Ghosh / Woodward / Przybyla Integrated Computational Materials Engineering (ICME)

Advancing Computational and Experimental Methods
1. Auflage 2020
ISBN: 978-3-030-40562-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 416 Seiten

?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.


Dr. Somnath Ghosh is Michael G. Callas Chair Professor in the Departments of Civil, Mechanical and Materials Science & Engineering, Johns Hopkins University and Director of the Center for Integrated Structure-Materials Modeling and Simulations (CISMMS).Dr. Christopher Woodward is Principal Materials Research Engineer within the Materials and Manufacturing Directorate, Air Force Research Laboratory/RX, Wright Patterson Air Force Base.Dr. Craig Przybyla is Senior Materials Engineer & Research Team Leader within the Air Force Research Laboratory/RX, Wright Patterson Air Force Base, OH.

Autoren/Hrsg.

Ghosh, Somnath

Woodward, Christopher

Przybyla, Craig

Inhaltsverzeichnis

1;Preface;6
2;Acknowledgment;11
3;Contents;12
4;Contributors;14
5;Acquisition of 3D Data for Prediction of Monotonic and Cyclic Properties of Superalloys;18
5.1;1 Superalloys and Fatigue;18
5.2;2 Importance of 3D Data;20
5.3;3 The TriBeam;21
5.4;4 Targeted 3D Data;28
5.5;5 Future Needs;30
5.6;Appendix;30
5.7;References;31
6;Data Structures and Workflows for ICME;36
6.1;1 Introduction;36
6.2;2 ICME Software Tools;39
6.3;3 Simulation Tools;39
6.3.1;3.1 Analytic Tools;40
6.3.2;3.2 Example Tools from Other Fields;41
6.4;4 Building an Extensible ICME Data Schema and Workflow Tool;42
6.4.1;4.1 Data Handling Requirements;43
6.4.2;4.2 Modular Workflow Requirements;44
6.4.3;4.3 Data Access and Metadata Labeling Requirements;46
6.5;5 SIMPL and DREAM.3D: Enabling ICME Workflows;46
6.5.1;5.1 SIMPL Data Structure;48
6.5.2;5.2 Filters, Pipelines, and Plugins;53
6.5.3;5.3 SIMPLView: The Standard SIMPL Graphical Interface;55
6.5.4;5.4 DREAM.3D: An ICME Workflow Tool;56
6.6;6 Case Study: Ti-6242Si Pancake Forging;57
6.6.1;6.1 Zoning Process Histories;58
6.6.2;6.2 Processing Characterization Data;62
6.6.3;6.3 Registration and Fusion;62
6.7;7 Summary;66
6.8;References;66
7;Multi-scale Microstructure and Property-Based Statistically Equivalent RVEs for Modeling Nickel-Based Superalloys;71
7.1;1 Introduction;71
7.2;2 M-SERVE and P-SERVE for Intragranular Microstructures at the Subgrain Scale;75
7.2.1;2.1 Experimental Data Acquisition and Image Processing;76
7.2.2;2.2 Parametric Representation of Precipitate Morphology and Statistical Distributions;78
7.2.3;2.3 Generating Intragranular Statistically Equivalent Virtual Microstructures;81
7.2.3.1;2.3.1 Finalizing SEVMs Through Optimization of the Two-Point Correlation Function;81
7.2.3.2;2.3.2 Validation of SEVM Generation Method by Convergence Tests;82
7.2.4;2.4 Determining the M-SERVE from Statistical Convergence;83
7.2.4.1;2.4.1 Convergence of Morphological Distributions;84
7.2.4.2;2.4.2 Convergence of Spatial Distributions;84
7.2.5;2.5 Determining the Property-Based Statistically Equivalent RVE (P-SERVE);85
7.2.5.1;2.5.1 Crystal Plasticity Models for Ni-Based Superalloys;86
7.2.5.2;2.5.2 CPFE Simulations for Analyzing Response Variables;87
7.2.5.3;2.5.3 Spatially Averaged Mechanical Fields;88
7.2.5.4;2.5.4 Local Response Field Variables;89
7.2.6;2.6 Summary of the Subgrain-Scale Analysis;90
