Wood fracture, acoustic emission, and the drying process Part 2. Acoustic emission pattern recognition analysis

Summary Signal features were extracted from AE signals collected during mixed mode fracture tests of ponderosa pine and California black oak conducted at 12 and 18 percent moisture content and temperatures of 20, 40 and 60 °C. Five features in the time domain and three in the frequency domain were selected for further analysis. Signal features were found to be significantly affected by both temperature and moisture content. Cluster analysis showed that while signals could be successfully classified, the resulting patterns showed little relationship to wood fracture. Only load levels well beyond maximum load could be consistently distinguished from lower load levels. It was concluded that pattern recognition analysis would have only limited application to control the wood drying process.This study was supported in part by USDA Grant No. 90-37291-5762