Götz, Benedict ; Kersting, Sebastian (2022)
Estimation of Uncertainty in the Lateral Vibration Attenuation of a Beam with Piezo-Elastic Supports by Neural Networks.
In: Applied Mechanics and Materials, 2018, 885
doi: 10.26083/tuprints-00020433
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
Quantification of uncertainty in technical systems is often based on surrogate models of corresponding simulation models. Usually, the underlying simulation model does not describe the reality perfectly, and consequently the surrogate model will be imperfect.In this article we propose an improved surrogate model of the vibration attenuation of a beam with shunted piezoelectric transducers. Therefore, experimentally observed and simulated variations in the vibration attenuation are combined in the model estimation process, by using multi-layer feedforward neural networks. Based on this improved surrogate model, we construct a density estimate of the maximal amplitude in the vibration attenuation.The density estimate is used to analyze the uncertainty in the vibration attenuation, resulting from manufacturing variations.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Götz, Benedict ; Kersting, Sebastian |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Estimation of Uncertainty in the Lateral Vibration Attenuation of a Beam with Piezo-Elastic Supports by Neural Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Publikationsdatum der Erstveröffentlichung: | 2018 |
| Verlag: | Trans Tech Publications Ltd. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Applied Mechanics and Materials |
| Jahrgang/Volume einer Zeitschrift: | 885 |
| DOI: | 10.26083/tuprints-00020433 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/20433 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Quantification of uncertainty in technical systems is often based on surrogate models of corresponding simulation models. Usually, the underlying simulation model does not describe the reality perfectly, and consequently the surrogate model will be imperfect.In this article we propose an improved surrogate model of the vibration attenuation of a beam with shunted piezoelectric transducers. Therefore, experimentally observed and simulated variations in the vibration attenuation are combined in the model estimation process, by using multi-layer feedforward neural networks. Based on this improved surrogate model, we construct a density estimate of the maximal amplitude in the vibration attenuation.The density estimate is used to analyze the uncertainty in the vibration attenuation, resulting from manufacturing variations. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-204333 |
| Zusätzliche Informationen: | Keywords: Density Estimation, Imperfect Model, Neural Network, Surrogate Model, Uncertainty Quantification |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 510 Mathematik 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik 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) 04 Fachbereich Mathematik 04 Fachbereich Mathematik > Stochastik |
| Hinterlegungsdatum: | 02 Feb 2022 14:01 |
| Letzte Änderung: | 03 Feb 2022 06:12 |
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Verfügbare Versionen dieses Eintrags
- Estimation of Uncertainty in the Lateral Vibration Attenuation of a Beam with Piezo-Elastic Supports by Neural Networks. (deposited 02 Feb 2022 14:01) [Gegenwärtig angezeigt]
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