7.3;3 M-SERVE and P-SERVE for Polycrystalline Microstructures of Ni-Based Superalloys;90
7.3.1;3.1 Image Extraction from Electron Backscattered Diffraction Maps;91
7.3.2;3.2 Statistically Equivalent Virtual Microstructure (SEVM) Generation from Characterization and Statistical Analysis;92
7.3.2.1;3.2.1 Validation of the SEVM Generation Method;95
7.3.3;3.3 Estimating M-SERVEs for Polycrystalline Microstructure with Twins;98
7.3.4;3.4 Estimating the P-SERVE Through Convergence Studies;99
7.3.4.1;3.4.1 P-SERVE Convergence Studies with the Crystal Plasticity Model;101
7.3.5;3.5 Summary of the Polycrystalline Scale Analysis;103
7.4;References;104
8;Microscale Testing and Characterization Techniques for Benchmarking Crystal Plasticity Models at Microstructural Length Scales;107
8.1;1 Introduction;107
8.2;2 Background;108
8.3;3 Machining Methods for Microscale Samples;113
8.3.1;3.1 Focused Ion Beam Machining;114
8.3.2;3.2 Wire EDM Machining;116
8.3.3;3.3 Femtosecond Laser Machining;118
8.3.4;3.4 Comparison of Machining Techniques;123
8.4;4 Sample Size Effects on Strength in René 88DT;125
8.5;5 Orientation and Deformation Maps;131
8.6;6 Chapter Summary;136
8.7;References;137
9;Computational Micromechanics Modeling of Polycrystalline Superalloys Application to Inconel 718;142
9.1;1 Introduction;142
9.2;2 Material Description;143
9.3;3 Experimental Characterization;144
9.3.1;3.1 Micromechanical Characterization;145
9.3.1.1;3.1.1 Experimental Procedure;145
9.3.1.2;3.1.2 Results;145
9.3.2;3.2 Macromechanical Characterization;148
9.3.2.1;3.2.1 Uniaxial Monotonic Tests;148
9.3.2.2;3.2.2 Low Cycle Fatigue Tests;150
9.4;4 Polycrystalline Homogenization Framework;153
9.4.1;4.1 Boundary Value Problem and Boundary Conditions;154
9.4.2;4.2 Microstructure Representation;156
9.4.3;4.3 Single Crystal Behavior;158
9.5;5 Monotonic Behavior;159
9.5.1;5.1 Elastic Behavior;160
9.5.2;5.2 Elastoplastic Behavior;160
9.5.3;5.3 Grain Size-Dependent Model;162
9.6;6 Cyclic Behavior;163
9.6.1;6.1 Crystal Plasticity Model for Cyclic Behavior;164
9.6.1.1;6.1.1 Model Parameter Identification;165
9.6.2;6.2 Simulation of the Cyclic Behavior;166
9.6.3;6.3 Grain Size-Dependent Cyclic Behavior;169
9.7;7 Microstructure-Dependent Fatigue Life Simulation;169
9.7.1;7.1 Microstructure-Sensitive Crack Initiation Model;169
9.7.2;7.2 Results;172
9.8;8 Conclusions;174
9.9;References;175
10;Non-deterministic Calibration of Crystal Plasticity ModelParameters;179
10.1;1 Introduction;179
10.2;2 Acquiring and Processing Experiment Data;182
10.2.1;2.1 Global Data;182
10.2.2;2.2 Local Data;182
10.2.2.1;2.2.1 Digital Image Correlation;183
10.2.2.2;2.2.2 High-Resolution EBSD;184
10.2.2.3;2.2.3 Combining DIC and HREBSD;185
10.3;3 Crystal Plasticity;185
10.3.1;3.1 Concepts;187
10.4;4 Calibration;189
10.4.1;4.1 General Process;189
10.4.2;4.2 Global Methods;190
10.4.2.1;4.2.1 Data Flow;191
10.4.2.2;4.2.2 Computational Model;192
10.4.3;4.3 Global-Local Methods;193
10.4.3.1;4.3.1 Data Flow;193
10.4.3.2;4.3.2 Computational Model;193
10.4.4;4.4 Local Methods;194
10.4.4.1;4.4.1 Data Flow;194
10.4.4.2;4.4.2 Computational Model;195
10.5;5 Uncertainty Quantification Model for Calibration;195
10.6;6 Demonstration Using Simulated Experiments;198
10.6.1;6.1 Using Global Calibration;202
10.6.2;6.2 Using Global-Local Calibration;204
10.6.3;6.3 Using Local Calibration;205
