Data Driven Evolutionary Modeling In Materials Technology

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Data-Driven Evolutionary Modeling in Materials Technology

Nirupam Chakraborti
Data Driven Evolutionary Modeling in Materials Technology Book PDF

Author : Nirupam Chakraborti
Publisher : CRC Press
Page : 507 pages
File Size : 47,6 Mb
Release : 2022-09-15
Category : Technology & Engineering
ISBN : 9781000635867

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Data-Driven Evolutionary Modeling in Materials Technology by Nirupam Chakraborti Pdf

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 Optimization

Yaochu Jin,Handing Wang,Chaoli Sun
Data Driven Evolutionary Optimization Book PDF

Author : Yaochu Jin,Handing Wang,Chaoli Sun
Publisher : Springer Nature
Page : 393 pages
File Size : 44,5 Mb
Release : 2021-06-28
Category : Computers
ISBN : 9783030746407

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Data-Driven Evolutionary Optimization by Yaochu Jin,Handing Wang,Chaoli Sun Pdf

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.

Data-Driven Modeling for Additive Manufacturing of Metals

National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,National Materials and Manufacturing Board,Board on Mathematical Sciences and Analytics
Data Driven Modeling for Additive Manufacturing of Metals Book PDF

Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,National Materials and Manufacturing Board,Board on Mathematical Sciences and Analytics
Publisher : National Academies Press
Page : 79 pages
File Size : 55,5 Mb
Release : 2019-11-09
Category : Technology & Engineering
ISBN : 9780309494205

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Data-Driven Modeling for Additive Manufacturing of Metals by National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,National Materials and Manufacturing Board,Board on Mathematical Sciences and Analytics Pdf

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.

Springback Assessment and Compensation of Tailor Welded Blanks

AB ABDULLAH,MF Jamaludin
Springback Assessment and Compensation of Tailor Welded Blanks Book PDF

Author : AB ABDULLAH,MF Jamaludin
Publisher : CRC Press
Page : 309 pages
File Size : 40,7 Mb
Release : 2022-12-27
Category : Science
ISBN : 9781000821949

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Springback Assessment and Compensation of Tailor Welded Blanks by AB ABDULLAH,MF Jamaludin Pdf

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 Model Learning for Engineers

Guillaume Mercère
Data Driven Model Learning for Engineers Book PDF

Author : Guillaume Mercère
Publisher : Springer Nature
Page : 218 pages
File Size : 52,9 Mb
Release : 2023-08-09
Category : Mathematics
ISBN : 9783031316364

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Data Driven Model Learning for Engineers by Guillaume Mercère Pdf

The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail. As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. Only a basic familiarity with advanced calculus, linear algebra and statistics is assumed, making the material accessible to students at the advanced undergraduate level.

Data-driven Modeling for Additive Manufacturing of Metals

Anonim
Data driven Modeling for Additive Manufacturing of Metals Book PDF

Author : Anonim
Publisher : Unknown
Page : 66 pages
File Size : 40,6 Mb
Release : 2019
Category : Electronic books
ISBN : 0309494214

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Data-driven Modeling for Additive Manufacturing of Metals by Anonim Pdf

"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

Materials Science and Engineering

Nirupam Chakraborti
Materials Science and Engineering Book PDF

Author : Nirupam Chakraborti
Publisher : Elsevier Inc. Chapters
Page : 542 pages
File Size : 47,7 Mb
Release : 2013-07-10
Category : Technology & Engineering
ISBN : 9780128059357

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Materials Science and Engineering by Nirupam Chakraborti Pdf

Artificial neural networks (ANNs) and genetic programming (GP) have already emerged as two very effective computing strategies for constructing data-driven models for systems of scientific and engineering interest. However, coming up with accurate models or meta-models from noisy real-life data is often a formidable task due to their frequent association with high degrees of random noise, which might render an ANN or GP model either over- or underfitted. This problem has recently been tackled in two emerging algorithms, Evolutionary Neural Net (EvoNN) and Bi-objective Genetic Programming (BioGP), which utilize the concept of Pareto tradeoff and apply a bi-objective genetic algorithm (GA) in the basic framework of both ANNs and GP. These concepts are elaborated in detail in this chapter.

Informatics for Materials Science and Engineering

Krishna Rajan
Informatics for Materials Science and Engineering Book PDF

Author : Krishna Rajan
Publisher : Butterworth-Heinemann
Page : 542 pages
File Size : 51,5 Mb
Release : 2013-07-10
Category : Technology & Engineering
ISBN : 9780123946140

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Informatics for Materials Science and Engineering by Krishna Rajan Pdf

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

Computational Sciences and Artificial Intelligence in Industry

Tero Tuovinen,Jacques Periaux,Pekka Neittaanmäki
Computational Sciences and Artificial Intelligence in Industry Book PDF

Author : Tero Tuovinen,Jacques Periaux,Pekka Neittaanmäki
Publisher : Springer Nature
Page : 278 pages
File Size : 48,8 Mb
Release : 2021-08-19
Category : Technology & Engineering
ISBN : 9783030707873

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Computational Sciences and Artificial Intelligence in Industry by Tero Tuovinen,Jacques Periaux,Pekka Neittaanmäki Pdf

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.

