期刊
COMPUTATIONAL MATERIALS SCIENCE
卷 34, 期 1, 页码 99-105出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.commatsci.2004.11.001
关键词
compressive strength; artificial neural network (ANN); polymer composites; preforming binder; multi-linear regression (MLR)
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. (c) 2004 Elsevier B.V. All rights reserved.
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