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

Abstract

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.

Keywords

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

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

Abstract

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.

Keywords

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

References

1. M. Shahwaz, P. Nath, I. Sen, A critical review on the microstructure and mechanical properties correlation of additively manufactured nickel-based superalloys, J. Alloys Compd. 907 (2022) 164530. https://doi.org/https://doi.org/10.1016/j.jallcom.2022.164530.
2. X. Wang, Y. Ding, Y. Gao, Y. Ma, J. Chen, B. Gan, Effect of grain refinement and twin structure on the strength and ductility of Inconel 625 alloy, Mater. Sci. Eng. A 823 (2021) 141739.
3. D. Dubey, R. Mukherjee, M. K. Singh, A review on tribological behavior of nickel-based Inconel superalloy, Proc. Inst. Mech. Eng. Part J J. Eng. Tribol. 238 (2024) 706–732. https://doi.org/10.1177/13506501241235724.
4. Y. Xie, D.M. Artymowicz, P.P. Lopes, A. Aiello, D. Wang, J.L. Hart, E. Anber, M.L. Taheri, H. Zhuang, R.C. Newman, K. Sieradzki, A percolation theory for designing corrosion-resistant alloys, Nat. Mater. 20 (2021) 789–793.https://doi.org/10.1038/s41563-021-00920-9.
5. H. Wang, Q. Guo, C. Li, L. Cui, H. Yao, Y. Liu, Microstructural evolution and strengthening mechanisms of inconel 718 alloy with 1wt.% Ti2AlC addition fabricated by laser powder bed fusion, Mater. Sci. Eng. A 925 (2025) 147872.https://doi.org/https://doi.org/10.1016/j.msea.2025.147872.
6. H. Zhang, Y. Liu, X. Liu, K. Zhu, T. Xiao, Z. Zhang, K. Feng, L. Chai, S. Guo, N. Guo, Laser weldability and interface microstructure of CoCrNi-based medium-entropy alloy to Inconel 718 superalloy, Mater. Charact. 216 (2024) 114290.https://doi.org/https://doi.org/10.1016/j.matchar.2024.114290.
7. P. Sun, N. Yan, S. Wei, D. Wang, W. Song, C. Tang, J. Yang, Z. Xu, Q. Hu, X. Zeng, Microstructural evolution and strengthening mechanisms of Inconel 718 alloy with different W addition fabricated by laser cladding, Mater. Sci. Eng. A 868 (2023) 144535.https://doi.org/10.1016/j.msea.2022.144535.
8. M.S. Alsoufi, S.A. Bawazeer, Predictive Modeling of Surface Integrity and Material Removal Rate in Computer Numerical Control Machining: Effects of Thermal Conductivity and Hardness, Materials (Basel). 18 (2025). https://doi.org/10.3390/ma18071557.

