Data-Driven Modeling for Additive Manufacturing of Metals PDF Download
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Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309494206
Category : Technology & Engineering
Languages : en
Pages : 79
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Book Description
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: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309494206
Category : Technology & Engineering
Languages : en
Pages : 79
Get Book Here
Book Description
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:
ISBN: 9780309494212
Category : Electronic books
Languages : en
Pages : 66
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Book Description
"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: Mutahar Safdar
Publisher: Springer Nature
ISBN: 3031321545
Category : Technology & Engineering
Languages : en
Pages : 151
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Book Description
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:
Publisher:
ISBN:
Category : Additive manufacturing
Languages : en
Pages : 0
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Book Description
Additive manufacturing (AM), which builds a single part directly from a 3D CAD model in a layer-by-layer manner, can fabricate complex component with intricate geometry in a time- and cost-saving manner.AM is thus gaining ever-increasing popularity across many industries. However, accompanied with its unique building manner and benefits thereof are the significantly complicated physics behind the AM process. This fact poses great challenges in modeling and understanding the underlying process-structure-property (P-S-P) relationship, which however is vital to efficient AM process optimization and quality control. With the advancement of machine learning (ML) models and increasing availability of AM-related digital data, ML-based data-driven modeling has recently emerged as a promising approach towards exhaustively exploring and fully understanding AM P-S-P relationship. Nonetheless, many of existing ML-based AM modeling severely under-utilize the powerful ML models by using them as simple regression tools, and largely neglect their distinct advantage in explicitly handling complex-data (e.g., image and sequence) involved data-driven modeling problems and other versatilities. To further explore and unlock the tremendous potential of ML, this research aims to attack two significant research problems: (1) from the data or pre-data-driven-modeling aspect: can we use ML to improve AM data via ML-assisted data collection, processing and acquirement? (2) from the data driven modeling aspect: can we use ML to build more capable data-driven models, which can act as a full (or maximum) substitute of physics-based model for high-level AM modeling or even realistic AM simulation? To adequately address the above questions, the current research presents a ML-based data-driven AM modeling framework. It attempts to provide a comprehensive ML-based solution to data-driven modeling and simulation of various physical events throughout the AM lifecycle, from process to structure and property. A variety of ML models, including Gaussian process (GP), multilayer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN) and their variants, are leveraged to handle representative data-driven modeling problems with different quantities of interest (QoI). They include data-driven process modeling (melt pool, temperature field), structure modeling (porosity structure) and property modeling (stress field, stress-strain curve). The results show that this research can break existing limitations of those five data-driven AM modeling in terms of modeling fidelity, accuracy and/or efficiency. It thus well addresses the two research questions that are key in significantly advancing data-driven AM modeling. In addition, although the current research uses five representative physical events in AM as examples, the data-driven methodologies developed should shed light on data-driven modeling of many other physical events in AM and beyond.
Author: Gurminder Singh
Publisher: Elsevier
ISBN: 0443221464
Category : Technology & Engineering
Languages : en
Pages : 291
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Book Description
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: Ziyang Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Sujit Rokka Chhetri
Publisher: Springer Nature
ISBN: 3030379620
Category : Technology & Engineering
Languages : en
Pages : 240
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Book Description
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
ISBN: 1498739997
Category : Business & Economics
Languages : en
Pages : 328
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Book Description
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: Dyuti Sarker
Publisher: John Wiley & Sons
ISBN: 1119210836
Category : Science
Languages : en
Pages : 624
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Book Description
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: Linkan Bian
Publisher: CRC Press
ISBN: 1351647482
Category : Business & Economics
Languages : en
Pages : 422
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Book Description
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.
