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Journals / Journal of Materials and Mechatronics: A / 2026 / Cilt: 6 Sayı: 1

Prediction of Tribological Properties of Micro-Particle-Filled Composites Using Artificial Neural Network

Yapay Sinir Ağı ile Mikro Parçacık Dolgulu Kompozitlerin Tribolojik Özelliklerinin Tahmini

Hüseyin Gürbüz(BATMAN ÜNİVERSİTESİ)0000-0003-1391-172X
Mehmet Emin Demir(BATMAN ÜNİVERSİTESİ)0000-0001-9630-6378
Şehmus Baday(BATMAN ÜNİVERSİTESİ)0000-0003-4208-8779
İbrahim Halil Akcan(BATMAN ÜNİVERSİTESİ)0009-0007-8075-2323
Pages
68–82
Publication Date
2025-06-19
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the regression coefficient values obtained from the ANN model created for the wear rate and COF values, the experimental results were consistent with high accuracy rates.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Artificial neural network (ANN) is one of the effective techniques used to solve complex engineering problems such as tribological performance of polymer composites.
📊
Key Findings & Data:The aim of this study is to model the wear performance of boron carbide (B4C) and graphite (Gr) filled epoxy matrix composite material with ANN.
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Conclusion & Impact:According to the regression coefficient values obtained from the ANN model created for the wear rate and COF values, the experimental results were consistent with high accuracy rates.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Mikro Parçacık · Cam Elyaf · Kompozit

Abstract

Artificial neural network (ANN) is one of the effective techniques used to solve complex engineering problems such as tribological performance of polymer composites. The aim of this study is to model the wear performance of boron carbide (B4C) and graphite (Gr) filled epoxy matrix composite material with ANN. Composites with 5 wt.%, 10 wt. % and 15 wt. % B4C and Gr fillers in glass fiber reinforced epoxy resin were prepared by simple hand lay-up technique. Wear tests were performed on these composites under dry sliding conditions according to Taguchi's orthogonal array design. Using experimental data, an ANN model was trained and tested to predict the effect of various control factors on wear behavior. In the created ANN model, the network structure is feed forward and back propagation, Levenberg–Marquardt training algorithm, tansig transfer function is used to estimate experimental results. The values of the training, validation, testing and general regression coefficients for coefficient of friction (COF) were 0.9936 – 0.99996 – 0.99807 and 0.9911, respectively, while for the wear rate, they were 0.9968 – 0.99891 – 0.83971 and 0.93886. According to the regression coefficient values obtained from the ANN model created for the wear rate and COF values, the experimental results were consistent with high accuracy rates.

Özet

Yapay sinir ağı (YSA), polimer kompozitlerin tribolojik performansı gibi karmaşık mühendislik problemlerini çözmek için kullanılan etkili tekniklerden biridir. Bu çalışmanın amacı bor karbür (B4C) ve grafit (Gr) dolgulu epoksi matrisli kompozit malzemenin aşınma performansını YSA ile modellemektir. Cam elyaf takviyeli epoksi reçine içerisinde ağırlıkça %5, %10 ve %15 oranında B4C ve Gr dolgulu kompozitler basit elle yatırma tekniği ile hazırlanmıştır. Bu kompozitlere, Taguchi'nin ortogonal dizi tasarımına göre kuru kayma koşullarında aşınma deneyleri gerçekleştirilmiştir. Deneysel veriler kullanılarak, aşınma davranışı üzerinde çeşitli kontrol faktörlerinin etkisini tahmin etmek amacıyla bir YSA modeli eğitilmiş ve test edilmiştir. Oluşturulan YSA modelinde, ağ yapısı ileri beslemeli ve geri yayılımlı, eğitim algoritması Levenberg–Marquardt, transfer fonksiyonu tansig kullanarak deneysel sonuçlar tahmin edilmiştir. Sürtünme katsayısı (COF) için eğitim, doğrulama, test ve genel regresyon katsayı değerleri sırasıyla 0,9936 – 0,99996 – 0,99807 ve 0,9911 iken, aşınma oranı için ise 0,9968 – 0,99891 – 0,83971 ve 0,93886 olarak elde edilmiştir. Aşınma oranı ve COF değerleri için oluşturulan YSA modelinden elde edilen regresyon katsayısı değerlerine göre, deneysel sonuçların yüksek doğruluk oranlarıyla tutarlı olduğu görülmüştür.

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Cite This Article

Gürbüz, H., Demir, M. E., Baday, Ş., Akcan, İ. H. (2025). Prediction of Tribological Properties of Micro-Particle-Filled Composites Using Artificial Neural Network. Journal of Materials and Mechatronics: A, Cilt: 6 Sayı: 1, 68–82. https://doi.org/10.55546/jmm.1581662

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