# Damage and fracture during sheet-metal forming of alloy 718

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## Abstract

Forming nickel-based superalloy aero-engine components is a challenging process, largely because of the risk of high degree of springback and issues with formability. In the forming tests conducted on alloy 718 at room temperature, open fractures are observed in the drawbead regions, which are not predicted while evaluating the formability using the traditional forming-limit diagram (FLD). This highlights the importance of an accurate prediction of failure during forming as, in some cases, may severely influence the springback and thereby the accuracy of the predicted shape distortions, leading the final shape of the formed component out of tolerance. In this study, the generalised incremental stress-state dependent damage model (GISSMO) is coupled with the isotropic von Mises and the anisotropic Barlat Yld2000-2D yield criteria to predict the material failure in the forming simulations conducted on alloy 718 using LS-DYNA. Their effect on the predicted effective plastic strains and shape deviations is discussed. The failure and instability strains needed to calibrate the GISSMO are directly obtained from digital image correlation (DIC) measurements in four different specimen geometries i.e. tensile, plane strain, shear, and biaxial. The damage distribution over the drawbeads is measured using a non-linear acoustic technique for validation purposes. The numerical simulations accurately predict failure at the same regions as those observed in the experimental forming tests. The expected distribution of the damage over the drawbeads is in accordance with the experimental measurements. The results highlight the potential of considering DIC to calibrate the GISSMO in combination with an anisotropic material model for forming simulations in alloy 718.

## Keywords

Alloy 718 Damage Fracture GISSMO Non-linear acoustic technique Optimisation## Introduction

Currently, the reductions in fuel consumption and carbon dioxide emissions are key factors for the aviation industry because of major concerns regarding climate change and more restrictive environmental laws. One of the methods of reducing both fuel consumption and CO_{2} emissions is by significantly decreasing the weight of vehicles while increasing the efficiency of the engine. According to the Advisory Council for Aeronautics Research in Europe (ACARE), the aviation industry has set targets for 2050 to reduce CO_{2} emissions by 75% and NO_{x} by 90% compared to levels observed in 2000 [1]. To meet these requirements, the European aero-engine industry is continuously focusing on improved engine designs and alternative manufacturing methods for load-carrying structures in advanced materials, such as titanium and nickel-based superalloys. Unlike conventional large-scale single castings, new manufacturing methods involve sheet-metal parts, small castings, and forgings assembled using welding. The new manufacturing methods imply possibilities to achieve flexible designs, where each part is made of the most suitable material state with advantages such as reduced product cost, lower weight, and increased engine efficiency.

The generalised incremental stress-state dependent damage model (GISSMO) is a model used to predict the ductile damage, which is implemented into the finite element (FE) code LS-DYNA and it can be coupled with existing elasto–plastic material models such as von Mises (*MAT_024), Barlat Yld2000-2D (*MAT_133), or Barlat ‘89 (*MAT_226) for both metal forming and crashworthiness simulations. In the model, the failure is assumed to occur because of the plastic-deformation history of the material, implying that the damage in the material is accumulated and fracture is expected when the damage reaches a critical value. To calibrate the GISSMO model, the fracture and instability strains versus triaxiality curves are required for different stress states which, in this study, are obtained through testing four different geometries until fracture using the digital image correlation system ARAMIS™. This approach has also been used by other authors in different materials such as Anderson [3] in the dual-phase steel sheet grade DP780, Till [4] in the HCT980C complex-phase steel or Huang [5] in an USIBOR steel grade. Another approach consisting of calibrating the GISSMO by inverse modelling has also been applied by Andrade [6] in DP800. Heibel [7] combined these two approaches and calibrated the GISSMO in DP1000 using both experimental data together with DIC measurements and inverse modelling. No previous applications of the GISSMO in nickel-based superalloys have been found in literature. A subsequent parameter calibration is performed using LS-OPT considering the smallest element length to obtain both the damage and fading exponents and to evaluate the hardening after the necking process. The mesh-size independence of the damage model is captured through the regularisation factors versus element size curve. It is determined after simulating a uniaxial tensile test with varying mesh sizes from 1 to 5 mm.

In this study, the GISSMO is calibrated using DIC and employed to predict the damage and failure in the forming simulation of the outer case component in alloy 718. The damage distribution over the part is measured using the NAW^{®} method with the help of non-linear ultrasounds [8, 9]. The predicted FEA results are compared with both experimental forming tests and damage measurements at room temperature.

