Published by Engineering Science Reference, 2020
ISBN 10: 1799872076 ISBN 13: 9781799872078
Language: English
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Published by Engineering Science Reference, 2020
ISBN 10: 1799872076 ISBN 13: 9781799872078
Language: English
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Published by Engineering Science Reference, 2020
ISBN 10: 1799872076 ISBN 13: 9781799872078
Language: English
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Published by Engineering Science Reference, 2020
ISBN 10: 1799872076 ISBN 13: 9781799872078
Language: English
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Published by Engineering Science Reference, 2020
ISBN 10: 1799872076 ISBN 13: 9781799872078
Language: English
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Published by Engineering Science Reference, 2020
ISBN 10: 1799872076 ISBN 13: 9781799872078
Language: English
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Published by Engineering Science Reference, 2020
ISBN 10: 1799872076 ISBN 13: 9781799872078
Language: English
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Published by Engineering Science Reference, 2020
ISBN 10: 1799872068 ISBN 13: 9781799872061
Language: English
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Published by Engineering Science Reference, 2020
ISBN 10: 1799872068 ISBN 13: 9781799872061
Language: English
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Published by Engineering Science Reference, 2020
ISBN 10: 1799872076 ISBN 13: 9781799872078
Language: English
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Published by Engineering Science Reference, 2020
ISBN 10: 1799872068 ISBN 13: 9781799872061
Language: English
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Published by Continental Academy Press, London
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Add to basketSoftcover. Condition: New. Dust Jacket Condition: no dj. First. Data-driven optimization has transformed the field of custom manufacturing, enabling companies to create high-quality, optimized products with unprecedented efficiency. This book provides a comprehensive overview of the latest techniques and tools for data-driven optimization in custom manufacturing, including the use of machine learning algorithms, data visualization, and process optimization. By examining real-world case studies and providing hands-on examples, the book empowers manufacturers to design and implement effective data-driven optimization solutions that can be applied to a wide range of custom manufacturing tasks. With its focus on practical applications and cutting-edge methodologies, this book is an essential resource for anyone working in the field of manufacturing and process optimization. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.
Published by Continental Academy Press, London
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Add to basketSoftcover. Condition: New. Dust Jacket Condition: no dj. First. 'Data-Driven Optimization in Custom Manufacturing Processes' examines how analytical techniques can streamline and enhance bespoke production workflows. The book emphasizes the role of data in optimizing process parameters, reducing waste, and improving quality. It discusses modeling and simulation tools that enable manufacturers to achieve higher efficiency and customization levels. This publication is crucial for manufacturing professionals seeking to leverage data insights for continuous process improvement and competitive advantage. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.
Published by Continental Academy Press, London
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Add to basketSoftcover. Condition: New. Dust Jacket Condition: no dj. First. Data-Driven Optimization in Custom Manufacturing Processes presents a forward-thinking approach to manufacturing optimization, leveraging the power of data analytics and machine learning to drive more efficient and effective production processes. By examining case studies and real-world applications, readers gain a deep understanding of how to harness the potential of data-driven optimization to improve product quality, reduce waste, and increase productivity. This book provides a comprehensive overview of the theoretical foundations and practical applications of data-driven optimization in custom manufacturing processes, making it an essential resource for manufacturers, engineers, and researchers seeking to drive more effective and efficient production processes. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.
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Published by Engineering Science Reference, 2020
ISBN 10: 1799872076 ISBN 13: 9781799872078
Language: English
Seller: moluna, Greven, Germany
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Klappentext This book is a compilation of chapters on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization, offering both soft computing approaches and machining process.
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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Add to basketHRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
HRD. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Published by Engineering Science Reference, 2020
ISBN 10: 1799872068 ISBN 13: 9781799872061
Language: English
Seller: moluna, Greven, Germany
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Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnAll machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doi.
Published by Engineering Science Reference, 2020
ISBN 10: 1799872076 ISBN 13: 9781799872078
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'This book is a compilation of chapters on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization, offering both soft computing approaches and machining processes'.
Published by Engineering Science Reference, 2020
ISBN 10: 1799872068 ISBN 13: 9781799872061
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
US$ 394.53
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Add to basketBuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.