Predicting physico-mechanical and thermal properties of loofa cylindrica fibers and Al2O3/Al-SiC reinforced polymer hybrid composites using artificial neural network techniques

Abstract

The physical, mechanical, and thermal characteristics of loofa fiber-alumina (Al2O3) and loofa fiber-aluminum silicon carbide (Al-SiC) reinforced with epoxy, vinyl-ester, and polyester hybrid composites were experimentally studied. The polymer hybrid laminates were fabricated using an ultrasonication probe-assisted wet-layup technique. The outcomes show that flexural, impact, and tensile characteristics were higher for loofa fiber-Al-SiC reinforcement than other samples due to minimal voids and uniform dispersion of fillers within the matrices as observed from scanning electron microscope (SEM) images. Minimum water absorption and thickness swelling characteristics were examined for the composite specimen reinforced with loofa fiber-Al-SiC fillers. Thermo-gravimetric analysis (TGA) was performed, and the laminate was observed to be thermally stable up to 436 °C. Also, forecasting models were simulated using an artificial neural network (ANN) to perceive schemes in data by varying certain factors. The outcomes collected signified that loofa fiber-Al-SiC-based laminate could substitute traditionally applied materials and provide real-world directions. A one-way ANOVA (Analysis of Variance) technique was performed to check the significance between the physical, mechanical, and thermal characteristics of developed polymer hybrid composites and found significant under a 95 % confidence level. Based on the results, the loofa fiber-Al-SiC reinforced polymer hybrid laminate exhibited improved tensile, impact, and flexural characteristics along with remarkable thermal stability, recommends multi-faceted applications in industries demanding lightweight yet strong materials like automotive and aerospace field, where structural integrity and decrement in weight are dominant.