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Journals / Politeknik Dergisi / 2025 / Cilt: 28 Sayı: 1

Predicting Surface Roughness with Machine Learning

Yüzey Pürüzlülüğünün Makine Öğrenmesi ile Tahmin Edilmesi

Bayram Sercan Bayram(GAZİ ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, İMALAT MÜHENDİSLİĞİ BÖLÜMÜ, İMALAT MÜHENDİSLİĞİ ANABİLİM DALI)0000-0002-9092-6274
Oktay Yıldız(Gazi Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Anabilim Dalı)0000-0001-9155-7426
İhsan Korkut(GAZİ ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, İMALAT MÜHENDİSLİĞİ BÖLÜMÜ, İMALAT MÜHENDİSLİĞİ ANABİLİM DALI)0000-0002-5001-4449
Pages
187–195
Publication Date
2025-01-24
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AI Summary & Key Takeaways

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

The study showed that the ANN method has a higher performance than the MLR method and can be used to predict surface roughness values.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:CNC turning is generally used to machine metal parts.
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Key Findings & Data:In order to optimise machining performance, it is necessary to investigate and predict the complex relationships between machining parameters and surface roughness values.
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Conclusion & Impact:The study showed that the ANN method has a higher performance than the MLR method and can be used to predict surface roughness values.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Makine öğrenmesi · Modelleme · Yüzey pürüzlülüğü

Abstract

CNC turning is generally used to machine metal parts. The quality of the surface finish is an important parameter in assessing the quality of the process and determining the machining conditions. In order to optimise machining performance, it is necessary to investigate and predict the complex relationships between machining parameters and surface roughness values. Artificial Neural Network (ANN) models can be used to investigate the effects of cutting conditions on surface roughness due to their ability to learn and predict complex relationships. In this study, prediction models were developed to predict surface roughness (Ra) values after turning using Multiple Linear Regression (MLR) and ANN methods. Machining experiments were conducted to develop the prediction models. Three levels of control factors of cutting speed (m/min), cutting depth (mm) and feed rate (mm/rev) were used in the experiments. The developed models were validated with experimental measurements and their performance was evaluated. It was calculated that the ANN predictions had an accuracy of 87.6% with respect to the actual values, while the multiple regression predictions had an accuracy of 78.4%. The study showed that the ANN method has a higher performance than the MLR method and can be used to predict surface roughness values.

Özet

CNC tornalama genellikle metal parçaları işlemek için kullanılır. Son yüzeyin kalitesi, işlemin kalitesini değerlendirilmesi ve işleme koşullarını belirlemesinde önemli bir parametredir. İşleme performansını optimize etmek için, işleme parametreleri ve yüzey pürüzlülüğü değerleri arasındaki karmaşık ilişkileri araştırmak ve tahmin etmek gerekir. Yapay Sinir Ağı (YSA) modelleri, karmaşık ilişkileri öğrenme ve tahmin etme yetenekleri nedeniyle kesme koşullarının yüzey pürüzlülüğü üzerindeki etkilerini araştırmak için kullanılabilir. Bu çalışmada, Çoklu Lineer Regresyon (ÇLR) ve YSA yöntemleri kullanılarak tornalama sonrası yüzey pürüzlülüğü (Ra) değerlerini tahmin etmek için tahmin modelleri geliştirilmiştir. Tahmin modellerini geliştirmek için işleme deneyleri yapılmıştır. Deneylerde kesme hızı (m/dak), kesme derinliği (mm) ve ilerleme hızı (mm/dev) olmak üzere üç kontrol faktörü seviyesi kullanılmıştır. Geliştirilen modeller deneysel ölçümlerle doğrulanmış ve performansları değerlendirilmiştir. YSA tahminlerinin gerçek değerlere göre %87,6 doğruluğa sahip olduğu, çoklu regresyon tahminlerinin ise %78,4 doğruluğa sahip olduğu hesaplanmıştır. Çalışma, YSA yönteminin MLR yönteminden daha yüksek bir performansa sahip olduğunu ve yüzey pürüzlülüğü değerlerini tahmin etmek için kullanılabileceğini göstermiştir.

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Cite This Article

Bayram, B. S., Yıldız, O., Korkut, İ., *, B. S. B. (2025). Predicting Surface Roughness with Machine Learning. Politeknik Dergisi, Cilt: 28 Sayı: 1, 187–195. https://doi.org/10.2339/politeknik.1447279

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