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Dergiler / Demiryolu Mühendisliği / 2026 / Sayı: 21

Ray Bağlantı Elemanlarının Deneysel ve Yapay Zekâ Destekli Korozyon Direncinin İncelenmesi

Experimental and Artificial Intelligence Supported Investigation of Corrosion Resistance of Rail Fasteners

Mustafa Dursunlar(YOZGAT BOZOK ÜNİVERSİTESİ)0000-0003-4278-2744
Zakir Taş(YOZGAT BOZOK ÜNİVERSİTESİ)0009-0000-0876-0547
Sayfa
83–95
Yayın Tarihi
2025-01-31
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Test sonuçlarında ilk pas 0,5 saat ve tamamen pas 1 saatte gözlemlenmiştir. 1 saat sonundaki ağırlık kaybı 0,0137 g olarak ölçülmüştür.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Gezegenimizdeki küresel iklim değişikliği son yıllarda olumsuz etkilerini iyice hissettirmeye başlamıştır.
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Temel Bulgular & Veri:Bu çalışmada HM tipi ray-travers bağlantısındaki 38Si7 kimyasal bileşime sahip SKL14 gergi kıskacının EN ISO 9227 standardında deneysel olarak yapay atmosferde tuz püskürtme korozyon testleri yapılmıştır.
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Sonuç & Katkı:Test sonuçlarında ilk pas 0,5 saat ve tamamen pas 1 saatte gözlemlenmiştir. 1 saat sonundaki ağırlık kaybı 0,0137 g olarak ölçülmüştür.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Demiryolu · 38Si7 · SKL

Özet

Gezegenimizdeki küresel iklim değişikliği son yıllarda olumsuz etkilerini iyice hissettirmeye başlamıştır. Bu durum atmosfer koşullarını değiştirmekte ve mühendislik yapıları için tehdit oluşturmaktadır. Ağır iklim ve atmosfer koşulları altında işletilen demiryolları için özellikle ray bağlantı elemanlarının mekanik özellikleri yetersiz kalmaktadır. Bu çalışmada HM tipi ray-travers bağlantısındaki 38Si7 kimyasal bileşime sahip SKL14 gergi kıskacının EN ISO 9227 standardında deneysel olarak yapay atmosferde tuz püskürtme korozyon testleri yapılmıştır. Test sonuçlarında ilk pas 0,5 saat ve tamamen pas 1 saatte gözlemlenmiştir. 1 saat sonundaki ağırlık kaybı 0,0137 g olarak ölçülmüştür. Korozyon hızı 0,000749 mm/yıl (0,749 µm/yıl) olarak hesaplanmıştır. Deneysel korozyon verileriyle yapay zekâ destekli makine öğrenmesi yardımıyla tahmin modelleri oluşturulmuştur. Sonuç olarak kaplamasız gergi kıskaçlarının korozyon performansının demiryolu standartları ve kullanımı için uygun olup olmadığı hem deneysel hem de yapay zekâ destekli olarak açıklanmıştır.

Abstract

Global climate change on our planet has started to make its negative effects felt in recent years. This situation changes atmospheric conditions and poses a threat to engineering structures. For railways operated under severe climatic and atmospheric conditions, especially the mechanical properties of rail fasteners are insufficient. In this study, experimental artificial atmosphere salt spray corrosion tests of SKL14 tension clamp with 38Si7 chemical composition in HM type rail-traverse connection were carried out in accordance with EN ISO 9227 standard. In the test results, the first rust was observed in 0.5 hours and the complete rust was observed in 1 hour. Weight loss at the end of 1 hour was measured as 0.0137 g. The corrosion rate was calculated as 0.000749 mm/y (0.749 µm/y). With experimental corrosion data, predictive models were created with the help of artificial intelligence supported machine learning. As a result, the corrosion performance of uncoated tension clamps is explained both experimentally and with artificial intelligence support whether they are suitable for railway standards and use.

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Bu Makaleye Atıf Yap

Dursunlar, M., Taş, Z. (2025). Ray Bağlantı Elemanlarının Deneysel ve Yapay Zekâ Destekli Korozyon Direncinin İncelenmesi. Demiryolu Mühendisliği, Sayı: 21, 83–95. https://doi.org/10.47072/demiryolu.1582533

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