Application of Meta-Heuristic Hybrid Artificial Intelligence Techniques for Modeling of Bonding Strength of Plywood Panels

Plywood, which is one of the most important wood based panels, has many usage areas changing from traffic signs to building constructions in many countries. It is known that the high quality plywood panel manufacturing has been achieved with a good bonding under the optimum pressure conditions depending on adhesive type. This is a study of determining the using possibilities of modern meta-heuristic hybrid artificial intelligence techniques such as IKE and AANN methods for prediction of bonding strength of plywood panels. This study has composed of two main parts as experimental and analytical. Scots pine, maritime pine and European black pine logs were used as wood species. The pine veneers peeled at 32°C and 50°C were dried at 110°C, 140°C and 160°C temperatures. Phenol formaldehyde and melamine urea formaldehyde resins were used as adhesive types. EN 314-1 standard was used to determine the bonding shear strength values of plywood panels in experimental part of this study. Then the intuitive k-nearest neighbor estimator (IKE) and adaptive artificial neural network (AANN) were used to estimate bonding strength of plywood panels. The best estimation performance was obtained from MA metric for kvalue=10. The most effective factor on bonding strength was determined as adhesive type. Error rates were determined less than 5% for both of the IKE and AANN. It may be recommended that proposed methods could be used in applying to estimation of bonding strength values of plywood panels.

[1]  Seref Sagiroglu,et al.  Application of Adaptive Artificial Neural Network Method to Model the Excitation Currents of Synchronous Motors , 2012, 2012 11th International Conference on Machine Learning and Applications.

[2]  Rickey Dubay,et al.  Nonlinear inversion-based control with adaptive neural network compensation for uncertain MIMO systems , 2012, Expert Syst. Appl..

[3]  I. Aydin,et al.  Formaldehyde Emission, Surface Roughness, and Some Properties of Plywood as Function of Veneer Drying Temperature , 2005 .

[4]  Ernestina Menasalvas,et al.  Prediction of MOR and MOE of structural plywood board using an artificial neural network and comparison with a multivariate regression model , 2012 .

[5]  I. Aydin,et al.  Technological and economic comparison of the usage of beech and alder wood in plywood and laminated veneer lumber manufacturing , 2006 .

[6]  Sundaram Suresh,et al.  Parkinson's disease prediction using gene expression - A projection based learning meta-cognitive neural classifier approach , 2013, Expert Syst. Appl..

[7]  Ramesh Sharda,et al.  Prediction of athletes performance using neural networks: An application in cricket team selection , 2009, Expert Syst. Appl..

[8]  Yong Lu,et al.  A robust stochastic genetic algorithm (StGA) for global numerical optimization , 2004, IEEE Transactions on Evolutionary Computation.

[9]  C. Vick.  Adhesive Bonding of Wood Materials , 1999 .

[10]  S. Hiziroglu,et al.  Surface characteristics of spruce veneers and shear strength of plywood as a function of log temperature in peeling process , 2006 .

[11]  W. Mcnamara,et al.  Comparison of the rate of glue-line strength development for Oak and Maple. , 1970 .

[12]  Seref Sagiroglu,et al.  A new approach to predict the excitation current and parameter weightings of synchronous machines based on genetic algorithm-based k-NN estimator , 2012 .

[13]  Luis García Esteban,et al.  Prediction of plywood bonding quality using an artificial neural network , 2011 .

[14]  S. Chow,et al.  Adhesion strength and wood failure relationship in wood-glue bonds , 1979 .

[15]  A. W. Christiansen.  HOW OVERDRYING WOOD REDUCES ITS BONDING TO PHENOL-FORMALDEHYDE ADHESIVES : A CRITICAL REVIEW OF THE LITERATURE . PART I . PHYSICAL RESPONSES , 2006 .

[16]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[17]  Atakan Aksoy,et al.  Application of the intuitive k-NN Estimator for prediction of the Marshall Test (ASTM D1559) results for asphalt mixtures , 2012 .

[18]  C. Dai,et al.  Theoretical Modeling of Bonding Characteristics and Performance of Wood Composites. Part III. Bonding Strength Between Two Wood Elements , 2007 .