10.7;7 Summary;207
10.8;8 Outlook;209
10.9;References;210
11;Local Stress and Damage Response of Polycrystal Materials to Light Shock Loading Conditions via Soft Scale-Coupling;213
11.1;1 Introduction;213
11.2;2 Nomenclature;215
11.3;3 Experimental Overview;215
11.4;4 Macroscale Damage Modeling;217
11.4.1;4.1 Damage Constitutive Model;217
11.4.2;4.2 Numerical Simulation Results;222
11.5;5 Local-Scale Modeling;223
11.5.1;5.1 Single Crystal Model;223
11.5.2;5.2 Polycrystal Numerical Results;226
11.6;6 Conclusion;233
11.7;References;233
12;A Framework for Quantifying Effects of Characterization Error on the Predicted Local Elastic Response in Polycrystalline Materials;236
12.1;1 Introduction;236
12.2;2 Methods;238
12.2.1;2.1 Step 1: Synthetic Material Generation – Phantoms;239
12.2.2;2.2 Step 2: Simulation of Data Collection;240
12.2.2.1;2.2.1 Resolution;240
12.2.2.2;2.2.2 Interaction Volume;240
12.2.2.3;2.2.3 Random Noise;241
12.2.2.4;2.2.4 Summary of Data Collection Model;242
12.2.3;2.3 Additional Notes on Methodology;243
12.3;3 Individual Parameter Variation Examples;243
12.3.1;3.1 Step 3: Error Measurements;244
12.3.2;3.2 Resolution;245
12.3.2.1;3.2.1 Analytical Model of Error Associated with Sample Spacing;247
12.3.3;3.3 Interaction Volume;250
12.3.4;3.4 Unindexed Pixels;251
12.3.5;3.5 Data Processing Parameters;251
12.3.6;3.6 Brief Discussion on Data Collection and Processing Error;253
12.4;4 Case Study: Application to Finite Element Model;254
12.4.1;4.1 Conclusions from the Case Study;257
12.5;5 Conclusions;258
12.6;References;259
13;Material Agnostic Data-Driven Framework to Develop Structure-Property Linkages;261
13.1;1 Introduction;261
13.2;2 Material Agnostic Data-Driven Framework to Process-Structure-Property Linkages;262
13.2.1;2.1 Microstructure Quantification;264
13.2.2;2.2 Data-Driven Workflow for Extracting P-S-P Linkages;266
13.3;3 Application of the Material Agnostic Framework to Different Material Systems;268
13.3.1;3.1 Composites;268
13.3.2;3.2 Polycrystalline Metallic Materials;272
13.4;4 Challenges;276
13.5;5 Summary;276
13.6;References;277
14;Multiscale Modeling of Epoxies and Epoxy-Based Composites;279
14.1;1 Introduction;279
14.2;2 Overview of Multiscale Simulation Methods for Epoxies;281
14.2.1;2.1 Molecular Dynamics Simulation;281
14.2.2;2.2 Coarse-Grained Molecular Dynamics Methods;283
14.2.3;2.3 Finite Element Method;284
14.3;3 Multiscale Simulations of Epoxies and Their Properties;285
14.3.1;3.1 Modeling the Curing Process of Epoxies;285
14.3.2;3.2 Epoxy Density and Volume Shrinkage;288
14.3.3;3.3 Glass Transition Temperature;289
14.3.4;3.4 Free Volume Distribution;291
14.3.5;3.5 Elastic Modulus;293
14.3.6;3.6 Failure Properties;294
14.4;4 Multiscale Simulations of Epoxy Interfacial Properties;296
14.4.1;4.1 Epoxy-Based Composites and the Interphase Region;296
14.4.2;4.2 Coatings and Adhesives;301
14.5;5 Summary and Conclusions;302
14.6;References;303
15;Microstructural Statistics Informed Boundary Conditions for Statistically Equivalent Representative Volume Elements (SERVEs) of Polydispersed Elastic Composites;309
15.1;1 Introduction;309
15.2;2 Formulation of the Exterior Statistics-Based Boundary Conditions for a SERVE;313
15.2.1;2.1 Exterior Statistics-Based Perturbed Fields;316
15.2.2;2.2 Implementation of the Exterior Statistics-Based Boundary Conditions (ESBCs);319
