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Innovative Lightweight and High-Strength Alloys
Multiscale Integrated Processing, Experimental, and Modeling Techniques
- 1st Edition - April 22, 2024
- Editor: Mohammed A. Zikry
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 9 5 3 9 - 9
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 9 5 4 0 - 5
Innovative Lightweight and High-Strength Alloys: Multiscale Integrated Processing, Experimental, and Modeling Techniques provides multiscale processing, experimental, and modeling… Read more
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Request a sales quoteInnovative Lightweight and High-Strength Alloys: Multiscale Integrated Processing, Experimental, and Modeling Techniques provides multiscale processing, experimental, and modeling techniques, overviews and perspectives, which highlight current roadblocks to the optimal design of new alloys, and provides viable solutions. Critical microstructural, chemical and mechanical aspects are considered with techniques for significantly improving mechanical properties. Case studies, applications and hands-on techniques that can be put into immediate practice are included throughout. Sections cover processing techniques for various alloys, including aluminum, titanium, martensitic, austenitic, and others. Additive manufacturing of alloys is also covered, along with updates on mechanical quasi-static, chemically-based, and dynamic experimentatal approaches.
The book concludes with a modeling section that features several chapters covering multiscale, microstructural, combinatorial computational, and machine learning modeling techniques. It is a key resource for academic researchers, materials researchers, mechanical engineering researchers, and professional engineers in mechanics, materials science, and chemistry.
- Provides solutions for designing innovative and durable alloys
- Demonstrates how to optimally combine alloys with other metallic and non-metallic material systems for longer life cycles and better durability in extreme environments and loading conditions
- Outlines a variety of experimentation, characterization and modeling techniques that can be put into immediate practice
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Chapter 1 Process-structure-property models for metal additive manufacturing using AI/ML approaches
- Abstract
- 1 Introduction
- 2 Dataset sources
- 3 Data science approaches to rationalize PSP linkages
- 4 Conclusion
- References
- Chapter 2 An overview on recent processing, metallurgy, and experiments related to microstructure engineering in additive manufacturing
- Abstract
- 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
- Abstract
- 1 Introduction
- 2 Application examples of the DDM
- 3 Conclusions
- References
- Chapter 4 Machine learning-enabled parametrically upscaled constitutive models for bridging length scales in Ti and Ni alloys
- Abstract
- Acknowledgments
- 1 Introduction
- 2 PUCMs for titanium alloys
- 3 PUCPM for Ni-based superalloys
- 4 Summary and conclusions
- Appendix Calibrated tables used in the CPFEM and PUCM for dual-phase Ti alloys
- References
- Chapter 5 Data-driven approaches for computational modeling for plasticity, fatigue, and fracture behavior of alloys
- Abstract
- 1 Introduction
- 2 State-of-the-art
- 3 Need for computational model updating
- 4 Current methods for data-driven computational model updating
- 5 Discussion and future trends
- References
- Chapter 6 Twin transmission and variant continuity in Mg bicrystals
- Abstract
- 1 Introduction
- 2 Modeling grain boundary mechanics
- 3 Application to magnesium bicrystals
- 4 Conclusions
- References
- Chapter 7 Thin-walled LPBF-manufactured Inconel 718 honeycomb structures: Multiscale characterization
- Abstract
- Acknowledgment
- 1 Introduction
- 2 Element size effect
- 3 Conclusion
- References
- Chapter 8 Sample size dependence of mechanical properties in metallic materials
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Size-dependent yield strength in metallic materials
- 3 Size-dependent creep rate in metallic materials
- 4 Size-dependent fracture toughness in metallic materials
- 5 Conclusions
- References
- Chapter 9 Intelligent processing and development of high-performance automotive aluminum alloys: Application of physics-based and data-driven modeling
- Abstract
- Acknowledgment
- 1 Introduction
- 2 Finite element modeling of extrusion
- 3 Mean-field modeling of clustering and precipitation in multicomponent aluminum alloys
- 4 Data-driven modeling for predicting mechanical properties
- 5 Future directions: Physics-informed machine learning
- References
- Chapter 10 Parameter effects on the fatigue behavior of aerospace aluminum alloys
- Abstract
- 1 Introduction
- 2 Review
- 3 Summary
- References
- Chapter 11 High-strength alloys with reduced hydrogen embrittlement susceptibility
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Hydrogen interaction with the microstructure
- 3 Theoretical methods of investigation
- 4 Material design strategies: Toward HE-resistant high-strength alloys
- 5 Conclusions
- References
- Chapter 12 Microstructural predictions of how processing and additive manufacturing can affect the mechanical behavior of Inconel alloys
- Abstract
- 1 Introduction
- 2 Dislocation-density-based multiple slip crystalline formulation
- 3 Microstructural IG fracture criterion and computational implementation
- 4 Results and discussion
- 5 Summary
- References
- Chapter 13 Breaking boundaries: Deformation processing techniques for the next generation of lightweight and high-strength materials
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Advanced material development through solid phase processing (SPP) techniques
- 3 Atomistic understanding
- 4 Emerging trends and required breakthroughs in current and future advanced materials research
- 5 Limitations and recent developments
- 6 Summary
- References
- Index
- No. of pages: 500
- Language: English
- Edition: 1
- Published: April 22, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780323995399
- eBook ISBN: 9780323995405
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