[1]CHEN Lichao,YAN Yaodong,ZHANG Rui,et al.Welding defect classification of stainless steel based on AlexNet neural network combined with transfer learning[J].CAAI Transactions on Intelligent Systems,2021,16(3):537-543.[doi:10.11992/tis.202005013]
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Welding defect classification of stainless steel based on AlexNet neural network combined with transfer learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 537-543 Column: 学术论文—人工智能基础 Public date: 2021-05-05
Title:
Welding defect classification of stainless steel based on AlexNet neural network combined with transfer learning
Author(s):
CHEN Lichao1;  YAN Yaodong1;  ZHANG Rui1;  FU Liuhu2;  CAO Jianfang1; 3
1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;
2. Mechanical Product Quality Supervision and Inspection Station, Shanxi Mechanical and Electrical Design & Research Institute, Taiyuan 030009, China;
3. Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou 034000, China
Keywords:
classification of weld defects in stainless steel; convolutional neural network; image preprocessing; AlexNet model; the migration study; data enhancement; weld data set; deep learning
CLC:
TP391.4
DOI:
10.11992/tis.202005013
Abstract:
In order to solve the problems of subjectivity and objectivity in feature extraction of stainless steel weld defects, an AlexNet convolutional neural network model based on transfer learning is proposed for automatic classification of stainless steel weld defects. First, due to the lack of stainless steel weld defect data, the first three layers of the network are frozen by transfer learning, which reduces the requirement of the network on the input data. In order to speed up the convergence of the network, the batch normalization (BN) of the feature information extracted from the two latter layers of convolution is carried out. The LeakyReLU function is used to activate the features in the negative interval so as to improve the robustness of the model and the ability of feature extraction. The results show that the accuracy of the model is 95.12%, and the recognition accuracy is 9.8% higher than that of the original structure. It has been verified that the improved method can classify five kinds of stainless steel weld defects such as crack, blowhole, slag inclusion, incomplete fusion, and incomplete penetration with high precision. Compared to the existing methods, this method has a wider recognition area, higher accuracy, and certain engineering significance.
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