Issue 50

G.V. Seretis et alii, Frattura ed Integrità Strutturale, 50 (2019) 517-525; DOI: 10.3221/IGF-ESIS.50.43 523 ସ ᇱ ൌ 407 െ 166 ൈ െ 10.29 ൈ ଵ ൅ 2.4 ൈ ℎ ଵ ൅ 55.8 ൈ ଶ ൅ 0.0491 ൈ ଵ ଶ െ 19.22 ൈ ℎ ଶ ൅ 11.77 ൈ ൈ ଵ െ 84.7 ൈ ൈ ℎ ଵ ൅ 1.399 ൈ ଵ ൈ ℎ ଵ െ 2.403 ൈ ଶ ൈ ଵ ൅ 18.51 ൈ ଶ ൈ ℎ ଵ െ 0.0654 ൈ ൈ ଵ ଶ ൅ 9.31 ൈ ൈ ℎ ଵ ଶ ൅ 0.01051 ൈ ଶ ൈ ଵ ଶ െ 1.597 ൈ ଶ ൈ ℎ ଵ ଶ Results and discussion Fig.5 presents the performance of the modified full quadratic regression model used for the tensile performance prediction. Fig.6 presents the performance of the Poisson regression model used for the flexural performance prediction. The above results when compared to the respective ones for laminated composites without GNPs reinforcement [23], lead to the con- clusion that the GNPs reinforcement does not affect which is the most significant parameter for both tensile and flexural performance. Thus, for the tensile performance of the specific composites, with and without GNPs reinforcement, the most significant parameter is the curing time and for the flexural performance the curing temperature. However, in the case of GNPs reinforced nanocomposites, the less significant parameter for the tensile performance is temperature, when the respective one for the composites without GNPs reinforcement is heating rate [23]. This may be related to the significantly different thermal expansion coefficient between matrix, fibers and graphene nanoparticles [25]. Figure 5 : Experimental and theoretical values of tensile strength (calculated using the modified full quadratic regression model). Figure 6 : Experimental and theoretical values of flexural strength (calculated using the Poisson regression model). C ONCLUSIONS NPs/woven E-glass fabric/epoxy laminated nanocomposites were produced using a hand lay-up process and underwent tensile and flexural testing according to a L 25 Taguchi design of experiments. Based on the experimental results as well as the subsequent statistical analysis, the following remarks may be drawn: G