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ORIGINAL RESEARCH ARTICLE

Data-driven prediction of strain fields in auxetic structures and non-contact validation with mechanoluminescence for structural health monitoring

Junheui Jo1† ,  Minwoo Park1† ,  Sukheon Kang1† ,  Hugon Lee1† ,  Chang-Yeon Gu1 ,  Taek-Soo Kim1 ,  Seunghwa Ryu1*
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1 Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Republic of Korea
IJAMD 2024, 1(2), 48–60; https://doi.org/10.36922/ijamd.3539
Submitted: 30 April 2024 | Accepted: 18 June 2024 | Published: 30 July 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Recent advancements in 3D printing technology have significantly enhanced the potential of auxetic structures, which are notable for their negative Poisson’s ratio, particularly in applications such as sensor technology and structural health monitoring. Central to the performance of these structures is the accurate estimation of the effective strain parameter, a critical metric for assessing structural integrity. However, as structural complexity increases, estimating this parameter becomes increasingly challenging. The fabrication and real-world validation of these structures are equally important challenges. This paper introduces two key innovations for the practical application of auxetic structures. First, we present a multi-kernel hierarchical deep neural network that leverages finite element simulation data to accurately predict effective strain fields in complex auxetic configurations. This model architecture not only reduces the number of parameters requiring training but also enhances feature learning and generalization capabilities, achieving over 90% accuracy in predicting strain fields. Second, we validate these predictions using a 3D-printed specimen embedded with mechanoluminescent (ML) particles. This approach enables direct, non-contact visualization of strain in real-time, offering high spatial and temporal resolution. The alignment observed between predicted and observed strain concentration areas demonstrates the efficacy of integrating ML technology into auxetic designs. This integration significantly improves the reliability and diagnostic capabilities of advanced structural systems.

Keywords
Mechanical metamaterials
Auxetic structure
Structural health monitoring
3D printing
Mechanoluminescence
Deep learning
Funding
This work was financially supported by the National Research Foundation of Korea (NRF) (RS-2023-00222166).
Conflict of interest
Seunghwa Ryu is an Editorial Board Member of this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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