Cross-modal deep learning for predicting material performance: example of thermal conductivity in thermal barrier coatings

Qiaochuan Chen; Sifan Han; Yuexing Han

doi:10.1117/12.3033107

5 July 2024 Cross-modal deep learning for predicting material performance: example of thermal conductivity in thermal barrier coatings

Qiaochuan Chen, Sifan Han, Yuexing Han

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Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131846N (2024) https://doi.org/10.1117/12.3033107
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

The performance of materials is closely linked to their structure, and can usually be evaluated by analyzing the microstructure. Thermal conductivity is an important property of thermal barrier coating materials, which can be predicted using data-driven methods such as deep learning. However, current deep learning methods have some limitations in this regard. Some methods are limited by the receptive field size of convolutional neural networks, ignoring the correlation between local detail features in the image. Other methods only consider microstructural information, ignoring other factors affecting material performance, such as temperature, current magnitude, and hydrogen concentration. In this paper, we propose a Cross-Modal Multi-Scale Feature Fusion deep learning model, aiming to accurately predict material performance using multimodal data. Meanwhile, a Scale-Agnostic Fusion Module is designed to effectively fuse microstructural features extracted at different scales. Experimental results show that our method outperforms most current data-driven methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Qiaochuan Chen, Sifan Han, and Yuexing Han "Cross-modal deep learning for predicting material performance: example of thermal conductivity in thermal barrier coatings", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131846N (5 July 2024); https://doi.org/10.1117/12.3033107

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KEYWORDS

Feature extraction

Performance modeling

Deep learning

Data modeling

Feature fusion

Image fusion

Image processing

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