TY  - BOOK
AU  - Alexopoulos,Kosmas
AU  - Makris,Sotiris
AU  - Stavropoulos,Panagiotis
ED  - SpringerLink (Online service)
TI  - Advances in Artificial Intelligence in Manufacturing II: Proceedings of the 2nd European Symposium on Artificial Intelligence in Manufacturing, October 16, 2024, Athens, Greece 
T2  - Lecture Notes in Mechanical Engineering,
SN  - 9783031864896
AV  - TJ212-225 
U1  - 629.8 23
PY  - 2025///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Control engineering
KW  - Robotics
KW  - Automation
KW  - Industrial engineering
KW  - Production engineering
KW  - User interfaces (Computer systems)
KW  - Human-computer interaction
KW  - Control, Robotics, Automation
KW  - Industrial and Production Engineering
KW  - User Interfaces and Human Computer Interaction
N1  -  -- Part I AI in Process Level -- 1 Digital Twins of manufacturing processes under Industry 5.0 -- 2 Recurrent Convolutional Neural Network based defect detection in Submerged Arc Welding processes -- 3 On the use of Generative AI to support in-line process monitoring in Zero-Defect Manufacturing -- 4 Laser Metal Deposition (LMD) process monitoring: from 3D visualization of sensor data towards anomaly detection -- 5 An explainable active learning approach for enhanced defect detection in manufacturing, etc; Open Access
N2  - 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
UR  - https://doi.org/10.1007/978-3-031-86489-6
ER  -