Integrated Computational Materials Engineering (ICME): Advancing Computational and Experimental Methods

Description

This book introduces research advances in Integrated Computational Materials Engineering (ICME) that have taken place under the aegis of the AFOSR/AFRL sponsored Center of Excellence on Integrated Materials Modeling (CEIMM) at Johns Hopkins University. Its author team consists of leading researchers in ICME from prominent academic institutions and the Air Force Research Laboratory. The book examines state-of-the-art advances in physics-based, multi-scale, computational-experimental methods and models for structural materials like polymer-matrix composites and metallic alloys. The book emphasizes Ni-based superalloys and epoxy matrix carbon-fiber composites and encompasses atomistic scales, meso-scales of coarse-grained models and discrete dislocations, and micro-scales of poly-phase and polycrystalline microstructures. Other critical phenomena investigated include the relationship between microstructural morphology, crystallography, and mechanisms to the material response at different scales; methods of identifying representative volume elements using microstructure and material characterization, and robust deterministic and probabilistic modeling of deformation and damage. Encompassing a slate of topics that enable readers to comprehend and approach ICME-related issues involved in predicting material performance and failure, the book is ideal for mechanical, civil, and aerospace engineers, and materials scientists, in in academic, government, and industrial laboratories. Preface Acknowledgment Contents Contributors Acquisition of 3D Data for Prediction of Monotonic and Cyclic Properties of Superalloys • McLean P. Echlin, William C. Lenthe, Jean-Charles Stinville, and Tresa M. Pollock 1 Superalloys and Fatigue 2 Importance of 3D Data 3 The TriBeam 4 Targeted 3D Data 5 Future Needs Appendix References Data Structures and Workflows for ICME • Sean P. Donegan and Michael A. Groeber 1 Introduction 2 ICME Software Tools 3 Simulation Tools 3.1 Analytic Tools 3.2 Example Tools from Other Fields 4 Building an Extensible ICME Data Schema and Workflow Tool 4.1 Data Handling Requirements 4.2 Modular Workflow Requirements 4.3 Data Access and Metadata Labeling Requirements 5 SIMPL and DREAM.3D: Enabling ICME Workflows 5.1 SIMPL Data Structure 5.2 Filters, Pipelines, and Plugins 5.3 SIMPLView: The Standard SIMPL Graphical Interface 5.4 DREAM.3D: An ICME Workflow Tool 6 Case Study: Ti-6242Si Pancake Forging 6.1 Zoning Process Histories 6.2 Processing Characterization Data 6.3 Registration and Fusion 7 Summary References Multi-scale Microstructure and Property-Based Statistically Equivalent RVEs for Modeling Nickel-Based Superalloys • Somnath Ghosh, George Weber, Maxwell Pinz, Akbar Bagri, Tresa M. Pollock, Will Lenthe, Jean-Charles Stinville, Michael D. Uchic, and Christopher Woodward 1 Introduction 2 M-SERVE and P-SERVE for Intragranular Microstructures at the Subgrain Scale 2.1 Experimental Data Acquisition and Image Processing 2.2 Parametric Representation of Precipitate Morphology and Statistical Distributions 2.3 Generating Intragranular Statistically Equivalent Virtual Microstructures 2.3.1 Finalizing SEVMs Through Optimization of the Two-Point Correlation Function 2.3.2 Validation of SEVM Generation Method by Convergence Tests 2.4 Determining the M-SERVE from Statistical Convergence 2.4.1 Convergence of Morphological Distributions 2.4.2 Convergence of Spatial Distributions 2.5 Determining the Property-Based Statistically Equivalent RVE (P-SERVE) 2.5.1 Crystal Plasticity Models for Ni-Based Superalloys 2.5.2 CPFE Simulations for Analyzing Response Variables 2.5.3 Spatially Averaged Mechanical Fields 2.5.4 Local Response Field Variables 2.6 Summary of the Subgrain-Scale Analysis 3 M-SERVE and P-SERVE for Polycrystalline Microstructures of Ni-Based Superalloys 3.1 Image Extraction from Electron Backscattered Diffraction Maps 3.2 Statistically Equivalent Virtual Microstructure (SEVM) Generation from Characterization and Statistical Analysis 3.2.1 Validation of the SEVM Generation Method 3.3 Estimating M-SERVEs for Polycrystalline Microstructure with Twins 3.4 Estimating the P-SERVE Through Convergence Studies 3.4.1 P-SERVE Convergence Studies