Prediction of Adhesion Strength of Some Varnishes Using Soft Computing Models

The purpose of this study was to predict the adhesion strength of the varnish, which is applied as a protective coating/finish on the surface of wooden material using soft computing models. In this study, the soft computing approaches were applied to oak (Quercus Petrea L.), chestnut (Castanea sativa M.), and scotch pine (Pinus sylvestris L.) with water-based, polyurethane, and acrylic varnishes. The adhesion strength of the varnish was determined in accordance with the Turkish Standard Institute-24624 and ASTM D4541. The outcome of the experiment was used to develop artificial neural network (ANN) and fuzzy logic (FL) prediction models. The total number of 360 data points was split as 80 % of training and 20 % of test for the model development. During the application of the ANN, 6 features were used as an input, while the adhesion strength was used as an output of the model. The coefficient of determination values (R2) for training and testing in the ANN models were 0.9939 and 0.9580, respectively. In the case of the ANFIS model, R2 values for training and testing were 0.9917 and 0.9929, respectively. Considering the MAPE, RMSE, and R2 values obtained from the results of both training and test values, it can be concluded that the ANFIS model showed a more successful performance in estimating varnish adhesion strength. Therefore, ANN and ANFIS have the potential to provide time and cost-efficient benefits in estimating wood adhesion strength. Cilj ovog istraživanja bio je uz pomoć modela mekog računalstva predvidjeti adhezivnu čvrstoću laka koji se nanosi kao zaštitni premaz na površinu drvnog materijala. Pristup mekog računalstva primijenjen je na uzorcima hrastovine (Quercus Petrea L.), kestenovine (Castanea sativa M.) i borovine (Pinus sylvestris L.) lakiranima vodenim poliuretanskim i vodenim akrilnim lakom. Adhezivna čvrstoća laka određena je prema normama TS EN 24624 i ASTM D4541. Rezultati istraživanja iskorišteni su za razvoj modela predviđanja umjetne neuronske mreže (ANN) i neizrazite logike (FL). Od ukupno 360 podatkovnih točaka razvoja modela 80 % njih upotrijebljeno je za trening, a 20 % za testiranje. Tijekom primjene ANN-a šest je svojstava poslužilo kao ulazna varijabla, dok je adhezivna čvrstoća primijenjena kao izlazna varijabla modela. Vrijednosti koeficijenta determinacije (R2) za trening i testiranje u ANN modelima bile su 0,9939 i 0,9580. Pri primjeni ANFIS modela R2 vrijednosti za trening i testiranje iznosile su 0,9917 i 0,9929. Uzi