Araştırma Makalesi
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Optimization of Vertical Machining Parameters of DIN 1.0038 Steels Using Hybrid Taguchi Based Grey-Fuzzy Algorithm in CNC Pocket Milling Process

Yıl 2026, Cilt: 41 Sayı: 1, 115 - 128, 25.03.2026
https://doi.org/10.21605/cukurovaumfd.1706626
https://izlik.org/JA75NH47PG

Öz

In CNC pocket milling, surface quality, material removal rate and production time are critical parameters. However, setting the optimum parameters related to the process is problematic due to the presence of many factors. In this study, a hybrid grey-based fuzzy algorithm with a Taguchi L16 orthogonal array experimental design was used to determine the optimum results by combining factors such as cutting speed, feed rate, cutting depth and cutting path strategy. The optimum results were found as 0.36 µm surface roughness, 10 s machining time and 120 mm³/min material removal rate. These results were achieved by using 1500 rpm cutting speed, 2.0 mm/rev feed rate, 1.25 mm cutting depth and zigzag cutting path strategy. In the analysis made using the Analysis of Variance (ANOVA), it was concluded that the process was affected by feed rate, cutting depth, cutting speed and cutting path strategy, respectively.

Kaynakça

  • 1. Ozturk, B. & Kara, F. (2020). Calculation and estimation of surface roughness and energy consumption in milling of 6061 alloy. Advances in Materials Science and Engineering, 2020(1), 5687951.
  • 2. Traini, E., Bruno, G. & Lombardi, F. (2020). Tool condition monitoring framework for predictive maintenance: a case study on milling process. International Journal of Production Research, 59(23), 7179-7193.
  • 3. Liu, Q., Chen, X., Liu, K., Cristino, V.A.M., Lo, K., Xie, Z., Guo, D., Tam, L. & Kwok, C. (2024). Influence of processing parameters on microstructure and surface hardness of hypereutectic Al-Si-Fe-Mg alloy via friction stir processing. Coatings, 14(2), 222.
  • 4. Viswanathan, R., Ramesh, S., Maniraj, S. & Subburam, V. (2020). Measurement and multi-response optimization of turning parameters for magnesium alloy using hybrid combination of Taguchi-GRA-PCA technique. Measurement, 159, 107800.
  • 5. Bien, D.X. (2023). Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 238(1-2), 137-150.
  • 6. Yang, H., Zheng, H. & Zhang, T. (2024). A review of artificial intelligent methods for machined surface roughness prediction. Tribology International, 199, 109935.
  • 7. Gologlu, C. & Sakarya, N. (2008). The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method. Journal of Materials Processing Technology, 206(1-3), 7-15.
  • 8. Deng, C., Miao, J., Ma, Y., Wei, B. & Feng, Y. (2019). Reliability analysis of chatter stability for milling process system with uncertainties based on neural network and fourth moment method. International Journal of Production Research, 58(9), 2732-2750.
  • 9. Chen, J., Lin, J., Zhang, M. & Lin, Q. (2024). Predicting surface roughness in turning complex-structured workpieces using vibration-signal-based gaussian process regression. Sensors, 24(7), 2117.
  • 10. Nguyen, A.T., Nguyen, V.H., Le, T.T. & Nguyen, N.T. (2022). Multiobjective optimization of surface roughness and tool wear in high‐speed milling of AA6061 by machine learning and NSGA‐II. Advances in Materials Science and Engineering, 2022(1), 5406570.
