Download or read book Data Driven Evolutionary Modeling in Materials Technology written by Nirupam Chakraborti and published by CRC Press. This book was released on 2022-09-15 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Materials Science and Engineering written by Nirupam Chakraborti and published by Elsevier Inc. Chapters. This book was released on 2013-07-10 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Springback Assessment and Compensation of Tailor Welded Blanks written by AB ABDULLAH and published by CRC Press. This book was released on 2022-12-27 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data driven Modeling Implementation Within Materials Development and Manufacturing Systems written by Allen Jonathan Roman and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predicting polymeric material behavior during processing and predicting final part properties continues to be a strong research focus within the scientific community as it involves taking into consideration a wide range of time-dependent variables. By use of data-driven modeling, the materials development process can be accelerated, and the highly predictive modeling techniques can facilitate the development of smart manufacturing systems. This dissertation worked on solving polymer engineering problems by use of data-driven modeling techniques. The first strategy was using data-driven modeling to provide a predictive model with statistical insights of the injection molding process to ensure part quality is maximized for a highly viscoelastic material blend. By injection molding highly viscoelastic materials, the probability of part defects is increased, therefore, it was crucial to use advanced computational techniques to understand the nuances of this highly non-linear process and to predict the outcome before creating material waste from faulty trials. The second strategy was in the use of data-driven modeling for reverse engineering purposes, specifically within materials development. By combining experimental characterization and data-driven modeling, algorithms were developed and compared to prove how highly predictive models can be used as reverse engineering toolboxes. This ultimately informed users of the optimal formulation which would reach the specified target material properties. The final strategy explored using data-driven modeling to validate the high influence of viscous heating within the pressure melt removal process, therefore, work was done in implementing a viscous heating system within a fused filament fabrication (FFF) 3D printer to accelerate the 3D printing process. The instrumented FFF 3D printer proved capable of accelerating print speeds and improving mechanical performance of 3D printed parts, working towards solving two of the largest bottlenecks within additive manufacturing: lead times and part quality. Given the unique capabilities of the data-driven modeling, the novel 3D printer was tested and evaluated via data-driven modeling to provide statistical information regarding which processing parameters were the most influential for improving overall performance of the 3D printing system. The results of this work provide a basis for future research endeavors related to combining data-driven modeling and polymer science, such as in optimizing the newly developed viscous heating 3D printer.

Download or read book Materials Science and Engineering written by Duane D. Johnson and published by Elsevier Inc. Chapters. This book was released on 2013-07-10 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: We exemplify and propose extending the use of genetic programs (GPs) – a genetic algorithm (GA) that evolves computer programs via mechanisms similar to genetics and natural selection – to symbolically regress key functional relationships between materials data, especially from electronic structure. GPs can extract structure–property relations or enable simulations across multiple scales of time and/or length. Uniquely, GP-based regression permits “data discovery” – finding relevant data and/or extracting correlations (data reduction/data mining) – in contrast to searching for what you know, or you think you know (intuition). First, catalysis-related materials correlations are discussed, where simple electronic-structure-based rules are revealed using well-developed intuition, and then, after introducing the concepts, GP regression is used to obtain (i) a constitutive relation between flow stress and strain rate in aluminum, and (ii) multi-time-scale kinetics for surface alloys. We close with some outlook for a range of applications (materials discovery, excited-state chemistry, and multiscaling) that could rely primarily on density functional theory results.

