Abstract
Particleboard specimens were subjected to various climatic conditions in Japan, and the relationships between climatic factors and internal bond strength (IB) were investigated using multiple regression analysis (MRA) or artificial neural networks (ANN). At low- and middle-temperature sites, the IB predicted using MRA (IBMRA) and ANN (IBANN) decreased linearly with increasing exposure time. In addition, at high-temperature sites, with increasing exposure time, IBMRA decreased linearly, whereas IBANN decreased exponentially. The trend of IBANN was almost identical to that of the measured IB of the specimens subjected to various climatic conditions. Moreover, IBMRA and IBANN for 1-, 3-, and 5-year exposures were predicted using nationwide climatic factors. The minimum IB is zero when the particleboard is deteriorated; however, negative IB was predicted using MRA when the exposure time increased in the high-temperature area. In addition, the IB for 1-year exposure in the low-temperature area near site 1 was higher than the initial IB of 0.833 MPa. MRA is not always valid because of the assumption of linearity. However, negative IB even for 5-year exposure in the high-temperature area and high IB even for 1-year exposure in the low-temperature area were not predicted using ANN. The IB reduction was predicted correctly using ANN, and the correct IB reduction could be mapped.
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André N, Cho H-W, Baek SH, Jeong M-K, Young TM (2008) Prediction of internal bond strength in a medium density fiberboard process using multivariate statistical methods and variable selection. Wood Sci Technol 42:521–534
Avramidis S, Iliadis L (2005a) Predicting wood thermal conductivity using artificial neural networks. Wood Fiber Sci 37:682–690
Avramidis S, Iliadis L (2005b) Wood-water sorption isotherm prediction with artificial neural networks: a preliminary study. Holzforschung 59:336–341
Avramidis S, Wu H (2007) Artificial neural network and mathematical modeling comparative analysis of nonisothermal diffusion of moisture in wood. Holz Roh Werkst 65:89–93
Avramidis S, Iliadis L, Mansfield SD (2006) Wood dielectric loss factor prediction with artificial neural networks. Wood Sci Technol 40:563–574
Ceylan I (2008) Determination of drying characteristics of timber by using artificial neural networks and mathematical models. Dry Technol 26:1469–1476
Cook DF, Chiu CC (1997) Predicting the internal bond strength of particleboard, utilizing a radial basis function neural network. Eng Appl Artif Intell 13:171–177
Crawley MJ (2012) Statics: an introduction using R (Japanese version). (Translator; Nomakuchi K, Kikuchi Y) Kyoritsu Shuppan, Tokyo, pp 226–227
Esteban LG, Fernández FG, de Palacios P (2009a) MOE prediction in Abies pinsapo Boiss. Timber: application of an artificial neural network using non-destructive testing. Comp Struct 87:1360–1365
Esteban LG, Fernández FG, de Palacios P, Conde M (2009b) Artificial neural networks in variable process control: application in particleboard manufacture. Invest Agrar Sist Recur For 18:92–100
Esteban LG, Fernández FG, de Palacios P, Rodrigo BG (2010) Use of artificial neural networks as a predictive method to determine moisture resistance of particle and fiber boards under cyclic testing. Wood Fiber Sci 42:335–345
Esteban LG, Fernández FG, de Palacios P (2011) Prediction of plywood bonding quality using an artificial neural network. Holzforschung 65:209–214
Fahlman SE, Lebiere C (1990) The cascade-correlation learning architecture. Technical Report CMU-CS-90-100. School of Computer Science, Carnegie Mellon University, Pittsburgh
Fernández FG, Esteban LG, de Palacios P, Navarro N, Conde M (2008) Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model. Invest Agrar Sist Recur For 17:178–187
Fernández FG, de Palacios P, Esteban LG, Garcia-Iruela A, Rodrigo BG, Menasalvas E (2012) Prediction of MOR and MOE of structural plywood board using an artificial neural network and comparison with a multivariate regression model. Comps B 43:3528–3533
Gatchell CJ, Heebink BG, Hefty FV (1966) Influence of component variables on properties of particleboard for exterior use. Forest Prod J 16(4):46–59
Hann RA, Black JM, Blomquist RF (1962) How durable is particleboard? Forest Prod J 12:577–584
Hasegawa M (1996) Climate indices for wood preservation (in Japanese). Wood preservation. 22:246–253
Japan Meteorological Agency (2014) http://www.jma.go.jp/jma/index.html. Accessed April 1, 2014
Japan Meteorological Business Support Center (2002) Mesh climatic data 2000. Japan Meteorological Agency (in Japanese, CD-ROM)
JIS (2003) JIS standard specification for particleboard. JIS A 5908. Japanese Industrial Standards, Japanese Standards Association, Tokyo
Kojima Y, Suzuki S (2011a) Evaluating the durability of wood-based panels using internal bonding strength results from accelerated aging treatments. J Wood Sci 57:7–13
