DISTRAL: Industrial Process Monitoring for Injection Molding with Distributed Transfer Learning
Description
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.
Key Data
Projectlead
Deputy Projectlead
Project team
Dr. Ahmed Abdulkadir, Paul-Philipp Luley, Simone Jana Schwizer, Damian Wildmann, Peng Yan
Project status
completed, 10/2022 - 03/2025
Institute/Centre
Centre for Artificial Intelligence (CAI); Institute of Embedded Systems (InES)
Funding partner
Innovationsprojekt / Projekt Nr. 62174.1 IP-ENG
Project budget
1'170'000 CHF
Publications
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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
