Innovative lightweight and high strength alloys : multiscale integrated processing, experimental, and modeling techniques /
Innovative Lightweight and High Strength Alloys: Multiscale Integrated Processing, Experimental, and Modeling Techniques provides multiscale processing, experimental and modeling techniques overviews and perspectives that highlight current roadblocks to optimal design of new alloys alongside solutio...
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| Інші автори: | |
|---|---|
| Формат: | Електронний ресурс eКнига |
| Мова: | Англійська |
| Опубліковано: |
Amsterdam :
Elsevier,
2024.
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| Предмети: | |
| Онлайн доступ: | ScienceDirect |
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| 245 | 0 | 0 | |a Innovative lightweight and high strength alloys : |b multiscale integrated processing, experimental, and modeling techniques / |c edited by Mohammed A. Zikry. |
| 260 | |a Amsterdam : |b Elsevier, |c 2024. | ||
| 300 | |a 1 online resource | ||
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| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a References. | ||
| 505 | 0 | |a Intro -- Innovative lightweight and highstrength alloys: Multiscale Integrated Processing, Experimental, and Modeling Techniques -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1: Process-structure-property models for metal additive manufacturing using AI/ML approaches -- 1. Introduction -- 2. Dataset sources -- 2.1. Physical experimentation -- 2.1.1. Synthesis of AM sample libraries -- 2.1.2. Process monitoring tools -- 2.1.3. Material structure characterization -- 2.1.3.1. Multiscale microstructural features -- 2.1.3.2. Quantification of microstructures -- 2.1.4. Property evaluation -- 2.1.4.1. Automation in mechanical testing -- 2.1.4.2. Rapid evaluation of mechanical properties using small samples -- 2.2. Physics-based simulations -- 2.2.1. Thermodynamic simulations -- 2.2.1.1. Thermal field simulations -- 2.2.1.2. Material modeling simulations -- 3. Data science approaches to rationalize PSP linkages -- 3.1. Data pipelines for PSP models -- 3.2. Application of AI/ML models for AM -- 3.2.1. Supervised learning -- 3.2.2. Unsupervised learning -- 4. Conclusion -- References -- Chapter 2: An overview on recent processing, metallurgy, and experiments related to microstructure engineering in additiv ... -- 1. Introduction -- 2. AM processes -- 3. Challenges with AM microstructures -- 4. Developments in AM microstructure engineering -- 5. Summary and future needs -- References -- Chapter 3: Dislocations in processing and material behavior -- 1. Introduction -- 2. Application examples of the DDM -- 2.1. A screw dislocation in a semiinfinite solid (Case 1) -- 2.2. A screw dislocation in a plate (Case 2) -- 2.3. A hole in a plate (Case 3) -- 2.4. A screw dislocation in a wedge (Case 4) -- 2.5. A hole in a wedge (Case 5) -- 2.6. Multiple interacting holes with screw dislocations (Case 6). | |
| 505 | 8 | |a 2.7. The static case of a dislocation segment near a planar free surface (Case 7) -- 2.8. Discrete dislocation dynamics (DDD) simulations on the effect of free surfaces (Case 8) -- 2.9. The DDM to solve eigenstrain problems (Case 9) -- 2.10. The stress field of a sharp crack: 2D or planar problem (Case 10) -- 2.11. The stress field of cylindrical crack (Case 11) -- 3. Conclusions -- References -- Chapter 4: Machine learning-enabled parametrically upscaled constitutive models for bridging length scales in Ti and Ni alloy -- 1. Introduction -- 2. PUCMs for titanium alloys -- 2.1. Generating microstructure-based statistically equivalent virtual microstructures -- 2.2. Summary of the crystal plasticity constitutive model -- 2.3. Sensitivity analysis and