Mikro/Nano SiO2 Takviyeli Aramid Kompozitlerde Aşınmanın Veriye Dayalı Tahmini ve Deneysel Analizi

Öz

Bu çalışmada, aramid elyaf takviyeli polimer matrisli kompozit malzemelere farklı oranlarda mikro ve nano boyutlu SiO2 partikülleri eklenerek elde edilen numunelerin kuru kayma koşulları altındaki aşınma davranışları belirlenmiştir. Aşınma performansı, kütle kaybı ve özgül aşınma oranları hesaplanarak analiz edilmiş ve aşınmış yüzeyler taramalı elektron mikroskobu kullanılarak incelenmiştir. Aşınma oranları 10, 20 ve 30 N yük ve 100-200 m kayma mesafeleri altında değerlendirilmiştir. Deneysel sonuçlar, özellikle %2-3 nano SiO₂ katkısının aşınma direncini belirgin biçimde artırdığını ve kütle kaybını yaklaşık %55-70 oranında azalttığını göstermiştir. Daha sonra, Yapay Sinir Ağı, Karar Ağacı, Rastgele Orman, Destek Vektör Makinesi, K-En Yakın Komşu ve XGBoost algoritmaları gibi tahmin modelleri kullanılarak aşınma davranışı tahmin edilmiştir Elde edilen aşınma verileri, Grid Search yöntemi ile hiperparametre optimizasyonu yapılarak belirtilen modellerle analiz edilmiştir. Model performansları, 5 katlı çapraz doğrulama ile Ortalama Karesel Hata (MSE) ve Belirleme Katsayısı (R2) değerlerine göre değerlendirilmiştir. Yapay Sinir Ağı için 83,4 MSE ve 0,92 R2, Karar Ağacı için 82,7 MSE ve 0,92 R2 ve K-En Yakın Komşular için 83,4 MSE ve 0,92 R2 ile en iyi sonuçlar elde edilmiştir. Sonuçlar, hiperparametre optimizasyonunun model performansında belirleyici bir rol oynadığını ve YSA, DT ve KNN modellerinin aşınma tahmini açısından yüksek doğruluk sağladığını göstermektedir.

Anahtar Kelimeler

• Aramid kompozit
• SiO2
• Aşınma davranışı
• Makine öğrenmesi
• Hiperparametre optimizasyonu

Data-Driven Prediction and Experimental Analysis of Wear in Micro/Nano SiO2-Reinforced Aramid Composites

Abstract

In this study, the wear behavior of samples obtained by adding micro and nano-sized SiO2 particle additives to aramid fiber reinforced polymer matrix composite materials at different rates were investigated under dry sliding conditions. The influence of both micro- and nanoscale additives was explicitly considered, and the composites were fabricated using a controlled hand lay-up method. The wear performance was analyzed by calculating the mass loss and specific wear rates, and the worn surfaces were examined using scanning electron microscopy (SEM). Wear rates were evaluated under 10 and 15 N loads and 100-200 m sliding distances. Experimental results revealed that the addition of 2-3 wt.% nano SiO2 significantly improved the wear resistance and reduced the mass loss by approximately 55-70% compared to the neat composite. SEM images revealed the presence of abrasive grooves, localized adhesion and material transfer, and micro-scale cracking associated with matrix fragmentation and particle pull-out. Then, predictive models such as Artificial Neural Network, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and XGBoost algorithms were used to predict wear behavior. The wear data obtained were analyzed with the mentioned models by performing hyperparameter optimization with Grid Search method. Model performances are evaluated according to Mean Squared Error (MSE) and Coefficient of Determination (R2) values with 5-fold cross validation. The best results were obtained with an MSE of 83.4 and an R2 of 0.92 for Artificial Neural Network, an MSE of 82.7 and an R2 of 0.92 for Decision Tree, and an MSE of 83.4 and an R2 of 0.92 for K-Nearest Neighbors. The results show that hyperparameter optimization plays a decisive role in model performance and ANN, DT and KNN models provide high accuracy in terms of wear prediction.

Keywords

• Aramid composites
• SiO2
• Wear behavior
• Machine learning
• Hyperparameter optimization

Kaynakça

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