Domain adaptation strategy for aging and damage classification model of high chromium martensitic heat-resistant steel based on transfer learning

Yaling Liu; Xu Yang; Hongbin Duan; Xu Bao

doi:10.1117/12.3009285

11 October 2023 Domain adaptation strategy for aging and damage classification model of high chromium martensitic heat-resistant steel based on transfer learning

Yaling Liu, Xu Yang, Hongbin Duan, Xu Bao

Author Affiliations +

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 1291828 (2023) https://doi.org/10.1117/12.3009285
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

Using deep learning to study the aging and damage classification of high-chromium martensite microscopic metallography requires a large amount of labeled data; usually a pre-trained deep learning model is used to deal with this problem, however, the pre-trained model contains a large number of available Features, although fine-tuning the entire pre-training model improves the model effect, it consumes a lot of computing power. In view of this, based on migration learning, this paper divides the VGG network into several parts according to the order of network reasoning from the perspective of domain adaptation, and divides and identifies the functions of each part from the perspective of down sampling; using high-chromium martensitic metallographic aging Each part of the network is fine-tuned separately with the impairment dataset. The research results show that the fine-tuning part of the model converges faster and saves more computing power than the fine-tuning of the entire model; different functional parts of the model have different effects on the final effect of the model, and fine-tuning the module closest to the input has less impact on the final effect of the model. In this way, a more efficient domain adaptation strategy for the aging and damage classification model of high-chromium martensitic heat-resistant steel is obtained.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yaling Liu, Xu Yang, Hongbin Duan, and Xu Bao "Domain adaptation strategy for aging and damage classification model of high chromium martensitic heat-resistant steel based on transfer learning", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 1291828 (11 October 2023); https://doi.org/10.1117/12.3009285

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KEYWORDS

Data modeling

Education and training

Machine learning

Feature extraction

Deep learning

Performance modeling

Convolution

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