9. M.R.D.D. Carvalho, A.Í.S. Antonialli, A.E. Diniz, A machinability evaluation based on the thermal and mechanical properties of two engine valve steels, Int. J. Adv. Manuf. Technol. 110 (2020) 3209–3219. https://doi.org/10.1007/s00170-020-06108-w.
10. S. Chauhan, R. Trehan, R.P. Singh, State of the art in finite element approaches for milling process: a review, Adv. Manuf. 11 (2023) 708–751.https://doi.org/10.1007/s40436-022-00417-x.
11. M.P. Sealy, Z.Y. Liu, D. Zhang, Y.B. Guo, Z.Q. Liu, Energy consumption and modeling in precision hard milling, J. Clean. Prod. 135 (2016) 1591–1601.https://doi.org/10.1016/j.jclepro.2015.10.094.
12. A. Czán, R. Joch, M. Šajgalík, J. Holubják, A. Horák, P. Timko, J. Valíček, M. Kušnerová, M. Harničárová, Experimental Study and Verification of New Monolithic Rotary Cutting Tool for an Active Driven Rotation Machining, Materials (Basel). 15 (2022).https://doi.org/10.3390/ma15051630.
13. N. Sihag, K.S. Sangwan, A systematic literature review on machine tool energy consumption, J. Clean. Prod. 275 (2020) 123125.https://doi.org/10.1016/j.jclepro.2020.123125.
14. R. Çakıroğlu, M. Günay, Analysis of surface roughness and energy consumption in turning of C17500 copper alloy under different machining environments and modellings with response surface method, Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. 237 (2022) 207–219. https://doi.org/10.1177/09544089221101368.
15. M.E. Korkmaz, M.K. Gupta, E. Çelik, N.S. Ross, M. Günay, A sustainable cooling/lubrication method focusing on energy consumption and other machining characteristics in high-speed turning of aluminum alloy, Sustain. Mater. Technol. 40 (2024) e00919.https://doi.org/10.1016/j.susmat.2024.e00919.
16. J. Lv, R. Tang, S. Jia, Y. Liu, Experimental study on energy consumption of computer numerical control machine tools, J. Clean. Prod. 112 (2016) 3864–3874.https://doi.org/10.1016/j.jclepro.2015.07.040.
17. B. Li, X. Tian, M. Zhang, Modeling and Multi-objective Optimization Method of Machine Tool Energy Consumption Considering Tool Wear, Int. J. Precis. Eng. Manuf. Technol. 9 (2022) 127–141. https://doi.org/10.1007/s40684-021-00320-z.
18. D. Bergh, E. Wagenmakers, F. Aust, Bayesian Repeated-Measures Analysis of Variance: An Updated Methodology Implemented in JASP, Adv. Methods Pract. Psychol. Sci. 6 (2023) 25152459231168024. https://doi.org/10.1177/25152459231168024.
19. R. Christensen, One-Way ANOVA BT - Plane Answers to Complex Questions: The Theory of Linear Models, in: R. Christensen (Ed.), Springer New York, New York, NY, 2011: pp. 91–103. https://doi.org/10.1007/978-1-4419-9816-3_4.
20. G. Douzas, F. Bacao, F. Last, Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE, Inf. Sci. (Ny). 465 (2018) 1–20.https://doi.org/10.1016/j.ins.2018.06.056.
21. M. Mujahid, E. Kına, F. Rustam, M.G. Villar, E.S. Alvarado, I. De La Torre Diez, I. Ashraf, Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering, J. Big Data 11 (2024) 87.https://doi.org/10.1186/s40537-024-00943-4.
22. M.A. Mahboob Ali, A.I. Azmi, A.N. Mohd. Khalil, Specific cutting energy of Inconel 718 under dry, chilled-air and minimal quantity nanolubricants, Procedia CIRP 77 (2018) 429–432. https://doi.org/10.1016/j.procir.2018.08.290.
23. N. Khanna, C. Agrawal, M. Dogra, C.I. Pruncu, Evaluation of tool wear, energy consumption, and surface roughness during turning of inconel 718 using sustainable machining technique, J. Mater. Res. Technol. 9 (2020) 5794–5804.https://doi.org/10.1016/j.jmrt.2020.03.104.
24. B. Özlü, H.B. Ulaş, F. Kara, Investigation of the Effects of Cutting Tool Coatings and Machining Conditions on Cutting Force, Specific Energy Consumption, Surface Roughness, Cutting Temperature, and Tool Wear in the Milling of Ti6Al4V Alloy, Lubricants 13 (2025).https://doi.org/10.3390/lubricants13080363.
25. A.K. Parida, K. Maity, Numerical and experimental analysis of specific cutting energy in hot turning of Inconel 718, Measurement 133 (2019) 361–369.https://doi.org/10.1016/j.measurement.2018.10.033.
26. Z. Zhou, K. Liu, J. Zhou, Y. Xu, L. Wang, A highly energy-efficient milling of Inconel 718 via modulated short electric arc machining, J. Manuf. Process. 78 (2022) 46–58. https://doi.org/10.1016/j.jmapro.2022.03.051.
27. M.U. Farooq, R. Kumar, A. Khan, J. Singh, S. Anwar, A. Verma, R. Haber, Sustainable machining of Inconel 718 using minimum quantity lubrication: Artificial intelligence-based process modelling, Heliyon 10 (2024) e34836. https://doi.org/10.1016/j.heliyon.2024.e34836.
28. P. Adishesha, S. Allada, PB Dhanish, J. Mathew, D. L. K, Analysis of the power during machining of Inconel 718 for different geometrical profiles and the development of power prediction models using multisensor data, Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. (2024) 09544089241291743. https://doi.org/10.1177/09544089241291743.
29. W. Frifita, S. Ben Salem, A. Haddad, M.A. Yallese, Optimization of machining parameters in turning of Inconel 718 Nickel-base super alloy, Mech. Ind. 21 (2020) 203.
30. N. Khanna, P. Shah, R.W. Maruda, G.M. Krolczyk, H. Hegab, Experimental investigation and sustainability assessment to evaluate environmentally clean machining of 15-5 PH stainless steel, J. Manuf. Process. 56 (2020) 1027–1038.https://doi.org/10.1016/j.jmapro.2020.05.016.
31. N. Khanna, P. Shah, N.M. Suri, C. Agrawal, S.K. Khatkar, F. Pusavec, M. Sarikaya, Application of Environmentally-friendly Cooling/Lubrication Strategies for Turning Magnesium/SiC MMCs, Silicon 13 (2021) 2445–2459.https://doi.org/10.1007/s12633-020-00588-x.
32. A. Dttmann, J. de Oliveira Gomes, Adapted versus Projected Machining Centers Energy Consumption for MQL Technique, U. Porto J. Eng. 7 (2021) 78–87.
33. J. Han, Y. Tang, L. Yue, X. Ma, H. Jia, N. Liu, P. Bai, Y. Meng, Y. Tian, Tribological Behavior of Polydiethylsiloxane (PDES) in a Si3N4 and M50 System under Low Temperatures from −80 to 25 °C, Lubricants 12 (2024).https://doi.org/10.3390/lubricants12050176.
34. C. Camposeco-Negrete, Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA, J. Clean. Prod. 53 (2013) 195–203.
35. M. Jamil, N. He, W. Zhao, H. Xiang, M.K. Gupta, A. Iqbal, A.M. Khan, Assessment of energy consumption, carbon emissions and cost metrics under hybrid MQL-Dry ice blasting system: A novel cleaner production technology for manufacturing sectors, J. Clean. Prod. 360 (2022) 132111. https://doi.org/10.1016/j.jclepro.2022.132111.
36. E. Aslan, Y. Özüpak, F. Alpsalaz, Z.M.S. Elbarbary, A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence, IEEE Access 13 (2025) 113618–113633.
37. R. Hassanzadeh, M. Farhadian, H. Rafieemehr, Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms, BMC Med. Res. Methodol. 23 (2023) 101. https://doi.org/10.1186/s12874-023-01920-w.
38. J. Kim, J.H. Kim, W. Jang, J. Pyo, H. Lee, S. Byeon, H. Lee, Y. Park, S. Kim, Enhancing Machine Learning Performance in Estimating CDOM Absorption Coefficient via Data Resampling, Remote Sens. 16 (2024). https://doi.org/10.3390/rs16132313.
39. F. Alpsalaz, Fault Detection in Power Transmission Lines: Comparison of Chirp-Z Algorithm and Machine Learning Based Prediction Models, Eksploat. i Niezawodn. – Maint. Reliab. (2025). https://doi.org/10.17531/ein/203949.