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Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis

Optimierung von Prozessparametern bei der OSB-Herstellung mittels künstlicher neuronaler Netzwerke

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Abstract

In the present work, an artificial neural network (ANN) model was developed for predicting the effects of some production factors such as adhesive ratio, press pressure and time, and wood density and moisture content on some physical properties of oriented strand board (OSB) such as moisture absorption, thickness swelling and thermal conductivity. The MATLAB Neural Network Toolbox was used for the training and optimization of the artificial neural network. The ANN model having the best prediction performance was determined by means of statistical and graphical comparisons. The results show that the prediction model is a useful, reliable and quite effective tool for predicting some physical properties of the OSB produced under different manufacturing conditions. Thus, this study has presented a novel and alternative approach to the literature to optimize process parameters in OSB manufacturing process.

Zusammenfassung

In dieser Studie wurde ein künstliches neuronales Netz (ANN) entwickelt, um den Einfluss einiger Produktionsfaktoren, wie zum Beispiel Klebstoffmenge, Pressdruck, Pressdauer, Holzdichte und Holzfeuchte, auf die physikalischen Eigenschaften von OSB, wie Wasseraufnahme, Dickenquellung und Wärmeleitfähigkeit zu ermitteln. Für die Trainingsphase und Optimierung des künstlichen neuronalen Netzes wurde die MATLAB Neural Network Toolbox verwendet. Anhand statistischer und graphischer Vergleiche wurde das ANN Modell mit der besten Vorhersageleistung bestimmt. Die Ergebnisse zeigen, dass dieses Modell ein nützliches, zuverlässiges und effektives Werkzeug zur Vorhersage verschiedener physikalischer Eigenschaften von unter verschiedenen Bedingungen hergestelltem OSB ist. Somit wird in dieser Studie ein neuer und alternativer Ansatz für die Optimierung von Prozessparametern bei der OSB-Herstellung vorgestellt.

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References

  • ASTM C 1113–99 (2004) Standard test method for thermal conductivity of refractories by hot wire (platinum resistance thermometer technique). ASTM, USA

    Google Scholar 

  • Avramidis S, Iliadis L (2005a) Predicting wood thermal conductivity using artificial neural networks. Wood Fiber Sci 37(4):682–690

    CAS  Google Scholar 

  • Avramidis S, Iliadis L (2005b) Wood–water sorption isotherm prediction with artificial neural networks: a preliminary study. Holzforschung 59(3):336–341

    Article  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 

  • Canakci A, Ozsahin S, Varol T et al (2012) Modeling the influence of a process control agent on the properties of metal matrix composite powders using artificial neural networks. Powder Technol 228:26–35

    Article  CAS  Google Scholar 

  • Castellani M, Rowlands H (2008) Evolutionary feature selection applied to artificial neural networks for wood veneer classification. Int J Prod Res 46(11):3085–3105

    Article  Google Scholar 

  • Ceylan I (2008) Determination of drying characteristics of timber by using artificial neural networks and mathematical models. Drying Technol 26(12):1469–1476

    Article  CAS  Google Scholar 

  • Choudhury TA, Hosseinzadeh N, Berndt CC (2012) Improving the generalization ability of an artificial neural network in predicting in-flight particle characteristics of an atmospheric plasma spray process. J Therm Spray Technol 21(5):935–949

    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 10(2):171–177

    Article  Google Scholar 

  • Cook DF, Whittaker AD (1993) Neural network process modeling of a continuous manufacturing operation. Eng Appl Artif Intell 6:559–564

    Article  Google Scholar 

  • Cook DF, Massey JG, Shannon RE (1991) A neural network to predict particleboard manufacturing process parameters. Forest Sci 37(5):1463–1478

    Google Scholar 

  • Cook DF, Ragsdale CT, Major RL (2000) Combining a neural network with a genetic algorithm for process parameter optimization. Eng Appl Artif Intell 13:391–396

    Article  Google Scholar 

  • Drake PR, Packianather MS (1998) A decision tree of neural networks for classifying images of wood veneer. Int Adv Manuf Technol 14:280–285

    Article  Google Scholar 

  • 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. Comput 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(1):92–100

    Google Scholar 

  • Esteban LG, Fernández FG, de Palacios P, Romero RM, Cano NN (2009c) Artificial neural networks in wood identification: the case of two juniperus species from The Canary Islands. IAWA J 30(1):87–94

    Article  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 

  • 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(2):178–187

    Google Scholar 

  • Fernández FG, de Palacios P, Esteban LG, Garcia–Iruela A, Rodrigo BG, Menasalvas E et al (2012) Prediction of MOR and MOE of structural plywood board using an artificial neural network and comparison with a multivariate regression model. Compos B 43:3528–3533

    Article  Google Scholar 

  • Khalid M, Lee ELY, Yusof R, Nadaraj M (2008) Design of an intelligent wood species recognition system. Int J Simul: Syst Sci Technol 9(3):9–19

    Google Scholar 

  • Kurdthongmee W (2008) Colour classification of rubberwood boards for fingerjoint manufacturing using a SOM neural network and image processing. Comput Electron Agric 64:85–92

    Article  Google Scholar 

  • Long W, Rice RW (2008) Detection of structural damage in medium density fiberboard panels using neural network method. J Compos Mater 42:1133–1145

    Article  Google Scholar 

  • Ma X, Zeng W, Tian F, Sun Y, Zhou Y (2012) Modeling constitutive relationship of BT25 titanium alloy during hot deformation by artificial neural network. J Mater Eng Perform 21(8):1591–1597