  • 11. Zhang, Y., Xie, M., He, Y. & Han, X. (2022). Capability-based remaining useful life prediction of machining tools considering non-geometry and tolerancing features with a hybrid model. International Journal of Production Research, 61(21), 7540-7556.
  • 12. Trinh, V.L. (2024). A review of the surface roughness prediction methods in finishing machining. Engineering, Technology & Applied Science Research, 14(4), 15297-15304.
  • 13. Shagwira, H., Mbuya, T.O., Akinlabi, E.T., Mwema, F.M. & Tanya, B. (2021). Optimization of material removal rate in the CNC milling of polypropylene+ 60 wt% quarry dust composites using the Taguchi technique. Materials Today: Proceedings, 44, 1130-1132.
  • 14. Lu, B. & Luo, Y. (2024). A dynamic condition-based maintenance policy for heterogeneous-wearing tools with considering product quality deterioration. International Journal of Production Research, 62(19), 7096-7113.
  • 15. Ko, J.H. & Yin, C. (2025). A review of artificial intelligence application for machining surface quality prediction: From key factors to model development. Journal of Intelligent Manufacturing, 1-24.
  • 16. Yang, J., Zhang, Y., Huang, Y., Lv, J. & Wang, K. (2022). Multi-objective optimization of milling process: Exploring trade-off among energy consumption, time consumption and surface roughness. International Journal of Computer Integrated Manufacturing, 36(2), 219-238.
  • 17. Kar, T., Mandal, N.K. & Singh, N.K. (2020). Multi-response optimization and surface texture characterization for CNC milling of Inconel 718 alloy. Arabian Journal for Science and Engineering, 45(2), 1265-1277.
  • 18. Kechagias, J.D., Aslani, K.E., Fountas, N.A., Vaxevanidis, N.M. & Manolakos, D.E. (2020). A comparative investigation of Taguchi and full factorial design for machinability prediction in turning of a titanium alloy. Measurement, 151, 107213.
  • 19. Yalcin, U., Karaoglan, A.D. & Korkut, I. (2013). Optimization of cutting parameters in face milling with neural networks and Taguchi based on cutting force, surface roughness and temperatures. International Journal of Production Research, 51(11), 3404-3414.
  • 20. Chandrasekaran, M., Muralidhar, M., Krishna, C.M. & Dixit, U.S. (2009). Application of soft computing techniques in machining performance prediction and optimization: A literature review. The International Journal of Advanced Manufacturing Technology, 46(5-8), 445-464.
  • 21. Isik, U., Demir, H. & Ozlu, B. (2025). Multi-objective optimization of process parameters for surface quality and geometric tolerances of AlSi10Mg samples produced by additive manufacturing method using taguchi-based gray relational analysis. Arabian Journal for Science and Engineering, 50(12), 9211-9229.
  • 22. Mantle, A. & Aspinwall, D. (2001). Surface integrity of a high speed milled gamma titanium aluminide. Journal of Materials Processing Technology, 118(1-3), 143-150.
  • 23. Benardos, P.G. & Vosniakos, G.C. (2002). Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments. Robotics and Computer-Integrated Manufacturing, 18(5-6), 343-354.
  • 24. Ertekin, Y.M., Kwon, Y. & Tseng, T. (2003). Identification of common sensory features for the control of CNC milling operations under varying cutting conditions. International Journal of Machine Tools and Manufacture, 43(9), 897-904.
  • 25. Paris, H., Peigne, G. & Mayer, R. (2004). Surface shape prediction in high speed milling. International Journal of Machine Tools and Manufacture, 44(15), 1567-1576.
