S. Srinivasa Moorthy and K. Manonmani
Statistical Analysis and Predictive Learning of Mechanical Parameters for TiO2 Filled GFRP Composite
119 - 123
2014
8
1
International Journal of Mechanical and Mechatronics Engineering
https://publications.waset.org/pdf/9997422
https://publications.waset.org/vol/85
World Academy of Science, Engineering and Technology
The new, polymer composites consisting of eglass fiber reinforcement with titanium oxide filler in the double bonded unsaturated polyester resin matrix were made. The glass fiber and titanium oxide reinforcement composites were made in three different fiber lengths (3cm, 5cm, and 7cm), filler content (2 wt, 4 wt, and 6 wt) and fiber content (20 wt, 40 wt, and 60 wt). 27 different compositions were fabricated and a sequence of experiments were carried out to determine tensile strength and impact strength. The vital influencing factors fiber length, fiber content and filler content were chosen as 3 factors in 3 levels of Taguchi’s L9 orthogonal array. The influences of parameters were determined for tensile strength and impact strength by Analysis of variance (ANOVA) and SN ratio. Using Artificial Neural Network (ANN) an expert system was devised to predict the properties of hybrid reinforcement GFRP composites. The predict models were experimentally proved with the maximum coincidence.
Open Science Index 85, 2014