9786209610462 - Advanced Artificial Intelligence Techniques in Mechanical Engineering de Thangavel, Gopalakrishnan; Aravindh, Revathi (7 resultados)

Condición: New.

Taschenbuch. Condición: Neu. Advanced Artificial Intelligence Techniques in Mechanical Engineering | Gopalakrishnan Thangavel (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209610462 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a comprehensive and application-oriented exploration of artificial intelligence in mechanical engineering, focusing on the transformation of traditional mechanical systems into intelligent, data-driven, and autonomous solutions. It bridges fundamental AI concepts with core mechanical engineering domains such as design optimization, smart manufacturing, predictive maintenance, digital twins, robotics, and advanced materials. Emphasis is placed on practical implementation using machine learning, deep learning, and computational intelligence techniques to solve real industrial problems. The book also discusses Industry 4.0 integration, sensor-driven analytics, and human-machine collaboration frameworks. Through case studies, algorithms, and workflow illustrations, the text equips students, researchers, and practicing engineers with the knowledge required to develop next-generation intelligent mechanical systems. The work is particularly relevant for those seeking to apply AI for efficiency, reliability, sustainability, and innovation in modern mechanical engineering environments. 208 pp. Englisch.

Paperback. Condición: new. Paperback. This book presents a comprehensive and application-oriented exploration of artificial intelligence in mechanical engineering, focusing on the transformation of traditional mechanical systems into intelligent, data-driven, and autonomous solutions. It bridges fundamental AI concepts with core mechanical engineering domains such as design optimization, smart manufacturing, predictive maintenance, digital twins, robotics, and advanced materials. Emphasis is placed on practical implementation using machine learning, deep learning, and computational intelligence techniques to solve real industrial problems. The book also discusses Industry 4.0 integration, sensor-driven analytics, and human-machine collaboration frameworks. Through case studies, algorithms, and workflow illustrations, the text equips students, researchers, and practicing engineers with the knowledge required to develop next-generation intelligent mechanical systems. The work is particularly relevant for those seeking to apply AI for efficiency, reliability, sustainability, and innovation in modern mechanical engineering environments. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a comprehensive and application-oriented exploration of artificial intelligence in mechanical engineering, focusing on the transformation of traditional mechanical systems into intelligent, data-driven, and autonomous solutions. It bridges fundamental AI concepts with core mechanical engineering domains such as design optimization, smart manufacturing, predictive maintenance, digital twins, robotics, and advanced materials. Emphasis is placed on practical implementation using machine learning, deep learning, and computational intelligence techniques to solve real industrial problems. The book also discusses Industry 4.0 integration, sensor-driven analytics, and human-machine collaboration frameworks. Through case studies, algorithms, and workflow illustrations, the text equips students, researchers, and practicing engineers with the knowledge required to develop next-generation intelligent mechanical systems. The work is particularly relevant for those seeking to apply AI for efficiency, reliability, sustainability, and innovation in modern mechanical engineering environments.