Download or read book Data driven Systems Engineering for Bioinspired Integrative Design written by Luca Gabriele De Vivo Nicoloso and published by . This book was released on 2021 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Engineering design problems can be discussed under the framework of decision making, namely, engineering design decisions. Inherently, accounting for uncertainty factors is an indispensable part of these decision processes. The goal of design decisions is to control or reduce the variational effect in decision consequences induced by many uncertainty factors. If we look at current technological trends, specifically the industry 4.0 movement, we can quickly appreciate the big push in science and technology for the digitalization of design, manufacturing, and management processes to reduce the amount of uncertainty present during innovation attempts. This work explores the value of data-driven integrated design and digital fabrication and how it allowed us to drive innovation in more than one domain. From examples in biomimicry discoveries to prostheses and unmanned aerial vehicle designs to the use of drones for emergency response, the key ideas of the proposed data-driven design paradigm are demonstrated. Earlier works on data-driven design and digital manufacturing have demonstrated its potential to disrupt the way we think about engineering design processes. However, constant modernization in these fields keeps pushing the boundaries on what is possible, and these territories remain relatively uncharted. This research aims to explore how a combination of spatial data sets can serve as a point of entry for data-driven innovative designs. The process starts with a different range of data acquisition tools and processing techniques, followed by computational analysis and optimization designs, all the way to digital manufacturing by means of 3D printing and validation via mechanical and functional testing. These data sets enabled the synthesis of digital twin models, which allowed us to begin a reverse engineering process for a series of multiple purposes. To begin our study, we focused on new methods of additive manufacturing with a special focus on composite 3D printing. We explored the current state of knowledge in the field of composite additive manufacturing. We investigated all different methods of 3D printing and the current broad range of materials available. We also gained a deep understanding of the different optimization opportunities that can be gained by incorporating fibers, chopped or continuous, into polymer filament additive manufacturing. Now that we know we can design and manufacture almost anything we can imagine we asked ourselves what would that be? At this point we explored new trends in the field of digital modeling, simulation, and optimization techniques. Starting in the cyber context we can create a digital twin that satisfies the objective functions of an engineering system such as lightweight, strong, controllable, manufacturable, and then use these objective functions as an opportunity to optimize over the design space. To prove this concept, we selected an engineering system design challenge: The design and optimization of a novel box wing vertical takeoff and landing aircraft intended to serve in long endurance environmental and archeological recognition missions as well as serving as the starting point for the development of the next generation of urban air mobility platforms. During the design of the Prandtl Box wing aircraft system we found that if we wanted to design better aeronautical systems, we needed to find a way to design lighter and stronger structures. This is the point when we decided to look into nature's library. We dived deep into biomimicry and proved how data and visualization driven research together with traditional mechanical testing, allows us to grasp a better insight on evolutionary optimization and its applications on structural design and material science. The ability to optimize and build stronger performing structures that follow form to function allowed us to add an extra level of complexity to our engineering system design. We added a human in the loop. This presented us with a unique set of functional requirements. We were faced with the challenge on how to translate these functional requirements into new objective functions. Using this new set of objective functions and the engineering system design methodology developed in previous studies we designed and tested a bioinspired transtibial prosthesis, which can be entirely 3D printed in a single piece allowing us to solve a global accessibility challenge. Additional work was done where we focused on the use of drones for emergency response after natural events and its applications on data-driven structural damage assessment during and after earthquakes. This work is not presented in this dissertation, but published material can be found online and the vita section of my thesis.

Download or read book Materials Discovery and Design written by Turab Lookman and published by Springer. This book was released on 2018-09-22 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.

Download or read book Informatics for Materials Science and Engineering written by Krishna Rajan and published by Butterworth-Heinemann. This book was released on 2013-07-10 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Data driven Analysis Modeling and Simulation of Fiber based Materials a Basis for Virtual Materials Testing written by Matthias Weber and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Development of Data Driven Models for Chemical Engineering Systems written by Nusrat Parveen and published by Mohammed Abdul Malik. This book was released on 2024-03-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling of any system or a process is one of the significant but challenging tasks in engineering. The challenge is either due to the physical complexity of natural phenomenon or our limited knowledge of mathematics. Recently, data driven modeling (DDM) has been found to be a very powerful tool in helping to overcome those challenges, by presenting opportunities to build basic models from the observed patterns as well as accelerating the response of decision makers in facing real world problems. Since DDM is able to map causal factors and consequent outcomes from the observed patterns (experimental data), without deep knowledge of the complex physical process, these modeling techniques are becoming popular among engineers. Soft computing and statistical models are the two commonly employed data-driven models for predictive modeling. As far as the statistical data-driven models are concerned, these models could be employed in the life of modern engineering. But the accuracy and generalizability of these models is very poor. The soft computing data- driven modeling techniques have attracted the attention of many researchers across the globe to overcome the limitations of statistical methods. The statistical data-driven modeling techniques such as least-squares methods, the maximum likelihood methods and traditional artificial neural network (ANN) are based on empirical risk minimization (ERM) principle while the support vector machine (SVM) method is based on the structural risk minimization (SRM) principle. According to it, the generalization accuracy is optimized over the empirical error and the flatness of the regression function or the capacity of SVM. On the other hand, the ANN and other traditional regression models which are based on ERM principle minimize the empirical error and do not consider the capacity of the learning machines. This results in model over fitting i.e. high prediction accuracy for the training data set and low for the test (unseen) data, giving poor generalization performance. SVMs belong to the supervised machine learning theory and are applied to both nonlinear classification called support vector classification (SVC) and regression or SVR. SVM possesses many advantages over traditional neural networks.

Download or read book Data Driven Evolutionary Optimization written by Yaochu Jin and published by Springer Nature. This book was released on 2021-06-28 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computational Sciences and Artificial Intelligence in Industry written by Tero Tuovinen and published by Springer Nature. This book was released on 2021-08-19 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Information Science for Materials Discovery and Design written by Turab Lookman and published by Springer. This book was released on 2019-03-27 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a “fourth leg’’ to our toolkit to make the “Materials Genome'' a reality, the science of Materials Informatics.