Kojima Y, Suzuki S (2011b) Evaluating the durability of wood-based panels using bending properties after accelerated aging treatments. J Wood Sci 57:126–133
Kojima Y, Norita H, Suzuki S (2009) Evaluating the durability of wood-based panels using thickness swelling results from accelerated aging treatments. Forest Prod J 59(5):35–41
Kojima Y, Shimoda T, Suzuki S (2012) Modified method for evaluating weathering intensity using outdoor exposure tests on wood-based panels. J Wood Sci 58:525–531
Korai H, Watanabe K (2015a) Effectiveness of principal component analysis for analyzing particleboard subjected to outdoor exposure. J Wood Sci 61:35–39
Korai H, Watanabe K (2015b) Comparison between climatic factors and climate deterioration index on strength reduction of particleboards subjected to various climatic conditions in Japan. Eur J Wood Prod 73(5):563–571
Korai H, Sekino N, Saotome H (2012) Effects of outdoor exposure angle on the deterioration of wood-based board properties. Forest Prod J 62:184–190
Korai H, Adachi K, Saotome H (2013) Deterioration of wood-based boards subjected to outdoor exposure in Tsukuba. J Wood Sci 59:24–34
Korai H, Saotome H, Ohmi M (2014) Effects of water soaking and outdoor exposure on modulus of rupture and internal bond strength of particleboard. J Wood Sci 60:127–133
Korai H, Watanabe K, Nakao K, Matsui T, Hayashi T (2015) Mapping of strength reduction of particleboard subjected to various climatic conditions using a climate deterioration index. Eur J Wood Prod. doi:10.1007/s00107-015-0952-7
Mansfield SD, Iliadis L, Avramidis S (2007) Neural network prediction of bending strength and stiffness in western hemlock (Tsuga heterophylla Raf.). Holzforschung 61:707–716
Mansfield SD, Kang K-Y, Iliadis L, Tachos S, Avramidis S (2011) Predicting the strength of Populus spp. clones using artificial neural networks and e-regression support vector machines (e-rSVM). Holzforschung 65:855–863
NeuralWare (2009) NeuralWorks Predict® User Guide. The complete solution for neural data modeling. NeuralWare, Pittsburgh
Ozsahin S (2013) Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis. Eur J Wood Prod 71:769–777
Özsahin Ş (2012) The use of an artificial neural network for modeling the moisture absorption and thickness swelling of oriented strand board. BioResources 7:1053–1067
River BH (1994) Outdoor aging of wood-based panels and correlation with laboratory aging. Forest Prod J 44(11/12):55–65
Samarasinghe S, Kulasiri D, Jamieson T (2007) Neural networks for predicting fracture toughness of individual wood samples. Silva Fennica 41:105–122
Sekino N, Sato H, Adachi K (2014) Evaluation of particleboard deterioration under outdoor exposure using several different types of weathering intensity. J Wood Sci 60:141–151
Suchsland (1973) Hygroscopic thickness swelling and related properties of selected commercial particleboards. Forest Prod J 22(7):26–30
Sukthomya W, Tannock A (2005) The training of neural networks to model manufacturing processes. J Intell Manuf 16:39–51
Suzuki S (2001) Evaluation of wood-based panel durability (in Japanese). Wood Indust 56:7–12
Watanabe K, Matsushita Y, Kobayashi I, Kuroda N (2013) Artificial neural network modeling for predicting final moisture content of individual Sugi (Cryptomeria japonica) samples during air-drying. J Wood Sci 59:112–118
Watanabe K, Kobayashi I, Matsushita Y, Saito S, Kuroda N, Noshiro S (2014) Application of near-infrared spectroscopy for evaluation of drying stress on lumber surface: a comparison of artificial neural networks and partial least-squares regression. Dry Technol 32:590–596
Watanabe K, Korai H, Matsumoto Y, Hayashi T (2015) Predicting internal bond strength of particleboard under outdoor exposure based on climate data. Comparison of multiple liner regression and artificial neural network. J Wood Sci 61(2):151–158
Wu H, Avramidis S (2006) Prediction of timber kiln drying rates by neural networks. Dry Technol 24:1541–1545
Acknowledgments
This study was supported by a Grant-in-Aid for Scientific Research (21380108) from the Ministry of Education, Culture, Sports, Science and Technology of Japan. The authors are grateful for the Grant received. The outdoor exposure test was conducted as part of a project organized by the Research Working Group on Wood-based Panels from the Japan Wood Research Society. The authors express their thanks to all participants of this project.
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Korai, H., Watanabe, K. Predicting the strength reduction of particleboard subjected to various climatic conditions in Japan using artificial neural networks. Eur. J. Wood Prod. 75, 385–396 (2017). https://doi.org/10.1007/s00107-016-1056-8
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DOI: https://doi.org/10.1007/s00107-016-1056-8