Kelmar, T.; Chierichetti, M.; Davoudi Kakhki, F. Optimization of Sensor Placement for Modal Testing Using Machine Learning. Appl. Sci.2024, 14, 3040.
Kelmar, T.; Chierichetti, M.; Davoudi Kakhki, F. Optimization of Sensor Placement for Modal Testing Using Machine Learning. Appl. Sci. 2024, 14, 3040.
Kelmar, T.; Chierichetti, M.; Davoudi Kakhki, F. Optimization of Sensor Placement for Modal Testing Using Machine Learning. Appl. Sci.2024, 14, 3040.
Kelmar, T.; Chierichetti, M.; Davoudi Kakhki, F. Optimization of Sensor Placement for Modal Testing Using Machine Learning. Appl. Sci. 2024, 14, 3040.
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 to 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, comparing their efficiency 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
