Due to problems associated with the design and manufacturing of composite materials, there is a need to introduce computational and intelligent systems engineering methodology in materials engineering. Soft Computing in the Design and Manufacturing of Composite Material offers an intelligent approach to advance material engineering, and significantly improves the process of designing and manufacturing a new material. This title includes chapters covering topics such as soft computing techniques, composite materials engineering, design and manufacturing of composite materials, numerical modeling, prediction, and optimization of the composite materials performance, development of the hybrid models, and control of the composite material performance. Introduction of soft computing in the composite materials engineering Includes accurate and detailed analysis of the current state of the art in the field Development of the intelligent models for design and manufacturing of composite material Details composite material performance prediction Optimization of the manufacturing process of composite materials

This book is a collection of some 47 research papers that were presented in June 1997 at the 2nd Online World Conference in Soft Computing. It covers the state-of-the-art techniques and applications of soft computing which will stimulate further advances towards the next generation of intelligent machines. Soft Computing in Engineering Design and Manufacturing will be of interest to graduate students and researchers involved in soft computing. It will also be useful for those working in related industrial environments.

This book aims at addressing the challenges of contemporary manufacturing in Industry 4.0 environment and future manufacturing (aka Industry 5.0), by implementing soft computing as one of the major sub-fields of artificial intelligence. It contributes to development and application of the soft computing systems, including links to hardware, software and enterprise systems, in resolving modern manufacturing issues in complex, highly dynamic and globalized industrial circumstances. It embraces heterogeneous complementary aspects, such as control, monitoring and modeling of different manufacturing tasks, including intelligent robotic systems and processes, addressed by various machine learning and fuzzy techniques; modeling and parametric optimization of advanced conventional and non-conventional, eco-friendly manufacturing processes by using machine learning and evolutionary computing techniques; cybersecurity framework for Internet of Things-based systems addressing trustworthiness and resilience in machine-to-machine and human-machine collaboration; static and dynamic digital twins integration and synchronization in a smart factory environment; STEP-NC technology for a smart machine vision system, and integration of Open CNC with Service-Oriented Architecture for STEP-NC monitoring system in a smart manufacturing. Areas of interest include but are not limited to applications of soft computing to address the following: dynamic process/system modeling and simulation, dynamic process/system parametric optimization, dynamic planning and scheduling, smart, predictive maintenance, intelligent and autonomous systems, improved machine cognition, effective digital twins integration, human-machine collaboration, robots, and cobots.

Describes the individual capabilities of each of 1,900 unique resources in the federal laboratory system, and provides the name and phone number of each contact. Includes government laboratories, research centers, testing facilities, and special technology information centers. Also includes a list of all federal laboratory technology transfer offices. Organized into 72 subject areas. Detailed indices.

This unique compendium presents a comprehensive and self-contained theory of material development under imperfect information and its applications. The book describes new approaches to synthesis and selection of materials with desirable characteristics. Such approaches provide the ability of systematic and computationally effective analysis in order to predict composition, structure and related properties of new materials.The volume will be a useful advanced textbook for graduate students. It is also suitable for academicians and practitioners who wish to have fundamental models in new material synthesis and selection.

This book offers a selection of the best papers presented at the annual international scientific conference “Digital Transformation in Industry: Trends, Management, Strategies (DTI2021),” held by the Institute of Economics, Ural Branch of the Russian Academy of Sciences, in Ekaterinburg (Russia) on October 29, 2021. The book focuses on the idea of introduction mechanisms for digitization processes and on highlighting successful digital transformation strategies in all sectors of industry. Key topics include the development of a cyber-physical production system for Industry 4.0; digital design technologies for enhancing the competitiveness of products and companies; digital twin-driven product manufacturing and services; and the effects of the industrial digital transformation on society and the environment. With regard to implementing IT and other technological innovations, lessons learned in developed and developing economies, as well as small and large enterprises, are included. Given its scope, the book offers a valuable asset for researchers and managers of industrial organizations alike.

This book highlights the importance of data-driven technologies and artificial intelligence in supply chain management. It covers important concepts such as enabling technologies in Industry 4.0, the impact of artificial intelligence, and data-driven technologies in lean manufacturing. "Provides solutions to solve complex supply chain management issues using artificial intelligence and data-driven technologies" Emphasizes the impact of a data-driven supply chain on quality management "Discusses applications of artificial intelligence, and data-driven technologies in the service industry, and lean manufacturing" Highlights the barriers to implementing artificial intelligence in small and medium enterprises Presents a better understanding of different risks such as procurement risks, process risks, demand risks, transportation risks, and operational risks The book comprehensively discusses the applications of artificial intelligence and data-driven technologies in supply chain management for diverse fields such as service industries, manufacturing industries, and healthcare. It further covers the impact of artificial intelligence and data-driven technologies in managing the FMGC supply chain. It will be a valuable resource for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, industrial engineering, manufacturing engineering, production engineering, and computer engineering.