representative aggregated microstructural parameter identification -- 2.4. Constitutive equations in the PUCM for dual-phase Ti alloys -- 2.4.1. Anisotropic elasticity -- 2.4.2. Plastic flow rule and anisotropic yield function -- 2.4.3. Anisotropic hardening model -- 2.5. ML-enabled functional forms of PUCM coefficients and parameters -- 2.6. Validation with specimen test data -- 2.7. UQ and UP in PUCM -- 2.7.1. Framework for UQ/UP implementation in PUCMs -- 2.7.2. Experimental validation of UQ-PUCM -- 2.7.3. Uncertainty in cyclic response due to microstructural variability -- 3. PUCPM for Ni-based superalloys -- 3.1. Steps in the development of single-crystal PUCPM -- 3.1.1. Generating M-SERVEs for micromechanical analysis -- 3.1.2. Image-based CPFEM for the M-SERVE with model calibration -- 3.1.3. GSA of microstructural descriptors and RAMP selection -- 3.1.4. Selected constitutive relations for the PUCPM -- 3.1.5. Self-consistent homogenization with a concurrent multiscale model for PUCPM coefficients -- 4. Summary and conclusions. | |
| 505 | 8 | |a Appendix. Calibrated tables used in the CPFEM and PUCM for dual-phase Ti alloys -- Acknowledgments -- References -- Chapter 5: Data-driven approaches for computational modeling for plasticity, fatigue, and fracture behavior of alloys -- 1. Introduction -- 2. State-of-the-art -- 2.1. Computational modeling for alloys -- 2.1.1. Computational plasticity models -- 2.1.2. Computational fracture models -- 2.1.3. Computational fatigue models -- 2.2. Experimental data for alloys -- 3. Need for computational model updating -- 4. Current methods for data-driven computational model updating -- 4.1. Data-driven plasticity -- 4.1.1. Problem description -- 4.1.2. Data-driven approaches for metal forming -- 4.1.2.1. Description of data and preprocessing steps -- 4.1.2.2. Data-driven model architecture -- 4.1.2.3. Data-driven texture prediction and comparison with experiments -- 4.1.3. Advantages and challenges -- 4.1.4. Related research and extensions -- 4.2. Data-driven fracture -- 4.2.1. Problem description -- 4.2.2. Data-driven approach of the problem -- 4.2.2.1. Description of data and preprocessing steps -- 4.2.2.2. Data-driven model -- 4.2.2.3. Data-driven fatigue crack prediction -- 4.2.3. Advantages and challenges -- 4.2.4. Related research and extensions -- 4.3. Data-driven fatigue -- 4.3.1. Problem description -- 4.3.2. Data-driven approach of the problem -- 4.3.2.1. Description of data and preprocessing steps -- 4.3.2.2. Data-driven model workflow -- 4.3.2.3. Data-driven fatigue crack prediction -- 4.3.3. Advantages and challenges -- 4.3.4. Related research and extensions -- 5. Discussion and future trends -- References -- Chapter 6: Twin transmission and variant continuity in Mg bicrystals -- 1. Introduction -- 2. Modeling grain boundary mechanics -- 3. Application to magnesium bicrystals -- 3.1. Bicrystal model -- 3.2. Experimental comparison. | |
| 505 | 8 | |a 4. Conclusions -- References -- Chapter 7: Thin-walled LPBF-manufactured Inconel 718 honeycomb structures: Multiscale characterization -- 1. Introduction -- 2. Element size effect -- 2.1. Scanning parameters -- 2.2. Element microstructure -- 2.3. Nanoindentation tests (homogeneity and mechanical anisotropy) -- 2.4. Miniature tensile test -- 2.5. Quasistatic compression tests on hexagonal honeycomb structures -- 3. Conclusion -- Acknowledgment -- References -- Chapter 8: Sample size dependence of mechanical properties in metallic materials -- 1. Introduction -- 2. Size-dependent yield strength in metallic materials -- 2.1. Dislocation source limitation mechanism in miniaturized sample -- 2.2. Unified model for size-dependent yield strength of single-crystalline materials -- 2.3. Unified model for size-dependent