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Research Article

Characterization and predictive modeling of thermally aged glass fiber reinforced plastic composites

Md Mijanur Rahman1, M Muzibur Rahman2

1Department of Mechanical and Aerospace Engineering, Oklahoma State University, U.S.A
2Department of Mechanical Engineering, Sonargaon University, Bangladesh

Keywords

Abstract


Glass Fiber Reinforced Plastic Composite;

 Thermal aging;

 Material characterization;

 Predictive modeling; 

Artificial neural network

Glass fiber reinforced plastics (GFRP) are exposed to thermal aging in their widespread aerospace applications. Evaluating the effect of mechanical properties due to thermal aging has remained a challenge. An experimental investigation to characterize the thermal aging effects of glass fiber epoxy composites as well as the development of a predictive modeling is presented here. Tensile test samples have been thermally aged at 50°C, 100°C, 150°C and 200°C for 30 mins, 60 mins, 90 mins and 120 mins. At higher temperatures, the samples have shown a gradually increasing brown color while emitting a burning smell. The tensile test shows that the UTS value decreases as the thermal aging temperature increases. The predictive model has been prepared by combining image processing, regression analysis and two cascaded artificial neural networks (ANNs). The model reads the photographic image of the sample and uses the color change as an identifier. Cascaded ANNs estimate the thermal aging temperature and time from the image processing program. Finally, the ANN’s output is forwarded to the developed regression equation to get the estimated UTS. The predictive model’s estimated UTS shows an average accuracy of 97% when compared to the experimental results.

© 2023 MIM Research Group. All rights reserved.

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LATEST AWARDS


2023 Reviewer Awards:

Please, visit Reviewer Awards section for the winners of the 2022 RESM reviewer awards.



2023 Best Paper Award:

The paper authored by Ferzan Fidan, Naim Aslan, Mümin Mehmet Koç entitled as “Morpho-structural and compressive mechanical properties of graphene oxide reinforced hydroxyapatite scaffolds for bone tissue applications” is awarded.



2023 Most Cited Paper Award:

The paper authored by Ercan Işık, Ehsan Harirchian, Hüseyin Bilgin, Kirti Jadhav entitled as “The effect of material strength and discontinuity in RC structures according to different site-specific design spectra" is awarded.


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