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 ORCID:

Ya-zhi ZHU

https://orcid.org/0000-0002-9783-2780

Shi-ping HUANG

https://orcid.org/0000-0002-0092-1753

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Journal of Zhejiang University SCIENCE A 2022 Vol.23 No.6 P.421-442

http://doi.org/10.1631/jzus.A2100369


Identification of ductile fracture model parameters for three ASTM structural steels using particle swarm optimization


Author(s):  Ya-zhi ZHU, Shi-ping HUANG, Hao HONG

Affiliation(s):  Department of Structural Engineering, Tongji University, Shanghai 200092, China; more

Corresponding email(s):   ctasihuang@scut.edu.cn

Key Words:  Parameter calibration, Void growth model (VGM), Gurson-Tvergaard-Needleman (GTN) model, A36 steel, A572 Gr. 50 steel, A992 steel, Particle swarm optimization (PSO)


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Ya-zhi ZHU, Shi-ping HUANG, Hao HONG. Identification of ductile fracture model parameters for three ASTM structural steels using particle swarm optimization[J]. Journal of Zhejiang University Science A, 2022, 23(6): 421-442.

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Abstract: 
Accurate prediction of ductile fracture requires determining the material properties, including the parameters of the constitutive and ductile fracture model, which represent the true material response. Conventional calibration of material parameters often relies on a trial-and-error approach, in which the parameters are manually adjusted until the corresponding finite element model results in a response matching the experimental global response. The parameter estimates are often subjective. To address this issue, in this paper we treat the identification of material parameters as an optimization problem and introduce the particle swarm optimization (PSO) algorithm as the optimization approach. We provide material parameters of two uncoupled ductile fracture models—the Rice and Tracey void growth model (RT-VGM) and the micro-mechanical void growth model (MM-VGM), and a coupled model—the gurson-Tvergaard-Needleman (GTN) model for ASTM A36, A572 Gr. 50, and A992 structural steels using an automated PSO method. By minimizing the difference between the experimental results and finite element simulations of the load-displacement curves for a set of tests of circumferentially notched tensile (CNT) bars, the calibration procedure automatically determines the parameters of the strain hardening law as well as the uncoupled models and the coupled GTN constitutive model. Validation studies show accurate prediction of the load-displacement response and ductile fracture initiation in V-notch specimens, and confirm the PSO algorithm as an effective and robust algorithm for seeking ductile fracture model parameters. PSO has excellent potential for identifying other fracture models (e.‍g., shear modified GTN) with many parameters that can give rise to more accurate predictions of ductile fracture. Limitations of the PSO algorithm and the current calibrated ductile fracture models are also discussed in this paper.

基于粒子群算法的ASTM结构钢延性断裂模型参数识别研究

作者:朱亚智1,黄仕平2,3,洪浩4
机构:1同济大学,建筑工程系,中国上海,200092;2华南理工大学,土木与交通学院,中国广州,510640;3中新国际联合研究院,中国广州,510700;4上海市政工程设计研究总院(集团)有限公司,中国上海,200092
目的:准确预测延性断裂需要确定材料参数(包括本构参数和延性断裂模型参数),以反映真实的材料响应。传统的材料参数标定方法往往依赖于试错法,需手动调整参数,直到相应的有限元模型得到与实验结果相匹配的材料力学响应。参数估计的过程通常是主观的。为了解决这一问题,本文将材料断裂参数辨识问题转化为优化问题,并引入粒子群优化(PSO)算法作为优化方法。
创新点:1.基于粒子群优化算法,给出了自动识别钢材应变硬化参数的方法;2.建立了ASTM结构钢材Gurson-Tvergaard-Needleman(GTN)损伤模型的参数识别方法。
方法:1.通过圆形缺口杆件的拉伸试验,以试验和有限元模拟的载荷-位移曲线差值为目标方程,采用PSO优化算法及参数自动校准程序,以最小化目标方程确定应变硬化准则和非耦合断裂模型的参数;2.基于文献调研的结果,确定GTN模型各参数的合理取值范围,以此确定PSO算法中参数的取值,从而能够高效、准确地确定GTN参数。
结论:1. PSO算法能够准确地预测V形缺口试件的载荷-位移响应和延性断裂萌发,是一种识别延性断裂模型参数的有效算法;2.PSO在识别其他具有更多参数的断裂模型(如剪切修正GTN模型)方面具有很好的潜力,这些模型可以更准确地预测延性断裂。

