Aramid Elyaf Takviyeli Polimer Matris Kompozitlerin Mekanik Deney Sonuçlarının Yapay Sinir Ağlarıyla Tahminleri ve İstatistiksel Analizleri

Kompozit malzemelerde polimer matris en çok tercih edilen malzeme türlerinden biri haline gelmesi personel koruma ya da diğer zırh malzemesi türlerinde de kullanımını artmakla birlikte gelişmeye devam etmektedir. Mekanik deney sonuçlarını birçok yöntemle matematiksel modellemesi yapılırken en fazla tercih edilenlerden bir tanesi de yapay sinir ağları olmaktadır. Kompozit malzemelerin birden fazla parçacık ya da malzemelerin birleşimiyle oluşmasından dolayı birleşime dahil olan parça ya da malzemenin mekanik sonuçlara etkisinin anlamlı veya anlamsız olduğunu söylemenin en güzel yollarından biri de istatistiksel analizlerdir. İstatistiksel analizlerle bilgi ve yorumlanmasıyla birlikte diğer oran ya da türevlerinin kıyaslama ya da karşılama yapılmasına da olanak sağlamaktadır. Bu çalışmada; Numuneler 8 katlı Aramid elyaf takviyeli ve dolgu malzemesi olarak ağırlıkça %0, %1, %2 ve %4 oranında TiB2 ilaveli olup ayrıca 450 ve 900 oryantasyona sahip olarak üretilmiştir. Yapay sinir ağları ve İstatistiksel analizler ağırlıkça %1 TiB2 ilaveli ve 900 oryantasyona sahip kompozitler diğer oranlara göre balistik amaca en anlamlı sonucu vermiştir.

Anahtar Kelimeler:

“İç İçe Tasarımlı ve Faktöriyel Varyans Analizi, Polimer Matrisli Kompozitler, Yapay Sinir Ağları, nftool, Kevlar-49, Aramid Elyaf”

Predictions and Statistical Analysis of Mechanical Experiment Results of Aramid Fiber Reinforced Polymer Matrix Composites with Artificial Neural Networks

In composite materials, the polymer matrix continues to develop as one of the most preferred material types, and its use in personnel protection or other armor material types is increasing. While mathematical modeling of mechanical test results with many methods is done, artificial neural networks are one of the most preferred ones. Statistical analysis is one of the best ways to say that the effect of the part or material included in the combination on the mechanical results is significant or insignificant, since composite materials are formed by more than one particle or combination of materials. It also allows comparison or compensation of other ratios or derivatives, along with information and interpretation through statistical analysis. In this study; The samples were produced with 8-layer Aramid fiber reinforced and 0%, 1%, 2% and 4% TiB2 additions as filling material and also with 450 and 900 orientations. Artificial neural networks and Statistical analysis, composites with 1% TiB2 addition and 900 orientation gave the most significant result for ballistic purpose compared to other ratios.

Keywords:

Nested Design and Factorial Analysis of Variance, Polymer Matrix Composites, Artificial Neural Networks, nftool, Kevlar-49,

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