  • 26. Franco, P., Estrems, M. & Faura, F. (2004). Influence of radial and axial runouts on surface roughness in face milling with round insert cutting tools. International Journal of Machine Tools and Manufacture, 44(15), 1555-1565.
  • 27. Shamsuddin, K.A., Ab-Kadir, A.R. & Osman, M.H. (2013). A Comparison of milling cutting path strategies for thin-walled aluminium alloys fabrication, The International Journal of Engineering and Science, 2(3),1-8.
  • 28. Ali, R.A., Mia, M., Khan, A.M., Chen, W., Gupta, M.K. & Pruncu, C.I. (2019). Multi-response optimization of face milling performance considering tool path strategies in machining of Al-2024. Materials, 12(7), 1013.
  • 29. Asadi, R., Yeganefar, A. & Niknam, S.A. (2019). Optimization and prediction of surface quality and cutting forces in the milling of aluminum alloys using ANFIS and interval type 2 neuro fuzzy network coupled with population-based meta-heuristic learning methods. The International Journal of Advanced Manufacturing Technology, 105(5-6), 2271-2287.
  • 30. Bawono, B., Anggoro, P., Bayuseno, A., Jamari, J. & Tauviqirrahman, M. (2019). Milling strategy optimized for orthotics insole to enhance surface roughness and machining time by Taguchi and response surface methodology. Journal of Industrial and Production Engineering, 36(4), 237-247.
  • 31. Daniel, S.A.A., Pugazhenthi, R., Kumar, R. & Vijayananth, S. (2019). Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi-grey relational analysis. Defence Technology, 15(4), 545-556.
  • 32. Mashinini, P.M., Soni, H. & Gupta, K. (2019). Investigation on dry machining of stainless steel 316 using textured tungsten carbide tools. Materials Research Express, 7(1), 016502.
  • 33. Datta, S., Bandyopadhyay, A. & Pal, P.K. (2008). Grey-based Taguchi method for optimization of bead geometry in submerged arc bead-on-plate welding. The International Journal of Advanced Manufacturing Technology, 39(11-12), 1136-1143.
  • 34. Abdulrazaq, M.M., Jaber, A.S., Hammood, A.S. & Abdulameer, A.G. (2019). Optimization of machining parameters for MRR and surface roughness for 7024 AL-alloy in pocket milling process. Association of Arab Universities Journal of Engineering Sciences, 26(1), 10-16.
  • 35. Esme, U., Kulekci, M.K., Ustun, D., Kahraman, F. & Kazancoglu, Y. (2015). Grey-based fuzzy algorithm for the optimization of the ball burnishing process. Materials Testing, 57(7-8), 666-673.
  • 36. Kumar, R., Khepar, J., Yadav, K., Kareri, E., Alotaibi, S.D., Viriyasitavat, W., Gulati, K., Kotecha, K. & Dhiman, G. (2022). A systematic review on generalized fuzzy numbers and its applications: past, present and future. Archives of Computational Methods in Engineering, 29(7), 5213-5236.
  • 37. Pandey, R.K. & Panda, S. (2014). Optimization of bone drilling parameters using grey-based fuzzy algorithm. Measurement, 47, 386-392.
  • 38. Lin, C.L. (2004). Use of the Taguchi method and grey relational analysis to optimize turning operations with multiple performance characteristics. Materials and Manufacturing Processes, 19(2), 209-220.
  • 39. Pınarbasi, A., Kulekci, M.K., Boga, C. & Esme, U. (2020). Optimization of the effect of processing parameters on surface roughness and cutting energy in CNC milling of Al-7075 material. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 35(2), 345-356.
  • 40. Demirdogen, M.F., Kilic, S. ve Ozturk, F. (2022). Yüzey pürüzlülüğünün tahmininde farklı yöntemlerin incelenmesi. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 37(2), 531-542.

