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FACTORIES OF THE FUTURE The book provides insight into various technologies adopted and to be adopted in the future by industries and measures the impact of these technologies on manufacturing performance and their sustainability. Businesses and manufacturers face a slew of demands beyond the usual issues of staying agile and surviving in a competitive landscape within a rapidly changing world. Factories of the Future deftly takes the reader through the continuous technology changes and looks ten years down the road at what manufacturing will mostly look like. The book is divided into two parts: Emerging technologies and advancements in existing technologies. Emerging technologies consist of Industry 4.0 and 5.0 themes, machine learning, intelligent machining, advanced maintenance, reliability, and green manufacturing. The advances of existing technologies consist of digital manufacturing, artificial intelligence in machine learning, Internet of Things, product life cycle, and the impact of factories on the future of manufacturing performance of the manufacturing industries. Readers will find in this illuminating book: A comprehensive discussion of almost all emerging technologies, including “green” manufacturing; An overview of the social, economic, and technical aspects of these technologies; An explanation of these technological advancements on manufacturing performance, through case studies and other analytical tools.
This book presents peer-reviewed papers from 14th International Conference on Learning Factories (CLF 2024) that took place from April 17-19, 2024, at the University of Twente, the Netherlands. CLF 2024 continued the successful CLF conference series targeting the latest research and development in the field of learning factories. The book is organized into two volumes and covers state-of-the-art research insights towards Learning Factories of the Future including learning factory design, Industry 5.0, digital twinning and VR/AR, 5G/6G in learning factories, AI for manufacturing systems, human-centred work design, human-robot collaboration, sustainability in learning factories, as well as cross-learning factory product/production systems. The book seamlessly integrates theory with real-world practice, empowering learners such as students, qualified engineers, and workers to keep pace with rapidly evolving technologies and methodologies, through enhancing learning factories. It also helps society and industry effectively manage future transitions with addressing current topics around digitalization, sustainability, and lifelong learning in industry.
This book provides state-of-the-art approaches to deep learning in areas of detection and prediction, as well as future framework development, building service systems and analytical aspects in which artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. Deep learning algorithms and techniques are found to be useful in various areas, such as automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delays in children. “Deep Learning Techniques for Automation and Industrial Applications” presents a concise introduction to the recent advances in this field of artificial intelligence (AI). The broad-ranging discussion covers the algorithms and applications in AI, reasoning, machine learning, neural networks, reinforcement learning, and their applications in various domains like agriculture, manufacturing, and healthcare. Applying deep learning techniques or algorithms successfully in these areas requires a concerted effort, fostering integrative research between experts from diverse disciplines from data science to visualization. This book provides state-of-the-art approaches to deep learning covering detection and prediction, as well as future framework development, building service systems, and analytical aspects. For all these topics, various approaches to deep learning, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms, are explained. Audience The book will be useful to researchers and industry engineers working in information technology, data analytics network security, and manufacturing. Graduate and upper-level undergraduate students in advanced modeling and simulation courses will find this book very useful.
The world is on the verge of fully ushering in the fourth industrial revolution, of which artificial intelligence (AI) is the most important new general-purpose technology. Like the steam engine that led to the widespread commercial use of driving machineries in the industries during the first industrial revolution; the internal combustion engine that gave rise to cars, trucks, and airplanes; electricity that caused the second industrial revolution through the discovery of direct and alternating current; and the Internet, which led to the emergence of the information age, AI is a transformational technology. It will cause a paradigm shift in the way’s problems are solved in every aspect of our lives, and, from it, innovative technologies will emerge. AI is the theory and development of machines that can imitate human intelligence in tasks such as visual perception, speech recognition, decision-making, and human language translation. This book provides a complete overview on the deep learning applications and deep neural network architectures. It also gives an overview on most advanced future-looking fundamental research in deep learning application in artificial intelligence. Research overview includes reasoning approaches, problem solving, knowledge representation, planning, learning, natural language processing, perception, motion and manipulation, social intelligence and creativity. It will allow the reader to gain a deep and broad knowledge of the latest engineering technologies of AI and Deep Learning and is an excellent resource for academic research and industry applications.
The world of manufacturing is undergoing significant changes driven by various factors and technological advancements. Automation and robotics technologies are revolutionizing manufacturing processes. Robotic systems are being increasingly used for repetitive and precise tasks, improving efficiency, quality, and safety. The Internet of Things (IoT) is enabling connectivity and data exchange between devices and systems. Manufacturing generates vast amounts of data and is leveraging this data through advanced analytics, providing valuable insights to optimize production processes, predict maintenance needs, and improve supply chain management. Additive Manufacturing has also gained significant traction in manufacturing. It enables the creation of complex parts and prototypes, customization, and rapid prototyping. Supply chains are becoming more interconnected and digitally integrated. Technologies such as blockchain enable transparent and secure transactions, traceability, and efficient inventory management. These trends and others are reshaping the manufacturing industry, promoting increased efficiency, agility, and sustainability. Manufacturers must be aware, understand, and embrace these changes to stay competitive and meet the evolving demands of customers in the modern era. This book enhances the awareness and understanding of these core technologies by explaining what they are and how they are being used in manufacturing. In addition, it provides practical suggestions on how to advance manufacturing in light of these changes. The book provides a view into the future and direction on how to navigate the journey to a more automated, smarter, and continuously learning factory. This book consolidates the major elements of the fourth industrial revolution and describes them in clear terms within the context of integrated manufacturing. It creates awareness and a fundamental understanding of the advanced technologies that are coming together to facilitate highly automated, smarter, agile, and sustainable operations.
This book is a part of the Blue Book series “Research on the Development of Electronic Information Engineering Technology in China,” which explores the cutting edge of deep learning studies. A subfield of machine learning, deep learning differs from conventional machine learning methods in its ability to learn multiple levels of representation and abstraction by using several layers of nonlinear modules for feature extraction and transformation. The extensive use and huge success of deep learning in speech, CV, and NLP have led to significant advances toward the full materialization of AI. Focusing on the development of deep learning technologies, this book also discusses global trends, the status of deep learning development in China and the future of deep learning.
Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science. Data lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakes. Industrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory, supply chain, 3D printing, education, research, and data science. The book covers: supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptors. What You Will Learn Generate and identify transformational disruptors of artificial intelligence (AI) Understand the field of machine learning (ML) and apply it to handle big data and process the data lakes in your environment Hone the skills required to handle the future of data engineering and data science Who This Book Is For Intermediate to expert level professionals in the fields of data science, data engineering, machine learning, and data management
The rapid development of new technologies in the industry of the future implies a major evolution in the industrial safety measures needed to be met, such as societal requirements. Towards Process Safety 4.0 in the Factory of the Future presents the concept of Safety 4.0 from the point of view of process safety, occupational safety and health, as well as systems’ cyber security. Numerous examples illustrate the different approaches of the identified methods and techniques of Safety 4.0. Their concepts, paradigms, structural bases, couplings, complexities and flaws are systematically analyzed. This comprehensive approach to Safety 4.0 is aimed at the wide variety of actors working in the industry of the future.
Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors Includes four case studies addressing real-world industrial problems solved with machine learning techniques A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka