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

Abstract

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 k-value=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.

Keywords

• Adaptive artificial neural network (AANN); Bonding Strength; Intuitive k-nearest neighbor estimator (IKE); Plywood

Kontrplaklarda Yapışma Direnci Modellenmesinde Meta-Buluşsal Yapay Sinir Ağları Tekniklerinin Kullanılması

Abstract

En önemli ahşap kökenli levha ürünlerinden biri olan kontrplak trafik levhalarından inşaata kadar pek çok kullanım yerine sahiptir. Yüksek kalitede kontrplak üretimi için tutkal türüne bağlı olarak optimum pres koşulları altında iyi bir yapışmanın sağlanması gerektiği bilinen bir gerçektir. Bu çalışmada kontrplağın yapışma direncinin tahmin edilmesi için modern meta buluşsal tekniklerden IKE ve AANN metotlarının kullanım imkanları araştırılmıştır. Çalışma deneysel ve analitik olarak iki kısımdan oluşmaktadır. Çalışmada ağaç türü olarak sarıçam, sahil çamı ve karaçam kullanılmıştır. Kaplamalar 2 farklı sıcaklıkta (32°C ve 50°C) soyulmuş ve 3 farklı sıcaklıkta (110°C, 140°C ve 160°C) kurutulmuştur. Kontrplak üretimi için fenol formaldehit ve melamin üre formaldehit tutkalları olmak üzere iki farklı tutkal türü kullanılmıştır. Deneysel olarak kontrplakların yapışma direnci değerleri EN 314-1 standardına göre yapılmıştır. IKE ve AANN teknikleri analitik olarak yapışma direnci tahmininde kullanılmışlardır. En iyi tahmin performansı k değeri 10 için elde edilmiştir. Yapışma direnci üzerine en etkili faktör olarak tutkal türü belirlenmiştir. IKE ve AANN için belirlenen hata oranları %5’in altında bulunmuştur. Çalışma neticesinde uygulanan tekniklerin kontrpalklarda yapışma direnci tahmininde kullanılabilir oldukları tespit edilmiştir.

Keywords

• Adaptif yapay sinir ağı (AANN), Yapışma Direnci, Sezgisel k en yakın komşu tahmincisi (IKE), Kontrplak

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