CNC Cep Frezeleme Prosesinde Hibrit Taguchi Tabanlı Gri-Bulanık Algoritma Kullanılarak DIN 1.0038 Çeliklerinin Dikey İşleme Parametrelerinin Optimizasyonu

Yıl 2026, Cilt: 41 Sayı: 1, 115 - 128, 25.03.2026
https://doi.org/10.21605/cukurovaumfd.1706626
https://izlik.org/JA75NH47PG

Öz

CNC cep frezelemede, yüzey kalitesi, talaş kaldırma oranı ve üretim süresi kritik parametrelerdir. Ancak prosese ilişkin optimum parametrelerin ayarlanması, çok sayıda faktörün bir arada bulunması nedeniyle sorun teşkil etmektedir. Bu çalışmada; kesme hızı, ilerleme oranı, kesme derinliği ve kesme yolu stratejisi gibi faktörler birleştirilerek optimum sonuçları belirlemek için bir Taguchi L16 ortogonal dizi deneysel tasarımına sahip hibrit gri tabanlı bulanık algoritma kullanılmıştır. Optimum sonuçlar 0,36 µm yüzey pürüzlülüğü, 10 sn işleme süresi ve 120 mm³/dk talaş kaldırma oranı şeklinde bulunmuştur. Bu sonuçlara 1500 rpm kesme hızı, 2,0 mm/dev ilerleme oranı, 1,25 mm kesme derinliği ve zikzak kesme yolu stratejisi kullanılarak ulaşılmıştır. Varyans Analizi (ANOVA) yöntemi kullanılarak yapılan analizlerde prosesin sırasıyla ilerleme oranı, kesme derinliği, kesme hızı ve kesme yolu stratejisinden etkilendiği sonucuna ulaşılmıştır.

