Application of BP Neural Network to Extract AE Characteristic Parameters of the Crack in the Drawing Parts
| Periodical | Advanced Materials Research (Volumes 181 - 182) |
|---|---|
| Main Theme | Advanced Materials Science and Technology |
| Edited by | Qi Luo and Yuanzhi Wang |
| Pages | 195-200 |
| DOI | 10.4028/www.scientific.net/AMR.181-182.195 |
| Citation | Zhi Gao Luo et al., 2011, Advanced Materials Research, 181-182, 195 |
| Online since | January, 2011 |
| Authors | Zhi Gao Luo, Xin He, Ai Cheng Xu, Qiang Chen |
| Keywords | Acoustic Emission (AE) Signal, BP Neural Network (BPNN), Characteristic Parameters, Crack, Drawing Parts |
| Price | US$ 28,- |
Using BP Neural Network to optimize AE characteristic parameters of crack in drawing parts.By detecting the optimized characteristic parameters of crack, the crack in drawing parts are identified.According to the quality of drawing parts,the output of the network are crack signal and normal signal.Comparing the sensitivity of the input characteristic parameters on the output characteristic parameters,then pick the characteristic parameters which have bigger sensitivity values.Finally,the AE characteristic parameters such as Rise Time、AE Event Counter、Energy、Amplitude、Frequency Centroid can represent the signal of crack in the drawing parts better.These five characteristic parameters can identify the crack signal in the forming process of the drawing parts.
