nach oben

Meccanica

Erschienen in:

01.05.2014

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

verfasst von: A. Garg, K. Tai, A. K. Gupta

Erschienen in: Meccanica | Ausgabe 5/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel Elastoplastic contact of rough surfaces: a line contact model for boundary regime of lubrication

Nächster Artikel Intelligent design of waste heat recovery systems using thermoelectric generators and optimization tools

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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

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 491:434–439CrossRef

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 35:589–595CrossRef

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 39:925–946CrossRefMATHMathSciNet

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

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

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

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

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 47:568–576CrossRef

11.

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

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 526:1–6CrossRef

13.

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

14.

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

15.

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

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 392:156–162CrossRef

17.

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

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 23:639–650CrossRef

19.

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

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 11:79–86CrossRef

21.

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

22.

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

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):1389–1399CrossRef

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):367–379CrossRef

27.

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

28.

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

29.

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

30.

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

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

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

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):1137–1151CrossRef

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):121–129CrossRef

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 45:616–627CrossRef

43.

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

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 13:18–28CrossRef

47.

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

48.

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

49.

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

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

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 47:2140–2152CrossRef

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 43:40–51CrossRef

53.

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

54.

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

Titel: A modified multi-gene genetic programming approach for modelling true stress of dynamic strain aging regime of austenitic stainless steel 304
verfasst von: A. Garg
K. Tai
A. K. Gupta
Publikationsdatum: 01.05.2014
Verlag: Springer Netherlands
Erschienen in: Meccanica / Ausgabe 5/2014
Print ISSN: 0025-6455
Elektronische ISSN: 1572-9648
DOI: https://doi.org/10.1007/s11012-013-9873-x

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 5/2014

2D problem of magneto-thermoelasticity fiber-reinforced medium under temperature dependent properties with three-phase-lag model

Steady mixed convection flow of Maxwell fluid over an exponentially stretching vertical surface with magnetic field and viscous dissipation

Stochastic finite element nonlinear free vibration analysis of piezoelectric functionally graded materials beam subjected to thermo-piezoelectric loadings with material uncertainties

Effect of rotation on ferromagnetic porous convection with a thermal non-equilibrium model

The onset of convection in a nanofluid saturated porous layer using Darcy model with cross diffusion

Velocity dispersion in an elastic plate with microstructure: effects of characteristic length in a couple stress model

Premium Partner

Marktübersichten