The Effect of Vegetable Cutting Oils on Turning AISI 5140 Steel for Sustainable Machining

Öz

This study presents a comprehensive machinability analysis of AISI 5140 steel widely used in industry by performing turning operations under dry and two different MQL (Minimum Quantity Lubrication) conditions. The analysis involves examining the machinability output parameters of cutting force and surface roughness at three different levels of feed rate and cutting speed, while maintaining a constant depth of cut. Physical experiments were conducted on a manual lathe, cutting forces were measured during the experiments, and surface roughness values were measured after the experiments were completed. The obtained data were first analyzed graphically and then subjected to regression, decision tree, heat map, and prediction table analyses, which represent machine learning models. The study concluded that low feed rates and high cutting speeds are required to achieve optimum cutting force. For surface roughness, low feed rate and high cutting speed values also yielded favorable results. The most advantageous performance was obtained in the MQL (Sunflower oil) environment. The accuracy of the prediction models was confirmed by achieving R² values higher than 70% for both output parameters. In conclusion, it was determined that integrating machine learning models into machinability studies can facilitate future manufacturing processes.

Anahtar Kelimeler

• AISI 5140
• Cutting oil
• Machinability
• Machine Learning

Destekleyen Kurum

This study was supported by TÜBİTAK 2209A program under project number 1919B012472605.

Sürdürülebilir İşleme için AISI 5140 Çeliğinin Tornalanmasında Bitkisel Kesme Yağlarının Etkisi

Öz

Bu çalışma, endüstride yaygın olarak kullanılan AISI 5140 çeliğinin kuru ve iki farklı Minimum Miktar Yağlama (MMY) koşulu altında tornalama işlemleri yapılarak kapsamlı bir işlenebilirlik analizi sunmaktadır. Analiz, sabit bir kesme derinliği korunurken, üç farklı ilerleme hızı ve kesme hızı seviyesinde kesme kuvveti ve yüzey pürüzlülüğü gibi işlenebilirlik çıktı parametrelerinin incelenmesini içermektedir. Fiziksel deneyler manuel bir torna tezgahında gerçekleştirilmiş, deneyler sırasında kesme kuvvetleri ölçülmüş ve deneyler tamamlandıktan sonra yüzey pürüzlülüğü değerleri ölçülmüştür. Elde edilen veriler önce grafiksel olarak analiz edilmiş, ardından makine öğrenme modellerini temsil eden regresyon, karar ağacı, ısı haritası ve tahmin tablosu analizlerine tabi tutulmuştur. Çalışma, optimum kesme kuvvetine ulaşmak için düşük ilerleme hızları ve yüksek kesme hızlarının gerekli olduğu sonucuna varmıştır. Yüzey pürüzlülüğü için de düşük ilerleme hızı ve yüksek kesme hızı değerleri olumlu sonuçlar vermiştir. En avantajlı performans MQL (Ayçiçek yağı) ortamında elde edilmiştir. Tahmin modellerinin doğruluğu, her iki çıktı parametresi için de %70'in üzerinde R² değerleri elde edilerek doğrulanmıştır. Sonuç olarak, makine öğrenimi modellerinin işlenebilirlik çalışmalarına entegre edilmesinin gelecekteki üretim süreçlerini kolaylaştırabileceği belirlenmiştir.

Anahtar Kelimeler

• AISI 5140
• Kesme yağı
• İşlenebilirlik
• Makine Öğrenmesi

Destekleyen Kurum

Bu çalışma, TÜBİTAK 2209A programı kapsamında 1919B012472605 proje numarasıyla desteklenmiştir.

Kaynakça

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