Dielectric properties and noncontact damage detection of plain-woven fabric glass fiber reinforced epoxy matrix composites using millimeter wavelength microwave

2012 ◽  
Vol 94 (2) ◽  
pp. 695-701 ◽  
Author(s):  
Kimiyoshi Naito ◽  
Yutaka Kagawa ◽  
Kanshi Kurihara
Keyword(s):  
Dielectric Properties ◽  
Damage Detection ◽  
Glass Fiber ◽  
Epoxy Matrix ◽  
Woven Fabric ◽  
Matrix Composites ◽  
Fiber Reinforced ◽  
Millimeter Wavelength ◽  
Glass Fiber Reinforced

scholarly journals The Effect of Moisture on the Properties of Glass Fiber Polymer Matrix Composites with MoS2 and CaCO3

2019 ◽  
Vol 8 (6) ◽  
pp. 4800-4806
Keyword(s):  
Mechanical Properties ◽  
Water Absorption ◽  
Glass Fiber ◽  
Epoxy Matrix ◽  
Polymer Matrix Composites ◽  
Tap Water ◽  
Matrix Composites ◽  
Fiber Reinforced ◽  
Glass Fiber Reinforced ◽  
The Matrix

The mechanical properties and water absorption behavior of a pure glass fiber reinforced epoxy matrix and a glass fiber reinforced epoxy filled composites immersed into a tap water were investigated. The main purpose of this experiment is addition of two different powdered fillers (CaCO3 and MoS2 ) into the epoxy matrix and comparing the properties of pure GFRP and filled GFRP. The composites specimens with fillers absorb less water when compared to pure GFRP specimens at room temperature. Water absorption curves and equilibrium moisture content were determined. The composites exhibit a positive deviation from the Fickan’s law with the addition of fillers into the matrix. The influence of water uptake has significant effect on the reduction of mechanical properties. It is observed that 3% filled MoS2 in epoxy matrix has less uptake of water and the tensile strength decreased is 3% , flexural strength decreased up to 18% and shear strength is 42% decreased when compared to CaCO3 filled composites and unfilled glass fiber reinforced polymer composite.

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.

scholarly journals Microstructure and mechanical behavior of metallic glass fiber-reinforced Al alloy matrix composites

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Z. Wang ◽  
K. Georgarakis ◽  
K. S. Nakayama ◽  
Y. Li ◽  
A. A. Tsarkov ◽  
...  
Keyword(s):  
Metallic Glass ◽  
Mechanical Behavior ◽  
Glass Fiber ◽  
Al Alloy ◽  
Matrix Composites ◽  
Fiber Reinforced ◽  
Alloy Matrix ◽  
Glass Fiber Reinforced
Sign in / Sign up

Export Citation Format

Share Document