## Material

Chemical composition of the specific batch of alloy 718 [wt%]

| | | | | | | | | | |

0.019 | <0.0003 | 0.07 | 0.07 | 17.78 | 2.88 | 0.15 | 1.05 | 0.48 | 0.004 | <0.01 |

| | | | | | | | | | |

18.47 | 0.03 | 53.86 | 0.008 | 5.05 | <0.01 | 0.02 | 0.03 | 5.06 | 1.53 | 54.01 |

## Experimental procedure

This section briefly describes the experimental procedures followed to determine the mechanical properties of the material and the experimental reference data for the calibration of the material models used. It also includes the non-linear acoustic technique used to compare the predicted damage values with experimental measurements.

### Material-characterization tests

^{−1}up to an engineering strain of 0.2% followed by a strain rate of 0.004 s

^{−1}until fracture. The deformation of every test was continuously monitored using a facet size/step of 19 × 15 in the ARAMIS™ digital image correlation system after applying a stochastic pattern on the surface of the specimens. This facet was equivalent to the smallest element size of 1 mm in the forming simulation. Fig. 5 shows the geometry of the tensile specimens.

A viscous bulge test [10] was performed at room temperature using a Wemhöner hydraulic press with a stamping force capability of 1300 tons at 5 mm/s to induce a balanced biaxial stress state in the specimen and determine the equibiaxial yield stress without the influence of friction. The diameter of the blank is 200 mm. The ARAMIS™ cameras were mounted on the upper die as depicted in Fig. 4b.

Experimental yield stresses and R-values used to obtain the Barlat Yld2000-2D anisotropic parameters (α_{1} – α_{8}) for alloy 718 at room temperature

| | | | | | | |
---|---|---|---|---|---|---|---|

504.62 | 491.12 | 481.67 | 538.00 | 0.761 | 0.912 | 0.960 | 1.0000 |

| | | | | | | |

0.8711 | 1.1130 | 0.8151 | 0.9941 | 0.9875 | 0.8421 | 1.0090 | 1.1720 |

^{−1}up to an engineering strain of 0.2% followed by a strain rate of 0.0065 s

^{−1}until fracture. The B specimens were tested at a strain rate of 0.003 s

^{−1}. Both the instability and failure strains were measured and evaluated using DIC with a facet size/step of 15 × 12 (A10), 18 × 14 (PS), and 15 × 12 (B) equivalent to a 1 mm element size. The S45 geometry was evaluated with a facet size/step of 10 × 7 which corresponded to an element size of 0.5 mm.

### Non-linear acoustic technique

^{®}) is a non-linear acoustic method used to measure the damage distribution over the drawbeads after forming. The method detects nonlinearities in the material by analysing the distortion of a low-amplitude wave which is introduced into the specimen through a transducer and by tapping a hammer on 47 different measurement points, therefore inspecting the material locally, see Fig. 3a. Cracks and other defects distort the acoustic waves generated when propagating over the drawbeads in a non-linear manner. The receiver acquires the signal and analyses de nonlinearity content of the propagated waves, as shown in Fig. 7a. The obtained signal shows the total value of the imperfections in the measured part, herein referred to as the damage value. For an undamaged specimen, a single frequency sinusoid is transmitted through the material. The signal is received without any distortion and the response from the transmitted signal is then the same undistorted sinusoid, see Fig. 7b. On the other hand, for a damaged specimen, nonlinearities such as higher harmonics appear in the received signal as depicted in Fig. 7c. The number and amplitude of nonlinearities is proportional to the amount of damage or defects in the specimen investigated. A damage value is obtained for each measurement point c.f. Fig. 17c.

## Numerical procedure

*f*is the yield function, Φ

^{′}and Φ

^{′′}are two isotropic convex functions with respect to the three principal stresses, s

_{1}and s

_{2}are the principal deviatoric stresses, \( \overline{\sigma} \) is the effective stress, and

*a*is a material coefficient based on the crystallographic structure of the material. The exponent

*a*is suggested in the Barlat and Lian [13] yield criterion to be equal to six for materials with a BCC crystal structure and eight for materials with an FCC crystal structure.

*X*=

*C*∙

*s*to each of the isotropic functions defined by Eqs. 2 and 3, the following equations are obtained:

where *s* is the deviatoric stress tensor, C^{′} and C^{′′} are the linear transformations, and X′_{1, 2} and X ′ ′_{1, 2} are the principal values of the linearly transformed stress tensors.

