Download or read book Model based Predictive Analytics for Additive and Smart Manufacturing written by Zhuo Yang and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Qualification and certification for additive and smart manufacturing systems can be uncertain and very costly. Using available historical data can mitigate some costs of producing and testing sample parts. However, use of such data lacks the flexibility to represent specific new problems which decreases predictive accuracy and efficiency. To address these compelling needs, in this dissertation modeling techniques are introduced that can proactively estimate results expected from additive and smart manufacturing processes swiftly and with practical levels of accuracy and reliability. More specifically, this research addresses the current challenges and limitations posed by use of available data and the high costs of new data by tailoring statistics-based metamodeling techniques to enable affordable prediction of these systems. The result is an integrated approach to customize and build predictive metamodels for the unique features of additive and smart manufacturing systems. This integrated approach is composed of five main parts that cover the broad spectrum of requirements. A domain-driven metamodeling approach uses physics-based knowledge to optimally select the most appropriate metamodeling algorithm without reliance upon statistical data. A maximum predictive error updating method iteratively improves predictability from a given dataset. A grey-box metamodeling approach combines statistics-based black-box and physics-based white-box models to significantly increase predictive accuracy with less expensive data overall. To improve computational efficiency for large datasets, a dynamic metamodeling method modifies the traditional Kriging technique to improve its efficiency and predictability for smart manufacturing systems. Finally, a super-metamodeling method optimizes results regardless of problem conditions by avoiding the challenge with selecting the most appropriate metamodeling algorithm. To realize the benefits of all five approaches, an integrated metamodeling process was developed and implemented into a tool package to systematically select the suitable algorithm, sampling method, and combination of models. All the functions of this tool package were validated and demonstrated by the use of two empirical datasets from additive manufacturing processes.
Download or read book Predictive Analytics for Smart Manufacturing written by Hyunsoo Jeong and published by . This book was released on 2016 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: The manufacturing industry has, recently, been facing tremendous challenges, including cost efficiency, system safety, and process automation, and manufacturing companies are required to adopt new technologies to keep themselves sustainable in the fast-changing world of technology. This research focuses, in particular, on how to prevent cutting tool failures and catastrophic accidents in Computerized Numerically Controlled (CNC) machining processes by using a predictive model based on the cutting sound data. With advances in machine learning algorithms and predictive analytics techniques, it becomes possible to create a noise-robust predictive model from an unstructured dataset of sound data. It is an obviously desirable decision to make use of every technology as required and benefit from it. The predictive model introduced in this research uses cutting sound data rather than acoustic emission or force/torque sensor data, which have been widely used for machine failure detection but have shown some limitations. The model is an important stepping stone for realizing an unmanned and fully automated manufacturing system, the so-called "smart factory," and it would be a meaningful movement for the government side as well, taking into account government's responsibility to keep people safe in the workplace. In this research, several experiments were carried out to collect sound data in the CNC machining center in Korea, and particular features were extracted from the analog waveform signals, using the unstructured data to make the predictive model using various advanced data analytics techniques and cutting-edge machine learning algorithms. Then, several analysis methods with systems thinking were used to explore potential impacts of the predictive model on the manufacturing system because the systems thinking approach is the most effective way to analyze a wide range of potential impacts from a holistic perspective. Specifically, the impact analysis was successfully conducted by using a "Causal Analysis based on STAMP (CAST)," which is a system safety analysis method. Also used was "system dynamics modeling," which is generally employed to identify dynamic behaviors in a complex system. Finally, a "complete value template" was constructed to portray how the new system delivers value to its stakeholders from a system architecture perspective.
Download or read book Technical Economic and Societal Effects of Manufacturing 4 0 written by Mikael Collan and published by Palgrave Macmillan. This book was released on 2020-10-01 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is among the first cross-disciplinary works about Manufacturing 4.0. It includes chapters about the technical, the economic, and the social aspects of this important phenomenon. Together the material presented allows the reader to develop a holistic picture of where the manufacturing industry and the parts of the society that depend on it may be going in the future. Manufacturing 4.0 is not only a technical change, nor is it a purely technically driven change, but it is a societal change that has the potential to disrupt the way societies are constructed both in the positive and in the negative. This book will be of interest to scholars researching manufacturing, technological innovation, innovation management and industry 4.0.
