DISTRAL: Industrial Process Monitoring for Injection Molding with Distributed Transfer Learning
Beschreibung
We develop a distributed machine learning system to sort out defect plastic parts during production. Main challenge is the transferability of learnt process know-how from case to case; the solution builds on domain adaptation, continual data-centric deep learning and federated edge computing.
Eckdaten
Projektleitung
Stellv. Projektleitung
Projektteam
Simone Jana Schwizer, Damian Wildmann, Peng Yan, Paul-Philipp Luley, Dr. Ahmed Abdulkadir
Projektstatus
abgeschlossen, 10/2022 - 03/2025
Institut/Zentrum
Centre for Artificial Intelligence (CAI); Institute of Embedded Systems (InES)
Drittmittelgeber
Innovationsprojekt / Projekt Nr. 62174.1 IP-ENG
Projektvolumen
1'170'000 CHF
Publikationen
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Learning actionable world models for industrial process control
2025 Yan, Peng; Abdulkadir, Ahmed; Schatte, Gerrit A.; Aguzzi, Giulia; Gha, Joonsu; Pascher, Nikola; Rosenthal, Matthias; Gao, Yunlong; Grewe, Benjamin F.; Stadelmann, Thilo
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A comprehensive survey of deep transfer learning for anomaly detection in industrial time series : methods, applications, and directions
2024 Yan, Peng; Abdulkadir, Ahmed; Luley, Paul-Philipp; Rosenthal, Matthias; Schatte, Gerrit A.; Grewe, Benjamin F.; Stadelmann, Thilo
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Automated process monitoring in injection molding via representation learning and setpoint regression
2024 Yan, Peng; Abdulkadir, Ahmed; Aguzzi, Giulia; Schatte, Gerrit A.; Grewe, Benjamin F.; Stadelmann, Thilo
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From concept to implementation : the data-centric development process for AI in industry
2023 Luley, Paul-Philipp; Deriu, Jan Milan; Yan, Peng; Schatte, Gerrit A.; Stadelmann, Thilo
School of Engineering
Technikumstrasse 71
8400 Winterthur