    Article  CAS  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(6):707–716

    Article  CAS  Google Scholar 

  • Moya L, Tze WTZ, Winandy JE et al (2009) The effect of cyclic relative humidity changes on moisture content and thickness swelling behavior of oriented strand board. Wood Fiber Sci 41(4):447–460

    CAS  Google Scholar 

  • Nordmark U (2002) Knot identification from CT images of young Pinus sylvestris sawlogs using artificial neural networks. Scand J Forest Res 17:72–78

    Article  Google Scholar 

  • Ozsahin S (2012) The use of an artificial neural network for modeling the moisture absorption and thickness swelling of oriented strand board. BioResources 7(1):1053–1067

    CAS  Google Scholar 

  • Packianather MS (1997) Design and optimization of neural network classifiers for automatic visual inspection of wood veneer. Ph. D. Thesis, University of Wales

  • Packianather MS, Drake PR (2000) Neural networks for classifying images of wood veneer. Part 2: Int. J Adv Manuf Technol 16:424–433

    Article  Google Scholar 

  • Packianather MS, Drake PR (2004) Modelling neural network performance through response surface methodology for classifying wood veneer defects. Proc Inst Mech Eng Part B: J Eng Manuf 218(4):459–466

    Article  Google Scholar 

  • Packianather MS, Drake PR (2005) Comparison of neural and minimum distance classifiers in wood veneer defect identification. Proc Inst Mech Eng Part B: J Eng Manuf 219(11):831–841

    Article  Google Scholar 

  • Packianather MS, Drake PR, Pham DT (2008) Feature selection method for neural network for the classification of wood veneer defects. In: Proceedings of the World Automation Congress, Waikoloa, September 28–October 2 (1–3):790–795

  • Pham DT, Sagiroglu S (2000) Neural network classification of defects in veneer boards. Proc Inst Mech Eng Part B: J Eng Manuf 214(3):255–258

    Article  Google Scholar 

  • Rojas G, Ortiz O (2010) Identification of knotty core in pinus radiata logs from computed tomography images using artificial neural network. Maderas, Ciencia y Tecnologia 12(3):229–239

    Google Scholar 

  • Samarasinghe S, Kularisi D, Jamieson T (2007) Neural networks for predicting fracture toughness of individual wood samples. Silva Fennica 41(1):105–122

    Google Scholar 

  • Thoemen H, Irle M, Sernek M (2010) Wood–Based Panels: An Introduction for Specialists. Brunel University Press, London

    Google Scholar 

  • Tou JY, Lau PY, Tay YH (2007) Computer vision–based wood recognition system. In: Proceedings of International Workshop on Advanced Image Technology (IWAIT). Bangkok, Thailand, p 197–202

  • TS 642/ISO 554 (1997) Standart atmospheres and/or testing; Specifications

  • TS–EN 322 (1999) Wood–Based panels, determination of moisture content. TSE, Ankara

    Google Scholar 

  • TS–EN 323 (1999) Wood–Based panels, determination of density. TSE, Ankara

    Google Scholar 

  • Wu Q (1999) In–plane dimensional stability of oriented strand panel: effect of processing variables. Wood Fiber Sci 31(1):28–40

    CAS  Google Scholar 

  • Wu H, Avramidis S (2006) Prediction of timber kiln drying rates by neural networks. Drying Technol 24(12):1541–1545

    Article  CAS  Google Scholar 

  • Wu Q, Piao C (1999) Thickness swelling and its relationship to internal bond strength loss of commercial oriented strand board. Forest Prod J 49(7/8):50–55

    Google Scholar 

  • Xu X, Yu ZT, Hu YC, Fan LW, Tian T, Cen KF (2007) Nonlinear fitting calculation of wood thermal conductivity using neural Networks. Zhejiang University Press 41(7):1201–1204

    Google Scholar 

  • Cook DF, Whittaker AD (1992) Neural network models for prediction of process parameters in wood products manufacturing. In: proceeding of first Industrial Engineering Research Conference. Chicago, 20–21 May, p. 209–211

  • Yapici F (2008) The Effect of Some Production Factors on The Properties of OSB Made from Scotch Pine (Pinus sylvestris L.) Wood. Ph. D. Thesis, Zonguldak Karaelmas University

  • Yildirim I, Ozsahin S, Akyuz KC (2011) Prediction of the financial return of the paper sector with artificial neural networks. BioResources 6(4):4076–4091

    CAS  Google Scholar 

  • Zhang D, Sun L, Cao J (2006a) Modeling of temperature–humidity for wood drying based on time–delay neural network. J For Res 17(2):141–144

    Article  Google Scholar 

  • Zhang J, Cao J, Zhang D (2006b) ANN–based data fusion for lumber moisture content sensors. Trans Inst Meas Control 28(1):69–79

    Article  CAS  Google Scholar 

  • Zhu XD, Cao J, Wang FH, Sun JP, Liu Y (2009) Wood defect identification based on artificial neural network. Comput Intell Intell Syst 51:207–214

    Article  Google Scholar 

Download references

Acknowledgments

The author is thankful to Dr. Fatih Yapici, Department of Furniture and Decoration, Technical Education Faculty, Karabuk University, Karabuk, Turkey, for providing the database used in the paper and for many fruitful discussions.

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Correspondence to Sukru Ozsahin.

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Ozsahin, S. Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis. Eur. J. Wood Prod. 71, 769–777 (2013). https://doi.org/10.1007/s00107-013-0737-9

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  • DOI: https://doi.org/10.1007/s00107-013-0737-9

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