[1]WANG Jiarui,LIU Nengfeng,QU Peng.Automatic identification of metallographic structure based on convolutional neural network[J].CAAI Transactions on Intelligent Systems,2022,17(4):698-706.[doi:10.11992/tis.202110035]
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Automatic identification of metallographic structure based on convolutional neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 4 Page number: 698-706 Column: 学术论文—机器学习 Public date: 2022-07-05
Title:
Automatic identification of metallographic structure based on convolutional neural network
Author(s):
WANG Jiarui1; 2;  LIU Nengfeng2;  QU Peng1
1. Mechanical and Electronic Engineering Department, Langfang Yanjing Polytechnic Inst., Langfang 065200, China;
2. Education Center of Experiments and Innovations, Harbin Institute of Technology, Shenzhen 518055, China
Keywords:
convolutional neural network; metallographic structure; image processing; network model; automatic identification; LeNet neural network; AlexNet neural network; VGGNet neural network
CLC:
TG141;TP391.4;TP183
DOI:
10.11992/tis.202110035
Abstract:
The convolutional neural model was used to automatically identify metallographic structure images to reduce the error rate of manual resolution of metallographic structure image categories and improve the resolution efficiency. Two kinds of metallographic structure images of ferrite and martensite obtained from metallographic sample blocks were analyzed, and a preprocessing scheme conforming to the distribution characteristics of the metallographic structure image was proposed. Image size normalization, gray value normalization, and Gaussian smoothing are used to establish the metallographic image sample set and training set. Aiming at the established image data sets of two types of metallographic structures such as ferrite and martensite, the improved models suitable for metallographic structure image recognition are proposed, which are named the LeNet-MetStr model, AlexNet-MetStr model, and VGGNet-MetStr model, respectively. Three improved convolutional neural networks were trained and analyzed. The results show that the VGGNet-MetStr model has higher accuracy for the automatic identification of two kinds of metallographic structure images.
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