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

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.ZusammenfassungIn 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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