Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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2005 - 200912010 - 20181

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Author: search.filters.author.Tanoğlu, MetinPublisher: search.filters.publisher.Elsevier Ltd.

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  • Article
    Citation - WoS: 97
    Citation - Scopus: 110
    Effect of Polyamide-6,6 (pa 66) Nonwoven Veils on the Mechanical Performance of Carbon Fiber/Epoxy Composites
    (Elsevier Ltd., 2018) Beylergil, Bertan; Tanoğlu, Metin; Aktaş, Engin
    In this study, carbon fiber/epoxy (CF/EP) composites were interleaved with polyamide-6,6 (PA 66) nonwoven veils at two different areal weight densities (17 and 50 gsm) to improve their delamination resistance against Mode-I loading. Mode-I fracture toughness (DCB), tensile, open hole tensile (OHT), flexural, compression, short beam shear (ILSS) and Charpy-impact tests were performed on the reference and PA 66 interleaved composite specimens. The DCB test results showed that the initiation and propagation Mode-I fracture toughness values of the composites were significantly improved by 84 and 171% using PA 66-17 gsm veils respectively, as compared to reference laminates. The use of denser PA 66-50 gsm veils in the interlaminar region led to higher improvement in fracture toughness values (349% for initiation and 718% for propagation) due to the higher amount of veil fibers involved in fiber bridging toughening mechanism. The incorporation of PA 66-50 gsm nonwoven veils also increased the ILSS and Charpy impact strength of the composites by 25 and 15%, respectively. On the other hand, the PA 66 veils reduced in-plane mechanical properties of CF/EP composites due to lower carbon fiber volume fraction and increased thickness.
  • Article
    Citation - WoS: 53
    Citation - Scopus: 63
    Artificial Neural Network (ann) Prediction of Compressive Strength of Vartm Processed Polymer Composites
    (Elsevier Ltd., 2005) Seyhan, Abdullah Tuğrul; Tayfur, Gökmen; Karakurt, Murat; Tanoğlu, Metin
    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.