Experimental force reconstruction on plates of arbitrary shape using neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Measuring the forces that excite a structure into vibration is an important tool in modeling the system and investigating ways to reduce the vibration. However, determining the forces that have been applied to a vibrating structure can be a challenging inverse problem, even when the structure is instrumented with a large number of sensors. Previously, an artificial neural network was developed to identify the location of an impulsive force on a rectangular plate. In this research, the techniques were extended to plates of arbitrary shape. The principal challenge of arbitrary shapes is that some combinations of network outputs (x- and y-coordinates) are invalid. For example, for a plate with a hole in the middle, the network should not output that the force was applied in the center of the hole. Different methods of accommodating arbitrary shapes were investigated, including output space quantization and selecting the closest valid region.

Original languageEnglish (US)
Title of host publicationProceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering
EditorsTyler Dare, Stuart Bolton, Patricia Davies, Yutong Xue, Gordon Ebbitt
PublisherThe Institute of Noise Control Engineering of the USA, Inc.
ISBN (Electronic)9781732598652
DOIs
StatePublished - 2021
Event50th International Congress and Exposition of Noise Control Engineering, INTER-NOISE 2021 - Washington, United States
Duration: Aug 1 2021Aug 5 2021

Publication series

NameProceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering

Conference

Conference50th International Congress and Exposition of Noise Control Engineering, INTER-NOISE 2021
Country/TerritoryUnited States
CityWashington
Period8/1/218/5/21

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics

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