ORIGINAL RESEARCH article
Front. Mater.
Sec. Computational Materials Science
Volume 12 - 2025 | doi: 10.3389/fmats.2025.1652484
This article is part of the Research TopicAdvancing Computational Material Science and Mechanics through Integrated Deep Learning ModelsView all 6 articles
Development of Defect Localization Method for Perforated Carbon-fiber-reinforced Plastic Specimens Using Finite Element Method and Graph Neural Network
Provisionally accepted
- 1Department of Science for Open and Environmental Systems, Graduate School of Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan
- 2Japan Aerospace Exploration Agency (JAXA), Research and Development Directorate, Research Unit IV, 2-1-1 Sengen, Tsukuba, Ibaraki 305-8505, Japan
- 3Department of Mechanical Engineering, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan
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In this study, we propose a novel defect localization method that integrates the graph neural network (GNN) with the finite element method (FEM) to estimate the three-dimensional location of defects in perforated carbon-fiberreinforced plastic (CFRP) interstage structures. Specifically, the model uses distributions of the sum of principal stresses on the surface (DSPSS) to predict the three-dimensional location of defects. FEM is employed to simulate tensile loading conditions and generate stress distribution data using Teflon sheets to represent predefined delaminations. These distributions serve as inputs to the graph attention network (GAT), which classifies defect positions into 19 categories. The proposed method achieved a macro-averaged F1-score of 61% and accurately predicted both the insertion layers and planar positions of defects.
Keywords: nondestructive testing, Infrared stress measurement, Finite element method, Graph neural network, Defect localization, carbon-fiber-reinforced plastic, Rocket Interstage Structure
Received: 23 Jun 2025; Accepted: 25 Aug 2025.
Copyright: © 2025 Nishioka, Kojima, Saito, Kawakami, Washiya and Muramatsu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mayu Muramatsu, Department of Mechanical Engineering, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan
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