with the Crystal Plasticity Model 3.5 Summary of the Polycrystalline Scale Analysis References Microscale Testing and Characterization Techniques for Benchmarking Crystal Plasticity Models at Microstructural Length Scales • David W. Eastman, Paul A. Shade, Michael D. Uchic, and Kevin J. Hemker 1 Introduction 2 Background 3 Machining Methods for Microscale Samples 3.1 Focused Ion Beam Machining 3.2 Wire EDM Machining 3.3 Femtosecond Laser Machining 3.4 Comparison of Machining Techniques 4 Sample Size Effects on Strength in René 88DT 5 Orientation and Deformation Maps 6 Chapter Summary References Computational Micromechanics Modeling of Polycrystalline Superalloys Application to Inconel 718 • Aitor Cruzado, Javier Llorca, and Javier Segurado 1 Introduction 2 Material Description 3 Experimental Characterization 3.1 Micromechanical Characterization 3.1.1 Experimental Procedure 3.1.2 Results 3.2 Macromechanical Characterization 3.2.1 Uniaxial Monotonic Tests 3.2.2 Low Cycle Fatigue Tests 4 Polycrystalline Homogenization Framework 4.1 Boundary Value Problem and Boundary Conditions 4.2 Microstructure Representation 4.3 Single Crystal Behavior 5 Monotonic Behavior 5.1 Elastic Behavior 5.2 Elastoplastic Behavior 5.3 Grain Size-Dependent Model 6 Cyclic Behavior 6.1 Crystal Plasticity Model for Cyclic Behavior 6.1.1 Model Parameter Identification 6.2 Simulation of the Cyclic Behavior 6.3 Grain Size-Dependent Cyclic Behavior 7 Microstructure-Dependent Fatigue Life Simulation 7.1 Microstructure-Sensitive Crack Initiation Model 7.2 Results 8 Conclusions References Non-deterministic Calibration of Crystal Plasticity Model Parameters • Jacob Hochhalter, Geoffrey Bomarito, Saikumar Yeratapally, Patrick Leser, Tim Ruggles, James Warner, and William Leser 1 Introduction 2 Acquiring and Processing Experiment Data 2.1 Global Data 2.2 Local Data 2.2.1 Digital Image Correlation 2.2.2 High-Resolution EBSD 2.2.3 Combining DIC and HREBSD 3 Crystal Plasticity 3.1 Concepts 4 Calibration 4.1 General Process 4.2 Global Methods 4.2.1 Data Flow 4.2.2 Computational Model 4.3 Global-Local Methods 4.3.1 Data Flow 4.3.2 Computational Model 4.4 Local Methods 4.4.1 Data Flow 4.4.2 Computational Model 5 Uncertainty Quantification Model for Calibration 6 Demonstration Using Simulated Experiments 6.1 Using Global Calibration 6.2 Using Global-Local Calibration 6.3 Using Local Calibration 7 Summary 8 Outlook References Local Stress and Damage Response of Polycrystal Materials to Light Shock Loading Conditions via Soft Scale-Coupling • C. A. Bronkhorst, P. W. Marcy, S. A. Vander Wiel, H. Cho, V. Livescu, and G. T. Gray III 1 Introduction 2 Nomenclature 3 Experimental Overview 4 Macroscale Damage Modeling 4.1 Damage Constitutive Model 4.2 Numerical Simulation Results 5 Local-Scale Modeling 5.1 Single Crystal Model 5.2 Polycrystal Numerical Results 6 Conclusion References A Framework for Quantifying Effects of Characterization Error on the Predicted Local Elastic Response in Polycrystalline Materials • Noah Wade, Michael D. Uchic, Amanda Criner, and Lori Graham-Brady 1 Introduction 2 Methods 2.1 Step 1: Synthetic Material Generation – Phantoms 2.2 Step 2: Simulation of Data Collection 2.2.1 Resolution 2.2.2 Interaction Volume 2.2.3 Random Noise 2.2.4 Summary of Data Collection Model 2.3 Additional Notes on Methodology 3 Individual Parameter Variation Examples 3.1 Step 3: Error Measurements 3.2 Resolution 3.2.1 Analytical Model of Error Associated with Sample Spacing 3.3 Interaction Volume 3.4 Unindexed Pixels 3.5 Data Processing Parameters 3.6 Brief Discussion on Data Collection and Processing Error 4 Case Study: Application to Finite Element Model 4.1 Conclusions from the Case Study 5 Conclusions References Material Agnostic Data-Driven Framework to Develop Structure-Property Linkages • Dipen Patel, Triplicane Parthasarathy, and Craig Przybyla 1 Introduction 2 Material Agnostic Data-Driven Framework to Process-Structure-Property Linkages 2.1 Microstructure Quantification 2.2 Data-Driven Workflow for Extracting P-S-P Linkages 3 Application of the Material Agnostic Framework to Different Material Systems 3.1 Composites 3.2 Polycrystalline Metallic Materials 4 Challenges 5 Summary References Multiscale Modeling of Epoxies and Epoxy-Based Composites • Xiawa Wu and Jaafar A. El-Awady 1 