NDLI: Investigation of damage identification of 16Mn steel based on artificial neural networks in tensile test

Content Provider	IEEE Xplore Digital Library
Author	Hongwei Wang Hongyun Luo Zhiyuan Han Qunpeng Zhong
Copyright Year	2009
Description	Author affiliation: Key Laboratory of Aerospace Materials and Performance (Ministry of Education) School of Materials Science and Engineering Beihang University Beijing, China (Hongwei Wang; Hongyun Luo; Zhiyuan Han; Qunpeng Zhong)
Abstract	In order to identify the damage modes of 16Mn steel, the tensile test of 16Mn steel plate specimens was developed and monitored with acoustic emission technique. Based on the acoustic emission signature analysis during material damage, 3-layer back-propagation neural networks (BPNN) model with Tansig-Logsig transfer function was applied to identify the damage modes of 16Mn steel in tensile test. In the model, amplitude, counts, energy, duration and rise time of acoustic emission parameters were selected as input neurons, and elastic deformation, yield deformation, strain hardening and necking deformation of damage modes were selected as output neurons. After the model has been trained with the experimental data, the discrimination rate of damage modes was equal to approximately 83%. It showed that it is feasible to identify the damage modes of 16Mn steel in tensile test based on acoustic emission technique and artificial neural network.
Starting Page	1057
Ending Page	1061
File Size	1244088
Page Count	5
File Format	PDF
ISBN	9781424449033
DOI	10.1109/ICRMS.2009.5269996
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Materials testing Acoustic testing Building materials Neurons Transfer functions Artificial neural networks Steel damage identification artificial neural networks Acoustic emission Acoustic materials 16Mn steel acoustic emission Monitoring
Content Type	Text
Resource Type	Article

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