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Estimation of Uncertainty in the Lateral Vibration Attenuation of a Beam with Piezo-Elastic Supports by Neural Networks

Götz, Benedict ; Kersting, Sebastian (2022):
Estimation of Uncertainty in the Lateral Vibration Attenuation of a Beam with Piezo-Elastic Supports by Neural Networks. (Publisher's Version)
In: Applied Mechanics and Materials, 885, pp. 293-303. Trans Tech Publications Ltd., ISSN 1660-9336, e-ISSN 1662-7482,
DOI: 10.26083/tuprints-00020433,
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Item Type: Article
Origin: Secondary publication service
Status: Publisher's Version
Title: Estimation of Uncertainty in the Lateral Vibration Attenuation of a Beam with Piezo-Elastic Supports by Neural Networks
Language: English
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.

Journal or Publication Title: Applied Mechanics and Materials
Volume of the journal: 885
Publisher: Trans Tech Publications Ltd.
Classification DDC: 500 Naturwissenschaften und Mathematik > 510 Mathematik
600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 16 Department of Mechanical Engineering > Research group System Reliability, Adaptive Structures, and Machine Acoustics (SAM)
04 Department of Mathematics > Stochastik
Date Deposited: 02 Feb 2022 14:01
Last Modified: 02 Feb 2022 14:01
DOI: 10.26083/tuprints-00020433
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-204333
Additional Information:

Keywords: Density Estimation, Imperfect Model, Neural Network, Surrogate Model, Uncertainty Quantification

URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20433
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