Introduction 2 Overview of Multiscale Simulation Methods for Epoxies 2.1 Molecular Dynamics Simulation 2.2 Coarse-Grained Molecular Dynamics Methods 2.3 Finite Element Method 3 Multiscale Simulations of Epoxies and Their Properties 3.1 Modeling the Curing Process of Epoxies 3.2 Epoxy Density and Volume Shrinkage 3.3 Glass Transition Temperature 3.4 Free Volume Distribution 3.5 Elastic Modulus 3.6 Failure Properties 4 Multiscale Simulations of Epoxy Interfacial Properties 4.1 Epoxy-Based Composites and the Interphase Region 4.2 Coatings and Adhesives 5 Summary and Conclusions References Microstructural Statistics Informed Boundary Conditions for Statistically Equivalent Representative Volume Elements (SERVEs) of Polydispersed Elastic Composites • Somnath Ghosh, Dhirendra V. Kubair, and Craig Przybyla 1 Introduction 2 Formulation of the Exterior Statistics-Based Boundary Conditions for a SERVE 2.1 Exterior Statistics-Based Perturbed Fields 2.2 Implementation of the Exterior Statistics-Based Boundary Conditions (ESBCs) 3 Validation of ESBCs for SERVEs in Nonhomogeneous Microstructures with Clustering 3.1 Comparing ESBCs Generated by the 2-Point Correlation and Radial Distribution Functions 3.2 ESBCs for SERVEs Intersecting Clustered Regions 4 Convergence of Elastic Homogenized Stiffness 4.1 Selection of SERVE Size from Convergence Characteristics 4.2 Comparing Convergence of ESBC-Based SERVE with Statistical Volume Elements (SVEs) 5 ESBCs for Polydispersed Microstructures of Carbon Fiber Polymer Matrix Composites 5.1 Microstructure Imaging, Characterization, and Mechanical Testing 5.2 Statistical Characterization of the Polydispersed Microstructure 5.3 Creating Statistically Equivalent MVEs from Experimental Micrographs 5.4 Micromechanical Analysis of the Polydispersed SERVE with ESBCs 5.5 Candidate SERVE Selection from Stiffness Convergence 5.6 Comparing the SERVE and SVE Stiffness with Experimental Observations 6 Summary and Conclusions Appendix: Eshelby Tensors for Circular Cylindrical Fibers References Transverse Failure of Unidirectional Composites: Sensitivity to Interfacial Properties • Scott Zacek, David Brandyberry, Anthony Klepacki, Chris Montgomery, Maryam Shakiba, Michael Rossol, Ahmad Najafi, Xiang Zhang, Nancy Sottos, Philippe Geubelle, Craig Przybyla, and George Jefferson 1 Introduction 2 Experimental Observations 3 Modeling 3.1 Cohesive Zone Model 3.2 Interface-Enriched Generalized Finite Element Method (IGFEM) 3.3 Mesoscale Simulations 3.4 Validation 4 Sensitivity Analysis: Formulation 5 Sensitivity Analysis: Verification 6 Sensitivity Analysis: Results 7 Conclusion Appendix: Sensitivity to Critical Displacement Jumps References Geometric Modeling of Transverse Cracking of Composites • Angel Agrawal, Scott Zacek, Kyle Nixon, Chris Montgomery, Philippe Geubelle, Nancy Sottos, Craig Przybyla, and George Jefferson 1 Introduction 2 Problem Description 3 Fiber-Pair Stress Concentration 4 Stress Shielding from Transverse Cracks 5 Model Testing and Calibration 6 Statistical Analysis of the Impact of the Interface Strength Distribution 7 Conclusion References Challenges in Understanding the Dynamic Behavior of Heterogeneous Materials • Manny Gonzales and Naresh N. Thadhani 1 Introduction 1.1 The Challenge of Dynamic Property Measurements 1.2 ICMSE Approaches to Probing Dynamic Behavior of Materials 1.2.1 Molecular Dynamics and Coarse-Grained Methods 1.2.2 Meso-scale and Microstructure-Based Simulation at the Continuum Scale 1.3 Outline of Chapter 2 Background on Shock Compression Science 2.1 Shock Compression Science and Theory 2.2 Conservation Relations for a Shock Wave 2.2.1 Theoretical Equations of State for Reactive Powders 2.3 Reactive Powder Mixtures and Explosives 3 Case Study: Dynamic Behavior of Reactive Powder Mixtures 3.1 Impact-Induced Chemical Reactions 3.2 Shock-Induced Chemical Reactions 4 Summary and Conclusions: Where Can ICMSE Continue to Provide Value in Understanding Dynamic Behavior of Heterogeneous Materials? References Correction to: Transverse Failure of Unidirectional Composites: Sensitivity to Interfacial Properties • Scott Zacek, David Brandyberry, Anthony Klepacki, Chris Montgomery, Maryam Shakiba, Michael Rossol, Ahmad Najafi, Nancy Sottos, Philippe Geubelle, Craig Przybyla, George Jefferson, and Xiang Zhang Index