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Journals / İmalat Teknolojileri ve Uygulamaları / 2026 / Cilt: 7 Sayı: 1

Machine Learning-Based Surface Roughness Prediction in Eco-Friendly Milling of AISI 420 Stainless Steel

AISI 420 Paslanmaz Çeliğin Çevre Dostu Frezelenmesinde Makine Öğrenmesi Tabanlı Yüzey Pürüzlülüğü Tahmini

Yusuf Furkan Yapan(YILDIZ TECHNICAL UNIVERSITY)0000-0001-9684-4117
Anuj Kumar Jha(Institute of Infrastructure Technology Research and Management)0009-0005-4201-427X
Navneet Khanna(Institute of Infrastructure Technology Research and Management)0000-0002-8094-4604
Alper Uysal(YILDIZ TECHNICAL UNIVERSITY)0000-0002-5513-4431
Pages
43–60
Publication Date
2026-04-29
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AI Summary & Key Takeaways

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

Titanium nitride (TiN)-coated tungsten carbide (WC) cutting tool, NMQL method containing 0.15 wt.% MWCNT and 40 mL/h MQL flow rate improved the cutting temperature and surface roughness by 25.3% and 49.9%, respectively, compared to dry cutting condition with the uncoated WC cutting tool. he K-nearest neighbors regression model achieved the highest predictive accuracy for surface roughness, with a coefficient of determination (R²) of 0.9789, outperforming the other evaluated machine learning approaches.

📌 3 Key Takeaways

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Objective & Hypothesis:Although martensitic stainless steels (MSS) are widely used due to their superior mechanical and chemical properties, their poor machinability can lead to surface quality degradation.
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Key Findings & Data:Therefore, in addition to improving the surface quality of the material using advanced machining methods, it is important to predict the surface quality to increase efficiency, reduce waste, and prevent faulty functionality.
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Conclusion & Impact:Titanium nitride (TiN)-coated tungsten carbide (WC) cutting tool, NMQL method containing 0.15 wt.% MWCNT and 40 mL/h MQL flow rate improved the cutting temperature and surface roughness by 25.3% and 49.9%, respectively, compared to dry cutting condition with the uncoated WC cutting tool. he K-nearest neighbors regression model achieved the highest predictive accuracy for surface roughness, with a coefficient of determination (R²) of 0.9789, outperforming the other evaluated machine learning approaches.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Paslanmaz çelik · Makine öğrenmesi · Yüzey pürüzlülüğü tahmini

Abstract

Although martensitic stainless steels (MSS) are widely used due to their superior mechanical and chemical properties, their poor machinability can lead to surface quality degradation. Therefore, in addition to improving the surface quality of the material using advanced machining methods, it is important to predict the surface quality to increase efficiency, reduce waste, and prevent faulty functionality. In this study, surface roughness was predicted for the first time using various machine learning models such as, polynomial regression, support vector regression, decision tree regression, random forest regression, and k-nearest neighbors regression, depending on the cutting condition, minimum quantity lubrication (MQL) flow rate, nanoparticle concentration, cutting tool coating, and cutting temperature in sustainable milling of AISI 420 MSS using the MQL method with multi-walled carbon nanotube (MWCNT)-reinforced nanofluid (NMQL). Titanium nitride (TiN)-coated tungsten carbide (WC) cutting tool, NMQL method containing 0.15 wt.% MWCNT and 40 mL/h MQL flow rate improved the cutting temperature and surface roughness by 25.3% and 49.9%, respectively, compared to dry cutting condition with the uncoated WC cutting tool. he K-nearest neighbors regression model achieved the highest predictive accuracy for surface roughness, with a coefficient of determination (R²) of 0.9789, outperforming the other evaluated machine learning approaches.

Özet

Martenzitik paslanmaz çelikler, üstün mekanik ve kimyasal özellikleri nedeniyle yaygın olarak kullanılmalarına rağmen, zayıf işlenebilirlik özellikleri yüzey kalitesinde bozulmalara yol açabilmektedir. Bu nedenle, gelişmiş talaşlı imalat yöntemleri kullanılarak malzemenin yüzey kalitesinin iyileştirilmesinin yanı sıra; verimliliğin artırılması, israfın azaltılması ve hatalı fonksiyonların önlenmesi amacıyla yüzey kalitesinin önceden tahmin edilmesi büyük önem taşımaktadır. Bu çalışmada, çok duvarlı karbon nanotüp (ÇDKNT) takviyeli nanoakışkan içeren minimum miktarda yağlama (NMMY) yöntemi kullanılarak sürdürülebilir frezeleme koşullarında işlenen AISI 420 martenzitik paslanmaz çeliğin yüzey pürüzlülüğü, kesme koşulu, MMY debisi, nanoparçacık konsantrasyonu, kesici takım kaplaması ve kesme sıcaklığına bağlı olarak polinomsal regresyon, destek vektör regresyonu, karar ağacı regresyonu, rastgele orman regresyonu ve k-en yakın komşu regresyonu gibi çeşitli makine öğrenmesi modelleri kullanılarak ilk kez tahmin edilmiştir. Titanyum nitrür (TiN) kaplamalı tungsten karbür (WC) kesici takım ile birlikte %0,15 (ağırlıkça) ÇDKNT içeren ve 40 mL/saat MMY debisine sahip NMMY yöntemi, kaplamasız WC kesici takım kullanılan kuru kesme koşullarına kıyasla kesme sıcaklığını ve yüzey pürüzlülüğünü sırasıyla %25,3 ve %49,9 oranında iyileştirmiştir. K-en yakın komşular regresyon modeli, 0,9789'luk belirleme katsayısı (R²) ile yüzey pürüzlülüğü için en yüksek tahmin doğruluğunu elde ederek değerlendirilen diğer makine öğrenme yaklaşımlarından daha iyi performans göstermiştir.

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Cite This Article

Yapan, Y. F., Jha, A. K., Khanna, N., Uysal, A. (2026). Machine Learning-Based Surface Roughness Prediction in Eco-Friendly Milling of AISI 420 Stainless Steel. İmalat Teknolojileri ve Uygulamaları, Cilt: 7 Sayı: 1, 43–60. https://doi.org/10.52795/mateca.1872574

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