Paslanmaz çeliklerin işlenmesi sırasında kuru ve MQL ortamlarının incelenmesi için makine öğrenimine dayalı bir yaklaşım

Öz

Paslanmaz çelik, aşındırıcı sıvılar için tanklar ve depolama kapları, kimya ve petrokimya endüstrisi, buhar kazanları, boya endüstrisi, gıda tesisleri ve madencilik gibi birçok alanda ideal kılan benzersiz malzeme özelliklerine sahiptir. Bu tür malzemeler, hassas boyutlar ve iyi yüzey kalitesi için son işlemlere (bitirme işlemleri) ihtiyaç duyar. Talaşlı imalat, yaygın olarak kullanılan uzun ömürlü malzemelerin şekillendirilmesinde en etkili yöntemlerden biridir. Bu makale hem deneysel hem de makine öğrenimi tabanlı veri değerlendirme yaklaşımıyla 316L çeliklerinin işlenebilirlik çalışmasına odaklanmaktadır. Farklı kesme parametre kombinasyonlarıyla (kesme hızı, ilerleme hızı ve talaş derinliği) kuru ve MMY ortamlarında bir dizi tornalama deneyi yapılmıştır. Bu çalışmanın sonuçları kesme kuvvetleri, kesme sıcaklıkları ve enerji tüketimidir. Makine öğrenimi tabanlı modeller (karar ağacı ve tahmin modeli), kesme hızı, ilerleme hızı ve kesme derinliği kullanılarak kuru ve MMY ortamlarında kesme kuvvetini ve kesme sıcaklığını yüksek doğrulukla tahmin etmiştir. Özellikle MMY koşullarında elde edilen daha düşük hata değerleri ve daha yüksek R² katsayıları, modelin işlenebilirlik analizindeki kararlılığını ve etkinliğini göstermektedir. Geliştirilen makine öğrenimi modellerinde, kesme kuvveti için R² değeri 0,975'e, sıcaklık için ise 0,957'ye ulaştı. RMSE değerleri sırasıyla 4,03–4,87 ve 2,76–3,32 aralığında elde edildi. Çalışma hem deneysel hemde makine öğrenmesini bir arada bulundurması nedeniyle işlenebilirlik deneylerinde gelecekte yapılacak araştırmalar için yardımcı olacaktır.

Anahtar Kelimeler

• Paslanmaz çelik
• Kesme koşulları
• Makine öğrenmesi
• AISI 316L
• MQL

A machine learning based approach for investigation of dry and MQL environments while turning stainless steels

Öz

Stainless steel has unique material properties that made these materials ideal in many areas such as in tanks and storage vessels for corrosive liquids, in the chemical and petrochemical industry, in steam boilers, in the paint industry, in food plants, in mining. Such types of materials require final operations (finish process) for precise dimensions and good surface quality. Machining is one of the most effective methods for shaping the long-lasting materials used in common area. This paper focuses on the machinability study of 316L steels in both experimental and machine learning based data evaluation approach. A series of turning experiments were conducted under dry and MQL environments with different cutting parameter (cutting Speed, feed rate and depth of cut) combinations. The outcomes of this study were cutting forces, cutting temperatures and energy consumption. Machine learning-based models (decision tree and prediction model) have predicted cutting force and cutting temperature with high accuracy in dry and MQL environments using cutting speed, feed rate, and depth of cut. The lower error values and higher R² coefficients obtained, particularly under MQL conditions, demonstrate the model's stability and effectiveness in machinability analysis. In the developed machine learning models, the R² value for cutting force reached 0.975, and for temperature it reached 0.957. RMSE values were obtained in the range of 4.03–4.87 and 2.76–3.32, respectively. Because the study combines both experimental and machine learning methods, it will be helpful for future research in machinability experiments.

Anahtar Kelimeler

• Stainless steel
• Cutting Environments
• Machine Learning
• AISI 316L
• MQL

Kaynakça

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