VIBRATION MITIGATION-BASED MACHINE LEARNING-DRIVEN DESIGN OF METASTRUCTURES

Ivana Kovačić, Željko Kanović, Ljiljana Teofanov, Vladimir Rajs

DOI Number
https://doi.org/10.22190/FUWLEP240929019K
First page
201
Last page
211

Abstract


This research is concerned with the development of a longitudinally excited metastructure, featuring periodically distributed external units, each equipped with internal oscillators functioning as vibration absorbers. Initially, the metastructure designed for vibration attenuation around the first structural resonance, is characterized by uniformity, with all absorbers being identical and consisting of cantilevers integrated into the external components, each cantilever terminating in a concentrated mass block. This study employs a machine learning approach to maximize vibration attenuation efficiency around the second resonance, as well as concurrently at the first and second resonant frequencies in two associated optimality criteria related to the width of the attenuation region and the amplitude reduction, respectively. The new metastructures redesigned based on these criteria are fabricated by 3D printing, and their enhanced vibration mitigation capabilities are verified experimentally.

Keywords

metastructure, vibration mitigation, auxiliary oscillators, machine learning

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References


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DOI: https://doi.org/10.22190/FUWLEP240929019K

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