Next-Gen Learning Factories
Description
This book provides practical insights into the latest developments and innovations in the field of learning factories, with a particular focus on next-generation approaches in engineering education and manufacturing systems. The book compiles selected contributions from the 16th Conference on Learning Factories (CLF 2026), held from May 5 to 7, 2026, at the Free University of Bozen-Bolzano. The proceedings cover five fundamental areas: (1) enabling technologies, (2) engineering education, (3) learning factory operations, (4) learning factory concepts and infrastructure, and (5) next-generation agile learning factories. This book is designed for a diverse audience. Researchers will find approaches to current research topics related to learning factories. Practitioners will gain insights from real-world applications and innovative learning factory solutions. Doctoral students and lecturers will gain insights into modern teaching methodologies and the integration of experiential learning environments into engineering education.
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Content
From MES Core to MES Minimal Lessons from Developing and Piloting Open Source MES Platforms in a Learning Factory.- 5G Applications in Manufacturing Systematic Literature Review and Case Applications in Learning Factories.- Competency Oriented Requirements for Learning Factories in Circular Battery Cell Production.- From Reality Capture to Industrial Metaverse: First Steps Towards a Self-Learning Manufacturing Systems.- From Signals To Decisions A Pedagogical Pipeline for Tool Wear Prediction Using Few Shot Learning.- Startups Development in Learning Factories A Case Study within a University Innovation Ecosystem.- Integrating Virtual Reality in Robotics Training A Project Based Learning Approach.- Integrating AI Assistance and Human-Machine Collaboration in Learning Factories for Industry 5 0.- From Raw Material to Finished Product Leveraging Learning Factories for End to End Product Traceability.- Smart Maintenance in Learning Factories Harnessing Data for Predictive Insights.