Utilização de imagens para a determinação automatizada da qualidade de colagem em painéis compensados

Image utilization to plywood panels automated bonding quality analysis The industrial use of fast-growing forest species can present characteristics that are very different from those seen in conventional materials, and their use requires adjustments to industrial processes to meet the quality standards required by the consumer market. In this context, the Brazilian plywood industry shows difficulty in participating in the most demanding markets for not meeting the quality standards of gluing and this is due to the high subjectivity of the activity and the lack of referential standards that effectively assist in the evaluation of products. Therefore, this study aimed to develop an automated methodology for visual assessment of the quality of bonding of plywood panels that has an accuracy equal to or greater than manual classification. In the present work, plywood panels with five veneers were produced to verify the influence of factors: species (Pinus kesiya and a hybrid of Eucalyptus grandis X Eucalyptus urophylla) and grammage (400 and 350 g/m2) of the phenolformaldehyde (FF) and urea-formaldehyde (UF) adhesives on the panels quality. The mechanical properties of static bending in the perpendicular and parallel directions were evaluated, obtaining the modulus of rupture (MOR) and elasticity (MOE). The bonding quality was determined by the glue-line shear test, the values of tension (Tmax), and the percentage of wood failure (WF%) were obtained. The experiment that used FF verified that for the MOR there was no statistical difference between the evaluated panels regardless of the test direction, while for the MOE the superior results were observed for treatments composed by the hybrid of Eucalyptus in the two evaluated directions. However, for bonding quality, treatments with the species P. kesiya had higher Tmax and WF%. For the UF panels, the treatments that used the Eucalyptus species showed better performance in most evaluations. For static bending, the grammage did not have a significant influence, and only in perpendicular MOR, there was no statistical difference between treatments. For the bonding quality in the dry condition, no statistical difference was observed between the treatments for Tmax, however, in the wet condition, the Eucalyptus treatments were superior. For WF% in both test conditions, the best results were observed in Eucalyptus panels and the worst result was verified in the treatment of P. kesiya with the grammage 350. The WF% information of the produced panels was used to create an image bank of the glue-line shear specimens (n= 4700), for the training and evaluation of five models of convolutional neural networks (CNN) to correctly identify the WF% of the samples. Initially, knowledge transfer was used in 25% of the image bank to develop models for the five WF% classes with a 20% amplitude (C1= 0-20; C2= 21-40; C3= 41–60; C4= 61–80; C5 = 80–100 of WF%). The produced models considered the production factors: two models for adhesives (FF and UF), two models for species (Pinus and Eucalyptus), and a Generalist model. All models used 75% of the remaining images for training and 25% for model validation, and the results obtained demonstrated the good classification performance of the models. The highest accuracy was verified for the FF model (94.96%) followed by Generalist (94.36%), Pinus (92.46%), Eucalyptus (91.84%), and UF (60.90%). The models had greater difficulty in classifying images belonging to the intermediate classes (C2, C3, and C4), while the extreme classes (C1 and C5) had lower error rates during training and validation, except for the UF model,