Flexible Automation and Intelligent Manufacturing: The Future of Automation and Manufacturing: Intelligence,...

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Krishnaswami Srihari Mohammad T. Khasawneh

Flexible Automation and Intelligent Manufacturing

The Future of Automation and Manufacturing: Intelligence, Agility, and Sustainability: Proceedings...

Krishnaswami Srihari, Mohammad T. Khasawneh, Sangwon Yoon , Daehan Won

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English

Springer Nature Switzerland AG
16 October 2025

Technical design; Industrial chemistry & manufacturing technologies; Production engineering; Automatic control engineering

Series: Lecture Notes in Mechanical Engineering

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.

Edited by: Krishnaswami Srihari, Mohammad T. Khasawneh, Sangwon Yoon, Daehan Won
Imprint: Springer Nature Switzerland AG
Country of Publication: Switzerland
Dimensions: Height: 235mm, Width: 155mm,
ISBN: 9783032056092
ISBN 10: 3032056098
Series: Lecture Notes in Mechanical Engineering
Pages: 699
Publication Date: 16 October 2025
Audience: College/higher education , Professional and scholarly , Further / Higher Education , Undergraduate
Format: Paperback
Publisher's Status: Active

.- 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.