Optimization of Wood Particleboard Drilling Operating Parameters by Means of the Artificial Neural Network Modeling Technique and Response Surface Methodology

Abstract

Drilling is one of the oldest and most important methods of processing wood and wood-based materials. Knowing the optimum value of factors that affect the drilling process could lead both to high-quality furniture and low-energy consumption during the manufacturing process. In this work, the artificial neural network modeling technique and response surface methodology were employed to reveal the optimum value of selected factors, namely, drill tip angle, tooth bite, and drill type of the delamination factor at the inlet and outlet, thrust force, and drilling torque. The data set that was used in this work to develop and validate the ANN models was collected from the literature. The results showed that the developed ANN models could reasonably predict the analyzed responses. By using these models and the response surface methodology, the optimum values of analyzed factors were revealed. Moreover, the influences of selected factors on the drilling process of wood particleboards were analyzed.

Combining Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Operating Parameters of MDF Panels

“…Based on the obtained performance indicators during the training, testing, and validation phase, it could be concluded that the developed ANN models are able to predict adequately the dependent variables. Compare these results with the results presented in the literature [14], in which the same methodology was applied in the case of prelaminated particleboards drilling, one could observe that the overall performance of the developed artificial neural networks for the MDF boards is slightly better than the performance of the ANN models, which were designed for particleboards (Table 6). This could be due to the fact that the MFD have more of a homogenous structure than the prelaminated wood particleboards.…”

“…By comparing the factor coefficients of Equation ( 7), one could observe that drill type (X 3 ) has a bigger influence than the drill tip angle (X 1 ) and tooth bite (X 2 ). This result is contrary to the results obtained in the case of prelaminated particleboards, where the most important factor that affects the delamination factor at inlet (Y 1 ) is the tooth bite [14]. This result is correlated with data reported in the literature; namely, the feed rate and drill tip angle play an important role on the value of delamination factor [2,6,8,9,12].…”

“…The most important interaction is between drill tip angle and tooth bite (X 1 X 2 ). By comparing the results regarding the most important factor that affects the delamination factor at outlet during drilling of MDF boards, one could observe that the obtained result is the same as that obtained in the case of prelaminated wood particleboards [14]. The ANOVA results (α = 0.05) are presented in Table 8.…”

“…Among other machine learning models, namely, fuzzy, neuro-fuzzy, and support vector machine (SVM), the ANN is applied in wood machining to model various processes. Bedelean et al [14] developed an ANN model that is able to predict the delamination factor, thrust force, and drilling torque during the drilling of prelaminated wood particleboards based on drill type, drill tip angle, and tooth bite. Szwajka et al [15] applied ANN to predict the tool wear during milling of wood.…”

Improved Drill State Recognition during Milling Process Using Artificial Intelligence

“…Various artificial intelligence solutions are widely used in the wood industry in general. Monitoring operational performance [ 11 ], optimizing drilling parameters [ 12 ] and AI algorithms perform well for various complex problems. The applications of such algorithms are also growing increasingly common in the wood industry in general, especially when it comes to problems encountered during the drilling process [ 13 ].…”