Skip to main content

Advertisement

Log in

Predicting the strength reduction of particleboard subjected to various climatic conditions in Japan using artificial neural networks

  • Original
  • Published:
European Journal of Wood and Wood Products Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • 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

    Article  Google Scholar 

  • Avramidis S, Iliadis L (2005a) Predicting wood thermal conductivity using artificial neural networks. Wood Fiber Sci 37:682–690

    CAS  Google Scholar 

  • Avramidis S, Iliadis L (2005b) Wood-water sorption isotherm prediction with artificial neural networks: a preliminary study. Holzforschung 59:336–341

    CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Avramidis S, Iliadis L, Mansfield SD (2006) Wood dielectric loss factor prediction with artificial neural networks. Wood Sci Technol 40:563–574

    Article  CAS  Google Scholar 

  • Ceylan I (2008) Determination of drying characteristics of timber by using artificial neural networks and mathematical models. Dry Technol 26:1469–1476

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    CAS  Google Scholar 

  • Esteban LG, Fernández FG, de Palacios P (2011) Prediction of plywood bonding quality using an artificial neural network. Holzforschung 65:209–214

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    CAS  Google Scholar 

  • Hann RA, Black JM, Blomquist RF (1962) How durable is particleboard? Forest Prod J 12:577–584

    Google Scholar 

  • Hasegawa M (1996) Climate indices for wood preservation (in Japanese). Wood preservation. 22:246–253

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Korai H, Watanabe K (2015a) Effectiveness of principal component analysis for analyzing particleboard subjected to outdoor exposure. J Wood Sci 61:35–39

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Korai H, Adachi K, Saotome H (2013) Deterioration of wood-based boards subjected to outdoor exposure in Tsukuba. J Wood Sci 59:24–34

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • NeuralWare (2009) NeuralWorks Predict® User Guide. The complete solution for neural data modeling. NeuralWare, Pittsburgh

    Google Scholar 

  • Ozsahin S (2013) Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis. Eur J Wood Prod 71:769–777

    Article  Google Scholar 

  • 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

    Google Scholar 

  • River BH (1994) Outdoor aging of wood-based panels and correlation with laboratory aging. Forest Prod J 44(11/12):55–65

    Google Scholar 

  • Samarasinghe S, Kulasiri D, Jamieson T (2007) Neural networks for predicting fracture toughness of individual wood samples. Silva Fennica 41:105–122

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Suchsland (1973) Hygroscopic thickness swelling and related properties of selected commercial particleboards. Forest Prod J 22(7):26–30

    Google Scholar 

  • Sukthomya W, Tannock A (2005) The training of neural networks to model manufacturing processes. J Intell Manuf 16:39–51

    Article  Google Scholar 

  • Suzuki S (2001) Evaluation of wood-based panel durability (in Japanese). Wood Indust 56:7–12

    Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hideaki Korai.

Ethics declarations

Ethical statement

Our manuscript complies with the Ethical Rules applicable for this journal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00107-016-1056-8

Keywords

Navigation