Implementation and verification of a microstructure-based capability for modeling microcrack nucleation in LSHR at room temperature

Cerrone, Albert; Stein, Clayton; Pokharel, Reeju; Hefferan, Christopher M.; Lind, Jonathan; Tucker, H. J.; Suter, Robert; Rollett, Anthony D.; Ingraffea, Anthony R.

doi:10.1088/0965-0393/23/3/035006

Cited by 39 publications

(22 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, microstructure explicit fatigue models for polycrystalline Ni-based superalloys have been developed [33][34][35][36][37][38][39][40] that confirm strain localization around microstructural features, primarily at twin boundaries, prior to nucleation of fatigue cracks. For this particular study, the finite elementbased crystal plasticity model was used to inform the design of microstructures and identify desirable meso-scale grain boundary character distributions that could be varied to enhance the fatigue performance of a commercially available Nibased superalloy RR1000.…”

Section: Introductionmentioning

confidence: 99%

Tailoring the Properties of a Ni-Based Superalloy via Modification of the Forging Process: an ICME Approach to Fatigue Performance

Detrois

Rotella

Hardy

et al. 2017

Integr Mater Manuf Innov

View full text Add to dashboard Cite

Traditionally, material design and property modifications are usually associated with compositional changes. Yet, subtle changes in the manufacturing process parameters can also have a dramatic effect on the resulting material properties. In this work, an integrated computational materials engineering (ICME) framework is adopted to tailor the fatigue performance of a Ni-based superalloy, RR1000. An existing fatigue model is used to identify microstructural features that promote enhanced fatigue life, namely a uniform, fine grain size distribution, random orientation, a distinct grain boundary distribution (specifically high twin boundary density and limited low-angle grain boundaries). A deformation mechanism map and process models for grain boundary engineering of RR1000 are used to identify the optimal thermo-mechanical processing parameters to realize these desirable microstructural features. For validation, small-scale forgings of RR1000 were produced and heat-treated to attain fine grain and coarse grain microstructures that represent the conventionally processed and grain boundary engineered (GBE) conditions, respectively. For each of the four microstructural variants of RR1000, the twin density and grain size were characterized and were in agreement with the desired microstructural attributes. In order to validate the deformation mechanisms and fatigue behavior of the material, high-resolution digital image correlation was performed to generate strain maps relative to the microstructural features. The high density of twin boundaries was confirmed to inhibit the length of slip bands, which is directly attributed to extended fatigue life. Thus, this study demonstrated the successful role of models, both process and performance, in the design and manufacture of Ni-based superalloy disk forgings.

show abstract

Section: Introductionmentioning

confidence: 99%

Tailoring the Properties of a Ni-Based Superalloy via Modification of the Forging Process: an ICME Approach to Fatigue Performance

Detrois

Rotella

Hardy

et al. 2017

Integr Mater Manuf Innov

View full text Add to dashboard Cite

show abstract

“…3 does not include the plasticity ahead of the crack tip, but contains terms for the shear stress along the crack propagating direction and the principal stress state. The majority of the existing metrics, describing fatigue crack propagation direction and rate, available in the literature Table 2 are proportional to the accumulated microplasticity ahead of the crack tip 21,[32][33][34][35]48 ; however, this assumption is not supported by the available data. The comparison of Fig.…”

Section: Discussionmentioning

confidence: 99%

“…The fatigue indicator parameters used for comparisons in the present work are summarized in Table 2. 21,[32][33][34][35] The resulting data are used to train the BNs. To obtain uniform prior distributions, the data associated with the crack propagation direction have been randomly sampled, while a stratification procedure has been applied to the ones pertaining to the crack propagation rate.…”

Section: Methodsmentioning

confidence: 99%

“…In the last decades, many analytical surrogate metrics for the small crack driving forces, which are commonly known as fatigue indicator parameters, have been postulated, 21,[32][33][34][35] albeit seldom of these theories have been directly compared to 3D experimental data. [36][37][38] Due to the nature of their construction, these fatigue metrics would benefit from a systematic analysis of the variables possibly influencing the small crack propagation process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials

Rovinelli¹,

Sangid²,

Proudhon³

et al. 2018

npj Comput Mater

151

View full text Add to dashboard Cite

The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented. Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation. A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data. The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation. The ability of the proposed expressions to capture the observed experimental behavior is quantified and compared to the results directly from the Bayesian network and from fatigue metrics that are common in the literature. Results indicate that the direction of small crack propagation can be reliably predicted using the proposed analytical model and compares more favorably than other fatigue metrics.

show abstract

“…25,26 DREAM.3D has been extensively used to characterize experimentally acquired microstructures and generate microstructure-based models for subsequent simulations. [27][28][29][30][31][32] Since it produces 3D virtual volumes, the experimentally obtained data were extrapolated from 2D to 3D based on some underlying assumptions. First, the diameter of the sphere-shaped grains in the original (hot-rolled) state was determined experimentally.…”

Section: Integrated Computational Materials Engineering Procedures Expmentioning

confidence: 99%

Identifying Structure–Property Relationships Through DREAM.3D Representative Volume Elements and DAMASK Crystal Plasticity Simulations: An Integrated Computational Materials Engineering Approach

Diehl

Groeber

Haase

et al. 2017

JOM

View full text Add to dashboard Cite

Predicting, understanding, and controlling the mechanical behavior is the most important task when designing structural materials. Modern alloy systems-in which multiple deformation mechanisms, phases, and defects are introduced to overcome the inverse strength-ductility relationship-give raise to multiple possibilities for modifying the deformation behavior, rendering traditional, exclusively experimentally-based alloy development workflows inappropriate. For fast and efficient alloy design, it is therefore desirable to predict the mechanical performance of candidate alloys by simulation studies to replace time-and resource-consuming mechanical tests. Simulation tools suitable for this task need to correctly predict the mechanical behavior in dependence of alloy composition, microstructure, texture, phase fractions, and processing history. Here, an integrated computational materials engineering approach based on the open source software packages DREAM.3D and DA-MASK (Dü sseldorf Advanced Materials Simulation Kit) that enables such virtual material development is presented. More specific, our approach consists of the following three steps: (1) acquire statistical quantities that describe a microstructure, (2) build a representative volume element based on these quantities employing DREAM.3D, and (3) evaluate the representative volume using a predictive crystal plasticity material model provided by DAMASK. Exemplarily, these steps are here conducted for a high-manganese steel.

show abstract

Implementation and verification of a microstructure-based capability for modeling microcrack nucleation in LSHR at room temperature

Cited by 39 publications

References 68 publications

Tailoring the Properties of a Ni-Based Superalloy via Modification of the Forging Process: an ICME Approach to Fatigue Performance

Tailoring the Properties of a Ni-Based Superalloy via Modification of the Forging Process: an ICME Approach to Fatigue Performance

Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials

Identifying Structure–Property Relationships Through DREAM.3D Representative Volume Elements and DAMASK Crystal Plasticity Simulations: An Integrated Computational Materials Engineering Approach

Contact Info

Product

Resources

About