Modeling and Simulation of Flexural Strength in Epoxy Graphene CNT Hybrids Using Python Tools
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Abstract
This paper provides a highly detailed experimental and predictive exploration of how to enhance flexural strength of polymer composites with the technique of bi-reinforced graphene nanoplatelets (GNP) and carbon nanotubes (CNT). One hundred and fifty hybrid combinations based on varying GNP (0-1 wt.%) and CNT (0-0.4 wt.%) contents were developed and prepared accordingly to determine the effects of hybrid fillers in flexural performance. Experimental findings showed that the un-reinforced baseline sample (0% GNP, 0% CNT) had flexural strength 68.90 MPa, whereas the strongest sample was recorded with 1% GNP and 0.3% CNT (120.20 MPa) which is an increment of 74.44 % as compared to the un-reinforced sample. Simultaneously, a regression model in the form of machine learning was trained to estimate the values of the flexural strength based on the filler contents as the input variables. The model proved very reliable with the highest strength being predicted as 100.85 MPa at the same optimum hybrid proportion, and lowest values of absolute and percent error in all specimens tested. The results of the model produced reasonably accurate results as compared to experimental data having R 2 values greater than 0.96, and an exclusively overlaying of predicted points on the 95 percent confidence interval.
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