Investigation of Effects of Process Parameters on Coating and Wear Properties in Boriding of Inconel 718 Through Machine Learning Methods

Year 2024, , 1929 - 1954, 15.12.2024

Faruk Çavdar , Ali Günen

https://doi.org/10.31466/kfbd.1505889

Abstract

Boronizing is an effective process that enhances the surface and wear properties of nickel-based superalloys. However, there is a need for a detailed understanding to optimize this process and improve coating quality. The primary objective of this study is to demonstrate the use of machine learning for optimizing boronizing process parameters and enhancing coating quality. Within the scope of the study, variations in coating thickness, surface roughness, microhardness, as well as the average coefficient of friction and wear volume loss at temperatures of 25, 400, and 750 °C were investigated in relation to boronizing temperature and duration. Initially, the most suitable machine learning algorithm was determined for each dependent variable, and three-dimensional surface graphs were generated using these algorithms to visualize the variations of dependent variables with boronizing temperature and duration. Upon examination of the graphs, it was observed that an increase in process temperature and duration led to an increase in coating thickness and hardness, along with an increase in surface roughness. The lowest coefficient of friction value (0.26) in friction tests conducted at room temperature and 750°C was found to be achieved within the 1000-1050 °C range with a process duration of 5-6 hours. When friction tests were conducted at 400°C, the lowest coefficient of friction (0.31) was determined to occur within the temperature range of 900-1000 °C and a process duration of 3-5 hours. Additionally, it was found that the effect of boronizing temperature on wear volume loss is more significant than that of the boronizing process duration.

Keywords

Inconel 718, Boriding, Machine learning, wear, Coefficient of friction

Inconel 718 Malzemenin Borlanmasında İşlem Parametrelerinin Kaplama ve Aşınma Özelliklerine Etkisinin Makine Öğrenmesi Yöntemleri Kullanılarak Araştırılması

Abstract

Borlama, nikel esaslı süper alaşımların yüzey ve aşınma özelliklerini iyileştiren etkili bir işlemdir. Ancak, bu işlemin optimize edilmesi ve kaplama kalitesinin arttırılması için detaylı bir anlayışın gerekliliği ortaya çıkmaktadır. Çalışmanın temel amacı, borlama işlem parametrelerinin optimize edilmesi ve kaplama kalitesinin arttırılması için makine öğrenmesi kullanımını ortaya koymaktır. Çalışma kapsamında, kaplama kalınlığı, yüzey pürüzlülüğü, mikrosertlik gibi özellikler yanında 25, 400 ve 750 °C sıcaklık şartlarındaki ortalama sürtünme katsayısı ve aşınma hacim kaybının borlama sıcaklığı ve süresine bağlı değişimi incelenmiştir. Çalışmada öncelikle her bağımlı değişken için en uygun makine öğrenmesi algoritması belirlenmiş ve bu algoritmalar kullanılarak oluşturulan üç boyutlu yüzey grafikleri yardımıyla bağımlı değişkenlerin borlama sıcaklığı ve süresi ile değişimi görselleştirilmiştir. Grafikler üzerinde yapılan incelemeler sonucunda işlem sıcaklığı ve süresinin artmasıyla kaplama kalınlığı ve sertliğiyle birlikte yüzey pürüzlülüğünün de arttığı görülmüştür. Oda sıcaklığında ve 750 C’de yapılan aşınma deneylerinde sürtünme katsayısının en düşük değerinin (0,26) 1000-1050 °C arasında 5-6 saat aralığındaki işlem süresinde 0,26 elde edileceği saptanmıştır. Aşınma testlerinin 400 °C’de yapılması durumunda ise en düşük sürtünme katsayısının 900-1000 °C sıcaklık ve 3-5 saat işlem süresi aralığında (0,31) olacağı tespit edilmiştir. Ek olarak borlama sıcaklığın aşınma hacim kaybı üzerindeki etkisinin borlama işlem süresinden daha fazla olduğu tespit edilmiştir.

Keywords

Inconel 718, Borlama, Makine öğrenmesi, Aşınma, Sürtünme katsayısı

Ethical Statement

Yapılan çalışmada araştırma ve yayın etiğine uyulmuştur.

Supporting Institution

Destekleyen kurum bulunmamaktadır.

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