Artificial Intelligence for Smart Manufacturing : Methods, Applications, and Challenges (Springer Series in Reliability Engineering)

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Artificial Intelligence for Smart Manufacturing : Methods, Applications, and Challenges (Springer Series in Reliability Engineering)

Tran, Kim Phuc (EDT)

ウェブストア価格 ¥35,183（本体¥31,985）
Springer International Publishing AG（2024/06発売）
外貨定価 US$ 169.99
ポイント 319pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 269 p.
言語 ENG
商品コード 9783031305122
DDC分類 670.28563

Full Description

This book provides readers with a comprehensive overview of the latest developments in the field of smart manufacturing, exploring theoretical research, technological advancements, and practical applications of AI approaches. With Industry 4.0 paving the way for intelligent systems and innovative technologies to enhance productivity and quality, the transition to Industry 5.0 has introduced a new concept known as augmented intelligence (AuI), combining artificial intelligence (AI) with human intelligence (HI).

As the demand for smart manufacturing continues to grow, this book serves as a valuable resource for professionals and practitioners looking to stay up-to-date with the latest advancements in Industry 5.0. Covering a range of important topics such as product design, predictive maintenance, quality control, digital twin, wearable technology, quantum, and machine learning, the book also features insightful case studies that demonstrate the practical applicationof these tools in real-world scenarios.

Overall, this book provides a comprehensive and up-to-date account of the latest advancements in smart manufacturing, offering readers a valuable resource for navigating the challenges and opportunities presented by Industry 5.0.

Chapter 1: Introduction to Artificial Intelligence for Smart Manufacturing.- Chapter 2: Artiﬁcial Intelligence for Smart Manufacturing in Industry 5.0: Methods, Applications, and Challenges.- Chapter 3: Quality control for Smart Manufacturing in Industry 5.0.- Chapter 4: Dynamic Process Monitoring Using Machine Learning Control Charts.- Chapter 5: Fault Prediction of Papermaking Process Based on Gaussian Mixture Model and Mahalanobis Distance.- Chapter 6: Multi-objective optimization of ﬂexible ﬂow-shop intelligent scheduling based on a hybrid intelligent algorithm.- Chapter 7: Personalized pattern recommendation system of men's shirts.- Chapter 8: Eﬃcient and Trustworthy Federated Learning-based Explainable Anomaly Detection: Challenges, Methods, and Future Directions.- Chapter 9: Multimodal machine learning in prognostics and health management of manufacturing systems.- Chapter 10: Explainable Artiﬁcial Intelligence for Cybersecurity in Smart Manufacturing.- Chapter 11: Wearable technology for Smart Manufacturing in Industry 5.0.- Chapter 12: Beneﬁts of using Digital Twin for online fault diagnosis of a manufacturing system.