Issue 50

G.V. Seretis et alii, Frattura ed Integrità Strutturale, 50 (2019) 517-525; DOI: 10.3221/IGF-ESIS.50.43 522 Parameter Optimal Parameters A 2 B 5 C 1 Experimental Predicted Flexural Strength (MPa) 285 278.007 Error % 2.45 % Table 6 : Confirmation table for optimum flexural performance. the tensile performance of the nanocomposites. It seems that the nanofillers act like cooling points within the epoxy matrix, negating in this manner the previously reported negative effect of temperature increase on E-glass fabric/ epoxy composite specimens’ performance. The heating rate, when it increases from 1°C/min to 2°C/min, results in a slight tensile increase, while greater increase of its value tend to decrease the tensile performance of the material. Specifically, for heating rate values ranging from 2°C/min to 3°C/min a significant performance drop can be observed. Subsequently, for heating rate value increase up to 4°C/min the tensile performance slightly increases and for further increase of a it decreases again. Overall, for heating rate values greater than 2 ο C/min the UTS values are considerably low. The curing time increase up to 6 hours leads to a consequent significant performance drop. However, for further curing time increase, up to 10 hours, the tensile performance increases again, overpassing the performance that corresponds to a curing time equal to 2 hours. The main effects plot for the main effect terms in flexural strength for factors a , T 1 , and h 1 are shown in Fig.4. The heating rate increase leads to an initial flexural performance increase, for heating rate value increase from 1°C/min to 2°C/min, and subsequently, for further increase of the heating rate value, to a flexural performance drop. Slight curing temperature increase, i.e. from 50°C to 80°C, dramatically decrease the flexural strength of the material. Further temperature increase causes a consequent increase in flexural performance. A progressive increase of the curing time significantly decreases the flexural strength of the material. M ULTIPLE REGRESSION ANALYSIS he above analysis was followed by a regression analysis, which was applied to create a model for prediction of the performance of the nanocomposites as regards both tensile and flexural performance. The commonly used full quadratic regression model, involving only the main factors as they occurred from the ANOVA analysis, achieved significantly low prediction accuracy in the case of the nanocomposite materials (about 62%). Due to the low accuracy of this regression model led to the necessity for a more leviable and accurate regression model. Therefore, a Poisson regression model was used to improve the prediction accuracy for the flexural performance and the full quadratic regression model enhanced with the interaction between the second order terms (modified full quadratic model) was used for tensile perform- ance prediction. The accuracy of these models was 94.9% and 90.99%, respectively. The backwards elimination method was applied again on all the parameters included in the regression. The F out factor for terms removal was selected equal to 4. All three curing process parameters selected were considered as independent variables. Therefore, two different regression models were created, i.e. a Poisson regression model for the flexural performance prediction and a modified full quadratic regression model for the tensile performance prediction of the nanocomposites. These two regression models are presented in Eqs.(4) and (5), respectively. ℎ ൌ ௒ య ᇲ (4) where ଷ ᇱ ൌ 6.09 െ 4.10 ൈ ൅ 0.0054 ൈ ଵ ൅ 4.703 ൈ ℎ ଵ െ 1.105 ൈ ଶ െ 0.001897 ൈ ଵ ଶ െ 0.12 ൈ ℎ ଶ ൅ 0.063 ൈ ൈ ଵ െ 0.841 ൈ ൈ ℎ ଵ െ 0.03293 ൈ ଵ ൈ ℎ ଵ ൅ 0.33 ൈ ଷ ൅ 0.00001 ൈ ଵ ଷ ൅ 0.0401 ൈ ℎ ଵ ଷ ൅ 0.0914 ൈ ଶ ൈ ℎ ଵ ൅ 0.0001 ൈ ൈ ଵ ଶ െ 0.001283 ൈ ൈ ℎ ଵ ൈ ଵ െ 0.01888 ൈ ସ െ 0.001775 ൈ ℎ ଵ ସ െ 0.01132 ൈ ଷ ൈ ℎ ଵ ℎ ൌ ସ ᇱ (5) where T