关键词:参数校准;孔洞增长模型;GTN模型;A36钢;A572 Gr. 50钢;A992钢;粒子群优化

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]AbbasiM, ShafaatMA, KetabchiM, et al., 2012a. Application of the GTN model to predict the forming limit diagram of IF-steel. Journal of Mechanical Science and Technology, 26(2):345-352.

[2]AbbasiM, BagheriB, KetabchiM, et al., 2012b. Application of response surface methodology to drive GTN model parameters and determine the FLD of tailor welded blank. Computational Materials Science, 53(1):368-376.

[3]AbbassiF, MistouS, ZghalA, 2013. Failure analysis based on microvoid growth for sheet metal during uniaxial and biaxial tensile tests. Materials & Design, 49:638-646.

[4]AbendrothM, KunaM, 2006. Identification of ductile damage and fracture parameters from the small punch test using neural networks. Engineering Fracture Mechanics, 73(6):710-725.

[5]AliAN, HuangSJ, 2019. Ductile fracture behavior of ECAP deformed AZ61 magnesium alloy based on response surface methodology and finite element simulation. Materials Science and Engineering: A, 746:197-210.

[6]AmaralR, TeixeiraP, AzinpourE, et al., 2016. Evaluation of ductile failure models in sheet metal forming. MATEC Web of Conferences, 80:03004.

[7]AndersonTL, 2017. Fracture Mechanics: Fundamentals and Applications, 3rd Edition. CRC Press, Boca Raton, USA.

[8]BenzergaAA, LeblondJB, 2010. Ductile fracture by void growth to coalescence. Advances in Applied Mechanics, 44:169-305.

[9]BernauerG, BrocksW, 2002. Micro-mechanical modelling of ductile damage and tearing–results of a European numerical round robin. Fatigue & Fracture of Engineering Materials & Structures, 25(4):363-384.

[10]BridgmanPW, 1964. Studies in Large Plastic Flow and Fracture. Harvard University Press, London, UK.

[11]BrinnelV, LangenbergJ, KordtomeikelF, et al., 2015. Numerical derivation of strain-based criteria for ductile failure: discussions on sensitivity and validity. Engineering Fracture Mechanics, 148:421-440.

[12]ChaWG, KimN, 2014. Quantification of micro-cracks on the bending surface of roll formed products using the GTN model. Metals and Materials International, 20(5):841-850.

[13]ChuCC, NeedlemanA, 1980. Void nucleation effects in biaxially stretched sheets. Journal of Engineering Materials and Technology, 102(3):249-256.

[14]CuestaII, AlegreJM, LacalleR, 2010. Determination of the Gurson–Tvergaard damage model parameters for simulating small punch tests. Fatigue & Fracture of Engineering Materials & Structures, 33(11):703-713.

[15]SystèmesDassault, 2016. ABAQUS Analysis User’s Manual. Dassault Systèmes Simulia, Providence, USA.

[16]DefaisseC, MazièreM, MarcinL, et al., 2018. Ductile fracture of an ultra-high strength steel under low to moderate stress triaxiality. Engineering Fracture Mechanics, 194:301-318.

[17]EberhartR, KennedyJ, 1995. A new optimizer using particle swarm theory. Proceedings of the 6th International Symposium on Micro Machine and Human Science, p.39-43.

[18]EberleA, KlingbeilD, SchickerJ, 2000. The calculation of dynamic JR-curves from the finite element analysis of a Charpy test using a rate-dependent damage model. Nuclear Engineering and Design, 198(1-2):75-87.

