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Treffer: Advances in Artificial Intelligence in Manufacturing II

Title:
Advances in Artificial Intelligence in Manufacturing II
Contributors:
Alexopoulos, Kosmas, Makris, Sotiris, Stavropoulos, Panagiotis
Publisher Information:
Cham: Springer Nature; Springer Nature Switzerland, 2025.
Publication Year:
2025
Collection:
Books
Imported or submitted locally
Original Material:
ed262202-3b35-40e0-a3bc-9d206ec72a33
6c6992af-b843-4f46-859c-f6e9998e40d5
Subject Terms:
Digital Twin in Manufacturing, AI in Human-Robot Interaction, AI in Manufacturing Processes, Big Data Management in Manufacturing, Machine Learning for Quality Inspection, Human-Robot Collaboration, Surrogate Models in Manufacturing, Generative AI in Manufacturing, Human Factors in Manufacturing, Large Language Models in Manufacturing, Automatic control engineering, Production and industrial engineering, Human–computer interaction
Contents Note:
...
Document Type:
E-Book book
File Description:
application/pdf
Language:
English
ISBN:
978-3-031-86488-9
978-3-031-86489-6
3-031-86488-3
3-031-86489-1
Relation:
Lecture Notes in Mechanical Engineering
DOI:
10.1007/978-3-031-86489-6
Access URL:
https://library.oapen.org/handle/20.500.12657/100799
Rights:
URL: http://creativecommons.org/licenses/by/4.0/
Notes:
ONIX_20250414_9783031864896_45

https://library.oapen.org/handle/20.500.12657/100799

https://link.springer.com/978-3-031-86489-6
Accession Number:
edsoap.20.500.12657.100799
Database:
OAPEN Library

Weitere Informationen

This open access book reports on recent developments of artificial intelligence applications in the manufacturing industry. Gathering contributions to the second European Symposium on Artificial Intelligence in Manufacturing, held on October 16, 2024, in Athens, Greece, it reports on machine learning, deep learning and generative AI models for process monitoring, optimization, and control, flexible and precise industrial robots, human-robot collaboration, data management and information technologies, digital twins, data augmentation and synthetic data. Giving a special emphasis to the integration of artificial intelligence in manufacturing systems, automation and processes, this book offers a timely and practice-oriented guide to a multidisciplinary audience of engineering researchers, system developers, AI scientists and industrial managers.