yield strength of polycrystalline materials -- 3. Size-dependent creep rate in metallic materials -- 3.1. Experimental procedure for creep tests of micropillars -- 3.2. Creep rate of Cu micropillars at different loading stresses -- 3.3. Mechanism-based model for size-dependent creep behavior -- 4. Size-dependent fracture toughness in metallic materials -- 4.1. Comparison of macro- and microscale fracture toughness tests -- 4.2. Transition of fracture toughness with increasing sample size -- 4.3. Mechanism-based model for size-dependent fracture toughness -- 5. Conclusions -- Acknowledgments -- References -- Chapter 9: Intelligent processing and development of high-performance automotive aluminum alloys: Application of physic -- 1. Introduction -- 2. Finite element modeling of extrusion -- 2.1. Extrusion process -- 2.2. Extrusion temperatures -- 2.3. Extrusion speed -- 2.4. Friction -- 2.5. Extrusion geometries -- 2.6. Extrusion ratio (ER) -- 2.7. Extrusion process -- 2.8. Finite element modeling of extrusion processing. | |
| 505 | 8 | |a 2.9. Field variables in FE output -- 2.9.1. Modeling assumptions for extrusion -- 2.9.2. Field variable for texture analysis -- 2.10. Constitutive material models -- 2.11. Summary -- 3. Mean-field modeling of clustering and precipitation in multicomponent aluminum alloys -- 3.1. Clustering and precipitation -- 3.2. Mean-field modeling of precipitation -- 3.2.1. Constitutive models -- 3.2.2. Nucleation, growth, and coarsening models -- 3.2.3. Class models -- 3.3. A unified model of clustering and precipitation -- 3.3.1. The core assumption for clustering -- 3.3.2. Framework for treating multistage aging -- 3.3.3. Nonclassical nucleation model -- 3.3.4. Formulation of growth -- 3.3.5. Formulation of coarsening -- 3.4. The effect of natural and preaging on the peak strength -- 3.5. Summary -- 4. Data-driven modeling for predicting mechanical properties -- 4.1. Data -- 4.2. Data size -- 4.3. Data quality -- 4.4. Data breadth -- 4.5. Data biases -- 4.6. Modeling -- 4.7. Models -- 4.7.1. Linear methods -- 4.7.2. K-nearest neighbor -- 4.7.3. Support vector regression -- 4.7.4. Decision tree-based methods -- 4.7.5. Neural networks -- 4.8. Validation methods -- 4.9. Comparison of models -- 4.10. Hyperparameter optimization -- 4.11. Feature engineering -- 4.11.1. POD -- 4.11.2. Physical metallurgy parameters -- 4.12. Feature importance -- 4.13. Extrapolation -- 4.13.1. Models for extrapolation -- 4.13.2. Active learning -- 4.14. Summary -- 5. Future directions: Physics-informed machine learning -- Acknowledgment -- References -- Chapter 10: Parameter effects on the fatigue behavior of aerospace aluminum alloys -- 1. Introduction -- 2. Review -- 2.1. Comparing 2050-T852 and 7050-T7452 alloys -- 2.2. Comparing other aluminum alloys with each other -- 2.2.1. 2024 T 351 vs 7075 T6-T351 -- 2.2.2. 2195 vs 2014 -- 2.2.3. 2017-T4 vs 5083-HIII vs 6082-T6 -- 3. Summary. | |
| 520 | |a Innovative Lightweight and High Strength Alloys: Multiscale Integrated Processing, Experimental, and Modeling Techniques provides multiscale processing, experimental and modeling techniques overviews and perspectives that highlight current roadblocks to optimal design of new alloys alongside solutions. | ||
| 650 | 0 | |a Alloys. | |
| 650 | 0 | |a Light metal alloys. | |
| 650 | 6 | |a Alliages. |0 (CaQQLa)201-0002256 | |
| 650 | 6 | |a Alliages légers. |0 (CaQQLa)201-0106831 | |
| 650 | 7 | |a alloy. |2 aat |0 (CStmoGRI)aat300010902 | |
| 650 | 7 | |a Alloys |2 fast |0 (OCoLC)fst00805706 | |
| 650 | 7 | |a Light metal alloys |2 fast |0 (OCoLC)fst00998555 | |
| 700 | 1 | |a Zikry, Mohammed A. | |
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| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780323995399 |