[19]GateaS, LuB, OuHG, et al., 2015. Numerical simulation and experimental investigation of ductile fracture in SPIF using modified GTN model. MATEC Web of Conferences, 21:04013.

[20]GursonAL, 1977. Continuum theory of ductile rupture by void nucleation and growth: part I—yield criteria and flow rules for porous ductile media. Journal of Engineering Materials and Technology, 99(1):2-15.

[21]HancockJW, BrownDK, 1983. On the role of strain and stress state in ductile failure. Journal of the Mechanics and Physics of Solids, 31(1):1-24.

[22]HaušildP, NedbalI, BerdinC, et al., 2002. The influence of ductile tearing on fracture energy in the ductile-to-brittle transition temperature range. Materials Science and Engineering: A, 335(1-2):164-174.

[23]JiaLJ, IkaiT, ShinoharaK, et al., 2016. Ductile crack initiation and propagation of structural steels under cyclic combined shear and normal stress loading. Construction and Building Materials, 112:69-83.

[24]KamiA, DarianiBM, VaniniAS, et al., 2015. Numerical determination of the forming limit curves of anisotropic sheet metals using GTN damage model. Journal of Materials Processing Technology, 216:472-483.

[25]KanvindeAM, DeierleinGG, 2006. The void growth model and the stress modified critical strain model to predict ductile fracture in structural steels. Journal of Structural Engineering, 132(12):1907-1918.

[26]KhandelwalK, El-TawilS, 2007. Collapse behavior of steel special moment resisting frame connections. Journal of Structural Engineering, 133(5):646-655.

[27]KiranR, KhandelwalK, 2013. A micromechanical model for ductile fracture prediction in ASTM A992 steels. Engineering Fracture Mechanics, 102:101-117.

[28]KiranR, KhandelwalK, 2014a. Experimental studies and models for ductile fracture in ASTM A992 steels at high triaxiality. Journal of Structural Engineering, 140(2):04013044.

[29]KiranR, KhandelwalK, 2014b. Fast-to-compute weakly coupled ductile fracture model for structural steels. Journal of Structural Engineering, 140(6):04014018.

[30]KiranR, KhandelwalK, 2014c. Gurson model parameters for ductile fracture simulation in ASTM A992 steels. Fatigue & Fracture of Engineering Materials & Structures, 37(2):171-183.

[31]KongDY, YangB, 2020. Enhanced voids growth model for ductile fracture prediction of high-strength steel Q690D under monotonic tension: experiments and numerical simulation. Journal of Structural Engineering, 146(6):04020107.

[32]KossakowskiPG, 2012. Prediction of ductile fracture for S235JR steel using the stress modified critical strain and Gurson-Tvergaard-Needleman models. Journal of Materials in Civil Engineering, 24(12):1492-1500.

[33]KulawinskiD, IdingK, SchornsteinR, et al., 2020. Improvement of the inverse finite element analysis approach for tensile and toughness predictions by means of small punch technique. ASME Turbo Expo: Turbomachinery Technical Conference and Exposition.

[34]KumarP, DuttaBK, ChattopadhyayJ, 2017. Fracture toughness prediction of reactor grade materials using pre-notched small punch test specimens. Journal of Nuclear Materials, 495:351-362.

[35]LemaitreJ, 1985. A continuous damage mechanics model for ductile fracture. Journal of Engineering Materials and Technology, 107(1):83-89.

[36]LiH, PanXF, YuanH, 2015. A nonlocal treatment technique based on the background cell concept for micro-mechanical damage modeling. Acta Mechanica, 226(5):1529-1547.

[37]LiJC, LiSP, XieZY, et al., 2015. Numerical simulation of incremental sheet forming based on GTN damage model. The International Journal of Advanced Manufacturing Technology, 81(9):2053-2065.

[38]LinseT, KunaM, ViehrigHW, 2014. Quantification of brittle-ductile failure behavior of ferritic reactor pressure vessel steels using the small-punch-test and micromechanical damage models. Materials Science and Engineering: A, 614:136-147.

[39]MahnkenR, 2002. Theoretical, numerical and identification aspects of a new model class for ductile damage. International Journal of Plasticity, 18(7):801-831.