Download or read book Data Driven Modeling for Additive Manufacturing of Metals written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-10-09 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Smart Manufacturing written by Anthony Tarantino and published by John Wiley & Sons. This book was released on 2022-05-10 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the dramatic changes brought on by the new manufacturing technologies of Industry 4.0 In Smart Manufacturing, The Lean Six Sigma Way, Dr. Anthony Tarantino delivers an insightful and eye-opening exploration of the ways the Fourth Industrial Revolution is dramatically changing the way we manufacture products across the world and especially how it will revitalize manufacturing in North America and Europe. The author examines the role and impact of a variety of new Smart technologies including industrial IoT, computer vision, mobile/edge computing, 3D printing, robots, big data analytics, and the cloud. He demonstrates how to apply these new technologies to over 20 continuous improvement/Lean Six Sigma tools, greatly enhancing their effectiveness and ease of use. The book also discusses the role Smart technologies will play in improving: Career opportunities for women in manufacturing Cyber security, supply chain risk, and logistics resiliency Workplace health, safety, and security Life on the manufacturing floor Operational efficiencies and customer satisfaction Perfect for anyone involved in the manufacturing or distribution of products in the 21st century, Smart Manufacturing, The Lean Six Sigma Way belongs in the libraries of anyone interested in the intersection of technology, commerce, and physical manufacturing.
Download or read book Digital Twins written by Yogini Borole and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-09-18 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning for Powder Based Metal Additive Manufacturing written by Gurminder Singh and published by Elsevier. This book was released on 2024-09-04 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Data driven Modeling for Additive Manufacturing of Metals written by and published by . This book was released on 2019 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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
Download or read book Digital Twin for Smart Manufacturing written by Rajesh Kumar Dhanaraj and published by Elsevier. This book was released on 2023-08-26 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital Twin for Smart Manufacturing: Emerging Approaches and Applications provides detailed descriptions on how to integrate and optimize novel digital technologies for smart manufacturing. The book discusses digital twins, which combine the industrial internet of things, artificial intelligence, machine learning and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change. In addition, they provide an effective way to integrate technologies like cyber-physical systems into a smart manufacturing system, potentially optimizing the entire business process and operating procedure of the manufacturing firm. Drawing on the latest research, the book addresses the topics and technologies key to successful implementation of a smart manufacturing system, including augmented and virtual reality, big data and energy management. Broader subjects such as additive manufacturing and robotics are also covered in this context, covering every aspect of production. Includes detailed case studies that show how digital twins have been successfully implemented Shows how digital twins can be used to improve sustainability through superior energy usage management Outlines potential future uses of the digital twin, thus pointing the way for future research directions
Download or read book Data Driven Smart Manufacturing Technologies and Applications written by Weidong Li and published by Springer Nature. This book was released on 2021-02-20 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing written by Amit Kumar Tyagi and published by . This book was released on 2024-09-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analytics and Artificial Intelligence (AI) play an important role in Predictive Maintenance (PdM) within the manufacturing industry. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries.
Download or read book Predictive Theoretical and Computational Approaches for Additive Manufacturing written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2016-11-21 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Additive manufacturing (AM) methods have great potential for promoting transformative research in many fields across the vast spectrum of engineering and materials science. AM is one of the leading forms of advanced manufacturing which enables direct computer-aided design (CAD) to part production without part-specific tooling. In October 2015 the National Academies of Sciences, Engineering, and Medicine convened a workshop of experts from diverse communities to examine predictive theoretical and computational approaches for various AM technologies. While experimental workshops in AM have been held in the past, this workshop uniquely focused on theoretical and computational approaches and involved areas such as simulation-based engineering and science, integrated computational materials engineering, mechanics, materials science, manufacturing processes, and other specialized areas. This publication summarizes the presentations and discussions from the workshop.