Kaynakça

  • 1. Ozturk, B. & Kara, F. (2020). Calculation and estimation of surface roughness and energy consumption in milling of 6061 alloy. Advances in Materials Science and Engineering, 2020(1), 5687951.
  • 2. Traini, E., Bruno, G. & Lombardi, F. (2020). Tool condition monitoring framework for predictive maintenance: a case study on milling process. International Journal of Production Research, 59(23), 7179-7193.
  • 3. Liu, Q., Chen, X., Liu, K., Cristino, V.A.M., Lo, K., Xie, Z., Guo, D., Tam, L. & Kwok, C. (2024). Influence of processing parameters on microstructure and surface hardness of hypereutectic Al-Si-Fe-Mg alloy via friction stir processing. Coatings, 14(2), 222.
  • 4. Viswanathan, R., Ramesh, S., Maniraj, S. & Subburam, V. (2020). Measurement and multi-response optimization of turning parameters for magnesium alloy using hybrid combination of Taguchi-GRA-PCA technique. Measurement, 159, 107800.
  • 5. Bien, D.X. (2023). Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 238(1-2), 137-150.
  • 6. Yang, H., Zheng, H. & Zhang, T. (2024). A review of artificial intelligent methods for machined surface roughness prediction. Tribology International, 199, 109935.
  • 7. Gologlu, C. & Sakarya, N. (2008). The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method. Journal of Materials Processing Technology, 206(1-3), 7-15.
  • 8. Deng, C., Miao, J., Ma, Y., Wei, B. & Feng, Y. (2019). Reliability analysis of chatter stability for milling process system with uncertainties based on neural network and fourth moment method. International Journal of Production Research, 58(9), 2732-2750.
  • 9. Chen, J., Lin, J., Zhang, M. & Lin, Q. (2024). Predicting surface roughness in turning complex-structured workpieces using vibration-signal-based gaussian process regression. Sensors, 24(7), 2117.
  • 10. Nguyen, A.T., Nguyen, V.H., Le, T.T. & Nguyen, N.T. (2022). Multiobjective optimization of surface roughness and tool wear in high‐speed milling of AA6061 by machine learning and NSGA‐II. Advances in Materials Science and Engineering, 2022(1), 5406570.
  • 11. Zhang, Y., Xie, M., He, Y. & Han, X. (2022). Capability-based remaining useful life prediction of machining tools considering non-geometry and tolerancing features with a hybrid model. International Journal of Production Research, 61(21), 7540-7556.
  • 12. Trinh, V.L. (2024). A review of the surface roughness prediction methods in finishing machining. Engineering, Technology & Applied Science Research, 14(4), 15297-15304.
  • 13. Shagwira, H., Mbuya, T.O., Akinlabi, E.T., Mwema, F.M. & Tanya, B. (2021). Optimization of material removal rate in the CNC milling of polypropylene+ 60 wt% quarry dust composites using the Taguchi technique. Materials Today: Proceedings, 44, 1130-1132.
  • 14. Lu, B. & Luo, Y. (2024). A dynamic condition-based maintenance policy for heterogeneous-wearing tools with considering product quality deterioration. International Journal of Production Research, 62(19), 7096-7113.
  • 15. Ko, J.H. & Yin, C. (2025). A review of artificial intelligence application for machining surface quality prediction: From key factors to model development. Journal of Intelligent Manufacturing, 1-24.
  • 16. Yang, J., Zhang, Y., Huang, Y., Lv, J. & Wang, K. (2022). Multi-objective optimization of milling process: Exploring trade-off among energy consumption, time consumption and surface roughness. International Journal of Computer Integrated Manufacturing, 36(2), 219-238.
  • 17. Kar, T., Mandal, N.K. & Singh, N.K. (2020). Multi-response optimization and surface texture characterization for CNC milling of Inconel 718 alloy. Arabian Journal for Science and Engineering, 45(2), 1265-1277.
  • 18. Kechagias, J.D., Aslani, K.E., Fountas, N.A., Vaxevanidis, N.M. & Manolakos, D.E. (2020). A comparative investigation of Taguchi and full factorial design for machinability prediction in turning of a titanium alloy. Measurement, 151, 107213.
  • 19. Yalcin, U., Karaoglan, A.D. & Korkut, I. (2013). Optimization of cutting parameters in face milling with neural networks and Taguchi based on cutting force, surface roughness and temperatures. International Journal of Production Research, 51(11), 3404-3414.
  • 20. Chandrasekaran, M., Muralidhar, M., Krishna, C.M. & Dixit, U.S. (2009). Application of soft computing techniques in machining performance prediction and optimization: A literature review. The International Journal of Advanced Manufacturing Technology, 46(5-8), 445-464.
  • 21. Isik, U., Demir, H. & Ozlu, B. (2025). Multi-objective optimization of process parameters for surface quality and geometric tolerances of AlSi10Mg samples produced by additive manufacturing method using taguchi-based gray relational analysis. Arabian Journal for Science and Engineering, 50(12), 9211-9229.
  • 22. Mantle, A. & Aspinwall, D. (2001). Surface integrity of a high speed milled gamma titanium aluminide. Journal of Materials Processing Technology, 118(1-3), 143-150.
