The emergence of reliable material characterization techniques in automotive and aeronautical industries, in particular sheet metal forming, promises to underpin a novel advance in materials research. In this regard, 5xxx series aluminium alloys deliver the largest formability range and can be deformed at room temperature. This study aims at determining the mechanical properties of the AA5352 aluminium alloy, using digital image correlation. Thus, tensile sheet specimens manufactured from the corresponding alloy are mechanically tested under a uniaxial condition and deformation fields are monitored. Considering the force/displacement response and stress/strain curves, the material Poisson’s ratio, Young’s modulus and anisotropy coefficient in the transverse direction are characterized by the experimental digital image correlation data. It intends to obtain accurate and reliable mechanical properties to be considered in the future processing analyses. Numerically, adopting the experimentally obtained material properties, the Gurson–Tvergaard–Needleman damage model is implemented using finite element method formulation to forecast the ductile fracture performance of the tested AA5352 sheet. The predicted results are then compared with the experimental digital image correlation solution verifying good agreement with the force/displacement response and the deformation fields. Overall, the acquired numerical results imply that the Gurson–Tvergaard–Needleman damage criterion is capable to render an accurate prediction upon a high stress triaxiality state.
ZhangRShaoZLinJ.A review on modelling techniques for formability prediction of sheet metal forming. Int J Lightweight Mater Manuf2018; 1(3): 115–125.
2.
BurgerGBGuptaAKJeffreyPW, et al. Microstructural control of aluminum sheet used in automotive applications. Mater Charact1995; 35(1): 23–39.
3.
JinHWagstaffBGallerneaultM, et al. Microstructure and deformation properties of AA5352/Al-6%Mg sheet produced by the direct chill casting fusion TM process. In: 12th international conference on aluminium alloys, Yokohama, Japan, 5–9 September 2010, pp.2186–2191. Tokyo, Japan: The Japan Institute of Light Metals.
4.
SlotaJŠiserMGajdošI.Failure prediction of axi-symmetric cup in deep drawing and expansion processes. Open Eng2018; 8(1): 354–336.
5.
PeixotoDSousaCRestivoT, et al. Young’s modulus determination: different methods and associated uncertainties. In: Experimental mechanics, new trends and perspectives, Porto, 22–27 July 2012, p.3837. Porto: INEGI.
6.
BruniCCabibboMCiccarelliD, et al. Characterization of double aluminium alloy specimens after ECAP. Procedia Manuf2018; 15: 1517–1524.
7.
FarahaniBVBelinhaJTavaresPJ, et al. A nonlinear simulation of a bi-failure specimen through improved discretisation methods: a validation study. J Strain Anal Eng Des2018; 53(8): 616–629.
8.
KumarADuttaAMakineniSK, et al. In-situ observation of strain partitioning and damage development in continuously cooled carbide-free bainitic steels using micro digital image correlation. Mat Sci Eng A: Struct2019; 757: 107–116.
9.
BabeshkoMEShevchenkoYN.Plastic incompressibility of anisotropic material. Int Appl Mech2005; 41(3): 256–263.
10.
BarnwalVKTewariANarasimhanK, et al. Effect of plastic anisotropy on forming behavior of AA-6061 aluminum alloy sheet. J Strain Anal Eng Des2016; 51(7): 507–517.
11.
FarahaniBVTavaresPJBelinhaJ, et al. Compact tension fracture specimen: experimental and computational implementations on stress intensity factor. J Strain Anal Eng Des2018; 53(8): 630–647.
12.
McCormickNLordJ.Digital image correlation. Mater Today2010; 13(12): 52–54.
13.
KasvayeeKASalomonssonKGhassemaliE, et al. Microstructural strain distribution in ductile iron; comparison between finite element simulation and digital image correlation measurements. Mat Sci Eng A: Struct2016; 655: 27–35.
14.
PereiraJLXavierJGhiassiB, et al. On the identification of earlywood and latewood radial elastic modulus of Pinus pinaster by digital image correlation: a parametric analysis. J Strain Anal Eng Des2018; 53(8): 566–574.
15.
FarahaniBVBelinhaJAmaralR, et al. Extending radial point interpolating meshless methods to the elasto-plastic analysis of aluminium alloys. Eng Anal Bound Elem2019; 100: 101–117.
16.
FarahaniBVEslamiSde MeloFQ, et al. Concept of stress dead zone in cracked plates: theoretical, experimental and computational studies. Fatigue Fract Eng M2019; 42: 2457–2467.
17.
StuparuFAApostolDAConstantinescuDM, et al. Failure analysis of dissimilar single-lap joints. Frat Ed Integrità Strutt2016; 10(36): 69–77.
18.
GariboldiENaumenkoKOzhoga-MaslovskajaO, et al. Analysis of anisotropic damage in forged Al–Cu–Mg–Si alloy based on creep tests, micrographs of fractured specimen and digital image correlations. Mat Sci Eng A: Struct2016; 652: 175–185.
19.
FarahaniBVBelinhaJTavaresPJ, et al. Elastoplastic response and failure assessment of steel alloys: empirical and computational analyses. Fatigue Fract Eng M2019; 42(6): 1247–1261.
20.
