Microstructure Sensitive Design for Performance Optimization
Title | Microstructure Sensitive Design for Performance Optimization PDF eBook |
Author | Brent L. Adams |
Publisher | Butterworth-Heinemann |
Pages | 425 |
Release | 2012-09-25 |
Genre | Science |
ISBN | 0123969891 |
The accelerating rate at which new materials are appearing, and transforming the engineering world, only serves to emphasize the vast potential for novel material structure and related performance. Microstructure Sensitive Design for Performance Optimization (MSDPO) embodies a new methodology for systematic design of material microstructure to meet the requirements of design in optimal ways. Intended for materials engineers and researchers in industry, government and academia as well as upper level undergraduate and graduate students studying material science and engineering, MSDPO provides a novel mathematical framework that facilitates a rigorous consideration of the material microstructure as a continuous design variable in the field of engineering design. Presents new methods and techniques for analysis and optimum design of materials at the microstructure level Authors' methodology introduces spectral approaches not available in previous texts, such as the incorporation of crystallographic orientation as a variable in the design of engineered components with targeted elastic properties Numerous illustrations and examples throughout the text help readers grasp the concepts
Metallurgy and Design of Alloys with Hierarchical Microstructures
Title | Metallurgy and Design of Alloys with Hierarchical Microstructures PDF eBook |
Author | Krishnan K. Sankaran |
Publisher | Elsevier |
Pages | 508 |
Release | 2017-06-14 |
Genre | Technology & Engineering |
ISBN | 0128120258 |
Metallurgy and Design of Alloys with Hierarchical Microstructures covers the fundamentals of processing-microstructure-property relationships and how multiple properties are balanced and optimized in materials with hierarchical microstructures widely used in critical applications. The discussion is based principally on metallic materials used in aircraft structures; however, because they have sufficiently diverse microstructures, the underlying principles can easily be extended to other materials systems. With the increasing microstructural complexity of structural materials, it is important for students, academic researchers and practicing engineers to possess the knowledge of how materials are optimized and how they will behave in service. The book integrates aspects of computational materials science, physical metallurgy, alloy design, process design, and structure-properties relationships, in a manner not done before. It fills a knowledge gap in the interrelationships of multiple microstructural and deformation mechanisms by applying the concepts and tools of designing microstructures for achieving combinations of engineering properties—such as strength, corrosion resistance, durability and damage tolerance in multi-component materials—used for critical structural applications. - Discusses the science behind the properties and performance of advanced metallic materials - Provides for the efficient design of materials and processes to satisfy targeted performance in materials and structures - Enables the selection and development of new alloys for specific applications based upon evaluation of their microstructure as illustrated in this work
Architecting Robust Co-Design of Materials, Products, and Manufacturing Processes
Title | Architecting Robust Co-Design of Materials, Products, and Manufacturing Processes PDF eBook |
Author | Anand Balu Nellippallil |
Publisher | Springer Nature |
Pages | 368 |
Release | 2020-06-13 |
Genre | Technology & Engineering |
ISBN | 3030453243 |
This book explores systems-based, co-design, introducing a “Decision-Based, Co-Design” (DBCD) approach for the co-design of materials, products, and processes. In recent years there have been significant advances in modeling and simulation of material behavior, from the smallest atomic scale to the macro scale. However, the uncertainties associated with these approaches and models across different scales need to be addressed to enable decision-making resulting in designs that are robust, that is, relatively insensitive to uncertainties. An approach that facilitates co-design is needed across material, product design and manufacturing processes. This book describes a cloud-based platform to support decisions in the design of engineered systems (CB-PDSIDES), which feature an architecture that promotes co-design through the servitization of decision-making, knowledge capture and use templates that allow previous solutions to be reused. Placing the platform in the cloud aids mass collaboration and open innovation. A valuable reference resource reference on all areas related to the design of materials, products and processes, the book appeals to material scientists, design engineers and all those involved in the emerging interdisciplinary field of integrated computational materials engineering (ICME).
Computational Materials System Design
Title | Computational Materials System Design PDF eBook |
Author | Dongwon Shin |
Publisher | Springer |
Pages | 239 |
Release | 2017-11-10 |
Genre | Technology & Engineering |
ISBN | 3319682806 |
This book provides state-of-the-art computational approaches for accelerating materials discovery, synthesis, and processing using thermodynamics and kinetics. The authors deliver an overview of current practical computational tools for materials design in the field. They describe ways to integrate thermodynamics and kinetics and how the two can supplement each other.
Machine Learning in Molecular Sciences
Title | Machine Learning in Molecular Sciences PDF eBook |
Author | Chen Qu |
Publisher | Springer Nature |
Pages | 323 |
Release | 2023-11-02 |
Genre | Computers |
ISBN | 3031371968 |
Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.
Horizons in Materials
Title | Horizons in Materials PDF eBook |
Author | Nicola Maria Pugno |
Publisher | Frontiers Media SA |
Pages | 189 |
Release | 2022-08-23 |
Genre | Technology & Engineering |
ISBN | 2889761630 |
The Frontiers in Materials Editorial Office team are delighted to present the “Horizons in Materials” article collection, showcasing high-impact, authoritative, and accessible Review articles covering important topics at the forefront of the materials science and engineering field. All contributing authors were nominated by the Chief Editors and Editorial Office in recognition of their prominence and influence in their respective fields. The cutting-edge work presented in this article collection highlights the diversity of research performed across the entire breadth of the materials science and engineering field and reflects on the latest advances in theory, experiment, and methodology with applications to compelling problems. This Editorial features the corresponding author(s) of each paper published within this important collection, ordered by section alphabetically, highlighting them as the great researchers of the future. The Frontiers in Materials Chief Editors and Editorial Office team would like to thank each researcher who contributed their work to this collection. We are excited to see each article gain the deserved visibility and traction within the wider community, ensuring the collection’s truly global impact and success. Emily Young Journal Manager
Microstructure modeling and crystal plasticity parameter identification for predicting the cyclic mechanical behavior of polycrystalline metals
Title | Microstructure modeling and crystal plasticity parameter identification for predicting the cyclic mechanical behavior of polycrystalline metals PDF eBook |
Author | Kuhn, Jannick |
Publisher | KIT Scientific Publishing |
Pages | 224 |
Release | 2023-04-04 |
Genre | Technology & Engineering |
ISBN | 3731512726 |
Computational homogenization permits to capture the influence of the microstructure on the cyclic mechanical behavior of polycrystalline metals. In this work we investigate methods to compute Laguerre tessellations as computational cells of polycrystalline microstructures, propose a new method to assign crystallographic orientations to the Laguerre cells and use Bayesian optimization to find suitable parameters for the underlying micromechanical model from macroscopic experiments.