1. A. Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Tai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. K. Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Tai.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Garg, A., Tai, K. & Gupta, A.K. A modified multi-gene genetic programming approach for modelling true stress of dynamic strain aging regime of austenitic stainless steel 304. Meccanica 49, 1193–1209 (2014). https://doi.org/10.1007/s11012-013-9873-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11012-013-9873-x

Keywords

Search

Navigation