Integration between Modeling and Experiments for Crystalline Metals: From Atomistic to Macroscopic Scales IV: Session III
Sponsored by: TMS Advanced Characterization, Testing, and Simulation Committee, TMS Materials Characterization Committee, TMS: Nanomaterials Committee
Program Organizers: Arul Kumar Mariyappan, Los Alamos National Laboratory; Irene Beyerlein, University of California, Santa Barbara; Levente Balogh, Queen's University; Caizhi Zhou, University of South Carolina; Lei Cao, University of Nevada; Josh Kacher, Georgia Institute of Technology
Tuesday 8:00 AM
October 11, 2022
Room: 401
Location: David L. Lawrence Convention Center
Session Chair: Ridwan Sakidja, Missouri State University; Michael Sangid, Purdue University
8:00 AM Invited
Direct Comparison of Microstructure-sensitive Fatigue Modeling Results to Situ High-energy X-ray Experiments: Veerappan Prithivirajan1; Priya Ravi1; Diwakar Naragani1; Michael Sangid1; 1Purdue University
The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation. In this talk, the methodology and demonstration for validating the location of microstructure-sensitive fatigue crack initiation as predicted by crystal plasticity finite element (CPFE) simulations, using high-energy X-ray diffraction and tomography experiments are presented. Realistic 3D microstructural models are created for the material of interest, IN718, with different twin instantiations, based on the experimental data for use in the CPFE simulations. The location of failure predicted using the extreme values of failure metrics (plastic strain accumulation and plastic strain energy density) resulted in an unambiguous one-to-one correlation with the experimentally observed location of crack-initiation for the models with statistical twin instantiations.
8:30 AM
Crystal Plasticity Modeling of Ultrasonic Softening Effect Considering Anisotropy in the Softening of Slip Systems: Jiarui Kang1; Xun Liu1; Stephen Niezgoda1; 1Ohio State University
A novel approach to modeling the ultrasonic softening effect during metal plasticity is developed, where the slip systems experience differential softening depending on their orientation relative to the ultrasonic direction. The directional softening model was implemented within a Visco-Plastic Self-Consistent (VPSC) model, where the material and ultrasonic softening parameters are calibrated and validated based on micro-tensile test data of pure copper. The VPSC modeling results provide new insights into ultrasonic softening, particularly that the stress reduction is not homogeneous in the whole aggregate. The degree of softening shows a strong dependence on grain orientation. A decrease in Taylor factor is predicted, especially in the grain subset with higher stress reduction, which agrees with experimental data. Although a traditional isotropic softening model is also capable in predicting the ultrasonically softened stress-strain response for different texture inputs, this decrease in Taylor factor cannot be captured.
8:50 AM
Atomistic Modeling of Twin Size Effect on the Localization of Cyclic Strain and Fatigue Crack Initiation in CrCoNi Medium-entropy Alloy: Veronika Mazanova1; Milan Heczko1; Mulaine Shih1; Connor Slone2; Easo George3; Jaroslav Polak4; Maryam Ghazisaeidi1; Michael Mills1; 1Ohio State University; 2Exponent; 3Oak Ridge National Laboratory; 4Institute of Physics of Materials CAS
Twin boundaries play an important role in the localization of cyclic plastic deformation and the fatigue crack initiation in structural materials. It is revealed that in fatigued CrCoNi alloy, thin deformation twins produced during the initial period of cyclic loading are the preferential sites of early strain localization. Comprehensive experimental workflow designed to extract information from the surface and the bulk of tested material by multi-scale electron microscopy suggests the deterministic role of the twin size. Experiments are complemented by atomistic modeling. Local stress fields associated with the twin boundaries both under tensile and compressive external load are generated and visualized by average-atom CrCoNi EAM potential utilized model. As deformation twinning in FCC alloys is of increasing interest, our work suggests that the effect of the twin thickness and its role in the generation of additional shear stress fields and the plastic strain localization should be taken into the considerations.
9:10 AM
Prisms-plasticity: An Open Source Crystal Plasticity Finite Element Software: Mohammadreza Yaghoobi1; Zhe Chen1; Duncan A. Greeley1; Aaditya Lakshmanan1; John E. Allison1; Veera Sundararaghavan1; 1University of Michigan
An open-source parallel 3-D crystal plasticity finite element (CPFE) software package PRISMS-Plasticity is presented here as a part of an overarching PRISMS Center integrated framework. Highly efficient rate-independent and rate-dependent crystal plasticity algorithms are implemented. Additionally, a new twinning-detwinning mechanism is incorporated into the framework based on an integration point sensitive scheme. The integration of the software as a part of the PRISMS Center framework is demonstrated. This integration includes well-defined pipelines for use of PRISMS-Plasticity software with experimental characterization techniques such as electron backscatter diffraction (EBSD), Digital Image Analysis (DIC), and high-energy synchrotron X-ray diffraction (HEDM), where appropriate these pipelines use popular open source software packages DREAM.3D and Neper. In addition, integration of the PRISMS-Plasticity results with the PRISMS Center information repository, the Materials Commons, will be presented. The parallel performance of the software demonstrates that it scales exceptionally well for large problems running on hundreds of processors.