The Barlat Yld2000-2D material model comprises eight material parameters which are determined, in this study, by conducting uniaxial tensile tests in three different directions with respect to the rolling direction and an equi-biaxial test wherein a balanced biaxial stress state is obtained. From these tests, the yield stresses and R-values are determined and used as inputs to the material model. The von Mises is represented using the Barlat material model. In this case, the yield stresses correspond to SIG00 in Table 2 and the R-values are set to one. For alloy 718, the exponent *a* is assumed eight for the Barlat and two for the von Mises yield criteria. Fig. 10 shows the calibrated yield surfaces for both yield criteria at room temperature along with the experimental yield stresses in the three different rolling directions, and for the biaxial yield stress state. In addition, the matrices C^{′} and C^{′′} can be expressed in terms of the eight anisotropy parameters as listed in Table 2.

## Damage and fracture model

*D*reaches a critical value i.e.

*D*= 1. Based on the study by Kachanov [16], the damage could be defined as the reduction in the nominal section area in a representative volume element due to micro-cracks and micro-voids as follows:

*A*

_{0}is the reference cross-sectional area, and

*A*

_{eff}is the effective cross-sectional area after subtracting the areas of the micro-cracks and micro-voids from the reference area. The formulation of the GISSMO is based on the damage model proposed by Johnson and Cook [17] where the linear accumulation of the damage

*D*depends on the ratio of the failure strain ε

_{f}to the actual equivalent plastic strain increment

*dε*

^{p}[18]:

*d*

_{1}…

*d*

_{5}are material constants, \( {\dot{\varepsilon}}_p \) is the rate of the von Mises equivalent plastic strain, \( {\dot{\varepsilon}}_0 \) is the reference strain rate,

*η*is the stress triaxiality, and

*T*is the temperature. As adiabatic heating usually does not play an important role in crash situations, the temperature dependency can be neglected, and Eq. 8 [18] can be rewritten as:

*ε*

_{f}is constant [18]:

*D*is the damage value (0 ≤

*D*≤ 1),

*n*is the damage exponent,

*ε*

_{f}(

*η*) is the fracture strain as a function of the triaxiality

*η*, and Δ

*ε*

_{p}is the plastic-strain increment. Assuming plane stress conditions (

*σ*

_{3}= 0), the triaxiality is defined as the ratio of the mean stress to the equivalent stress [15]:

Here, *σ*_{m} is the hydrostatic stress, *σ*_{v} is the equivalent von Mises stress, and *σ*_{1} and *σ*_{2} are the two principal stresses. The plastic deformation occurs when *σ*_{v} reaches the yield strength of the material. Nonetheless, the failure strains are input in this work as a weighing function defined as the curve of failure strain vs. triaxiality.

*F*is the forming intensity,

*n*is the new accumulation exponent,

*ε*

_{p, loc}is the equivalent plastic strain for localisation, and Δ

*ε*

_{p}is the increment in the current equivalent plastic strain. The forming intensity parameter

*F*is then accumulated in a manner similar to that of the damage parameter

*D*; however, a different weighting function is employed, which is defined as the curve of the instability strain vs. triaxiality. The forming intensity represents the onset of material instability and marks the beginning of the need for regularisation of different mesh sizes. The basic idea is to regularise the amount of energy dissipated during crack development and propagation. When

*F*reaches unity, the coupling of the accumulated damage to the stresses is initiated by modifying the effective-stress concept proposed by Lemaitre [20]:

*σ*

^{∗}is the stress coupled to the damage, and

*σ*is the current stress. By combining the process of material instability, a damage threshold could be defined [19]. As soon as the damage parameter

*D*reaches this value, the damage and flow stresses will be coupled. The current implementation helps in either entering the damage threshold as a fixed input parameter or using the damage value corresponding to the instability point. After reaching the post-critical range of the deformation or beyond the point of instability, a critical damage

*D*

_{CRIT}is determined and is used to calculate the effective stress tensor as follows:

*m*is the instability exponent governing the rate of stress fading, which directly influences the amount of energy dissipated during the element fade out. Both the damage exponent

*n*(dmgexp) and fading exponent

*m*(fadexp) control the way in which the true plastic stress–strain curve decreases (softening). When the material is fully damaged (

*D*= 1), the stress in the integration point will be zero. When a user-defined number of integration points have failed, the element is deleted.

