Proceedings of the Annual Conference of JSAI
Online ISSN : 2758-7347
38th (2024)
Session ID : 1K5-OS-15b-03
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Estimating Material Property Values from Fracture Surface Images with Vision Transformers
*Shota YAMANAKAToshimitsu ARITAKEYoshifumi AMAMOTOYoh-ichi MOTOTAKE
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CONFERENCE PROCEEDINGS FREE ACCESS

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Abstract

Analyzing fracture surfaces to determine the type of fracture mechanics is important to improve the safety use of materials. Recent approaches apply deep neural networks to estimate fracture types or property values. This study aims to verify whether the combination of transfer learning with fine tuning and the vision transformer (ViT) model improves the accuracy of fracture surface image analysis. The verification results showed that the ViT with the attention mechanism displays superior performance to the convolutional neural networks (CNN) used in conventional fractography. It was also confirmed that the ViT particularly improves the systematic errors observed in conventional methods using CNN.

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© 2024 The Japanese Society for Artificial Intelligence
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