Statistical characterization of the impact strengths of vapor‐grown carbon nanofiber/vinyl ester nanocomposites using a central composite design

Abstract

The effects of vapor-grown carbon nanofiber (VGCNF) weight fraction, high-shear mixing time, and ultrasonication time on the Izod impact strengths of VGCNF/vinyl ester (VE) nanocomposites were studied using a central composite design. A response surface model (RSM) for predicting impact strengths was developed using regression analysis. RSM predictions suggested that an 18% increase in impact strength was possible for nanocomposites containing only 0.170 parts per hundred parts resin (phr) of VGCNFs ($0.1 v%) that were high-shear mixed for 100 min when compared to that of neat VE. In general, the predicted impact strengths increased for high-shear mixing times above 55 min and VGCNF weight fractions below 0.400 phr. The predicted strengths decreased as the VGCNF weight fraction was further increased. Scanning electron micrographs of the nanocomposite fracture surfaces showed that increased impact strength could be directly correlated to better nanofiber dispersion in the matrix.

Comprehensive mechanical property classification of vapor-grown carbon nanofiber/vinyl ester nanocomposites using support vector machines

“…The majority of data samples used in this work were generated using various statistics-based designed experiments, utilizing a general mixed level full factorial and central composite designs [26][27][28][29]35,36]. Different datasets were merged into a larger one incorporating a total of 583 data points, i.e.…”

“…The weight vector is scaled so that the value of g(x) at the closest point to the separating hyperplane is Table 1 The experimental design factors, their levels and the high ranges of the mechanical property responses [26][27][28][29]35,36]. equal to 1 for class one and À1 for class two.…”

“…They have been the subject of extensive research recently [23,24]. Abuomar et al [25] applied data mining and knowledge discovery techniques in order to analyze a thermosetting viscoelastic VGCNF/VE nanocomposite material system [26][27][28][29]. These techniques included SOMs, which are sometimes referred to as Kohonen maps [30,31] and fuzzy C-means (FCM) clustering [32,33].…”

“…This was accomplished by including a wider range of measured mechanical properties, i.e., viscoelastic [26], compressive and tensile [35], flexural [36], and impact strengths [29]. Abuomar et al [37] implemented this idea initially for a smaller dataset, where SVMs technique was used to analyze and classify a VGCNF/VE dataset, including viscoelastic data, compressive and tensile property data, and flexural property data into three classes of desired mechanical properties, i.e., high storage modulus, high true ultimate strength, and high flexural modulus.…”

Carbon nanofiber-reinforced polymeric nanocomposites

“…Vapor-grown carbon nanofibers (VGCNFs) offer a less expensive alternative to carbon nanotubes (CNTs) while still providing stiffness [1] and strength improvements when added to an infusion-class thermoset polymeric resin such as vinyl ester (VE) [2][3][4][5][6][7]. VE is an inexpensive structural grade resin that represents an alternative to the traditional and more expensive epoxy resin systems often used in automotive applications due to its good strength, toughness, and chemical and corrosion resistance [8][9][10][11][12][13].…”