Modeling and optimization of dynamic-mechanical properties of hybrid polymer composites by multiple nonlinear neuro-regression method

Year 2023, Volume: 41 Issue: 6, 1243 - 1254, 29.12.2023

Melih Savran Mustafa Öncül Muhammed Yılmaz Levent Aydın Kutlay Sever

Abstract

The purpose of this research is to improve the dynamic-mechanical properties of the polypro-pylene filled by artichoke stem (AS) particles and wollastonite (W) in different weight frac-tions. The effect of weight ratios of fillers in polypropylene was mathematically modeled using the data obtained as a result of the experimental work. In the modeling phase, multiple nonlin-ear neuro-regression analysis was used. In this context, proposed linear and nonlinear models have been examined by performing R2training, R2adjusted, R2testing, and boundedness check. The models that satisfy these four criteria were selected as the objective functions for the optimiza-tion phase. Finally, Modified Differential Evolution Algorithm was used to obtain maximum storage modulus and loss modulus by adjusting weight percent ratio of artichoke stem particle and wollastonite. The experimental results and the modeling optimization results showed that when the polypropylene-artichoke stem particle-wollastonite hybrid polymer composite was used instead of other non-hybrid polymer composite, the storage modulus and the loss mod-ulus improved by approximately 40%.

Keywords

Multiple Nonlinear Neuroregression, Design and Optimization, Hybrid Polymer Composites, Modeling, Storage Modulus, Loss Modulus

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