15.3;3 Validation of ESBCs for SERVEs in Nonhomogeneous Microstructures with Clustering;319
15.3.1;3.1 Comparing ESBCs Generated by the 2-Point Correlation and Radial Distribution Functions;321
15.3.2;3.2 ESBCs for SERVEs Intersecting Clustered Regions;324
15.4;4 Convergence of Elastic Homogenized Stiffness;325
15.4.1;4.1 Selection of SERVE Size from Convergence Characteristics;325
15.4.2;4.2 Comparing Convergence of ESBC-Based SERVE with Statistical Volume Elements (SVEs);327
15.5;5 ESBCs for Polydispersed Microstructures of Carbon Fiber Polymer Matrix Composites;329
15.5.1;5.1 Microstructure Imaging, Characterization, and Mechanical Testing;329
15.5.2;5.2 Statistical Characterization of the Polydispersed Microstructure;330
15.5.3;5.3 Creating Statistically Equivalent MVEs from Experimental Micrographs;331
15.5.4;5.4 Micromechanical Analysis of the Polydispersed SERVE with ESBCs;333
15.5.5;5.5 Candidate SERVE Selection from Stiffness Convergence;334
15.5.6;5.6 Comparing the SERVE and SVE Stiffness with Experimental Observations;335
15.6;6 Summary and Conclusions;336
15.7;Appendix: Eshelby Tensors for Circular Cylindrical Fibers;336
15.8;References;338
16;Transverse Failure of Unidirectional Composites: Sensitivity to Interfacial Properties;341
16.1;1 Introduction;341
16.2;2 Experimental Observations;343
16.3;3 Modeling;345
16.3.1;3.1 Cohesive Zone Model;345
16.3.2;3.2 Interface-Enriched Generalized Finite Element Method (IGFEM);347
16.3.3;3.3 Mesoscale Simulations;348
16.3.4;3.4 Validation;349
16.4;4 Sensitivity Analysis: Formulation;350
16.5;5 Sensitivity Analysis: Verification;353
16.6;6 Sensitivity Analysis: Results;355
16.7;7 Conclusion;357
16.8;Appendix: Sensitivity to Critical Displacement Jumps;357
16.9;References;358
17;Geometric Modeling of Transverse Cracking of Composites;360
17.1;1 Introduction;360
17.2;2 Problem Description;362
17.3;3 Fiber-Pair Stress Concentration;367
17.4;4 Stress Shielding from Transverse Cracks;370
17.5;5 Model Testing and Calibration;371
17.6;6 Statistical Analysis of the Impact of the Interface Strength Distribution;374
17.7;7 Conclusion;376
17.8;References;377
18;Challenges in Understanding the Dynamic Behavior of Heterogeneous Materials;378
18.1;1 Introduction;378
18.1.1;1.1 The Challenge of Dynamic Property Measurements;379
18.1.2;1.2 ICMSE Approaches to Probing Dynamic Behavior of Materials;381
18.1.2.1;1.2.1 Molecular Dynamics and Coarse-Grained Methods;381
18.1.2.2;1.2.2 Meso-scale and Microstructure-Based Simulation at the Continuum Scale;385
18.1.3;1.3 Outline of Chapter;388
18.2;2 Background on Shock Compression Science;388
18.2.1;2.1 Shock Compression Science and Theory;389
18.2.2;2.2 Conservation Relations for a Shock Wave;390
18.2.2.1;2.2.1 Theoretical Equations of State for Reactive Powders;393
18.2.3;2.3 Reactive Powder Mixtures and Explosives;394
18.3;3 Case Study: Dynamic Behavior of Reactive Powder Mixtures;395
18.3.1;3.1 Impact-Induced Chemical Reactions;396
18.3.2;3.2 Shock-Induced Chemical Reactions;400
18.4;4 Summary and Conclusions: Where Can ICMSE Continue to Provide Value in Understanding Dynamic Behavior of Heterogeneous Materials?;403
18.5;References;404
19;Correction to: Transverse Failure of Unidirectional Composites: Sensitivity to Interfacial Properties;409
20;Index;410