Investigation of Damage Identification of 16Mn Steel Based on Artificial Neural Networks in Tensile Test
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 backpropagation neural networks (BPNN) model with TansigLogsig 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.
16Mn steel Acoustic emission Damage identification Artificial Neural Networks
Hongwei Wang Hongyun Luo Zhiyuan Han Qunpeng Zhong
Key Laboratory of Aerospace Materials and Performance (Ministry of Education) School of Materials Sc Key Laboratory of Aerospace Materials and Performance (Ministry of Education) School of Materials Sc
国际会议
2009 8th International Conference on Reliability,Maintainability and Safety(第八届中国国际可靠性、维修性、安全性会议)
成都
英文
1057-1061
2009-08-24（万方平台首次上网日期，不代表论文的发表时间）