Indentation, finite element modeling and artificial neural network studies on mechanical behavior of GFRP composites in an acidic environment

• Published in: Journal of materials research and technology 2023-05, Vol.24, p.5042-5058
• Main Authors: Dadras, Hooman, Teimouri, Amir, Barbaz-Isfahani, Reza, Saber-Samandari, Saeed
• Format: Article
• Language: English
• Subjects: Acidic environment; Artificial neural network; Finite element Model; GFRP composites; Indentation; Nanoparticles
• ISSN: 2238-7854
• DOI: 10.1016/j.jmrt.2023.04.146

In this study, the indentation tests are performed with various forces using the Vickers indenter to investigate the mechanical properties, including the elastic modulus, hardness, and the plasticity index of pure, and incorporated glass fiber reinforced polymer (GFRP) composites with nanosilica and nanoclay. To study the effect of adding different nanoparticles on reducing the mechanical properties of immersed specimens in acid for 0, 1 and 3 months, incorporated composite specimens with 3 wt percent (wt. %) of nanoparticles were fabricated using hand lay-up method. Accordingly, the reduction in mechanical properties and increase in plasticity index was attributed to the penetration of acid into composite specimens during the immersion period, which resulted in the failure of matrix and fiber following increased moisture penetration. Adding the nanoparticles especially nanoclay, has alleviated the trend of drop in mechanical properties. In fact, for the incorporated composite specimen with nanoclay, the hardness and elastic modulus of the immersed samples in acid for 0 and 3 months indicated a decrease of 10.55 and 7.88%, respectively on grounds of hydrophobic nature of nanoclay. Conversely, incorporating nanosilica increased the plasticity index leading to higher rate of degradation, which was even more than pure sample, yet the pure sample had the lowest mechanical properties after 3 months of immersion. In addition, Finite element modeling (FEM) and artificial neural network (ANN) were used to respectively predict the indentation behavior of fabricated composite specimens and study the effects of immersion time in an acidic environment.