Download or read book Handbook of Smart Manufacturing written by Ajay and published by CRC Press. This book was released on 2023-07-17 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook covers smart manufacturing development, processing, modifications, and applications. It provides a complete understanding of the recent advancements in smart manufacturing through its various enabling manufacturing technologies, and how industries and organizations can find the needed information on how to implement smart manufacturing towards sustainability of manufacturing practices. Handbook of Smart Manufacturing: Forecasting the Future of Industry 4.0 covers all related advances in manufacturing such as the integration of reverse engineering with smart manufacturing, industrial internet of things (IIoT), and artificial intelligence approaches, including Artificial Neural Network, Markov Decision Process, and Heuristics Methodology. It offers smart manufacturing methods like 4D printing, micro-manufacturing, and processing of smart materials to assist the biomedical industries in the fabrication of human prostheses and implants. The handbook goes on to discuss how to accurately predict the requirements, identify errors, and make innovation for the manufacturing process more manageable by implementing various advanced technologies and solutions into the traditional manufacturing process. Strategies and algorithms used to incorporate smart manufacturing into different sectors are also highlighted within the handbook. This handbook is an invaluable resource for stakeholders, industries, professionals, technocrats, academics, research scholars, senior graduate students, and human healthcare professionals.
Download or read book Smart Manufacturing written by Masoud Soroush and published by Elsevier. This book was released on 2020-08-04 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research efforts in the past decade have led to considerable advances in the concepts and methods of smart manufacturing. Smart Manufacturing: Applications and Case Studies includes information about the key applications of these new methods, as well as practitioners’ accounts of real-life applications and case studies. Written by thought leaders in the field from around the world, Smart Manufacturing: Applications and Case Studies is essential reading for graduate students, researchers, process engineers and managers. It is complemented by a companion book titled Smart Manufacturing: Concepts and Methods, which describes smart manufacturing methods in detail. Includes examples of applications of smart manufacturing in process industries Provides a thorough overview of the subject and practical examples of applications through well researched case studies Offers insights and accounts of first-hand experiences to motivate further implementations of the key concepts of smart manufacturing
Download or read book Artificial Intelligence Enabled Digital Twin for Smart Manufacturing written by Amit Kumar Tyagi and published by John Wiley & Sons. This book was released on 2024-10-15 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.
Download or read book Additive Manufacturing Technology written by Kun Zhou and published by John Wiley & Sons. This book was released on 2022-12-20 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: Additive Manufacturing Technology Highly comprehensive resource covering all key aspects of the current developments of additive manufacturing Additive Manufacturing Technology: Design, Optimization, and Modeling provides comprehensive and in-depth knowledge of the latest advances in various additive manufacturing technologies for polymeric materials, metals, multi-materials, functionally graded materials, and cell-laden bio-inks. It also details the application of numerical modeling in facilitating the design and optimization of materials, processes, and printed parts in additive manufacturing. The topics covered in this book include: Fundamentals and applications of 4D printing, 3D bioprinting of cell-laden bio-inks, and multi-material additive manufacturing Alloy design for metal additive manufacturing, mechanisms of metallurgical defect formation, and the mechanical properties of printed alloys Modified inherent strain method for the rapid prediction of residual stress and distortion within parts fabricated by additive manufacturing Modeling of the different stages in polymer and metal additive manufacturing processes, including powder spreading, melting, and thermal stress evolution By providing extensive coverage of highly relevant concepts and important topics in the field of additive manufacturing, this book highlights its essential role in Industry 4.0 and serves as a valuable resource for scientists, engineers, and students in materials science, engineering, and biomedicine.
Download or read book Intelligent and Transformative Production in Pandemic Times written by Chin-Yin Huang and published by Springer Nature. This book was released on 2023-02-02 with total page 874 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the proceeding of the 26th International Conference on Production Research (ICPR). ICPR is a biennial conference that has been hosted for more than a half century. It is regarded worldwide as one of the leading conferences of production research, industrial engineering, and related subjects. The acute impact of the pandemic on human lives is spurring further research and advances: because modern life relies on production and supply networks. The future of production calls for transformative research exploiting the possibilities of artificial intelligence in particular to respond to the challenge of sustainability. This book is of interest to researchers, students, and professionals in industry.