FarahaniBVBelinhaJAmaralR, et al. A digital image correlation analysis on a sheet AA6061-T6 bi-failure specimen to predict static failure. Eng Fail Anal2018; 90: 179–196.
21.
BertinMHildFRouxS, et al. Integrated digital image correlation applied to elastoplastic identification in a biaxial experiment. J Strain Anal Eng Des2016; 51(2): 118–131.
22.
AmbuRAymerichFBertolinoF.Investigation of the effect of damage on the strength of notched composite laminates by digital image correlation. J Strain Anal Eng Des2005; 40(5): 451–461.
23.
ClausenAHBørvikTHopperstadOS, et al. Flow and fracture characteristics of aluminium alloy AA5083–H116 as function of strain rate, temperature and triaxiality. Mat Sci Eng A: Struct2004; 364(1–2): 260–272.
24.
TaleffEMTakataKIchitaniK. Hot and warm deformation of AA5182 sheet materials: ductility and microstructure evolution. In: 12th international conference on aluminium alloys, Yokohama, Japan, 5–9 September 2010, pp.1231–1236. Tokyo, Japan: The Japan Institute of Light Metals.
25.
GursonAL.Continuum theory of ductile rupture by void nucleation and growth: part I – yield criteria and flow rules for porous ductile media. J Eng Mater: T ASME1977; 99(1): 2–15.
26.
TvergaardVNeedlemanA.Analysis of the cup-cone fracture in a round tensile bar. Acta Metall1984; 32(1): 157–169.
27.
BonoraN.Identification and measurement of ductile damage parameters. J Strain Anal Eng Des1999; 34(6): 463–478.
28.
WcislikW.Experimental determination of critical void volume fraction fF for the Gurson Tvergaard Needleman (GTN) model. Procedia Struct Integr2016; 2: 1676–1683.
29.
MitchellMRLinkREZhouZ, et al. Ductile damage analysis for small punch specimens of type 304 stainless steel based on GTN model. J Test Eval2009; 37(6): 538–544.
30.
TengBWangWXuY.Ductile fracture prediction in aluminium alloy 5A06 sheet forming based on GTN damage model. Eng Fract Mech2017; 186: 242–254.
31.
Society of Automotive Engineers and American Society for Testing Materials. Metals and alloys in the unified numbering system. 8th ed.Warrendale, PA: Society of Automotive Engineers, 1999.
32.
ASTM E8 / E8M-13:2013. Standard test methods for tension testing of metallic materials.
33.
MazièreMBessonJForestS, et al. Numerical aspects in the finite element simulation of the Portevin-Le Chatelier effect. Comput Method Appl M2010; 199(9–12): 734–754.
34.
AbbadiMHähnerPZeghloulA.On the characteristics of Portevin–Le Chatelier bands in aluminum alloy 5182 under stress-controlled and strain-controlled tensile testing. Mat Sci Eng A: Struct2002; 337(1–2): 194–201.
35.
WatsonMDickRHuangYH, et al. Benchmark 1 – failure prediction after cup drawing, reverse redrawing and expansion. Part A: benchmark description. J Phys Conf Ser2016; 734(2): 022001.
36.
ASTM E517-18:2018. Standard test method for plastic strain ratio r for sheet metal.
37.
TvergaardV.Influence of voids on shear band instabilities under plane strain conditions. Int J Fracture1981; 17(4): 389–407.
38.
KossakowskiPGWciślikW.Effect of critical void volume fraction on results of ductile fracture simulation for S235JR steel under multi-axial stress states. Key Eng Mater2014; 598: 113–118.
39.
QianL-YFangGZengP, et al. Experimental and numerical investigations into the ductile fracture during the forming of flat-rolled 5083-O aluminum alloy sheet. J Mater Process Tech2015; 220: 264–275.
40.
GateaSOuHLuB, et al. Modelling of ductile fracture in single point incremental forming using a modified GTN model. Eng Fract Mech2017; 186: 59–79.
41.
TaherizadehAGreenDEGhaeiA, et al. A non-associated constitutive model with mixed iso-kinematic hardening for finite element simulation of sheet metal forming. Int J Plasticity2010; 26: 288–309.
42.
VoceE.The relationship between stress and strain for homogeneous deformations. J I Met1948; 74: 537–562.
43.
ButcherCChenZWorswickM.A lower bound damage-based finite element simulation of stretch flange forming of Al–Mg alloys. Int J Fracture2007; 142(3–4): 289–298.
44.
ChenZY.The role of heterogeneous particle distribution in the prediction of ductile fracture. PhD Thesis, University of Waterloo, Waterloo, ON, Canada, 2004.
45.
ChenZTWorswickMJLloydDJ.Damage-based finite element modeling of stretch flange forming of aluminum-magnesium alloy. Mater Sci Forum2006; 519–521: 815–820.
46.
KimJGaoXSrivatsanTS.Modeling of void growth in ductile solids: effects of stress triaxiality and initial porosity. Eng Fract Mech2004; 71(3): 379–400.
47.
BrünigMGerkeSHagenbrockV.Micro-mechanical studies on the effect of the stress triaxiality and the Lode parameter on ductile damage. Int J Plasticity2013; 50: 49–65.
48.
SchiffmannRBleckWDahlW.The influence of strain history on ductile failure of steel. Comp Mater Sci1998; 13(1–3): 142–147.
