著者

足立 吉隆 田口 茂樹 弘川 奨悟

出版者

一般社団法人 日本鉄鋼協会

雑誌

鉄と鋼 (ISSN:00211575)

巻号頁・発行日

vol.102, no.12, pp.722-729, 2016 (Released:2016-11-30)

参考文献数

9

被引用文献数

19

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Deep learning by convolution neural network (CNN) was applied to recognize a microstructure of steels. Three typical CNN-models such as LeNet5, AlexNet, and GoogLeNet were examined their accuracy of recognition. In addition to a model, an effect of learning rate, dropout ratio, and mean image subtraction on recognition accuracy were also investigated. Through this study, the potency of deep learning for microstructural classification is demonstrated.