Document Type
Article
Publication Date
3-29-2024
Publisher
MDPI
Abstract
Modal testing is a common step in aerostructure design, serving to validate the predicted natural frequencies and mode shapes obtained through computational methods. The strategic placement of sensors during testing is crucial for accurately measuring the intended natural frequencies. However, conventional methodologies for sensor placement are often time-consuming and involve iterative processes. This study explores the potential of machine learning techniques to enhance sensor selection methodologies. Three machine learning-based approaches are introduced and assessed, and their efficiencies are compared with established techniques. The evaluation of these methodologies is conducted using a numerical model of a beam to simulate real-world scenarios. The results offer insights into the efficacy of machine learning in optimizing sensor placement, presenting an innovative perspective on enhancing the efficiency and precision of modal testing procedures in aerostructure design.
Recommended Citation
Kelmar, T., Chierichetti, M., & Davoudi Kakhki, F. (2024). Optimization of Sensor Placement for Modal Testing Using Machine Learning. Applied Sciences, 14(7), Article 7. https://doi.org/10.3390/app14073040 2.7).
Comments
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).