DISTRAL: Industrial Process Monitoring for Injection Molding with Distributed Transfer Learning

Auf einen Blick

Projektleiter/in : Prof. Dr. Thilo Stadelmann
Stellv. Projektleiter/in : Prof. Dr. Matthias Rosenthal
Projektteam : Dr. Ahmed Abdulkadir, Paul-Philipp Luley, Simone Jana Schwizer, Damian Wildmann, Peng Yan
Projektvolumen : CHF 1'170'000
Projektstatus : laufend
Drittmittelgeber : Innosuisse (Innovationsprojekt / Projekt Nr. 62174.1 IP-ENG)
Kontaktperson : Thilo Stadelmann

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.

Publikationen

Yan, Peng; Abdulkadir, Ahmed; Aguzzi, Giulia; Schatte, Gerrit A.; Grewe, Benjamin F.; Stadelmann, Thilo,

2024.

Automated process monitoring in injection molding via representation learning and setpoint regression [Paper].

In:

11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024.

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

Verfügbar unter: https://doi.org/10.21256/zhaw-30430
Yan, Peng; Abdulkadir, Ahmed; Luley, Paul-Philipp; Rosenthal, Matthias; Schatte, Gerrit A.; Grewe, Benjamin F.; Stadelmann, Thilo,

2024.

A comprehensive survey of deep transfer learning for anomaly detection in industrial time series : methods, applications, and directions.

IEEE Access.

12, S. 3768-3789.

Verfügbar unter: https://doi.org/10.1109/ACCESS.2023.3349132
Luley, Paul-Philipp; Deriu, Jan Milan; Yan, Peng; Schatte, Gerrit A.; Stadelmann, Thilo,

2023.

From concept to implementation : the data-centric development process for AI in industry [Paper].

In:

2023 10th IEEE Swiss Conference on Data Science (SDS).

10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22-23 June 2023.

IEEE.

S. 73-76.

Verfügbar unter: https://doi.org/10.1109/SDS57534.2023.00017

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