Machine Learning Based Modeling of Coating Properties and Corrosion of Borided AISI H1 Tool Steel

Yıl 2024, , 625 - 638, 03.10.2024

Faruk Çavdar , Ali Günen , Mustafa Sert

https://doi.org/10.21605/cukurovaumfd.1560038

Öz

Due to the significant increases in hardness, wear, and corrosion resistance it provides, boronizing is one of the most commonly used thermochemical coating processes. In this study, the effect of process temperature and duration on coating thickness, surface roughness, microhardness, and corrosion rate in boronized hot work tool steel AISI H11 material with pack boronizing technique has been modeled and investigated using machine learning methods. Multiple linear, K-nearest neighbors, support vector machine, decision tree, random forest, and extreme gradient boosting regression algorithms were employed to create models, and their performances were compared using R2, mean absolute error, and mean squared error criteria. Coating thickness and hardness increase with process temperature and duration. However, process temperature has a more significant effect on these properties compared to duration. Upon examining the results of the regression model, it was observed that the effects of coating parameters on thickness and roughness cumulatively transformed into an effect on the corrosion rate. As a result of the study, optimum parameter values for corrosion resistance in boronized AISI H11 steel were determined to be 1000°C and 2.2 hours.

Anahtar Kelimeler

AISI H11, Pack boronizing, Corrosion rate, Machine learning, Regression model

Borlanmış AISI H11 Takım Çeliğinin Kaplama Özellikleri ve Korozyon Oranının Makine Öğrenmesi Temelli Modellenmesi

Öz

Yüksek sertlik, aşınma ve korozyon direncinde önemli ölçüde artışlar sağlaması nedeniyle borlama işlemi kullanılan en yaygın termokimyasal kaplama işlemlerinden birisidir. Bu çalışmada sıcak iş takım çeliklerinden AISI H11 malzemenin kutu borlama tekniğiyle borlanmasında işlem sıcaklığı ve süresinin kaplama kalınlığı, yüzey pürüzlülüğü, sertliği ve korozyon oranına etkisi makine öğrenmesi yöntemleriyle modellenmiş ve incelenmiştir. Çalışma kapsamında çoklu doğrusal, K en yakın komşu, destek vektör makinesi, karar ağacı, rastgele orman ve ekstrem eğim arttırma regresyon algoritmaları ile modeller oluşturulmuş ve bu modellerin performansları R2, ortalama mutlak hata ve ortalama kare hatası kriterleri kullanılarak kıyaslanmıştır. Kaplama tabakası kalınlık ve sertlikleri işlem sıcaklığı ve süresi ile artmaktadır. Diğer yandan bu özellikleri üzerinde borlama sıcaklığı süreye göre daha etkilidir. Regresyon modeli sonuçları incelendiğinde kaplama parametrelerinin kaplama kalınlığına ve pürüzlülüğe olan etkilerinin birleşerek korozyon oranı üzerinde kümülatif bir etkiye dönüştüğü görülmüştür. Çalışma sonucunda AISI H11 çeliğinin borlanmasında korozyon direnci için optimum parametre değerlerinin 1000 °C ve 2,2 saat olduğu sonucuna varılmıştır.

Anahtar Kelimeler

AISI H11, Kutu borlama, Korozyon oranı, Makine öğrenmesi, Regresyon modeli

Kaynakça

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