Data Driven Modeling For Additive Manufacturing Of Metals
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| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 79 |
| Release | : 2019-11-09 |
| Genre | : Technology & Engineering |
| ISBN | : 0309494206 |
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.
| Author | : |
| Publisher | : |
| Total Pages | : 66 |
| Release | : 2019 |
| Genre | : Electronic books |
| ISBN | : 9780309494212 |
"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
| Author | : Sujit Rokka Chhetri |
| Publisher | : Springer Nature |
| Total Pages | : 240 |
| Release | : 2020-02-08 |
| Genre | : Technology & Engineering |
| ISBN | : 3030379620 |
This book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect of the CPS. This book provides insight on how a data-driven modeling approach can be utilized to take advantage of the relation between the cyber and the physical domain of the CPS to aid the first-principle approach in capturing the stochastic phenomena affecting the CPS. The authors provide practical use cases of the data-driven modeling approach for securing the CPS, presenting novel attack models, building and maintaining the digital twin of the physical system. The book also presents novel, data-driven algorithms to handle non- Euclidean data. In summary, this book presents a novel perspective for modeling the CPS.
| Author | : Linkan Bian |
| Publisher | : CRC Press |
| Total Pages | : 328 |
| Release | : 2017-08-09 |
| Genre | : Business & Economics |
| ISBN | : 1498739997 |
Laser-Based Additive Manufacturing (LBAM) technologies, hailed by some as the "third industrial revolution," can increase product performance, while reducing time-to-market and manufacturing costs. This book is a comprehensive look at new technologies in LBAM of metal parts, covering topics such as mechanical properties, microstructural features, thermal behavior and solidification, process parameters, optimization and control, uncertainty quantification, and more. The book is aimed at addressing the needs of a diverse cross-section of engineers and professionals.
| Author | : Mutahar Safdar |
| Publisher | : Springer Nature |
| Total Pages | : 151 |
| Release | : 2023-06-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3031321545 |
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.
| Author | : Gurminder Singh |
| Publisher | : Elsevier |
| Total Pages | : 291 |
| Release | : 2024-09-04 |
| Genre | : Technology & Engineering |
| ISBN | : 0443221464 |
Machine Learning for Powder-based Metal Additive Manufacturing outlines machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs. The book combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications. The book covers ML for design in AM, ML for materials development and intelligent monitoring in metal AM, both geometrical deviation and physics informed machine learning modeling, as well as data-driven cost estimation by ML. In addition, optimization for slicing and orientation, ML to create models of materials for AM processes, ML prediction for better mechanical and microstructure prediction, and feature extraction by sensing data are all covered, and each chapter includes a case study. - Covers machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs - Combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications - Discusses algorithm development of ML for metal AM, metal AM process modeling and optimization, mathematical and simulation studies of metal AM, and pre- and post-processing smart methods for metal AM
| Author | : Parnika Shrivastava |
| Publisher | : CRC Press |
| Total Pages | : 269 |
| Release | : 2023-12-05 |
| Genre | : Technology & Engineering |
| ISBN | : 1003803245 |
The text presents the latest research and development, technical challenges, and future directions in the field of hybrid metal additive manufacturing. It further discusses the modeling of hybrid additive manufacturing processes for metals, hybrid additive manufacturing of composite materials, and low-carbon hybrid additive manufacturing processes. THIS BOOK •Presents cutting-edge advancements and limitations in hybrid additive manufacturing technologies. • Discusses fabrication methods and rapid tooling techniques focusing on metals, composites, and alloys. •Highlights the importance of low-carbon additive manufacturing technologies toward achieving sustainability. •Emphasizes the challenges and solutions for integrating additive manufacturing and Industry 4.0 to enable rapid manufacturing of customized and tailored products. • Covers hybrid additive manufacturing of composite materials and additive manufacturing for fabricating high-hardness components. The text discusses the recent advancements in additive manufacturing of high-hardness components and covers important engineering materials such as metals, alloys, and composites. It further highlights defects and post-processing of hybrid additive manufacturing components, sustainability solutions for hybrid additive manufacturing processes, and recycling of machining waste into metal powder feedstock. It will serve as an ideal reference text for senior undergraduate and graduate students, and researchers in fields including mechanical engineering, aerospace engineering, manufacturing engineering, and production engineering.