9:30 AM
Multi-scale Characterization of Monotonic and Cyclic Properties of Ultra-high Strength CrCoNi Medium-entropy Alloy with Heterogeneous Partially Recrystallized Microstructure: Milan Heczko1; Veronika Mazanova1; Connor Slone2; Mulaine Shih1; Tomas Kruml3; Maryam Ghazisaeidi1; Easo George4; Jaroslav Polak3; Michael Mills1; 1Ohio State University; 2Exponent; 3Institute of Physics of Materials CAS; 4Oak Ridge National Laboratory
The effect of tunable partially recrystallized (PRX) heterostructures on the monotonic and cyclic plasticity and overall performance of equiatomic CrCoNi was investigated. After specific processing steps of cold-rolling and heat-treatment the alloy was subjected to monotonic and strain-controlled cyclic tests at room temperature. Fatigue hardening/softening curves, cyclic stress-strain curves and fatigue life curves were evaluated. The evolution of the internal critical stresses and the effective saturated stress during cyclic loading was analyzed using a generalized statistical theory of the hysteresis loop. The deformation mechanisms and substructure evolution was studied by multi-scale electron microscopy based characterization supported by atomistic modeling and correlated with the mechanical properties. Key performance aspects associated with the gradient heterogeneous microstructure were identified. It is revealed that CrCoNi alloy with PRX microstructure exhibits ultra-high monotonic and cyclic strength and extraordinary fracture toughness while preserving good ductility and resistance to cyclic plastic deformation.
9:50 AM Break
10:10 AM Invited
Alloying Design and Deep Learning Applications for Concentrated and High-entropy-Driven Ni-based Superalloys: Ridwan Sakidja1; Marium Mou1; 1Missouri State University
In this work, we have developed an alloying design that is based on the application of Deep Learning to model the mechanical properties of concentrated and/or high-entropy-driven Ni-based Superalloys. In developing the atomistic-based model, we utilized the results from electronic structure calculations generated from ab-initio molecular dynamics including the energy, forces, and virial database, and constructed the interatomic potentials for the multi-component system through Deep Learning algorithms. The compositions used can be strategically sampled and varied to maximize their statistical variations so as to ensure a wide range of compositional/phase transferability of the potentials. The validity of the developed potentials was then tested through molecular dynamics (MD) simulations to model various types of thermomechanical properties that are consistent with the experimental observations. We would like to acknowledge the support from the National Energy Technology Laboratory (NETL) and also National Energy Research Scientific Computing Center (NERSC) with their supercomputer resources.
10:40 AM
Investigating Effects of Particles and Voids in Plastic Deformation of Al6061 Using Finite Element Simulations: Hojun Lim1; Philip Noell1; Raiyan Seede2; John Emery1; Kyle Johnson1; 1Sandia National Laboratories; 2Texas A&M University
Effects of secondary particles and voids in plastic deformation of aluminum alloy 6061 are investigated using finite element simulations with isotropic J2 plasticity and crystal plasticity (CP) material models. Micro-computed tomography (u-CT) is used to fully construct 3D microstructure of polycrystalline Al6061, and finite element simulations of uniaxial tension are conducted with and without defects. In computational microstructures with defects, various volume fractions, shapes and sizes of soft particles, hard particles or voids are assigned and both macroscopic and local mechanical response from J2 and CP simulations are compared. This work demonstrates effects of incorporating realistic distributions of secondary particles and voids in finite element predictions of macroscopic response as well as failure and fracture behavior.
11:00 AM
Validation of Representative Volume Element (RVE) Finite Element Models of Dual Phase Steels Using SEM In-situ Tensile Tests and Digital Image Correlation (DIC): Alexander Bardelcik1; Quade Butler1; Amin Latifi Vanjani1; Hari Simha1; 1University of Guelph
3-Dimensional representative volume element (RVE) finite element models have been developed for two grades of dual phase steels. The meso-scale RVE models are constructed using realistic grain morphology and predict the macro-scale flow stress of the steels. We further evaluate the flow stress and strain partitioning predicted by the RVE models at high strains, where damage evolution phenomena lead to ductile fracture. To validate these models, in-situ uniaxial tensile tests were conducted within a field emission scanning electron microscope. Micrographs were taken at a point within the neck of the uniaxial tensile specimens, up to local fracture strains of ~0.50. The contrast of the etched microstructure was used to quantify strain partitioning up to failure using digital image correlation. The grain level strains predicted by the RVE’s were within the range of the quantified strains and provide valuable insight into the damage/fracture performance of these steels.
11:20 AM
Tailoring the Properties of Multi-phase Titanium Through the Use of Correlative Microscopy and Machine Learning: Gunnar Blaschke1; David Field1; Colin Merriman2; 1Washington State University; 2Idaho National Lab
High strength alloys with good ductility, hardness, and toughness are needed to meet rigorous design requirements for extreme environments. However, metals rarely exhibit both high strength and good fracture toughness as the underlying mechanisms work in opposition. An exception can be found in multiphase alloys that form microstructures of mixed phases. Machine learning (ML) techniques are used to identify and correlate critical microstructural features in a Ti-10V-2Fe-3Al alloy that is reported to exhibit high strength and fracture toughness. Metallurgical specimens are characterized in a correlative manor using optical microscopy, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and electron backscatter diffraction (EBSD). Microstructural features such as α platelet dimensions and locations and α/β phase boundaries are analyzed and correlated with strength and fracture toughness. Conclusion of this project resulted in a Convolutional Neural Network (CNN) with the ability to segment and classify the microstructures of Ti 10V-2Fe-3Al.