## Calibration

The GISSMO is implemented into the LS-DYNA using the keyword *MAT_ADD_EROSION and is activated using the first flag IDAM = 1. When DMGTYP = 1, the damage is accumulated, and element failure occurs when *D* reaches unity in each of its seven integration points through the thickness. To calibrate the model, the inputs of the two load curves are required: the failure curve LCSDG, which defines the equivalent plastic strain required for failure vs. triaxiality, and the instability curve ECRIT, which defines the critical equivalent plastic strain vs. triaxiality. The experimental strain data is obtained after testing the geometries of the four specimens (A10, PS, S45, and B) corresponding to different stress states, ranging from shear to biaxial. Seventeen specimens are tested: 3 (A10), 5 (PS), 7 (S45), and 2 (B). The fracture and instability strains in each test are directly obtained using DIC at the instability point (maximum force) and at the last stage prior to the fracture, respectively. The stress triaxiality values are calculated using the algorithm proposed by Marth [21], which is used to export the local deformation gradients from ARAMIS™ to the algorithm and obtain the true stress–strain relationship beyond the point of necking until fracture. Using the von Mises yield criterion, the local strain state is used to calculate the local stress state while assuming a plane-stress condition. Fig. 11a shows the strain paths as a function of the triaxiality of the specimens tested for each geometry. Each strain path has been smoothed using the smoothing-spline function in Matlab R2014b. The average values of the failure and instability strains and their corresponding stress triaxiality are obtained for each specimen geometry. The failure and instability curves are obtained after fitting a grade 5 polynomial through the average experimental points in Matlab R2014b, as depicted in Fig. 11b.

Optimised GISSMO parameters in alloy 718

| | | | | | | | |
---|---|---|---|---|---|---|---|---|

2.59695 | 1.2766 | 0.6300 | 0.5605 | 0.7196 | 22.13 | 8.86 | 9.50 | |

0.6325 | 0.5839 | 0.7281 | 22.55 | 8.80 | 9.00 | | ||

0.39 | 4.01 | 1.17 | 1.86 | 0.68 | 5.56 | |

To obtain mesh-size independence, the fracture strain is scaled using a factor that depends on the element size. The scale factor, or regularisation factor, is determined after simulating the A80 uniaxial tensile test specimen with different mesh sizes varying from 1 to 5 mm. The last input for calibrating the GISSMO is a load curve termed the regularisation curve, or LCREGD, wherein the abscissa and ordinate represent the element size and regularisation factors, respectively, as shown in Figs. 14 and 15.

## Results

## Discussion

The uniaxial tensile tests conducted in the three different directions with respect to the rolling direction give similar yield stresses, with the variation between the maximum (SIG00) and the minimum (SIG90) being less than 5%. The difference between the highest (R90) and lowest (R00) Lankford coefficients increases up to 21%, see Fig. 10b and Table 2. These results indicate that the specific batch of alloy 718 can be considered anisotropic in strain yet quite isotropic in stress. Therefore, the anisotropic Barlat Yld2000-2D material model is chosen for the forming simulations.

The A10, PS, and S45 tests do not follow a strain path of constant triaxiality during the loading, see Fig. 11a. Effelsberg [19] concluded that this behaviour is largely because of the geometrical changes in the section during deformation. The experimental strain paths are in good agreement with the theoretical stress triaxiality values found in literature by Haufe [22]. A strain path of constant triaxiality is used in the B geometry tests because of the inability of the algorithm proposed by Marth [21] in calculating the stress triaxiality values from the biaxial test. The LCSDG and ECRIT curves in Fig. 11b are bounded by equivalent plastic strains greater than zero.

The accuracy of the fracture strains measured on the surface of the specimens via optical strain measurements such as ARAMIS™ has been extensively discussed by other authors. On the one hand, Till [4] concluded for the complex phase steel that failure strains should be obtained using thickness measurements since surface strain measurements may underestimate the real failure strains. On the other hand, Heibel [7] found that the failure onset is overestimated for every stress state when the failure strains are obtained from tactilely measured thickness in a DP1000 steel sheet. However, the onset of fracture initiation could be accurately predicted when the experimental points of the fracture curve in GISSMO were inversely determined based on optically-measured equivalent plastic strains at fracture and then optimized based on force-displacement curves. A similar conclusion was reached by Huang [5] in a USIBOR steel grade where they validated the accuracy of the surface strain measurements using DIC after comparison with the measured thinning from equibiaxial tests. In this work, the failure strains measured on the surface of the GISSMO specimens are considered to be accurate enough to calibrate the model.

