Comparison of predicted thickness swelling of particleboard with fuzzy systems and artificial neural networks

Document Type : Research Paper

Authors

1 M.Sc., Department of Wood and Paper Science and Technology, Faculty of Natural Resources, University of Zabol

2 Associate Professor, Department of aquiferous, Faculty of Natural Resources, University of Tehran, Tehran, Iran

Abstract

Swelling percent is a very important physical property of the final product. Swelling test is timely and needed to cost. Therefore, swelling prediction of particleboard in production during can operate of the process line and consistent quality of production. In this research, variables such as moisture content of particle, the amount of adhesive, press time, press temperature, press pressure and swelling properties of particleboard were collected from Debalkhazae mill. The normalized data was analyzed by artificial neural network and fuzzy systems. Also, swelling percent was predicted by optimal model. The best model of swelling prediction is 5-5 basis of artificial neural networks, and the best function of fuzzy systems is Z-shaped curve membership function. Means absolute percent errors of the predictions are equal 5 and 22 percent, respectively. ANN method has better performance compared with fuzzy systems.

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References

[1]     Alborzi, M., 2001. Introduction to Artificial Neural Networks. Sharif University press, 137 p. (In Persian).
[2]     Menhaj, M., 1998. Introduction to Artificial Neural Networks. Hesabi press, 716 p. (In Persian).
[3]     Esteban, L.G., Fernández, F.G., de Palacios, P. and Conde, M., 2009. Artificial neural networks in variable process control: application in particleboard manufacture. Investigación Agraria: Sistemas y Recursos Forestales, 18(1):92-100.
[4]     Cook, D. and Chiu, C.C., 1997. Predicting the internal bond strength of particleboard, utilizing a radial basis function neural network. Engineering Applications of Artificial Intelligence, (10)2: 171–177.
[5]     Fernandez, F.G., Esteban, L.G., de Palacios, P., Navarro, N. and Conde, M., 2008. Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model. Investigación Agraria: Sistemas y Recursos Forestales, 17(2):178-187
[6]     Bayatkashkoli, A., 2013. Evaluation of process variables effect on the bursting strength of newsprint, printing and writing paper. Journal Indian Academy Wood Science, 10(1):55–61.
[7]     Jahanilomer, Z., FarrokhPayam, S.R. and Shamsian, M., 2014. An intelligent neural networks system for prediction of particleboard properties. Iranian Journal of Wood and Paper Science Research, 29(2): 242-253. (In Persian).
[8]     Oduguwa, V., Tiwari, A. and Roy, R., 2005. Evolutionary computing in manufacturing industry: an overview of recent applications. Applied Soft Computing Journal, 5: 281–299.
[9]     Stefansson, A., Koncar, N. and Jones, A.J., 1997. A note on the Gamma test. Neural Computing and Applications, 5: 131-133.
[10]  Monte, R.A., 1999. A random walk for dummies. Mit Undergraduate Journal of Mathematics, 1: 143- 148.
[11]  Yagar, R.R. and Zadeh, L.A., 1992. An introduction to fuzzy logic applications in intelligent systems. Boston,  Kluwer Academic Publishers, 356 p.
[12]  Jang, J.S.R. and Gulley, N., 1995. Fuzzy logic Toolbox for use with MATLAB. Natick Mall, Boston,  the math works, Inc. 208 p.
[13]  Mamdani, E.H. and Assilian. S., 1975. An experimental in linguistic synthesis with a fuzzy logic control. International Journal Man-Machine Studies, 7: 1-13.
[14] Zadeh, L. A., 1965. Fuzzy set. Information and Control. 8:338-353.
[15]  Dubois, D. and Prade, H., 1980. Fuzzy set and systems: Theory and Application. Academic press. New York, 411 p.
[16]  Malinov, S., Sha, W. and Mckeown, J.J., 2001. Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural network. Computational Materials Science Journal, 21: 375-394.
[17]  Cheng, C.S., 1995. A multi-layer neural network model for detecting changes in the process mean. Computers and Industrial Engineering, 28: 51-61.
[18]  Cook, D.F., Massey, J.G. and Shannon, R.E., 1991. A neural network to predict particleboard manufacturing process parameters. Forest Science, 5: 1463-1478.
[19]  Cook, D.F., Zobel, C.W. and Nottingham, Q.J., 2001. Utilization of neural networks for the recognition of variance shifts in correlated manufacturing process parameters. International Journal Products Resource, 39(17): 3881-3887.
[20]  Cook, D.F., Ragsdale, C.T. and Major, R.L., 2000. Combining a neural network with a genetic algorithm for process parameter optimization. Engineering Applications of Artificial Intelligence, 13(4):391–396.
[21]  Esteban, L.G., Garcia Fernandez, F., de Palacios, P. and Gonzalez Rodrigo, B., 2010. Use of Artificial Neural Networks as a predictive method to determine moisture resistance of particle and fiber boards under cyclic testing conditions (UNE-EN 321). Wood and Fiber Science, 42(3): 1-11.
[22]  Dousthosyni, K. 2001. Production technology and use of wood based panel. Tehran University press, 420 p. (In Persian).
[23] Moslemi, A.A., 1974. Particleboard, Volumes 1 and 2. Southern illinois University Press, Carbondale, illinois. 256 p.
[24] Nemli, G., Zekovic, E. and Aydin, I., 2007. Some of the parameters influencing surface roughness of particleboard. Building and Environment Journal, 40(10): 1337-1340.
[25] Nemli, G., Aydin, I. and Zekovic, E., 2007. Evaluation of some of the properties of particleboard as function of manufacturing parameters. Materials and Design, 28: 1169-1176.

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