TY - GEN
T1 - Investigation of damage identification of 16Mn steel based on artificial neural networks in tensile test
AU - Wang, Hongwei
AU - Luo, Hongyun
AU - Han, Zhiyuan
AU - Zhong, Qunpeng
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - 16Mn steel
KW - Acoustic emission
KW - Artificial neural networks
KW - Damage identification
UR - https://www.scopus.com/pages/publications/77955963846
U2 - 10.1109/ICRMS.2009.5269996
DO - 10.1109/ICRMS.2009.5269996
M3 - 会议稿件
AN - SCOPUS:77955963846
SN - 9781424449057
T3 - Proceedings of 2009 8th International Conference on Reliability, Maintainability and Safety, ICRMS 2009
SP - 1057
EP - 1061
BT - Proceedings of 2009 8th International Conference on Reliability, Maintainability and Safety, ICRMS 2009
T2 - 2009 8th International Conference on Reliability, Maintainability and Safety, ICRMS 2009
Y2 - 20 July 2009 through 24 July 2009
ER -