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arXiv · 2409.06084

Symmetry constrained neural networks for detection and localization of damage in metal plates

James Amarel·Christopher Rudolf·Athanasios Iliopoulos·John Michopoulos·Leslie N. Smith
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Abstract

The present paper is concerned with deep learning techniques applied to detection and localization of damage in a thin aluminum plate. We used data collected on a tabletop apparatus by mounting to the plate four piezoelectric transducers, each of which took turn to generate a Lamb wave that then traversed the region of interest before being received by the remaining three sensors. On training a neural network to analyze time-series data of the material response, which displayed damage-reflective features whenever the plate guided waves interacted with a contact load, we achieved a model that detected with greater than $99\%$ accuracy in addition to a model that localized with $2.58 \pm 0.12$ mm mean distance error. For each task, the best-performing model was designed according to the inductive bias that our transducers were both similar and arranged in a square pattern on a nearly uniform plate.

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James Amarel, Christopher Rudolf, Athanasios Iliopoulos, John Michopoulos, Leslie N. Smith. 2024-09-09. Symmetry constrained neural networks for detection and localization of damage in metal plates. https://doi.org/10.1063/5.0242345

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