Prediction of Tensile Properties of CNF/Epoxy Composites Using Artificial Neural Network

Xing Kai Chen

doi:10.4028/www.scientific.net/AMR.898.111

Paper Titles

Hydrogen Storage Properties of LiNH₂/MgH₂ Complex Materials with Ti Catalyst by Mechanical Alloying
p.93

Study on Toughening of Chinese Herb Residue Fiber/Recycled ABS Composites
p.98

Effect of Tackifier on Thermal Oxidation Kinetics and Effective Storage Time of Raw Natural Rubber
p.102

Effects of Nanoclay and CO₂Content on Foaming Behaviors of PP/PS Alloys
p.107

Prediction of Tensile Properties of CNF/Epoxy Composites Using Artificial Neural Network
p.111

Research on a Zinc Flow Cell Prototype
p.115

I-V Characterization Study of Porous Silicon Formation by Doubled-Cell Electrochemical Etching
p.119

Anode-Supported LSGM Films Prepared by Slurry Spin Coating
p.123

Preparation and Characterization of NiO-LSCM/LSCM Dual Composite Anode for IT-SOFC
p.128

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 898Prediction of Tensile Properties of CNF/Epoxy...

Prediction of Tensile Properties of CNF/Epoxy Composites Using Artificial Neural Network

Abstract:

In present study, artificial neural network (ANN) was used to predict the tensile moduli of carbon nanofibers (CNF)/epoxy composites. The tensile properties of CNF/epoxy composites made by different dispersion technique were measured by tensile test. It was found that the tensile properties are seriously affected by the CNF fraction, ultrasonication time and mechanical stirring time. According to the test results, ANN was trained and used to predict the tensile moduli of CNF/epoxy composites. By compared the predicted values with the experimental data, it was demonstrated that the back propagation ANN model is a promising tool for prediction of properties of composites.

You might also be interested in these eBooks

Applied Material Science and Related Technologies

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 898)

Pages:

111-114

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.898.111

Citation:

Cite this paper

Online since:

February 2014

Authors:

Xing Kai Chen*

Keywords:

Artificial Neural Network (ANN), Carbon Nanofiber, Composite, Tensile Properties

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Hongming Ma, Jijun Zeng, Mary Lynn Realff, Satish Kumar, David A. Schiraldi. Processing, Structure, and properties of fibers from poluester/carbon nanofiber composites. Composites Science and Technology, 2003, 63: 1617-1628.

DOI: 10.1016/s0266-3538(03)00071-x

Google Scholar

[2] Jijun Zeng, Bethany Saltysiak, W.S. Johnson, Savid A. Schiraldi, Satish Kumar. Processing and properties of poly(methyl methacrylate)/carbon nano fiber composites. Composites: Part B, 2004, 35: 173-178.

DOI: 10.1016/s1359-8368(03)00051-9

Google Scholar

[3] Kumar S., Doshi H., Srinivasarao M., Park J.O., Schiraldi D.A. Fibers from polypropylene/nanocarbon fiber composites. Polymer, 2002, 43: 1701-1708.

DOI: 10.1016/s0032-3861(01)00744-3

Google Scholar

[4] M.D. Sanchez-Garcia, J.M. Lagaron, S.V. Hoa. Effect of addition of carbon nanofibers and carbon nanotubes on properties of thermoplastic biopolymers. Composites Science and Technology, 2010, 70: 1095-1105.

DOI: 10.1016/j.compscitech.2010.02.015

Google Scholar

[5] Smrutisikha Bal. Experimental study of mechanical and electrical properties of carbon nanofiber/epoxy composites. Materials and Design, 2010, 31: 2406-2413.

DOI: 10.1016/j.matdes.2009.11.058

Google Scholar

[6] Rodica-Mariana Diaconescu, Marinela Barbuta, Maria Harja. Prediction of properties of polymer concrete composite with tire rubber using neural networks. Materials Science and Engineering B, 2013, 178: 1259-1267.

DOI: 10.1016/j.mseb.2013.01.014

Google Scholar

[7] Temel Varol, Aykut Canakci, Sukru Ozsahin. Artificial neural network modeling to effect of reinforcement properties on the physical and mechanical properties of Al2024-B4C composites produced by powder metallurgy. Composites: Part B, 2013, 54: 224-233.

DOI: 10.1016/j.compositesb.2013.05.015

Google Scholar