The effect of granite powder on mechanical, structural and water absorption characteristics of alkali treated cordia dichotoma fiber reinforced polyester composite

2020 ◽  
Vol 91 ◽  
pp. 106782 ◽  
Author(s):  
B. Madhusudhan Reddy ◽  
R. Meenakshi Reddy ◽  
B.Chandra Mohan Reddy ◽  
P. Venkateshwar Reddy ◽  
H. Raghavendra Rao ◽  
...  
Keyword(s):  
Water Absorption ◽  
Fiber Reinforced ◽  
Polyester Composite ◽  
Granite Powder ◽  
Absorption Characteristics

Mechanical and water absorption behaviors of corn stalk/sisal fiber-reinforced hybrid composites

2018 ◽  
Vol 135 (26) ◽  
pp. 46405 ◽  
Author(s):  
Juan Chen ◽  
Yu Zou ◽  
Heyi Ge ◽  
Zedong Cui ◽  
Shanshan Liu
Keyword(s):  
Water Absorption ◽  
Hybrid Composites ◽  
Sisal Fiber ◽  
Corn Stalk ◽  
Fiber Reinforced

Probabilistic Study of Tensile and Flexure Properties of Untreated Jute Fiber Reinforced Polyester Composite

2017 ◽  
Vol 4 (10) ◽  
pp. 11050-11055 ◽  
Author(s):  
A.P. Abhishek ◽  
B.S. Keerthi Gowda ◽  
G.L. Easwara Prasad ◽  
R. Velmurugan
Keyword(s):  
Jute Fiber ◽  
Fiber Reinforced ◽  
Polyester Composite ◽  
Probabilistic Study

Synergy of fiber length and content on free vibration and damping behavior of natural fiber reinforced polyester composite beams

2014 ◽  
Vol 56 ◽  
pp. 379-386 ◽  
Author(s):  
K. Senthil Kumar ◽  
I. Siva ◽  
P. Jeyaraj ◽  
J.T. Winowlin Jappes ◽  
S.C. Amico ◽  
...  
Keyword(s):  
Free Vibration ◽  
Fiber Length ◽  
Natural Fiber ◽  
Composite Beams ◽  
Fiber Reinforced ◽  
Damping Behavior ◽  
Polyester Composite

scholarly journals Influence of Fiber Orientation on Impact Resistance Behavior of Woven Sisal Fiber Reinforced Polyester Composite

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Genetu A. Dress ◽  
M. H. Woldemariam ◽  
D. T. Redda
Keyword(s):  
Fiber Orientation ◽  
Test Specimen ◽  
Impact Resistance ◽  
Impact Testing ◽  
Sisal Fiber ◽  
Fiber Reinforced ◽  
Average Impact ◽  
Polyester Composite ◽  
Izod Impact

Woven natural fiber reinforced polymer composites have better tensile, flexural, and compressive strength compared to the mechanical properties of unidirectional and randomly oriented NFRPC because of the interlacing of fiber bundles. However, the characterization of impact behavior with different fiber orientation such as 30°/60°, 0/90°, 30°/−45°, and 45°/−45° woven sisal fiber reinforced polyester composite was not studied vigorously. Thus, this paper focuses on the experimental characterization of the impact resistance behavior on woven sisal fiber reinforced polyester composite materials for semistructural part by using Izod impact testing setup. The 30°/60°, 30°/−45°, 0°/90°, and 45°/−45° woven sisal fiber was prepared using nailed wooden frame as a warp and weft guider. The woven sisal fiber was impregnated in order to make woven sisal fiber dimensionally stable. Using 40% by weight of fiber and 60% by weight of polyester, the composite was developed using hand layup process. The morphology and cross-sectional elemental detection was carried out using scanning electron microscope (SEM) assessment in leather development institute (LDI). Finally, impact tests were carried out using Izod impact testing setup in Addis Ababa Science and Technology University (ASTU). The average impact strength of a 40 wt% fiber 45°/−45° woven sisal fiber reinforced unsaturated polyester composite (WSFRPC) test specimen with consecutive warp and weft tow spacing of 2 mm was 342.67 J/m and this was greater energy compared to the other orientations. But the average impact strength of a 40 wt% fiber 30°/60° WSFRPC of test specimen with consecutive warp and weft tow spacing of 2 mm was 241.33 J/m.

scholarly journals Damage Analysis and Prediction in Glass Fiber Reinforced Polyester Composite Using Acoustic Emission and Machine Learning

2021 ◽  
Author(s):  
S. Gholizadeh
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Damage Detection ◽  
Glass Fiber ◽  
Fatigue Cracking ◽  
Great Success ◽  
Fiber Reinforced ◽  
Structural Problems ◽  
Glass Fiber Reinforced ◽  
Polyester Composite

One of the most pervasive types of structural problems in aircraft industries is fatigue cracking that can potentially occur without anticipation with catastrophic failures and unexpected downtime. Acoustic emission (AE) is a passive structural health monitoring (SHM) technique, since it offers real time damage detection based on stress waves generated by cracking in the structure. Machine learning techniques have presented great success over the past few years with a large number of applications. This study assesses the progression of damage occurring on glass fiber reinforced polyester composite specimens using two approaches of machine learning, namely, Supervised and Unsupervised learning. A methodology for damage detection and characterization of composite is presented. The result shows that machine learning can predict the failure. All predictive models and their performance as well as AE parameters had a direct relationship with the applied stress values, suggesting that these correlation coefficients are reliable means of predicting fatigue life in a composite material.

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