[40]MalcherL, PiresFMA, deSá JMAC, 2014. An extended GTN model for ductile fracture under high and low stress triaxiality. International Journal of Plasticity, 54:193-228.

[41]MansouriLZ, ChalalH, Abed-MeraimF, 2014. Ductility limit prediction using a GTN damage model coupled with localization bifurcation analysis. Mechanics of Materials, 76:64-92.

[42]McClintockFA, 1968. A criterion for ductile fracture by the growth of holes. Journal of Applied Mechanics, 35(2):363-371.

[43]NeimitzA, GalkiewiczJ, DziobaI, 2018. Calibration of constitutive equations under conditions of large strains and stress triaxiality. Archives of Civil and Mechanical Engineering, 18(4):1123-1135.

[44]NguyenHH, NguyenTN, VuHC, 2018. Ductile fracture prediction and forming assessment of AA6061-T6 aluminum alloy sheets. International Journal of Fracture, 209(1-2):143-162.

[45]PalS, WathugalaGW, KunduS, 1996. Calibration of a constitutive model using genetic algorithms. Computers and Geotechnics, 19(4):325-348.

[46]PineauA, BenzergaAA, PardoenT, 2016. Failure of metals I: brittle and ductile fracture. Acta Materialia, 107:424-483.

[47]RiceJR, TraceyDM, 1969. On the ductile enlargement of voids in triaxial stress fields. Journal of the Mechanics and Physics of Solids, 17(3):201-217.

[48]RossollA, BerdinC, PrioulC, 2002. Determination of the fracture toughness of a low alloy steel by the instrumented Charpy impact test. International Journal of Fracture, 115(3):205-226.

[49]RousselierG, 1987. Ductile fracture models and their potential in local approach of fracture. Nuclear Engineering and Design, 105(1):97-111.

[50]SajidHU, KiranR, 2018. Influence of high stress triaxiality on mechanical strength of ASTM A36, ASTM A572 and ASTM A992 steels. Construction and Building Materials, 176:129-134.

[51]SeupelA, KunaM, 2019. A gradient-enhanced damage model motivated by engineering approaches to ductile failure of steels. International Journal of Damage Mechanics, 28(8):1261-1296.

[52]SeupelA, HütterG, KunaM, 2020. On the identification and uniqueness of constitutive parameters for a non-local GTN-model. Engineering Fracture Mechanics, 229:106817.

[53]SmithC, KanvindeA, DeierleinG, 2017. Calibration of continuum cyclic constitutive models for structural steel using particle swarm optimization. Journal of Engineering Mechanics, 143(5):04017012.

[54]SoyarslanC, GülçimenB, BargmannS, et al., 2016. Modeling of fracture in small punch tests for small- and large-scale yielding conditions at various temperatures. International Journal of Mechanical Sciences, 106:266-285.

[55]SteglichD, BrocksW, 1998. Micromechanical modelling of damage and fracture of ductile materials. Fatigue & Fracture of Engineering Materials & Structures, 21(10):1175-1188.

[56]SunGQ, SunFY, CaoFL, et al., 2015. Numerical simulation of tension properties for Al-Cu alloy friction stir-welded joints with GTN damage model. Journal of Materials Engineering and Performance, 24(11):4358-4363.

[57]SunQ, LuYB, ChenJJ, 2020. Identification of material parameters of a shear modified GTN damage model by small punch test. International Journal of Fracture, 222(1-2):25-35.

[58]TengBA, WangWN, LiuYQ, et al., 2014. Bursting prediction of hydroforming aluminium alloy tube based on Gurson-Tvergaard-Needleman damage model. Procedia Engineering, 81:2211-2216.

[59]TengBG, WangWN, XuYC, 2017. Ductile fracture prediction in aluminium alloy 5A06 sheet forming based on GTN damage model. Engineering Fracture Mechanics, 186:242-254.

[60]TvergaardV, 1981. Influence of voids on shear band instabilities under plane strain conditions. International Journal of Fracture, 17(4):389-407.

