Experimental investigation and prediction of bonding strength of Oriental beech (Fagus orientalisLipsky) bonded with polyvinyl acetate adhesive

Using artificial neural networks for modeling surface roughness of wood in machining process

(2014) Sebahattin Tiryaki et al.   CONSTRUCTION AND BUILDING MATERIALS

Determination of the percentage of wood failure by 3D scanning of the adhesive bondline

(2014) Mirko Kariz et al.   European Journal of Wood and Wood Products

Comparison of artificial neural network and multiple linear regression models to predict optimum bonding strength of heat treated woods

(2014) Sebahattin Tiryaki et al.   INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES

Predicting internal bond strength of particleboard under outdoor exposure based on climate data: comparison of multiple linear regression and artificial neural network

(2014) Ken Watanabe et al.   JOURNAL OF WOOD SCIENCE

Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model

(2014) F. García Fernández et al.   Investigación agraria. Sistemas y recursos forestales.

Artificial neural network modeling to effect of reinforcement properties on the physical and mechanical properties of Al2024–B4C composites produced by powder metallurgy

(2013) Temel Varol et al.   COMPOSITES PART B-ENGINEERING

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

(2013) Sukru Ozsahin   European Journal of Wood and Wood Products

Optimization of some panel manufacturing parameters for the best bonding strength of plywood

(2013) Cenk Demirkir et al.   INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES

Mechanical properties of adhesives for bonding wood—A review

(2013) Frank Stoeckel et al.   INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES

Measurement of bonding strength of pine, kapur and meranti wood species as function of their surface quality

(2013) Salim Hiziroglu et al.   MEASUREMENT

Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks

(2013) Sebahattin Tiryaki et al.   MEASUREMENT

Application of artificial neural networks for the prediction of roll force and roll torque in hot strip rolling process

(2012) Mahdi Bagheripoor et al.   APPLIED MATHEMATICAL MODELLING

Effect of pressing parameters on the shear strength of beech specimens bonded with low solvent liquefied wood

(2012) Aleš Ugovšek et al.   JOURNAL OF ADHESION SCIENCE AND TECHNOLOGY

Prediction of MOR and MOE of structural plywood board using an artificial neural network and comparison with a multivariate regression model

(2011) Francisco García Fernández et al.   COMPOSITES PART B-ENGINEERING

Adhesive penetration in beech wood: experiments

(2011) Philipp Hass et al.   WOOD SCIENCE AND TECHNOLOGY

Influence of wood properties and bonding parameters on bond durability of European Beech (Fagus sylvatica L.) glulams

(2010) Denny Ohnesorge et al.   ANNALS OF FOREST SCIENCE

Prediction of plywood bonding quality using an artificial neural network

(2010) Luis García Esteban et al.   HOLZFORSCHUNG

MOE prediction in Abies pinsapo Boiss. timber: Application of an artificial neural network using non-destructive testing

(2009) Luis García Esteban et al.   COMPUTERS & STRUCTURES

Evolutionary Artificial Neural Network Design and Training for wood veneer classification

(2009) Marco Castellani et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Prediction of density, porosity and hardness in aluminum–copper-based composite materials using artificial neural network

(2008) Adel Mahamood Hassan et al.   JOURNAL OF MATERIALS PROCESSING TECHNOLOGY

Effects of machining method and grain orientation on the bonding strength of some wood species

(2007) Ayhan Özçifçi et al.   JOURNAL OF MATERIALS PROCESSING TECHNOLOGY