Tin-Chih Toly Chen – författare

This book provides a thorough overview of the applications of 3D printing technologies to ubiquitous manufacturing (UM). UM itself represents an application of ubiquitous computing in the manufacturing sector, and this book reveals how it offers convenient, on-demand network access to a shared pool of configurable manufacturing resources, including software tools, equipment, and capabilities. 

Given its scope, the book will be of considerable interest to researchers in the areas of manufacturing, mechanical engineering, operations management, production control, ubiquitous computing, and sensor technologies, as well as practicing managers and engineers. 

Advances in Fuzzy Group Decision Making will be of interest to researchers in decision making, artificial intelligence, soft computing, operations management, and information management, as well as practicing managers and engineers.

This book applies artificial intelligence to lean production and shows how to practically combine the advantages of these two disciplines. 

Lean manufacturing originated in Japan and is a well-known tool for improving manufacturers'' competitiveness. Prevalent tools for lean manufacturing include Kanban, Pacemaker, Value Stream Map, 5s, Just-in-Time and Pull Manufacturing. Lean Manufacturing and  the Toyota Manufacturing System has been successfully applied to various factories and supply chains around the world. A lean manufacturing system can not only reduce wastes and inventory, but also respond to customer needs more immediately. Artificial intelligence is a subject that has attracted much attention recently. Many researchers and practical developers are working hard to apply artificial intelligence to our daily lives, including in factories. For example, fuzzy rules have been established to optimize machine settings. Bionic algorithms have been proposed to solve production sequencing and scheduling problems. Machine learning technologies are applied to detect possible product quality problems and diagnose the health of a machine. 

This book will be of interest to production engineers, managers, as well as students and researchers in manufacturing engineering.

This book systematically analyzes the applicability of big data analytics and Industry 4.0 from the perspective of semiconductor manufacturing management. It reports in real examples and presents case studies as supporting evidence. In recent years, technologies of big data analytics and Industry 4.0 have been frequently applied to the management of semiconductor manufacturing. However, related research results are mostly scattered in various journal issues or conference proceedings, and there is an urgent need for a systematic integration of these results. In addition, many related discussions have placed too much emphasis on the theoretical framework of information systems rather than on the needs of semiconductor manufacturing management. This book addresses these issues.

This book provides a comprehensive overview of the latest developments in Explainable AI (XAI) and its applications in manufacturing. It covers the various methods, tools, and technologies that are being used to make AI more understandable and communicable for factory workers. With the increasing use of AI in manufacturing, there is a growing need to address the limitations of advanced AI methods that are difficult to understand or explain to those without a background in AI. This book addresses this need by providing a systematic review of the latest research and advancements in XAI specifically tailored for the manufacturing industry.

The book includes real-world case studies and examples to illustrate the practical applications of XAI in manufacturing. It is a valuable resource for researchers, engineers, and practitioners working in the field of AI and manufacturing.

This book shows how smart technology applications to mobile healthcare will be different in the post-pandemic era. Prior to the Covid-19 pandemic, smart technologies had been widely applied to mobile health care. It will be the same in the post pandemic. However, the widely used smart technologies before and after the Covid-19 pandemic may be different. First, users’ motivations for applying smart technologies have changed. In addition, some innovative ways of applying smart technologies within the Covid-19 pandemic have emerged. Further, users’ acceptance of smart technology applications has increased. Furthermore, new smart technologies are still being proposed. This book discusses these topics.

Dieses Buch wendet künstliche Intelligenz auf die schlanke Produktion an und zeigt, wie sich die Vorteile dieser beiden Disziplinen praktisch kombinieren lassen. 

Die schlanke Produktion hat ihren Ursprung in Japan und ist ein bekanntes Instrument zur Verbesserung der Wettbewerbsfähigkeit der Hersteller. Zu den gängigen Werkzeugen der schlanken Produktion gehören Kanban, Pacemaker, Wertstromkarte, 5s, Just-in-Time und Pull Manufacturing. Lean Manufacturing und das Toyota-Produktionssystem wurden in verschiedenen Fabriken und Lieferketten auf der ganzen Welt erfolgreich eingesetzt. Ein schlankes Fertigungssystem kann nicht nur Verschwendung und Lagerbestände reduzieren, sondern auch unmittelbarer auf Kundenbedürfnisse reagieren. Künstliche Intelligenz ist ein Thema, das in letzter Zeit viel Aufmerksamkeit auf sich gezogen hat. Viele Forscher und praktische Entwickler arbeiten hart daran, künstliche Intelligenz in unserem täglichen Leben anzuwenden, auch in Fabriken. So wurden beispielsweise Fuzzy-Regeln zur Optimierung von Maschineneinstellungen entwickelt. Es wurden bionische Algorithmen vorgeschlagen, um Probleme bei der Produktionsreihenfolge und -planung zu lösen. Technologien des maschinellen Lernens werden eingesetzt, um mögliche Probleme mit der Produktqualität zu erkennen und den Zustand einer Maschine zu diagnostizieren. 