In the present calibration, the identified damage and fading exponents are listed in Table 3. The force vs. displacement curves in Fig. 12 reproduce fairly accurate the behaviour of the geometries tested. For the PS and S45 geometries, the simulated softening part of the force vs. displacement curve (PS_OPT and S45_OPT) fit reasonably well with the experimental one (PS_avg and S45_avg) after parameter identification in LS-OPT. In contrast, the simulated (A10_OPT) curve for the A10 geometry exhibits an earlier softening compared to the experimental one (A10_avg). The predicted failure strains for the three considered geometries (A10epsf, PSepsf, and S45epsf) are slightly lower than the experimental ones but within less than 4%. The simulated failure displacements (A10zf, PSzf, and S45zf) have a maximum error of 5.56% compared to the experiments.

The regularisation curve is calibrated only considering uniaxial loading (*η* = 1/3). The effect of using the regularisation curve but for different triaxialities is not studied in this work. The A80 geometry is chosen because it allows to run the simulations with varying mesh sizes from 1 to 5 mm while maintaining a great accuracy of the geometry of the specimen. Other geometries such as the PS (*η* = 1/√3), and S45 (*η* = 0) contain smaller measurement areas than the A80, making the highest element size of 5 mm too large to accurately represent the correct geometry of the measurement area i.e. radii of the notches.

With respect to the forming simulations, the isotropic von Mises predicts slightly higher effective plastic strains over the outer case geometry compared to the anisotropic Barlat Yld2000-2D material model, see Fig. 16a and b, respectively. The measured damage distribution over the upper drawbead in Fig. 17c is in better agreement with the predicted damage values considering the anisotropic material model c.f. Fig. 17b where the highest damage values (presented in red) correspond to the first elements deleted from the centre of the drawbead. The alternation of high and low damage values along the drawbead, as well as higher damage values on the right-hand side of the drawbead compared to those on the left-hand side where the damage values are lower are also captured.

The predicted effective plastic strains for the trimmed outer case geometry are also lower when considering the anisotropic Barlat compared to the isotropic von Mises, as depicted in Fig. 18. Overall, the predicted distribution of shape deviation considering both material models is in good agreement with the measured one c.f. Fig. 19. The anisotropic Barlat model predicts the shape deviations over the part more accurately than the von Mises, especially along the lower-left edge and around the hole. The open fractures observed in the drawbead regions could influence the behaviour of the material and therefore the resulting shape distortions.

## Conclusions

In this study, the GISSMO is coupled with the isotropic von Mises and the anisotropic Barlat Yld2000-2D yield criteria to simulate the behaviour of alloy 718 in a forming procedure at room temperature using LS-DYNA. The effect of using both models in the predicted amount of shape deviation and the distribution of damage over the part is discussed. The calibration procedure of the GISSMO is also evaluated. The strain paths for the geometries tested are in good agreement with the theoretical stress triaxiality values found in literature. The parameter-optimisation procedure yields close results with the failure strains measured with DIC. The use of the damage model is found to be of major importance in predicting localisation and failure. The GISSMO can predict fracture in the forming process studied whereas the traditional FLD does not indicate any risk of failure. The predicted shape distortions are compared to the 3D-scanned measurements showing a closer agreement when considering the anisotropic model. The NAW^{®} method can determine the distribution of the damage over the part with great accuracy. These measurements correlate best with the predicted distribution of the damage using the anisotropic yield criterion. Finally, the industry can benefit from the use of DIC to calibrate the GISSMO in forming simulations in order to design successful forming procedures without risk of material failure and enhance the accuracy of the prediction of springback of sheet-metal forming processes of interest to the aviation industry.

## Notes

### Acknowledgements

The support given by GKN Aerospace Sweden AB, ITE Fabriks AB, Acoustic Agree AB, VINNOVA – Swedish Governmental Agency for Innovation Systems NFFP6 program for SME, Swedish Armed Forces, and Swedish Defence Materiel Administration is gratefully appreciated. Grant No. 2013-01173.

### Funding

This study was funded by VINNOVA – Swedish Governmental Agency for Innovation Systems NFFP6 program for SME, Swedish Armed Forces, and Swedish Defence Materiel Administration (grant no. 2013-01173).

### Compliance with Ethical Standards

### Conflict of Interest

The authors declare that they have no conflict of interest.

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