| Author | : Hang Z. Yu |
| Publisher | : John Wiley & Sons |
| Total Pages | : 421 |
| Release | : 2024-07-10 |
| Genre | : Technology & Engineering |
| ISBN | : 3527350934 |
Timely summary of state-of-the-art solid-state metal 3D printing technologies, focusing on fundamental processing science and industrial applications Solid-State Metal Additive Manufacturing: Physics, Processes, Mechanical Properties, and Applications provides detailed and in-depth discussion on different solid-state metal additive manufacturing processes and applications, presenting associated methods, mechanisms and models, and unique benefits, as well as a detailed comparison to traditional fusion-based metal additive manufacturing. The text begins with a high-level overview of solid-state metal additive manufacturing with an emphasis on its position within the metal additive manufacturing spectrum and its potential for meeting specific demands in the aerospace, automotive, and defense industries. Next, each of the four categories of solid-state additive technologies—cold spray additive manufacturing, additive friction stir deposition, ultrasonic additive manufacturing, and sintering-based processes—is discussed in depth, reviewing advances in processing science, metallurgical science, and innovative applications. Finally, the future direction of these solid-state processes, especially the material innovation and artificial intelligence aspects, are discussed. Sample topics covered in Solid-State Metal Additive Manufacturing include: Physical processes and bonding mechanisms in impact-induced bonding and microstructures and microstructural evolution in cold sprayed materials Process fundamentals, dynamic microstructure evolution, and potential industrial applications of additive friction stir deposition Microstructural and mechanical characterization and industrial applications of ultrasonic additive manufacturing Principles of solid-state sintering, binder jetting-based metal printing, and sintering-based metal additive manufacturing methods for magnetic materials Critical issues inherent to melting and solidification, such as porosity, high residual stress, cast microstructure, anisotropic mechanical properties, and hot cracking Solid-State Metal Additive Manufacturing is an essential reference on the subject for academic researchers in materials science, mechanical, and biomedicine, as well as professional engineers in various manufacturing industries, especially those involved in building new additive technologies.
| Author | : Dyuti Sarker |
| Publisher | : John Wiley & Sons |
| Total Pages | : 624 |
| Release | : 2021-10-26 |
| Genre | : Science |
| ISBN | : 1119210836 |
METAL ADDITIVE MANUFACTURING A comprehensive review of additive manufacturing processes for metallic structures Additive Manufacturing (AM)—also commonly referred to as 3D printing—builds three-dimensional objects by adding materials layer by layer. Recent years have seen unprecedented investment in additive manufacturing research and development by governments and corporations worldwide. This technology has the potential to replace many conventional manufacturing processes, enable the development of new industry practices, and transform the entire manufacturing enterprise. Metal Additive Manufacturing provides an up-to-date review of all essential physics of metal additive manufacturing techniques with emphasis on both laser-based and non-laser-based additive manufacturing processes. This comprehensive volume covers fundamental processes and equipment, governing physics and modelling, design and topology optimization, and more. The text adresses introductory, intermediate, and advanced topics ranging from basic additive manufacturing process classification to practical and material design aspects of additive manufacturability. Written by a panel of expert authors in the field, this authoritative resource: Provides a thorough analysis of AM processes and their theoretical foundations Explains the classification, advantages, and applications of AM processes Describes the equipment required for different AM processes for metallic structures, including laser technologies, positioning devices, feeder and spreader mechanisms, and CAD software Discusses the opportunities, challenges, and current and emerging trends within the field Covers practical considerations, including design for AM, safety, quality assurance, automation, and real-time control of AM processes Includes illustrative cases studies and numerous figures and tables Featuring material drawn from the lead author’s research and professional experience on laser additive manufacturing, Metal Additive Manufacturing is an important source for manufacturing professionals, research and development engineers in the additive industry, and students and researchers involved in mechanical, mechatronics, automatic control, and materials engineering and science.
| Author | : Yi Zhang |
| Publisher | : Elsevier |
| Total Pages | : 252 |
| Release | : 2020-06-29 |
| Genre | : Technology & Engineering |
| ISBN | : 0128225599 |
Multiscale Modeling of Additively Manufactured Metals: Application to Laser Powder Bed Fusion Process provides comprehensive coverage on the latest methodology in additive manufacturing (AM) modeling and simulation. Although there are extensive advances within the AM field, challenges to predictive theoretical and computational approaches still hinder the widespread adoption of AM. The book reviews metal additive materials and processes and discusses multiscale/multiphysics modeling strategies. In addition, coverage of modeling and simulation of AM process in order to understand the process-structure-property relationship is reviewed, along with the modeling of morphology evolution, phase transformation, and defect formation in AM parts. Residual stress, distortion, plasticity/damage in AM parts are also considered, with scales associated with the spatial, temporal and/or material domains reviewed. This book is useful for graduate students, engineers and professionals working on AM materials, equipment, process, development and modeling. - Includes the fundamental principles of additive manufacturing modeling techniques - Presents various modeling tools/software for AM modeling - Discusses various design methods and how to optimize the AM process using these models
