Wenzel, Sören ; Slomski-Vetter, Elena ; Melz, Tobias (2022)
Optimizing System Reliability in Additive Manufacturing Using Physics-Informed Machine Learning.
In: Machines, 2022, 10 (7)
doi: 10.26083/tuprints-00022069
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
Fused filament fabrication (FFF), an additive manufacturing process, is an emerging technology with issues in the uncertainty of mechanical properties and quality of printed parts. The consideration of all main and interaction effects when changing print parameters is not efficiently feasible, due to existing stochastic dependencies. To address this issue, a machine learning method is developed to increase reliability by optimizing input parameters and predicting system responses. A structure of artificial neural networks (ANN) is proposed that predicts a system response based on input parameters and observations of the system and similar systems. In this way, significant input parameters for a reliable system can be determined. The ANN structure is part of physics-informed machine learning and is pretrained with domain knowledge (DK) to require fewer observations for full training. This includes theoretical knowledge of idealized systems and measured data. New predictions for a system response can be made without retraining but by using further observations from the predicted system. Therefore, the predictions are available in real time, which is a precondition for the use in industrial environments. Finally, the application of the developed method to print bed adhesion in FFF and the increase in system reliability are discussed and evaluated.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Wenzel, Sören ; Slomski-Vetter, Elena ; Melz, Tobias |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Optimizing System Reliability in Additive Manufacturing Using Physics-Informed Machine Learning |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Machines |
| Jahrgang/Volume einer Zeitschrift: | 10 |
| (Heft-)Nummer: | 7 |
| Kollation: | 23 Seiten |
| DOI: | 10.26083/tuprints-00022069 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/22069 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung aus gefördertem Golden Open Access |
| Kurzbeschreibung (Abstract): | Fused filament fabrication (FFF), an additive manufacturing process, is an emerging technology with issues in the uncertainty of mechanical properties and quality of printed parts. The consideration of all main and interaction effects when changing print parameters is not efficiently feasible, due to existing stochastic dependencies. To address this issue, a machine learning method is developed to increase reliability by optimizing input parameters and predicting system responses. A structure of artificial neural networks (ANN) is proposed that predicts a system response based on input parameters and observations of the system and similar systems. In this way, significant input parameters for a reliable system can be determined. The ANN structure is part of physics-informed machine learning and is pretrained with domain knowledge (DK) to require fewer observations for full training. This includes theoretical knowledge of idealized systems and measured data. New predictions for a system response can be made without retraining but by using further observations from the predicted system. Therefore, the predictions are available in real time, which is a precondition for the use in industrial environments. Finally, the application of the developed method to print bed adhesion in FFF and the increase in system reliability are discussed and evaluated. |
| Freie Schlagworte: | reliability optimization; physics-informed machine learning; recurrent neural network; knowledge transfer; additive manufacturing; Latin hypercube sampling |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-220695 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Fachgebiet Systemzuverlässigkeit, Adaptronik und Maschinenakustik (SAM) |
| Hinterlegungsdatum: | 24 Aug 2022 12:18 |
| Letzte Änderung: | 15 Feb 2023 11:52 |
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| Suche nach Titel in: | TUfind oder in Google |
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