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Optimization of Composite Process Parameters on Fracture Toughness of Glass Fiber Reinforced Plastics
Publication year - 2019
Publication title -
international journal of recent technology and engineering (ijrte)
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1009.0982s1119
Subject(s) - materials science , fracture toughness , composite material , taguchi methods , fracture mechanics , fracture (geology) , stacking , toughness , stress intensity factor , linear regression , scanning electron microscope , glass fiber , response surface methodology , mathematics , statistics , physics , nuclear magnetic resonance
The objective of the work was to investigate statistical analysis of fracture toughness of the glass fiber reinforced composites at different stacking sequences, strain rate and crack length. The glass reinforced Vinylester composites prepared by hand lay-up technique with various stacking sequences such as 0/90, 45/-45 and chopped strand orientations. 3-point bending for fracture toughness test for various notch depth ratio’s as per linear elastic fracture mechanics concept was conducted. Further the initial notch depth method was adopted to find the critical stress intensity factor KIC for the given notch depth ratio. Design of experiment using the Taguchi L9 array was formulated to understand the influence of parameters on fracture toughness. ANOVA was performed to find out the influencing weightage factor for fracture toughness. Regression analysis was used to develop a mathematical model that gave the contribution of individual parameters on responses. The fracture surface was investigated using scanning electron microscopy (SEM).The DOE response surface methodology showed the stacking sequence of [45/-45/0/90/Chopped]3s, with strain rate of 0.5 mm/min and without notch having the highest fracture toughness of 81.36 Mpa . ANOVA was carried out to know the statistical significance of factors influencing fracture toughness, Results showed that notch depth ratio was the most influencing factor with 68.67 % and stacking sequence with 24%. Accuracy of developed regression model was 90.61 % (R2), which indicating the confidence level of regression.

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