自组织CNN建模识别耐热钢金相组织研究

doi:10.11896/cldb.21030032

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Abstract Heat-resistant steel has complex metallographic structure, hence it is difficult to extract features using traditional image processing methods to accurately achieve auto recognition. Manual recognition is susceptible to subjective factors and thus resulting in poor accuracy and repeatability. Convolutional neural networks (CNN) can extract complex features from raw images, only with the problem of difficulty in model training and selection and optimization of topological hyperparameters. In this work, a Hyperband algorithm, which calculates resource allocation based on hyperparameter sets, was used to optimize hyperparameter in CNN model and finally the self-organizing modelling was achieved. Relevant problems were solved such as low efficiency and large consumption in computing resources due to grid searching, random searching and Bayesian optimization as well as optimization instability. Based on above-mentioned Hyperband algorithm optimization, a 33 layers' CNN model was constructed, trained, simulated and its recognition performance evaluated with confusion matrix analysis. Results showed that, in recognition of metallographic structure for heat-resistant steel, an average accuracy of 94.2%, an average precision of 94.1%, an average sensitivity of 94.2 and an average specificity of 98.1% were obtained after using the established model. Our model demonstrated a good generalization ability and could be used to recognize metallagraphic structure accurately. This approach provided a new method in intelligent recognition of complex metallagraphic structures.

Key words： computational materials science heat-resistant steel metallographic structure deep learning convolutional neural network hyperparameter optimization identification

Published: 25 June 2022 Online: 2022-06-24

CLC number:

TG142.1+5

Fund:National Natural Science Foundation of China (52061037) and the High-level Talents Introduction Research Project of Inner Mongolia Agricultural University(NDYB2016-20).

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	Articles by authors
	ZHANG Yongzhi
	LI Xuying
	XIN Quanzhong
	KONG Xiangming
	WANG Yongliang

Cite this article:

ZHANG Yongzhi,LI Xuying,XIN Quanzhong, et al. Research on Self-organized CNN Modeling to Identify Metallographic Structure of Heat-resistant Steel[J]. Materials Reports, 2022, 36(12): 21030032-6.

URL:

http://www.mater-rep.com/EN/10.11896/cldb.21030032 OR http://www.mater-rep.com/EN/Y2022/V36/I12/21030032

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