Smart Innovations in Energy and Mechanical Systems

Proceedings of SIEMS-2026
Erscheinungsjahr 2026
ISBN: 978-3-032-31556-4
Verlag: Springer

Buch, Englisch, 383 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Lecture Notes in Networks and Systems

This book offers a comprehensive review of smart innovations and provides perspectives on their applications in energy and  mechanical engineering. It covers a wide range of topics, from intelligent manufacturing and aerospace technologies to renewable energy systems and artificial intelligence in engineering, offering readers new visions and ideas for future research and interdisciplinary collaborations.
The book presents selected papers from the 2nd International Conference on Smart Innovations in Energy and Mechanical Systems (SIEMS-2026), held in Zaporizhzhia, Ukraine on May 11–13, 2026. The conference was organized by the National University Zaporizhzhia Polytechnic, in partnership with University of Stuttgart (Germany), Gdansk University of Technology (Poland), Offenburg University of Applied Sciences (Germany), International Association for Technological Development and Innovations (IATDI) (Ukraine), GPMS International Inc (USA), JSC “Ivchenko-Progress”(Ukraine), JSC “Motor Sich” (Ukraine) and Cardiff University (the UK). The volume offers a comprehensive overview of recent progress and research in mechanical engineering, energy systems, artificial intelligence, smart manufacturing, and sustainable technologies. It covers a broad range of topics including digital manufacturing, aerospace technologies, renewable energy, providing valuable insights and fostering collaboration amongst researchers, academics, and industry professionals in the fields of mechanical and electrical engineering.

Zielgruppe

Research

Autoren/Hrsg.

Tryshyn, Pavlo

Pavlenko, Dmytro

Honchar, Natalia

Kozlova, Olena

Fachgebiete

• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
• Geisteswissenschaften Design Produktdesign, Industriedesign
• Technische Wissenschaften Technik Allgemein Konstruktionslehre und -technik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau

Inhaltsverzeichnis

Enhancing Cyber-Physical System Resilience: Machine Learning Analysis of Credential Reliability in Smart Manufacturing.- Modeling and Optimal Neural Network Parameter Selection for Predicting Leakage Current of 35 kV Insulators under Nonlinear Environmental Effects.- Research of the SMART Greenhouse Automatic Control System.