Symmetry constrained neural networks for detection and localization of damage in metal plates
<|reference_start|>Symmetry constrained neural networks for detection and localization of damage in metal plates: 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.<|reference_end|>
@article{amarel2024symmetry,
    title={Symmetry constrained neural networks for detection and localization of
  damage in metal plates},
    author={James Amarel, Christopher Rudolf, Athanasios Iliopoulos, John
  Michopoulos, Leslie N. Smith},
    journal={arXiv preprint arXiv:2409.06084},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06084},
    primaryClass={cs.LG eess.SP}
}