Determining the stress intensity factor of a material with an artificial neural network from acoustic emission measurements |
| |
Authors: | Ki-Bok Kim Dong-Jin Yoon Jung-Chae Jeong Seung-Seok Lee |
| |
Affiliation: | NDE Group, Center for Safety Measurement, Korea Research Institute of Standards and Science, P.O. Box 102, 305-600, Yuseong, Daejeon, South Korea |
| |
Abstract: | An artificial neural (ANN) network was trained to recognize the stress intensity factor in the interval from microcrack to fracture from acoustic emission (AE) measurements on compact tension specimens. The specimens were made from structural steel SWS490B whilst the ANN had a 5-14-1 structure. The number of neurons in the input layers was five inputs of the AE parameters such as ring-down counts, rise time, energy, event duration and peak amplitude. The performance of the ANN was tested using a specific set of the AE data. The ANN is a promising tool for predicting the stress intensity factor of material using AE data. |
| |
Keywords: | Acoustic emission Artificial neural network Stress intensity factor Fatigue crack propagation |
本文献已被 ScienceDirect 等数据库收录! |
|