Machine Learning for Sustainable Manufacturing in Industry 4.0 : Concept, Concerns and Applications
Book Details
Format
Paperback / Softback
ISBN-10
1032592117
ISBN-13
9781032592114
Publisher
Taylor & Francis Ltd
Imprint
CRC Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Jun 26th, 2025
Print length
234 Pages
Weight
460 grams
Product Classification:
Production & quality control managementProduction and quality control managementPurchasing & supply managementPurchasing and supply managementEngineering: generalOther manufacturing technologiesMechanical engineeringElectrical engineeringElectronics engineeringHydraulic engineeringEnvironmental science, engineering & technologyEnvironmental science, engineering and technologyAutomotive technology & tradesAutomotive technology and trades
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The book focuses on the recent developments in the areas of error reduction, resource optimization, and revenue growth in sustainable manufacturing using machine learning. It presents the integration of smart technologies such as machine learning in the field of Industry 4.0 for better quality products and efficient manufacturing methods.
The book focuses on the recent developments in the areas of error reduction, resource optimization, and revenue growth in sustainable manufacturing using machine learning. It presents the integration of smart technologies such as machine learning in the field of Industry 4.0 for better quality products and efficient manufacturing methods. Focusses on machine learning applications in Industry 4.0 ecosystem, such as resource optimization, data analysis, and predictions. Highlights the importance of the explainable machine learning model in the manufacturing processes. Presents the integration of machine learning and big data analytics from an industry 4.0 perspective. Discusses advanced computational techniques for sustainable manufacturing. Examines environmental impacts of operations and supply chain from an industry 4.0 perspective. This book provides scientific and technological insight into sustainable manufacturing by covering a wide range of machine learning applications fault detection, cyber-attack prediction, and inventory management. It further discusses resource optimization using machine learning in industry 4.0, and explainable machine learning models for industry 4.0. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the fields including mechanical engineering, manufacturing engineering, production engineering, aerospace engineering, and computer engineering.
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