Data Driven Evolutionary Modeling In Materials Technology
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Data Driven Evolutionary Modeling In Materials Technology
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Author : Nirupam Chakraborti
language : en
Publisher: CRC Press
Release Date : 2022-09-15
Data Driven Evolutionary Modeling In Materials Technology written by Nirupam Chakraborti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-15 with Technology & Engineering categories.
Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.
Data Driven Evolutionary Modeling In Materials Technology
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Author : Nirupam Chakraborti
language : en
Publisher: CRC Press
Release Date : 2022-09-15
Data Driven Evolutionary Modeling In Materials Technology written by Nirupam Chakraborti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-15 with Technology & Engineering categories.
Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.
Data Driven Modeling For Additive Manufacturing Of Metals
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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2019-11-09
Data Driven Modeling For Additive Manufacturing Of Metals written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-09 with Technology & Engineering categories.
Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have the potential to improve AM predictive modeling capabilities, particularly when combined with existing knowledge of the underlying mechanics. These predictive models have the potential to reduce the cost of and time for concept-to-final-product development and can be used to supplement experimental tests. The National Academies convened a workshop on October 24-26, 2018 to discuss the frontiers of mechanistic data-driven modeling for AM of metals. Topics of discussion included measuring and modeling process monitoring and control, developing models to represent microstructure evolution, alloy design, and part suitability, modeling phases of process and machine design, and accelerating product and process qualification and certification. These topics then led to the assessment of short-, immediate-, and long-term challenges in AM. This publication summarizes the presentations and discussions from the workshop.
Data Driven Modeling For Additive Manufacturing Of Metals
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Author :
language : en
Publisher:
Release Date : 2019
Data Driven Modeling For Additive Manufacturing Of Metals written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Electronic books categories.
"Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have the potential to improve AM predictive modeling capabilities, particularly when combined with existing knowledge of the underlying mechanics. These predictive models have the potential to reduce the cost of and time for concept-to-final-product development and can be used to supplement experimental tests. The National Academies convened a workshop on October 24-26, 2018 to discuss the frontiers of mechanistic data-driven modeling for AM of metals. Topics of discussion included measuring and modeling process monitoring and control, developing models to represent microstructure evolution, alloy design, and part suitability, modeling phases of process and machine design, and accelerating product and process qualification and certification. These topics then led to the assessment of short-, immediate-, and long-term challenges in AM. This publication summarizes the presentations and discussions from the workshop"--Publisher's description
Springback Assessment And Compensation Of Tailor Welded Blanks
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Author : AB ABDULLAH
language : en
Publisher: CRC Press
Release Date : 2022-12-27
Springback Assessment And Compensation Of Tailor Welded Blanks written by AB ABDULLAH and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-27 with Science categories.
Focusing on techniques developed to evaluate the forming behaviour of tailor welded blanks (TWBs) in sheet metal manufacturing, this edited collection details compensation methods suited to mitigating the effects of springback. Making use of case studies and in-depth accounts of industry experience, this book gives a comprehensive overview of springback and provides essential solutions necessary to modern-day automotive engineers. Sheet metal forming is a major process within the automotive industry, with advancement of the technology including utilization of non-uniform sheet metal in order to produce light or strengthened body structures. This is critical in the reduction of vehicle weight in order to match increased consumer demand for better driving performance and improved fuel efficiency. Additionally, increasingly stringent international regulations regarding exhaust emissions require manufacturers to seek to lighten vehicles as much as possible. To aid engineers in optimizing lightweight designs, this comprehensive book covers topics by a variety of industry experts, including compensation by annealing, low-power welding, punch profile radius and tool-integrated springback measuring systems. It ends by looking at the future trends within the industry and the potential for further innovation within the field. This work will benefit car manufacturers and stamping plants that face springback issues within their production, particularly in the implementation of TWB production into existing facilities. It will also be of interest to students and researchers in automotive and aerospace engineering.
Data Driven Evolutionary Optimization
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Author : Yaochu Jin
language : en
Publisher: Springer Nature
Release Date : 2021-06-28
Data Driven Evolutionary Optimization written by Yaochu Jin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-28 with Computers categories.
Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
Machine Learning For Materials Discovery
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Author : N. M. Anoop Krishnan
language : en
Publisher: Springer
Release Date : 2024-03-19
Machine Learning For Materials Discovery written by N. M. Anoop Krishnan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-19 with Technology & Engineering categories.
Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect—each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials.
Informatics For Materials Science And Engineering
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Author : Krishna Rajan
language : en
Publisher: Butterworth-Heinemann
Release Date : 2013-07-10
Informatics For Materials Science And Engineering written by Krishna Rajan and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-10 with Technology & Engineering categories.
Materials informatics: a ‘hot topic’ area in materials science, aims to combine traditionally bio-led informatics with computational methodologies, supporting more efficient research by identifying strategies for time- and cost-effective analysis. The discovery and maturation of new materials has been outpaced by the thicket of data created by new combinatorial and high throughput analytical techniques. The elaboration of this "quantitative avalanche"—and the resulting complex, multi-factor analyses required to understand it—means that interest, investment, and research are revisiting informatics approaches as a solution. This work, from Krishna Rajan, the leading expert of the informatics approach to materials, seeks to break down the barriers between data management, quality standards, data mining, exchange, and storage and analysis, as a means of accelerating scientific research in materials science. This solutions-based reference synthesizes foundational physical, statistical, and mathematical content with emerging experimental and real-world applications, for interdisciplinary researchers and those new to the field. Identifies and analyzes interdisciplinary strategies (including combinatorial and high throughput approaches) that accelerate materials development cycle times and reduces associated costs Mathematical and computational analysis aids formulation of new structure-property correlations among large, heterogeneous, and distributed data sets Practical examples, computational tools, and software analysis benefits rapid identification of critical data and analysis of theoretical needs for future problems
Engineering Of Additive Manufacturing Features For Data Driven Solutions
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Author : Mutahar Safdar
language : en
Publisher: Springer Nature
Release Date : 2023-06-01
Engineering Of Additive Manufacturing Features For Data Driven Solutions written by Mutahar Safdar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-01 with Technology & Engineering categories.
This book is a comprehensive guide to the latest developments in data-driven additive manufacturing (AM). From data mining and pre-processing to signal processing, computer vision, and more, the book covers all the essential techniques for preparing AM data. Readers willl explore the key physical and synthetic sources of AM data throughout the life cycle of the process and learn about feature engineering techniques, pipelines, and resulting features, as well as their applications at each life cycle phase. With a focus on featurization efforts from reviewed literature, this book offers tabular summaries for major data sources and analyzes feature spaces at the design, process, and structure phases of AM to uncover trends and insights specific to feature engineering techniques. Finally, the book discusses current challenges and future directions, including AI/ML/DL readiness of AM data. Whether you're an expert or newcomer to the field, this book provides a broader summary of the status and future of data-driven AM technology.
Computational Sciences And Artificial Intelligence In Industry
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Author : Tero Tuovinen
language : en
Publisher: Springer Nature
Release Date : 2021-08-19
Computational Sciences And Artificial Intelligence In Industry written by Tero Tuovinen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-19 with Technology & Engineering categories.
This book is addressed to young researchers and engineers in the fields of Computational Science and Artificial Intelligence, ranging from innovative computational methods to digital machine learning tools and their coupling used for solving challenging industrial and societal problems.This book provides the latest knowledge from jointly academic and industries experts in Computational Science and Artificial Intelligence fields for exploring possibilities and identifying challenges of applying Computational Sciences and AI methods and tools in industrial and societal sectors.