Download or read book Lead Free Piezoelectrics written by Shashank Priya and published by Springer Science & Business Media. This book was released on 2011-11-19 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ecological restrictions in many parts of the world are demanding the elimination of Pb from all consumer items. At this moment in the piezoelectric ceramics industry, there is no issue of more importance than the transition to lead-free materials. The goal of Lead-Free Piezoelectrics is to provide a comprehensive overview of the fundamentals and developments in the field of lead-free materials and products to leading researchers in the world. The text presents chapters on demonstrated applications of the lead-free materials, which will allow readers to conceptualize the present possibilities and will be useful for both students and professionals conducting research on ferroelectrics, piezoelectrics, smart materials, lead-free materials, and a variety of applications including sensors, actuators, ultrasonic transducers and energy harvesters.

Download or read book Data driven Free form Modeling of Biological Systems written by Theodore William Cornforth and published by . This book was released on 2014 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: The quantity of data available to scientists in all disciplines is increasing at an exponential rate, yet the insight necessary to distill data into scientific knowledge must still be supplied by human experts. This widening gap between data and insight can be bridged with data-driven modeling, in which computational methods shift much of the work in creating models from humans to computers. Traditional approaches to data-driven modeling require that the form of the model be fixed in advance, which requires substantial human effort and limits the complexity of problems that can be addressed. In contrast, a newer approach to automated modeling based on evolutionary computation (EC) removes such restrictions on the form of models. This free-form modeling has the potential both to reduce human effort for routine modeling and to make complex problems more tractable. Although major advances in EC-based modeling have been made in recent years, many challenges remain. These challenges include three features often seen in biological systems: complex nonlinear behavior, multiple time scales, and hidden variables. This work addresses these challenges by developing new approaches to ECbased modeling, with applications to neuroscience, systems biology, ecology, and other fields. The contributions of this work consist of three primary lines of research. In the first line of research, EC-based methods for the automated design of analog electrical circuits are adapted for the modeling of electrical systems studied in neurophysiology that display complex, nonlinear behavior, such as ion channels. In the second line of research, EC-based methods for symbolic modeling are extended to facilitate the modeling of dynamical systems with multiple time scales, such as those found throughout ecology and other fields. Finally, in the third line of research, established EC-based algorithms are extended with the capability to model dynamical systems as systems of differential equations with hidden variables, which can contribute in an essential way to the observed dynamics of a physical system yet historically have presented a particularly difficult challenge to automated modeling.

Download or read book Data driven Modeling of Mechanical Behaviors of Additively Manufactured Materials written by Ziyang Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Additive manufacturing (AM) is a revolutionary technology that greatly improves the flexibility of fabricating parts with complex structures and eliminates the cost of making molds. While AM techniques offer unique benefits over traditional manufacturing processes, it is challenging to predict the mechanical behaviors of additively manufactured parts based on design and process parameters. With recent advances in machine learning, data-driven methods have the potential to overcome such limitations. In this work, data-driven modeling frameworks were proposed to predict the tensile, flexural, and compressive behaviors of additively manufactured plastics and composites. Ensemble learning was used to predict the tensile strength of polylactic acid (PLA) with cooperative AM process parameters. A 12.97% mean absolute percentage error (MAPE) was achieved by combining lasso, support vector regression, and extreme gradient boosting in the computational framework. An enhanced ensemble learning method that combines eight different machine learning algorithms was introduced to predict the flexural strength of continuous carbon fiber and short carbon fiber reinforced nylon (CCF-SCFRN) composites with design parameters. Learned knowledge from CCF-SCFRN composites was transferred to continuous glass fiber and short carbon fiber reinforced nylon (CGF-SCFRN) composites for flexural stress-strain curve prediction using an optimal transport (OT) integrated transfer learning framework. Compared with traditional transfer learning, the OT-integrated framework improves the stress-strain curve prediction accuracy by 10.46% in terms of MAPE. The transfer learning framework was further demonstrated in predicting the compressive stress-strain curves of PLA scaffolds with both AM process and design parameters. Three cases were studied by selecting different parameters for domain transfer to validate the generalizability of the proposed framework in predicting mechanical behaviors of additively manufactured materials with limited data.

Download or read book Development and Expansion of Tools for Data driven Materials Development written by Jessica Kong and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning and natural language processing techniques are being integrated into chemistry and materials science, finding utility at field and domain levels of research. While these tools have existed, the relative recent emergence of these tools within high-level programming languages like Python means that they have only recently begun to be utilized at scale. In this dissertation, I explore the ways in which these tools can be applied in field-specific settings and a general, domain-level one. In one, I develop a new analysis methodology utilizing image registration, dimensionality reduction, and multivariate analysis to derive information from multimodal atomic force microscopy images. In a second, I utilize and develop reusable code for a Python package within the scanning probe community to obtain insights about and examine impacts of different physical contributions to a measured signal in a specialized atomic force microscopy technique. In another, I introduce a practitioner-centric framework for evaluating topic models that moves away from the dichotomic approach utilized in model development with a critical downstream benefit of advancing data-driven materials research via natural language processing. These works illustrate the ways in which existing machine learning and natural language processing are powerful tools and makes a case for the need of domain expertise in their development, much like the symbiotic work of computationalists, experimentalists, and theorists.