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A Comparative Study of the Estimated Bending Strength of Sandwich Panels by an Artificial Neural Network and an Adaptive Neuro-Fuzzy Inference System

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Mechanics of Composite Materials Aims and scope

The effects of cattail/poplar straw weight ratio, sandwich panel thickness, and the number of kraft paper layers at the core of sandwich panels on the modulus of rupture (MOR) of the panels were predicted using an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The prediction accuracy of the MOR based on experimental data, in terms of the mean absolute percentage error, was smaller than 1.4 and 1.1%, with coefficients of determination 0.94 and 0.995 for the ANN and ANFIS models, respectively. These models can easily predict the bending strength of lignocellulosic-based sandwich panels with a high precision in the manufacturing process.

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Authors and Affiliations

  1. Faculty of New Technologies and Aerospace Engineering, Shahid Beheshti University, Tehran, Iran

    M. Nazerian, B. S. Deghatkar & E. Vatankhah

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  1. M. Nazerian
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  2. B. S. Deghatkar
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  3. E. Vatankhah
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Correspondence to M. Nazerian.

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Nazerian, M., Deghatkar, B.S. & Vatankhah, E. A Comparative Study of the Estimated Bending Strength of Sandwich Panels by an Artificial Neural Network and an Adaptive Neuro-Fuzzy Inference System. Mech Compos Mater 59, 595–608 (2023). https://doi.org/10.1007/s11029-023-10118-6

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  • DOI: https://doi.org/10.1007/s11029-023-10118-6

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