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.

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.

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.

"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

This book develops the core system science needed to enable the development of a complex industrial internet of things/manufacturing cyber-physical systems (IIoT/M-CPS). Gathering contributions from leading experts in the field with years of experience in advancing manufacturing, it fosters a research community committed to advancing research and education in IIoT/M-CPS and to translating applicable science and technology into engineering practice. Presenting the current state of IIoT and the concept of cybermanufacturing, this book is at the nexus of research advances from the engineering and computer and information science domains. Readers will acquire the core system science needed to transform to cybermanufacturing that spans the full spectrum from ideation to physical realization.

This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress. This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.

Additive manufacturing (AM) of metals and composites using laser energy, direct energy deposition, electron beam methods, and wire arc melting have recently gained importance due to their advantages in fabricating the complex structure. Today, it has become possible to reliably manufacture dense parts with certain AM processes for many materials, including steels, aluminum and titanium alloys, superalloys, metal-based composites, and ceramic matrix composites. In the near future, the AM material variety will most likely grow further, with high-performance materials such as intermetallic compounds and high entropy alloys already under investigation. Additive Manufacturing Applications for Metals and Composites is a pivotal reference source that provides vital research on advancing methods and technological developments within additive manufacturing practices. Special attention is paid to the material design of additive manufacturing of parts, the choice of feedstock materials, the metallurgical behavior and synthesis principle during the manufacturing process, and the resulted microstructures and properties, as well as the relationship between these factors. While highlighting topics such as numerical modeling, intermetallic compounds, and statistical techniques, this publication is ideally designed for students, engineers, researchers, manufacturers, technologists, academicians, practitioners, scholars, and educators.

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.

This textbook covers in detail digitally-driven methods for adding materials together to form parts. A conceptual overview of additive manufacturing is given, beginning with the fundamentals so that readers can get up to speed quickly. Well-established and emerging applications such as rapid prototyping, micro-scale manufacturing, medical applications, aerospace manufacturing, rapid tooling and direct digital manufacturing are also discussed. This book provides a comprehensive overview of additive manufacturing technologies as well as relevant supporting technologies such as software systems, vacuum casting, investment casting, plating, infiltration and other systems. Reflects recent developments and trends and adheres to the ASTM, SI and other standards; Includes chapters on topics that span the entire AM value chain, including process selection, software, post-processing, industrial drivers for AM, and more; Provides a broad range of technical questions to ensure comprehensive understanding of the concepts covered.

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