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Flexible Automation and Intelligent Manufacturing: The Future of Automation and Manufacturing: Intelligence, Agility, and Sustainability : Proceedings of FAIM 2025, June 21–24, 2025, New York City, NY, USA, Volume 2 / edited by Krishnaswami Srihari, Mohammad T. Khasawneh, Sangwon Yoon, Daehan Won.
Springer eBooks EBA - Intelligent Technologies and Robotics Collection 2026 Available online
View online- Format:
- Book
- Author/Creator:
- Srihari, Krishnaswami.
- Series:
- Lecture Notes in Mechanical Engineering, 2195-4364
- Language:
- English
- Subjects (All):
- Automatic control.
- Robotics.
- Automation.
- Manufactures.
- Engineering design.
- Control, Robotics, Automation.
- Machines, Tools, Processes.
- Engineering Design.
- Local Subjects:
- Control, Robotics, Automation.
- Machines, Tools, Processes.
- Engineering Design.
- Physical Description:
- 1 online resource (980 pages)
- Edition:
- 1st ed. 2026.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2026.
- Summary:
- This book reports on cutting-edge research and developments in manufacturing, giving a special emphasis to intelligent, agile and sustainable solutions. It covers applications of machine learning in manufacturing and advances in cyber-physical systems, human-robot collaboration, and machine tools and assembly systems. It also reports on advances in logistics and supply chain, and lean manufacturing. Based on the proceedings of the 33rd International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2025), held on 21–24, 2025, in New York City, NY, USA, this second volume of a 2-volume set provides academics and professionals with extensive, technical information on trends and technologies in manufacturing, yet it also discusses challenges and practice-oriented experience in all the above-mentioned areas.
- Contents:
- Application of Ensemble Learning to Classify Failures in Lithium-ion Batteries
- Implementation of a Reinforcement Learning Application for Production Scheduling Including Practical Constraints
- Prediction of Machined Surface Roughness Using Cutting Load and Machining History Data
- Prediction of Tensile Strength and Impact Strength in Fused Deposition Modeling Using a Machine Learning Pipeline, etc.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-032-05610-1
- 9783032056108
- OCLC:
- 1546965563
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