Investigation of Damage Identification of 16Mn Steel Based on Artificial Neural Networks and Data Fusion Techniques in Tensile Test

Authors:
Hongwei Wang;Hongyun Luo;Zhiyuan Han;Qunpeng Zhong
Affiliations:
Key Laboratory of Aerospace Materials and Performance (Ministry of Education), School of Materials Science and Engineering, Beihang University, Beijing 100191;Key Laboratory of Aerospace Materials and Performance (Ministry of Education), School of Materials Science and Engineering, Beihang University, Beijing 100191;Key Laboratory of Aerospace Materials and Performance (Ministry of Education), School of Materials Science and Engineering, Beihang University, Beijing 100191;Key Laboratory of Aerospace Materials and Performance (Ministry of Education), School of Materials Science and Engineering, Beihang University, Beijing 100191
Venue:
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Year:
2009

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Abstract

This paper proposes a damage identification method based on back propagation neural network (BPNN) and dempster-shafer (D-S) evidence theory to analyze the acoustic emission (AE) data of 16Mn steel in tensile test. Firstly, the AE feature parameters of each sensor in 16Mn steel tensile test are extracted. Secondly, BPNNs matching sensor number are trained and tested by the selected features of the AE data, and the initial damage decision is made by each BPNN. Lastly, the outputs of each BPNN are combined by D-S evidence theory to obtain the finally damage identification of 16Mn steel in tensile test. The experimental results show that the damage identification method based on BPNN and D-S evidence theory can improve damage identification accuracy in comparison with BPNN alone and decrease the effect of the environment noise.