  • 23. Benardos, P.G. & Vosniakos, G.C. (2002). Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments. Robotics and Computer-Integrated Manufacturing, 18(5-6), 343-354.
  • 24. Ertekin, Y.M., Kwon, Y. & Tseng, T. (2003). Identification of common sensory features for the control of CNC milling operations under varying cutting conditions. International Journal of Machine Tools and Manufacture, 43(9), 897-904.
  • 25. Paris, H., Peigne, G. & Mayer, R. (2004). Surface shape prediction in high speed milling. International Journal of Machine Tools and Manufacture, 44(15), 1567-1576.
  • 26. Franco, P., Estrems, M. & Faura, F. (2004). Influence of radial and axial runouts on surface roughness in face milling with round insert cutting tools. International Journal of Machine Tools and Manufacture, 44(15), 1555-1565.
  • 27. Shamsuddin, K.A., Ab-Kadir, A.R. & Osman, M.H. (2013). A Comparison of milling cutting path strategies for thin-walled aluminium alloys fabrication, The International Journal of Engineering and Science, 2(3),1-8.
  • 28. Ali, R.A., Mia, M., Khan, A.M., Chen, W., Gupta, M.K. & Pruncu, C.I. (2019). Multi-response optimization of face milling performance considering tool path strategies in machining of Al-2024. Materials, 12(7), 1013.
  • 29. Asadi, R., Yeganefar, A. & Niknam, S.A. (2019). Optimization and prediction of surface quality and cutting forces in the milling of aluminum alloys using ANFIS and interval type 2 neuro fuzzy network coupled with population-based meta-heuristic learning methods. The International Journal of Advanced Manufacturing Technology, 105(5-6), 2271-2287.
  • 30. Bawono, B., Anggoro, P., Bayuseno, A., Jamari, J. & Tauviqirrahman, M. (2019). Milling strategy optimized for orthotics insole to enhance surface roughness and machining time by Taguchi and response surface methodology. Journal of Industrial and Production Engineering, 36(4), 237-247.
  • 31. Daniel, S.A.A., Pugazhenthi, R., Kumar, R. & Vijayananth, S. (2019). Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi-grey relational analysis. Defence Technology, 15(4), 545-556.
  • 32. Mashinini, P.M., Soni, H. & Gupta, K. (2019). Investigation on dry machining of stainless steel 316 using textured tungsten carbide tools. Materials Research Express, 7(1), 016502.
  • 33. Datta, S., Bandyopadhyay, A. & Pal, P.K. (2008). Grey-based Taguchi method for optimization of bead geometry in submerged arc bead-on-plate welding. The International Journal of Advanced Manufacturing Technology, 39(11-12), 1136-1143.
  • 34. Abdulrazaq, M.M., Jaber, A.S., Hammood, A.S. & Abdulameer, A.G. (2019). Optimization of machining parameters for MRR and surface roughness for 7024 AL-alloy in pocket milling process. Association of Arab Universities Journal of Engineering Sciences, 26(1), 10-16.
  • 35. Esme, U., Kulekci, M.K., Ustun, D., Kahraman, F. & Kazancoglu, Y. (2015). Grey-based fuzzy algorithm for the optimization of the ball burnishing process. Materials Testing, 57(7-8), 666-673.
  • 36. Kumar, R., Khepar, J., Yadav, K., Kareri, E., Alotaibi, S.D., Viriyasitavat, W., Gulati, K., Kotecha, K. & Dhiman, G. (2022). A systematic review on generalized fuzzy numbers and its applications: past, present and future. Archives of Computational Methods in Engineering, 29(7), 5213-5236.
  • 37. Pandey, R.K. & Panda, S. (2014). Optimization of bone drilling parameters using grey-based fuzzy algorithm. Measurement, 47, 386-392.
  • 38. Lin, C.L. (2004). Use of the Taguchi method and grey relational analysis to optimize turning operations with multiple performance characteristics. Materials and Manufacturing Processes, 19(2), 209-220.
  • 39. Pınarbasi, A., Kulekci, M.K., Boga, C. & Esme, U. (2020). Optimization of the effect of processing parameters on surface roughness and cutting energy in CNC milling of Al-7075 material. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 35(2), 345-356.
  • 40. Demirdogen, M.F., Kilic, S. ve Ozturk, F. (2022). Yüzey pürüzlülüğünün tahmininde farklı yöntemlerin incelenmesi. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 37(2), 531-542.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Mühendisliğinde Optimizasyon Teknikleri
Bölüm Araştırma Makalesi
Yazarlar

Kaan Emre Engin 0000-0002-6439-7700

Uğur Eşme 0000-0002-0672-7943

Mustafa Kemal Külekci 0000-0002-5829-3489

Gönderilme Tarihi 26 Mayıs 2025
Kabul Tarihi 6 Ocak 2026
Yayımlanma Tarihi 25 Mart 2026
DOI https://doi.org/10.21605/cukurovaumfd.1706626
IZ https://izlik.org/JA75NH47PG
Yayımlandığı Sayı Yıl 2026 Cilt: 41 Sayı: 1

Kaynak Göster

APA Engin, K. E., Eşme, U., & Külekci, M. K. (2026). Optimization of Vertical Machining Parameters of DIN 1.0038 Steels Using Hybrid Taguchi Based Grey-Fuzzy Algorithm in CNC Pocket Milling Process. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 41(1), 115-128. https://doi.org/10.21605/cukurovaumfd.1706626