Название: Manufacturing Strategies and Systems: Technologies, Processes, and Machine Tools
Автор: Ajay Kumar, Parveen Kumar, Ashish Kumar Srivastava, Lokesh Saharan
Издательство: CRC Press
Год: 2025
Страниц: 326
Язык: английский
Формат: pdf (true), epub
Размер: 25.3 MB

Advancements in manufacturing strategies and systems have sparked a profound transformation, ushering in a new era of efficiency, precision, and sustainability, driven by the integration of automation, Artificial Intelligence (AI), and advanced materials, reshaping industries, boosting productivity, reducing costs, and improving the overall quality of products. This book focuses on practical applications of manufacturing technologies, providing case studies and real-world examples of how these advancements in manufacturing are being implemented to solve manufacturing challenges and improve efficiency.

Manufacturing Strategies and Systems: Technologies, Processes, and Machine Tools presents numerical, experimental, and computational approaches for various methods of manufacturing and offers different concepts from cross-disciplinary fields, including discussions from mechanical engineering, production engineering, and industrial engineering, and acts as a guide on the modeling and optimization of various manufacturing methods. The book explores key emerging trends in manufacturing technologies, such as Industry 4.0, additive manufacturing, robotics and automation, advanced materials, digital twins, augmented reality/virtual reality, edge computing, sustainable manufacturing, and cyber security. Key chapters on micro- and nanomanufacturing and cellular manufacturing are included and details on the advances in machining, joining, forming, powder metallurgy, casting, and molding science are discussed. Included are original theoretical, experimental, and modeling results of advancements in manufacturing techniques along with recent developments, outlook, and advanced and analytical modeling techniques of manufacturing with examples backed by experimental and numerical data.

In contemporary manufacturing, technology and industry are fundamentally reshaping traditional paradigms. Machine Learning (ML) and Deep Learning (DL), formidable branches of Artificial Intelligence (AI), are revolutionizing our conceptualization, design, and optimization of manufacturing processes. In an era of abundant data, manufacturing processes generate vast amounts of information. ML with its ability to identify patterns, trends, and anomalies within this deluge of data is proving pivotal as a catalyst for informed decision-making. ML algorithms demonstrate their ability to extract valuable insights from complex manufacturing data, whether applied to predictive maintenance to reduce downtime, quality control to improve product reliability, or supply chain optimization for efficient operations.

Beyond the realms of conventional ML lies the intricate domain of DL, drawing inspiration from the intricate neural networks of the human brain. DL models, propelled by layered neural networks, excel in tasks demanding nuanced understanding, including natural language processing (NLP), image recognition as well as intricate pattern recognition. In the manufacturing domain, these capabilities open avenues to revolutionary applications, encompassing advanced defect detection, autonomous robotic systems, and adaptive manufacturing processes that continuously evolve based on real-time feedback.

This reference title provides logical, technical, and analytical solutions and ideas to complex problems faced by researchers and professionals in the field of advancements in manufacturing. Academicians and students will get a comprehensive update on the state of the arts in this area and ample ideas for further research and innovation in manufacturing strategies.

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