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Information Journal Paper

Title

Prediction of the flexural strength of particleboard using artificial neural network modeling in comparison with regression models

Pages

  283-297

Abstract

 Today, several modeling methods have been developed to predict the physical and mechanical properties of wood-based panel products, cost-efficiently. Two common modeling methods include regression and Artificial neural networks (ANN). In this study, the possibility of predicting the Modulus of Rupture (MOR) and Modulus of Elasticity (MOE) of Particleboard by simple and Multiple linear regression and ANN models were evaluated based on the structural parameters including density in three levels (0. 65, 0. 7, and 0. 75 g/cm3), slenderness ratio of particles in three levels (47, 30, and 13), and adhesive percent in three levels of (8, 9. 5, and 11%). experimental and predicted data by different models were compared and assed with several criteria including mean absolute percentage error (MAPE), mean squared error (MSE), and coefficient of determination (R2). The results revealed that although both Multiple linear regression and Artificial neural network models were able to predict MOR and MOE values with acceptable accuracy, but ANN model predicted them with higher R2 and lower MAPE than the Multiple linear regression model. The value of MAPE and R2, for prediction of MOR and MOE by ANN model were 7. 72% and 0. 77, and 7% and 0. 86, respectively. The corresponding value for the multiple regression model were 8. 3% and 0. 738, and 9. 06% and 0. 783, respectively. These levels of error are industrially and practically satisfactory for the prediction of flexural strength in Particleboard.

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    • Cite

    APA: Copy

    ARABI, M., ROSTAMPOUR HAFTKHANI, A., & POURBABA, R.. (2021). Prediction of the flexural strength of particleboard using artificial neural network modeling in comparison with regression models. IRANIAN JOURNAL OF WOOD AND PAPER INDUSTRIES, 12(2 ), 283-297. SID. https://sid.ir/paper/959665/en

    Vancouver: Copy

    ARABI M., ROSTAMPOUR HAFTKHANI A., POURBABA R.. Prediction of the flexural strength of particleboard using artificial neural network modeling in comparison with regression models. IRANIAN JOURNAL OF WOOD AND PAPER INDUSTRIES[Internet]. 2021;12(2 ):283-297. Available from: https://sid.ir/paper/959665/en

    IEEE: Copy

    M. ARABI, A. ROSTAMPOUR HAFTKHANI, and R. POURBABA, “Prediction of the flexural strength of particleboard using artificial neural network modeling in comparison with regression models,” IRANIAN JOURNAL OF WOOD AND PAPER INDUSTRIES, vol. 12, no. 2 , pp. 283–297, 2021, [Online]. Available: https://sid.ir/paper/959665/en

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