[61]TvergaardV, NeedlemanA, 1984. Analysis of the cup-cone fracture in a round tensile bar. Acta Metallurgica, 32(1):157-169.

[62]Vaz JrM, Muñoz-RojasPA, CardosoEL, et al., 2016. Considerations on parameter identification and material response for Gurson-type and Lemaitre-type constitutive models. International Journal of Mechanical Sciences, 106:254-265.

[63]WangLY, LiL, 2017. Parameter identification of GTN model using response surface methodology for high-strength steel BR1500HS. Journal of Materials Engineering and Performance, 26(8):3831-3838.

[64]WenHJ, MahmoudH, 2016. New model for ductile fracture of metal alloys. I: monotonic loading. Journal of Engineering Mechanics, 142(2):04015088.

[65]YanS, ZhaoXZ, 2018. A fracture criterion for fracture simulation of ductile metals based on micro-mechanisms. Theoretical and Applied Fracture Mechanics, 95:127-142.

[66]YanS, ZhaoXZ, WuAH, 2018. Ductile fracture simulation of constructional steels based on yield-to-fracture stress–strain relationship and micromechanism-based fracture criterion. Journal of Structural Engineering, 144(3):04018004.

[67]YanYX, SunQ, ChenJJ, et al., 2013. The initiation and propagation of edge cracks of silicon steel during tandem cold rolling process based on the Gurson‍–‍Tvergaard‍–Needleman damage model. Journal of Materials Processing Technology, 213(4):598-605.

[68]YingL, WangDT, LiuWQ, et al., 2018. On the numerical implementation of a shear modified GTN damage model and its application to small punch test. International Journal of Material Forming, 11(4):527-539.

[69]YoshidaF, UrabeM, HinoR, et al., 2003. Inverse approach to identification of material parameters of cyclic elasto-plasticity for component layers of a bimetallic sheet. International Journal of Plasticity, 19(12):2149-2170.

[70]YuHL, TieuK, LuC, et al., 2014. Tensile fracture of ultrafine grained aluminum 6061 sheets by asymmetric cryorolling for microforming. International Journal of Damage Mechanics, 23(8):1077-1095.

[71]YuenyongJ, SuthonM, KingklangS, et al., 2018. Formability prediction for tube hydroforming of stainless steel 304 using damage mechanics model. Journal of Manufacturing Science and Engineering, 140(1):011006.

[72]YunGJ, ShangS, 2011. A self-optimizing inverse analysis method for estimation of cyclic elasto-plasticity model parameters. International Journal of Plasticity, 27(4):576-595.

[73]ZhangTR, LuK, ManoA, et al., 2021. A novel method to uniquely determine the parameters in Gurson–Tvergaard–Needleman model. Fatigue & Fracture of Engineering Materials & Structures, 44(12):3399-3415.

[74]ZhangWW, CongS, 2016. Failure analysis of SUS304 sheet during hydro-bulging based on GTN ductile damage model. The International Journal of Advanced Manufacturing Technology, 86(1):427-435.

[75]ZhangY, LorentzE, BessonJ, 2018. Ductile damage modelling with locking-free regularised GTN model. International Journal for Numerical Methods in Engineering, 113(13):1871-1903.

[76]ZhaoPJ, ChenZH, DongCF, 2016. Failure analysis based on microvoids damage model for DP600 steel on in-situ tensile tests. Engineering Fracture Mechanics, 154:152-168.

[77]ZhongJ, XuT, GuanK, et al., 2016. Determination of ductile damage parameters using hybrid particle swarm optimization. Experimental Mechanics, 56(6):945-955.

[78]ZhuYZ, EngelhardtMD, 2018a. A nonlocal triaxiality and shear dependent continuum damage model for finite strain elastoplasticity. European Journal of Mechanics-A/Solids, 71:16-33.

[79]ZhuYZ, EngelhardtMD, 2018b. Prediction of ductile fracture for metal alloys using a shear modified void growth model. Engineering Fracture Mechanics, 190:491-513.

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