Issue 50

G.V. Seretis et alii, Frattura ed Integrità Strutturale, 50 (2019) 517-525; DOI: 10.3221/IGF-ESIS.50.43 518 Taguchi analysis is widely used in the research of composite materials. For example, it is used to investigate the influence of surface treatment on the composites’ performance [15] and the importance of V-ring indenter parameters [16], to deter- mine the optimum machining conditions leading to minimum surface roughness in drilling of GFRP composite [17], to de- velop multiphase hybrid epoxy matrix composites reinforced with glass-fiber and filled with rice husk particulates [18], etc. Analysis of Variance (ANOVA) and Regression or Multiple Regression models regularly follow the Taguchi method to create an effective prediction model. The commonly used Multiple Regression models [16,17] are not always able to achieve high accuracy in prediction. Therefore, researchers started working on more accurate multiple regression models [19,20]. In this study, a multi-parameter analysis, using Taguchi method for design of experiments, has been conducted to investigate the optimum curing conditions for GNPs/E-glass fabric/epoxy laminated nanocomposites. The independent variables in the L 25 Taguchi orthogonal array were heating rate, curing temperature and curing time, addressing five levels each. Tensile and 3-point bending tests were performed for each experiment number (run number) of the Taguchi L 25 . According to the analysis of variance, the significant parameter for tensile performance was the curing time and for flexural performance was the curing temperature, at a 95% confidence level. The Full Quadratic regression model was used to predict the tensile response of the nanocomposites, since the respective main effects plots were more linear or shown clear trends. On the other hand, for the flexural performance, where the respective main effects plots neither were linear nor shown clear trends, the Poisson regression model was used to achieve high accuracy in prediction. The R 2 of the regression models used was greater than 90% for the prediction of both tensile and flexural values. E XPERIMENTAL PROTOCOL Materials he matrix material used for the nanocomposite specimens was the low-viscosity Araldite GY 783 epoxy resin together with the low-viscosity, phenol free, modified cycloaliphatic polyamine hardener. The glass transition temperature (Tg) was 100°C and the gel time for the specific matrix composition at 20°C and 65% relative humidity (RH), conditioning requirements which were obeyed during the preparation of the nanocomposites laminates, was 35 min. Woven E-glass fabric of 282 g/m 2 density was used for matrix reinforcement, as presented in Fig.1. Table 1 presents the characteristics of the fabric used. The warp direction is the enhanced one, as can be seen in, and therefore it was the main weave direction. Thus, the laminae orientations in the stacking sequence of the composites will be based on the warp direction. Graphene nanoplatelets (GNPs) by Alfa Aesar of surface area (S.A.) 500 m 2 /g were also used as filler material. Figure 1 : The woven E-glass fabric used in positioning angle (layer orientation) 0º. Characteristics Warp Weft Fiber description Glass EC11 204 fiber Glass EC11 204 fiber Thread count (ends/cm) 8 6 Weight distribution (%) 57 43 Table 1 : E-glass fabric’s characteristics. Preparation of E-glass fabric/epoxy laminated composites To prepare the GNPs-reinforced matrix, weighed amount of pre-dried graphene nanoplatelets were stirred gently into the epoxy resin using a laboratory mixer for mechanical stirring for a process time of 25 min at 200 rpm, to ensure homogeneity of the suspension [22]. Weighed amount of hardener was added into the GNPs reinforced epoxy resin mixture at the manu- T