Inconel 718 Alaşımının Frezelenmesinde Enerji Tüketiminin Optimizasyonu ve Makine Öğrenmesi ile Tahmin Modeli

Öz

Bu çalışma işlenebilirliği zor olarak bilinen Inconel 718 süperalaşımının frezelenmesi sırasında ortaya çıkan güç tüketimini optimize etmeyi ve aynı zamanda makine öğrenmesi tabanlı tahmin modelleri geliştirmeyi amaçlamaktadır. Deneyler üç farklı kesme hızı (40, 60 ve 90 m/dak) ve dört farklı kesme koşulu (kuru, Minimum Miktarda Yağlama (MQL), kriyojenik ve kriyojenik+MQL) altında Taksan TMC 500 V CNC dik işleme merkezinde yürütülmüştür. Enerji tüketimi KAEL Multiser sinyal analizörü kullanılarak gerçek zamanlı olarak kaydedilmiş elde edilen veriler ise ANOVA ve regresyon yöntemleri ile analiz edilmiştir. İstatistiksel analiz sonuçları, enerji talebini belirleyen en önemli faktörün kesme hızı olduğunu (p<0,001), soğutma/yağlama stratejilerinin ise istatistiksel olarak anlamlı bir etki göstermediğini ortaya koymuştur. Ayrıca, veri kümesindeki sınıf dengesizliğini gidermek için SMOTE tabanlı bir veri çoğaltma yöntemi kullanılmış ve ardından güç tüketimi tahmini amacıyla topluluk (ensemble) ve regresyon tabanlı makine öğrenmesi modelleri (Rastgele Orman, Gradient Boosting ve Doğrusal Regresyon) eğitilmiştir. Bulgular Gradient Boosting algoritmasının tüm kesme ortamlarında en yüksek doğruluk seviyesine ulaştığını, performans değerlerinin R²≈0,97 ve RMSE≈7 W olduğunu göstermiştir. Elde edilen sonuçlar deneysel verilerin hesaplamalı yöntemlerle birleştirilmesinin talaşlı imalatta enerji tüketimini azaltmada etkili olduğunu ve sürdürülebilir üretim hedeflerine katkı sunduğunu kanıtlamaktadır. Bu yaklaşım Inconel 718’in endüstriyel işlenmesinde hem enerji verimliliği hem de çevresel sürdürülebilirlik açısından önemli bir yöntem önermektedir.

Anahtar Kelimeler

• Frezeleme
• Enerji Tüketimi
• MMY
• Makine Öğrenimi
• SMOTE

Optimisation of Energy Consumption in Milling of Inconel 718 Alloy and Prediction Model with Machine Learning

Öz

This study aims to optimize power consumption observed while milling Inconel 718 superalloy—well known for its poor machinability—and to develop machine learning-based prediction models. Experiments were carried out on a Taksan TMC 500 V CNC milling machining center at three cutting speeds (40, 60, and 90 m/min) under four distinct cutting conditions: dry, Minimum Quantity Lubrication (MQL), cryogenic, and cryogenic+MQL. Energy consumption was monitored in real-time using a KAEL Multiser signal analyzer and the collected data were analyzed through ANOVA and regression approaches. The ANOVA results revealed that cutting speed is the most significant factor influencing energy demand (p<0.001), whereas cooling/lubrication strategies exhibited no statistically significant effect. To address class imbalance the dataset was augmented via a SMOTE-based method and ensemble and regression-based ML models (Random Forest, Gradient Boosting, Linear Regression) were trained for power prediction. The findings indicated that the Gradient Boosting algorithm consistently achieved superior accuracy across all cutting environments with performance levels reaching R²≈0.97 and RMSE≈7 W. Results indicate that combining experimental data with computational methods is effective for decreasing energy consumption in machining and advancing sustainable production goals. The proposed methodology contributes to enhancing both efficiency and environmental sustainability in the industrial processing of Inconel 718.

Anahtar Kelimeler

• Milling
• Energy Consumption
• MQL
• Machine Learning
• SMOTE

Kaynakça

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