Dieses Buch ist für Produktionsingenieure, Manager sowie für Studenten und Forscher im Bereich der Fertigungstechnik von Interesse.

Dieses Buch bietet einen umfassenden Überblick über die Anwendungen von 3D-Drucktechnologien in der allgegenwärtigen Fertigung (Ubiquitous Manufacturing, UM). UM selbst stellt eine Anwendung des Ubiquitären Computings im Fertigungssektor dar, und dieses Buch zeigt, wie es bequemen, bedarfsgerechten Netzwerkzugang zu einem gemeinsamen Pool konfigurierbarer Fertigungsressourcen, einschließlich Software-Tools, Ausrüstung und Fähigkeiten, bietet. 

Aufgrund seines Umfangs wird das Buch für Forscher in den Bereichen Fertigung, Maschinenbau, Betriebsmanagement, Produktionssteuerung, Ubiquitäres Computing und Sensortechnologien sowie für praktizierende Manager und Ingenieure von großem Interesse sein.

This book systematically reviews the progress of Explainable Ambient Intelligence (XAmI) and introduces its methods, tools, and applications.

Ambient intelligence (AmI) is a vision in which an environment supports the people inhabiting it in an unobtrusive, interconnected, adaptable, dynamic, embedded, and intelligent way. So far, artificial intelligence (AI) technologies have been widely applied in AmI. However, some advanced AI methods are not easy to understand or communicate, especially for users with insufficient background knowledge of AI, which undoubtedly limits the practicability of these methods. To address this issue, explainable AI (XAI) has been considered a viable strategy. Although XAI technologies and tools applied in other fields can also be applied to explain AI technology applications in AmI, users should be the main body in the application of AmI, which is slightly different from the application of AI technologies in other fields.

This book examines smart technologies and their invaluable role in augmenting the walking experience of mobile users.

From meticulously planned walking routes to precise footprint detection and analysis, as well as cutting-edge fall detection and prevention mechanisms, these intelligent technologies have the potential to revolutionize healthy and smart walking. Against the backdrop of the post-COVID-19 era, where unrestricted mobility has become pivotal for restoring normalcy, the demand for smart healthcare solutions has soared.

The book explores latest advances in sensor technology, cloud computing, deep learning, and networking and related innovative applications that can leverage smart technologies to enhance healthy walking.

This book provides a comprehensive exploration of semiconductor supply chain localization, offering insights into the formulation of effective strategies and ways to enhance the competitiveness and sustainability of semiconductor manufacturers through supply chain localization.

The semiconductor industry is currently witnessing a significant localization wave, with a growing trend of wafer foundries relocating their production capacity closer to chip designers. This shift is primarily driven by factors such as the US-China trade war, geopolitical considerations, the impact of Covid-19, the Russia-Ukraine conflict, and the increasing importance of environmental awareness. By localizing semiconductor supply chains, companies can effectively address these challenges while also improving their competitive edge and ensuring long-term sustainability in the face of political and war risks.

However, achieving supply chain localization in the semiconductor industry is a complex endeavor, as traditional factors considered in semiconductor supply chain management may no longer be decisive. Moreover, existing research on the subject is often scattered across various journal issues and conference proceedings, necessitating a systematic integration of these findings. Furthermore, most of the available supply chain management-related books do not specifically focus on this topic. This book aims to bridge these gaps by providing a comprehensive resource that combines relevant references, real-world cases, and supporting evidence.

This book systematically reviews the progress in explainable AI (XAI) and introduces the methods, tools, and applications of XAI technologies in job sequencing and scheduling. Relevant references and real case studies are provided as supporting evidence.

To date, artificial intelligence (AI) technologies have been widely applied in job sequencing and scheduling. However, some advanced AI methods are not easy to understand or communicate, especially for factory workers with insufficient background knowledge of AI. This undoubtedly limits the practicability of these methods. To address this issue, explainable AI has been considered a viable strategy. XAI methods suitable for job sequencing and scheduling differ from those for other fields in manufacturing, such as pattern recognition, defect analysis, estimation, and prediction. This is the first book to systematically integrate current knowledge in XAI and demonstrate its application to manufacturing.