Use of acoustic emission to characterize corrosion fatigue damage accumulation in glass fiber reinforced polyester laminates

1999 ◽  
Vol 20 (5) ◽  
pp. 689-696 ◽  
Author(s):  
G. Kotsikos ◽  
J. T. Evans ◽  
A. G. Gibson ◽  
J. Hale
Polymers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 2250
Author(s):  
Mohammad Amjadi ◽  
Ali Fatemi

Short glass fiber-reinforced (SGFR) thermoplastics are used in many industries manufactured by injection molding which is the most common technique for polymeric parts production. Glass fibers are commonly used as the reinforced material with thermoplastics and injection molding. In this paper, a critical plane-based fatigue damage model is proposed for tension–tension or tension–compression fatigue life prediction of SGFR thermoplastics considering fiber orientation and mean stress effects. Temperature and frequency effects were also included by applying the proposed damage model into a general fatigue model. Model predictions are presented and discussed by comparing with the experimental data from the literature.


Author(s):  
S. Gholizadeh

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.


2020 ◽  
pp. 096739112091533
Author(s):  
Anjana Jain ◽  
Shivkumar Minajagi ◽  
Enoos Dange ◽  
Sushma U Bhover ◽  
YT Dharanendra

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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