Evaluation of mechanical properties of Al₂O₃ and TiO₂ nano filled enhanced glass fiber reinforced polymer composites

Abstract

The present investigation has emphasized the implication of nano fillers (Al2O3 and TiO2) combined additions in glass fiber reinforced polymer composite on the variation of interlaminar shear strength (ILSS). The experiment has also investigated the effect of crosshead speed during testing and subsequent fracture surface analysis to find out the possible failure modes by scanning electron micrograph examinations. Loss and storage modulus have been experimentally evaluated to support the new findings in the present experimental design. It has been observed that the concurrent presence of nano Al2O3 and TiO2 fillers improves the ILSS. Dynamic mechanical thermal analysis indicated that, the addition of nano fillers reduces the storage and loss modulus of the composites. However, glass transition temperature has not been altered by the nano fillers addition. Box–Behnken design of experiment of surface response methodology has been adopted to optimize the filler content and crosshead speed. A second order mathematical model has been developed and the predicted optimum input parameters are 0.3 wt % Al2O3, 0.15 wt %TiO2, and 500 mm/min crosshead speed. Furthermore, the model predicted results are compared with experimental one and found a close agreement between them. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016, 133, 44274.

Influence of glass/carbon fiber stacking sequence on mechanical and three-body abrasive wear resistance of hybrid composites

“…The residuals approximate the straight line showing normal distribution of the experimental data. Thus, the experimental data are statistically acceptable and reliable [24].…”

“…The test samples were cut to dimensions according to ASTM specifications. The test specimens were post-cured in an oven at 140°C for 6 h [24]. The hybrid composites are symmetrical about the central axis by design to ensure same mechanical and tribological properties on both sides of the composites.…”

Application of box‐behnken design and neural computation for tribo‐mechanical performance analysis of iron‐mud‐filled glass‐fiber/epoxy composite and parametric optimization using PSO

“…and is appropriate for the investigation of quadratic response surfaces. This design helps in reducing the experimental run through process optimization . The four‐variable randomized Box‐Behnken design of experiment (DOE) technique based on the response surface methodology was used to study the interrelationship between significant abrasion wear characteristics and analyze the combined effect of iron‐mud content, normal load, sliding distance, and sliding velocity on volume loss during the three‐body wear of iron‐mud‐filled GF‐epoxy hybrid composite.…”

“…ANN was implemented to learn and simulate a randomly chosen test dataset of 29 independent wear measurement data generated during the abrasion trials on the composites using the “Neural Network Toolbox” of MATLAB . A structured ANN (4‐[6] 1 ‐1) with one hidden layer (six neurons in the “tansig” layer) along with an automated Bayesian regularization (“trainbr”) back‐propagation algorithm was finally selected with an acceptable predictive quality with a low value of mean square error (MSE).…”

Thermomechanical Properties of Carbon Fabric Reinforced Epoxy Laminates with h-BN and MoS2 Fillers

“…In this regard, several researchers paid attention to improve the thermal stability, storage modulus (E′), Tg, and toughness of the epoxy matrix by incorporating the fillers. The commonly used fillers are alumina (Al 2 O 3 ) 17 , boron carbide (B 4 C) 18 , silicon dioxide (SiO 2 ) 19 , nanoclay 20,21 , graphene [22][23][24] , boron nitride (BN) 25,26 , molybdenum disulfide (MoS 2 ) 27,28 . The thermomechanical properties improvement depends on the type, size, shape, and quality of the filler dispersion.…”