Optimization of Sensor Placement for Modal Testing Using Machine Learning
Todd Kelmar,
Maria Chierichetti,
Fatemeh Davoudi Kakhki
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 … Show more
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