scholarly journals Relationship between acoustic emission characteristics and fiber content of glass fiber reinforced polycarbonate.

1986 ◽  
Vol 35 (392) ◽  
pp. 521-526
Author(s):  
Megumu SUZUKI ◽  
Hiroshi NAKANISHI ◽  
Masaharu IWAMOTO ◽  
Eiichi JINEN ◽  
Zenichiro MAEKAWA ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Glass Fiber ◽  
Fiber Content ◽  
Emission Characteristics ◽  
Fiber Reinforced ◽  
Glass Fiber Reinforced

Fracture and acoustic emission characteristics of glass fiber reinforced plastics panels for hot water

1994 ◽  
Vol 15 (3) ◽  
pp. 206-216 ◽  
Author(s):  
Tohru Morii ◽  
Toshio Tanimoto ◽  
Hiroyuki Hamada ◽  
Zen-Ichiro Maekawa ◽  
Takahiro Hirano ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Glass Fiber ◽  
Hot Water ◽  
Emission Characteristics ◽  
Fiber Reinforced ◽  
Reinforced Plastics ◽  
Glass Fiber Reinforced ◽  
Fiber Reinforced Plastics

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.

Impact and acoustic emission performance of polyvinylidene fluoride sensor embedded in glass fiber-reinforced polymer composite structure

2020 ◽  
pp. 096739112091533
Author(s):  
Anjana Jain ◽  
Shivkumar Minajagi ◽  
Enoos Dange ◽  
Sushma U Bhover ◽  
YT Dharanendra
Keyword(s):  
Acoustic Emission ◽  
Glass Fiber ◽  
Smart Materials ◽  
Polyvinylidene Fluoride ◽  
Piezoelectric Materials ◽  
Fiber Reinforced Polymer ◽  
Fiber Reinforced ◽  
Glass Fiber Reinforced Polymer ◽  
Reinforced Polymer ◽  
Glass Fiber Reinforced

Smart materials find vital applications in the aerospace industry due to their ability to adapt to surrounding conditions according to design requirements and applicability. Piezoelectric materials are commonly used under the category of smart materials for transducer applications. Among piezoelectric materials, piezo polymer polyvinylidene fluoride (PVDF) is widely used for structural health monitoring (SHM) applications of composite structures, acoustic emission (AE) sensor, accelerometer, strain gauge, pressure sensor, and so on because of its outstanding piezo stress constant ( g 33), piezo strain constant ( d 33), flexibility, and lightweight. In this article, glass fiber-reinforced polymer (GFRP) laminates have been prepared by embedding the PVDF sensor into GFRP for the first time. A detailed study has been done on the behavior and characterization of the PVDF sensor embedded in GFRP. The PVDF sensors embedded in laminates were subjected to impact test, where a constant weight of 5.5 kg was dropped from a height of 10–60 mm in the interval of 10 mm, and the voltage response of the PVDF sensor was recorded. Sensitivity analysis and AE test of the PVDF sensor in GFRP were also carried out. This is useful for various aerospace applications especially for SHM of aircraft.

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