Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-30430Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Yan, Peng | - |
| dc.contributor.author | Abdulkadir, Ahmed | - |
| dc.contributor.author | Aguzzi, Giulia | - |
| dc.contributor.author | Schatte, Gerrit A. | - |
| dc.contributor.author | Grewe, Benjamin F. | - |
| dc.contributor.author | Stadelmann, Thilo | - |
| dc.date.accessioned | 2024-04-12T08:31:42Z | - |
| dc.date.available | 2024-04-12T08:31:42Z | - |
| dc.date.issued | 2024-05-31 | - |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/30430 | - |
| dc.description.abstract | Online process monitoring is essential to detect failures and respond promptly in automated industrial processes such as injection molding. Traditional systems rely on experienced operators manually defining operational boundaries around a reference signal. We propose a data-driven representation that auto-tunes the sensitivity to a pre-set specificity threshold and automatically detects anomalies alongside interpretable indices that help identify root causes. Our automated system achieved an average AUC of 0.998 and detected 100 percent of the anomalies with the proposed dynamic calibration of the data-driven embedding method. The dynamic calibration, which accounted for drift, boosts the average specificity from 0.362 to 0.869. The outputs also indicate the direction and relative magnitude of characteristic deviations caused by machine parameters, including holding pressure, mold temperature, and injection speed. The AI-derived process boundaries are superior to manual annotation in tested real-world production environments. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | ZHAW Zürcher Hochschule für Angewandte Wissenschaften | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | Anomaly detection | de_CH |
| dc.subject | Time series | de_CH |
| dc.subject | Variational autoencoder | de_CH |
| dc.subject | Root-cause analysis | de_CH |
| dc.subject | Explainable AI | de_CH |
| dc.subject | Transfer learning | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.title | Automated process monitoring in injection molding via representation learning and setpoint regression | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Centre for Artificial Intelligence (CAI) | de_CH |
| dc.identifier.doi | 10.21256/zhaw-30430 | - |
| zhaw.conference.details | 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | acceptedVersion | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.webfeed | Datalab | de_CH |
| zhaw.webfeed | Machine Perception and Cognition | de_CH |
| zhaw.webfeed | ZHAW digital | de_CH |
| zhaw.funding.zhaw | DISTRAL: Industrial Process Monitoring for Injection Molding with Distributed Transfer Learning | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2024_Yan-etal_Automated-process-monitoring-in-injection-molding_SDS.pdf | Accepted Version | 4.45 MB | Adobe PDF |  View/Open |
Show simple item record
Yan, P., Abdulkadir, A., Aguzzi, G., Schatte, G. A., Grewe, B. F., & Stadelmann, T. (2024, May 31). Automated process monitoring in injection molding via representation learning and setpoint regression. 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. https://doi.org/10.21256/zhaw-30430
Yan, P. et al. (2024) ‘Automated process monitoring in injection molding via representation learning and setpoint regression’, in 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-30430.
P. Yan, A. Abdulkadir, G. Aguzzi, G. A. Schatte, B. F. Grewe, and T. Stadelmann, “Automated process monitoring in injection molding via representation learning and setpoint regression,” in 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024, May 2024. doi: 10.21256/zhaw-30430.
YAN, Peng, Ahmed ABDULKADIR, Giulia AGUZZI, Gerrit A. SCHATTE, Benjamin F. GREWE und Thilo STADELMANN, 2024. Automated process monitoring in injection molding via representation learning and setpoint regression. In: 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. Conference paper. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. 31 Mai 2024
Yan, Peng, Ahmed Abdulkadir, Giulia Aguzzi, Gerrit A. Schatte, Benjamin F. Grewe, and Thilo Stadelmann. 2024. “Automated Process Monitoring in Injection Molding via Representation Learning and Setpoint Regression.” Conference paper. In 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-30430.
Yan, Peng, et al. “Automated Process Monitoring in Injection Molding via Representation Learning and Setpoint Regression.” 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2024, https://doi.org/10.21256/zhaw-30430.
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