AUTHOR=Natrayan L. , Janardhan Gorti , Paramasivam Prabhu , Dhanasekaran Seshathiri 

TITLE=Enhancing mechanical performance of TiO2 filler with Kevlar/epoxy-based hybrid composites in a cryogenic environment: a statistical optimization study using RSM and ANN methods

JOURNAL=Frontiers in Materials

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2023.1267514

DOI=10.3389/fmats.2023.1267514

ISSN=2296-8016

ABSTRACT=The main aim of the present research work is to investigate the mechanical performance of the different weight proportions of nano TiO2 combined with Kevlar fiber-based hybrid composites under cryogenic conditions. To achieve the mentioned objectives, the following parameters were considered: (i) Kevlar fiber mat type (100 and 200 gsm), (ii) weight proportions of TiO2 nanofiller (2 and 6 wt.%), and (iii) cryogenic processing time (10 to 30 min at -196 ºC). The composites were fabricated through compression molding techniques. After fabrication, the mechanical characteristics of the prepared nanocomposites, like tensile, bending, and impact properties, were evaluated. The optimal mechanical strength of nano-filler-based composites was analyzed using response surface methodology (RSM) and artificial neural networks (ANN). The following compositions, such as 4 weight percentages of nano TiO2 filler, 200 gsm of Kevlar fiber mat, and 20 minutes of cryogenic treatment, were shown to produce the maximum mechanical strength (65.47 MPa of tensile, 97.34 MPa of flexural, and 52.82 J/m2 of impact). This is because residual strains are produced at low temperatures (cryogenic treatment) due to unstable matrices and fiber contraction. The aforementioned interfacial stress helps to maintain a relationship between the reinforcement and resin and improve adhesion, leading to improved results. Based on the statistical evaluation, the ratio of correlation (R2), mean square deviation and average error function of the experimental and validation data sets of the experimental models were analyzed. The ANN displays 0.9864 values for impact, 0.9842 for flexural, and 0.9764 for tensile. ANN and RSM models were used to forecast the mechanical efficiency of the suggested nanocomposites with up to 95% reliability.