Data-Driven Optimization of Manufacturing Processes

Kalita, Kanak,Ghadai, Ranjan Kumar,Gao, Xiao-Zhi
Data Driven Optimization of Manufacturing Processes Book PDF

Author : Kalita, Kanak,Ghadai, Ranjan Kumar,Gao, Xiao-Zhi
Publisher : IGI Global
Page : 298 pages
File Size : 42,8 Mb
Release : 2020-12-25
Category : Technology & Engineering
ISBN : 9781799872085

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Data-Driven Optimization of Manufacturing Processes by Kalita, Kanak,Ghadai, Ranjan Kumar,Gao, Xiao-Zhi Pdf

All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.

Computational Approaches to Materials Design: Theoretical and Practical Aspects

Datta, Shubhabrata,Davim, J. Paulo
Computational Approaches to Materials Design Theoretical and Practical Aspects Book PDF

Author : Datta, Shubhabrata,Davim, J. Paulo
Publisher : IGI Global
Page : 475 pages
File Size : 51,7 Mb
Release : 2016-06-16
Category : Technology & Engineering
ISBN : 9781522502913

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Computational Approaches to Materials Design: Theoretical and Practical Aspects by Datta, Shubhabrata,Davim, J. Paulo Pdf

The development of new and superior materials is beneficial within industrial settings, as well as a topic of academic interest. By using computational modeling techniques, the probable application and performance of these materials can be easily evaluated. Computational Approaches to Materials Design: Theoretical and Practical Aspects brings together empirical research, theoretical concepts, and the various approaches in the design and discovery of new materials. Highlighting optimization tools and soft computing methods, this publication is a comprehensive collection for researchers, both in academia and in industrial settings, and practitioners who are interested in the application of computational techniques in the field of materials engineering.

Machine Learning for Materials Discovery

N. M. Anoop Krishnan,Hariprasad Kodamana,Ravinder Bhattoo
Machine Learning for Materials Discovery Book PDF

Author : N. M. Anoop Krishnan,Hariprasad Kodamana,Ravinder Bhattoo
Publisher : Springer
Page : 0 pages
File Size : 43,7 Mb
Release : 2024-03-19
Category : Technology & Engineering
ISBN : 3031446216

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Machine Learning for Materials Discovery by N. M. Anoop Krishnan,Hariprasad Kodamana,Ravinder Bhattoo Pdf

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.

Applications of Metaheuristics in Process Engineering

Jayaraman Valadi,Patrick Siarry
Applications of Metaheuristics in Process Engineering Book PDF

Author : Jayaraman Valadi,Patrick Siarry
Publisher : Springer
Page : 444 pages
File Size : 46,7 Mb
Release : 2014-08-07
Category : Computers
ISBN : 9783319065083

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Applications of Metaheuristics in Process Engineering by Jayaraman Valadi,Patrick Siarry Pdf

Metaheuristics exhibit desirable properties like simplicity, easy parallelizability and ready applicability to different types of optimization problems such as real parameter optimization, combinatorial optimization and mixed integer optimization. They are thus beginning to play a key role in different industrially important process engineering applications, among them the synthesis of heat and mass exchange equipment, synthesis of distillation columns and static and dynamic optimization of chemical and bioreactors. This book explains cutting-edge research techniques in related computational intelligence domains and their applications in real-world process engineering. It will be of interest to industrial practitioners and research academics.

Introduction to Evolutionary Computing

Agoston E. Eiben,J.E. Smith
Introduction to Evolutionary Computing Book PDF

Author : Agoston E. Eiben,J.E. Smith
Publisher : Springer Science & Business Media
Page : 307 pages
File Size : 45,7 Mb
Release : 2013-03-14
Category : Computers
ISBN : 9783662050941

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Introduction to Evolutionary Computing by Agoston E. Eiben,J.E. Smith Pdf

The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.

Data-Driven Modeling & Scientific Computation

J. Nathan Kutz
Data Driven Modeling Scientific Computation Book PDF

Author : J. Nathan Kutz
Publisher : Oxford University Press
Page : 657 pages
File Size : 51,7 Mb
Release : 2013-08-08
Category : Computers
ISBN : 9780199660339

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Data-Driven Modeling & Scientific Computation by J. Nathan Kutz Pdf

Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.