Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Pattern Classification of Acoustic Emission Signals During Wood Drying by Artificial Neural Network

인공신경망을 이용한 목재건조 중 발생하는 음향방출 신호 패턴분류

  • Published : 2004.06.01
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Abstract

This study was Performed to classify the acoustic emission(AE) signal due to surface cracking and moisture movement in the flat-sawn boards of oak(Quercus Variablilis) during drying using the principal component analysis(PCA) and artificial neural network(ANN). To reduce the multicollinearity among AE parameters such as peak amplitude, ring-down count event duration, ring-down count divided by event duration, energy, rise time, and peak amplitude divided by rise time and to extract the significant AE parameters, correlation analysis was performed. Over 96 of the variance of AE parameters could be accounted for by the first and second principal components. An ANN analysis was successfully used to classify the Af signals into two patterns. The ANN classifier based on PCA appeared to be a promising tool to classify the AE signals from wood drying.

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References

  1. Anastasopoulos, A. A. and T. P. Philippidis. 1995. Clustering Methodology for the Evaluation of Acoustic Emission from Composites, Journal of Acoustic Emission, 13(1/2):11-22
  2. Booker, J. D. 1992. Acoustic emission related to instantaneous strain in Tasmanian eucalypt timber during seasoning. Wood Science and Technology, 28:249-259
  3. Breese, M. C., S. Zhao and G. McLeod. 1995. The use of acoustic emissions and steaming to reduce checking during the drying of European oak, Holz als Roh und Werkstoff, 53(6):393-396
  4. Hertz, J., A. Krogh and R. G. Palmer. 1991. Introduction to the theory of neural computation. Addison-Wesley Publishing Company, USA
  5. Honeycutt, R. M., C. Skaar and W. Simpson. 1985. Use of acoustic emission to control drying rate of red oak. Forest Products Journal, 35(1):48-50
  6. Kaiser, J. 1950. Untersuchungen uber das aufreten von gerauschen beim zugversuch(An investigation into the occurrence of noises in tensile tests). Ph.D. thesis, Technische Hochschule, Munich
  7. Krug, D., S. Tobisch, R. Emmler and K. Froehlich. 1995. The use of certain parameters of the acoustic emission analysis for the control of wood drying processes. Holz als Roh und Werkstoff, 53(4):253-256 https://doi.org/10.1007/s001070050083
  8. Kwon, O. Y. and K. Ono. 1990. Acoustic Emission Characterization of the Deformation and Fracture of an SiC-Reinforced, Aluminum Matrix Composite. Journal of Acoustic Emission, 9(2): 123-129
  9. Lee, S. H., S. Quarles and A. Schniewind. 1995. Wood fracture, acoustic emission, and the drying process. Part 2. Acoustic emission associated with fracture. Wood Science and Technology, 30:283-292
  10. Noguchi, M., Y. Kagawa and J. Katagiri. 1980a. Detection of acoustic emissions during hardwood drying. Mokuzai Gakkaishi, 26(9):637-647
  11. Noguchi, M., S. Okomura and S. Kawamoto. 1980b. Characteristics of acoustic emission during wood drying. Mokuzai Gakkaishi. 31(3):171-175
  12. Ogino, S., K. Kaino and M. Suzuki. 1986. Prediction of lumber checking during drying by means of acoustic emission technique. Journal of Acoustic Emission, 5(2):61-65
  13. Ono, K. and J. Wu. 1996. Pattern Recognition Analysis of Acoustic Emission from Fatigue of 2024-T4 Aluminum. Progress in Acoustic Emission: 237-242
  14. Sharaf, M. A., D. L. Illman and B. R. Kowalski. 1986. Chemometrics, John Wiley & Sons, New York
  15. Skaar, C., W. Simpson and R. Honeycutt. 1980. Use of acoustic emissions to identify high levels of stress during oak lumber drying. Forest Products Journal, 30(2):21-22
  16. Kang, H. Y. and K. B. Kim. 2003. Analysis and classification of acoustic emission signals during wood drying using the principal component analysis, J. of the Korean Society for Nondestructive Testing 23(3):254-262.(In Korean)
  17. Kim, K. B., D. J. Yoon, J. C. Jeong and S. S. Lee. 2002. Classification of acoustic emission signals for fatigue crack opening and closure by artificial neural network based on principal component analysis, Jo of the Korean Society for Nondesturctive Testing 22(5):532-538.(In Korean)