کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10644602 | 999654 | 2005 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Artificial neural network (ANN) prediction of compressive strength of VARTM processed polymer composites
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مکانیک محاسباتی
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چکیده انگلیسی
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 6Â 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Materials Science - Volume 34, Issue 1, August 2005, Pages 99-105
Journal: Computational Materials Science - Volume 34, Issue 1, August 2005, Pages 99-105
نویسندگان
A. TugËrul Seyhan, Gökmen Tayfur, Murat Karakurt, Metin TanogËlu,