Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2010
Title: Artificial neural network (ANN) prediction of compressive strength of VARTM processed polymer composites
Authors: Seyhan, Abdullah Tuğrul
Tayfur, Gökmen
Karakurt, Murat
Tanoğlu, Metin
Seyhan, A. Tuğrul
Tayfur, Gökmen
Karakurt, Murat
Tanoğlu, Metin
Izmir Institute of Technology. Mechanical Engineering
Izmir Institute of Technology. Civil Engineering
Keywords: Artificial neural network (ANN)
Compressive strength
Multi-linear regression (MLR)
Polymer composites
Preforming binder
Neural networks
Issue Date: Aug-2005
Publisher: Elsevier Ltd.
Source: Seyhan, A. T., Tayfur, G., Karakurt, M., and Tanoǧlu, M. (2005). Artificial neural network (ANN) prediction of compressive strength of VARTM processed polymer composites. Computational Materials Science, 34(1), 99-105. doi:10.1016/j.commatsci.2004.11.001
Abstract: A three layer feed forward artificial neural network (ANN) model having three input neurons, one output neuron and two hidden neurons was developed to predict the ply-lay up compressive strength of VARTM processed E-glass/ polyester composites. The composites were manufactured using fabric preforms consolidated with 0, 3 and 6 wt.% of thermoplastic binder. The learning of ANN was accomplished by a backpropagation algorithm. A good agreement between the measured and the predicted values was obtained. Testing of the model was done within low average error levels of 3.28%. Furthermore, the predictions of ANN model were compared with those obtained from a multi-linear regression (MLR) model. It was found that ANN model has better predictions than MLR model for the experimental data. Also, the ANN model was subjected to a sensitivity analysis to obtain its response. As a result, the ANN model was found to have an ability to yield a desired level of ply-lay up compressive strength values for the composites processed with the addition of the thermoplastic binder.
URI: http://doi.org/10.1016/j.commatsci.2004.11.001
http://hdl.handle.net/11147/2010
ISSN: 0927-0256
0927-0256
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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