Predicting the Impacts of Various Factors on Failure Load of Screw Joints for Particleboard Using Artificial Neural Networks

Selahattin Bardak


Innovations in the furniture industry have an important place in the global competitive environment. The use of mechanical joining techniques is rapidly increasing in the furniture industry. One of the most common mechanical joining techniques is screwing. This study investigated the impacts of screw diameter, screw length, and the distance between the screws on the failure load of screw joints in particleboard. Additionally, a model was developed on an artificial neural network model (ANN), based on experimental data, to predict the failure load of joints. The results indicated that the highest tension and compression strengths of joints were achieved when the distance is 140 mm between the screws. Joint strengths of all specimens were improved when the screw length and diameter were increased. It is necessary to estimate the effect of various factors to improve furniture joint performance. Coefficients of determination at 0.98 (tension strength test) and 0.96 (compression strength test) were predicted for the testing phase by the ANN model. All these findings established that the prediction was compatible with experimental data of tension and compression strengths. The results of the analysis showed that the neural network approach was effective in predicting the failure load of screw joints and showed that the ANN model has great potential in the design optimization of furniture assemblies.


Screw; Joint; Furniture; Artificial neural networks

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