TEPKİ YÜZEYİ TASARIMI VE YAPAY SİNİR AĞLARI YAKLAŞIMI UYGULANARAK EPOKSİ MATRİSLİ KOMPOZİT MALZEMENİN AŞINMA DAYANIMININ TAHMİNİ VE MODELLENMESİ

Abstract

Yapılan çalışmada; inşaat, otomotiv ve havacılık gibi birçok sektörde geniş bir kullanım alanına sahip olan epoksi matrisli kompozit malzemenin aşınma davranışına etki eden faktörler incelenmiş olup, süreç optimizasyonu gerçekleştirilmiştir. Cam ve ferrokrom (karbür) katkı maddelerinin epoksi matrisli kompozit malzemenin aşınma dayanımına etkisini tahmin etmek için, Merkezi Birleşik Tasarım (MBT) uygulanarak toplam 18 deney noktasında 54 adet deney numunesi üretilmiştir. Üretilen numunelerin aşınma tepki değerleri ölçülerek Tepki Yüzeyleri Tasarımı (TYT) ve Yapay Sinir Ağları (YSA) aşınma tahmin modelleri oluşturulmuş ve bu modellerin tahmin performansı değerleri karşılaştırılmıştır. YSA yaklaşımının, sınama setinin aşınma oranı tahmininde ortalama yüzde hata değeri (MAPE) %8,18 olarak hesaplanmış olup, TYT yaklaşımının MAPE değeri %9,42 olarak bulunmuştur. Tepki değişkenindeki değişkenliğin açıklanmasında ve epoksi matrisli kompozit malzemenin aşınma davranışının tahmin edilmesinde R 2 ve ortalama kare hata (MSE) istatistikleri de incelenmiş olup, bu istatistiklerde MSE için 1,317 ve R 2 için %81,1 değerleri ile TYT yaklaşımının YSA yaklaşımına göre daha başarılı olduğu sonucuna ulaşılmıştır. Ayrıca, cam katkı oranının artması ile aşınma oranının büyük ölçüde azaldığı görülmüştür. Minimum aşınma oranı; küçük parçacıklarda cam ve ferrokrom katkı oranının sırasıyla %17,07 ve %2,93 olduğu, büyük parçacıklarda iki katkı oranının da %17,07 olduğu durumda elde edilmiştir.

Keywords

• Tepki yüzeyi tasarımı
• aşınma
• epoksi matrisli kompozit malzeme
• regresyon
• yapay sinir ağları

Prediction And Modelling Wear Resistance of Epoxy Matrix Composite Using Artificial Neural Network and Response Surface Design

Abstract

Epoxy resin is a widely used material in various of industries especially construction, aviation and automative. Factors that affect epoxy-based composite’s wear rate have been investigated and process optimization has been conducted in this paper. In order to predict the effect of glass and ferrochromium reinforcement in wear resistance of epoxy, total number of 54 sample has been produced where design points are determined by Central Composite Design (CCD). After samples have been tested via wear test machine, results are compared with Artificial Neural Network (ANN) and Response Surface Methodology (RSM) wear predictions. Mean absolute percentage error (MAPE) shows that ANN (8.18%) outperforms RSM (9.42%) in terms of wear prediction accuracy. Mean square error (MSE) and R 2 statistics are also examined in order to explain variability in response variable and it is concluded that RSM yields better results which are 1.317 and %81.1, respectively. Besides, it is found that glass reinforcement results in decrease in wear rate. Minimum wear rate for small sized particle is obtained at level where glass and ferrochromium reinforcement rates are 17.07% and 2.93%, respectively. For large sized particles, minimum wear rate is obtained where both reinforcements are at rate 17.07%.

Keywords

• Response surface design
• wear
• epoxy matrix composite
• regression
• artificial neural networks

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