Estimation of Physical and Mechanical Properties of Composite Board via Adaptive Neural Networks, Polynomial Curve Fitting, and the Adaptive Neuro-Fuzzy Inference System

The aim of this paper was to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) and to predict the flexural strength of the sandwich panels made with thin medium density fiberboard as surface layers, and polyurethane foam as a core layer, by applying metaheuristic optimization methods. For this purpose, various models, namely ant colony optimization for the continuous domain (ACOR), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) were applied and compared, as different efficient bio-inspired paradigms, to assess their suitability for training the adaptive neuro-fuzzy inference system model. The predicted values of the flexural strength resulting from applying adaptive neuro-fuzzy inference system trained by ACOR, DE, GA, and PSO, were compared with the values derived from adaptive neuro-fuzzy inference system classical model. The molar ratio of formaldehyde to melamine and urea, sandwich panel thickness, and the weight ratio of the modified starch to MUF resin (OS/MUF weight ratio) were used as an input variables and the modulus of rupture was used as an output one. The developed hybrid models were used to predict the values of the modulus of rupture obtained from experimental tests. In order to evaluate and compare the performance of the models, three performance criteria were employed namely, determination coefficient (R2), root mean square error, and mean absolute percentage error. It was found that ANFIS–ACOR, ANFIS–DE, ANFIS–GA, and ANFIS–PSO showed different performance ratios compared to the predicting model. In addition, the ANFIS–GA model is found to be by far more accurate than the other hybrid models.

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Developing adaptive neuro-fuzzy inference system-based models to predict the bending strength of polyurethane foam-cored sandwich panels

Nazerian

Naderi

Partovinia

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part L:

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Control of system parameters by estimating screw withdrawal strength values of particleboards using artificial neural network-based statistical control charts

Kurt

2022

J Wood Sci

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In this study, with data obtained from a particleboard factory, screw withdrawal strength (SWS) values of particleboards were estimated using artificial neural networks (ANNs). Predictive control charts were also created. A total of seven independent variables were used for the ANN model: modulus of elasticity (MoE), surface soundness (SS), internal bond strength (IBS), density, press time, press temperature, and press pressure. The results showed that the ANN-based individual moving range (I-MR) and cumulative sum (CUSUM) control charts created for SWS values detected out-of-control signal points close to those of the real-time control charts. Among the selected independent variables, IBS was the most important parameter affecting SWS. The most suitable press temperatures and times for high SWS values were determined as 198–201 °C and 165–175 s, respectively. Moreover, the boards with 2500–2800 N/mm2 MoE and 0.55 N/mm2 IBS values exhibited the best SWS.

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Estimation of Physical and Mechanical Properties of Composite Board via Adaptive Neural Networks, Polynomial Curve Fitting, and the Adaptive Neuro-Fuzzy Inference System

Cited by 2 publications

References 6 publications

Developing adaptive neuro-fuzzy inference system-based models to predict the bending strength of polyurethane foam-cored sandwich panels

Developing adaptive neuro-fuzzy inference system-based models to predict the bending strength of polyurethane foam-cored sandwich panels

Control of system parameters by estimating screw withdrawal strength values of particleboards using artificial neural network-based statistical control charts

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