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BibTex RIS Kaynak Göster

Finish turning of toolox 33 to improve machining parameters with different nose radius tools

Yıl 2025, Cilt: 9 Sayı: 3, 234 - 245

Kübra Kaya , Tayfun Çetin , Rüstem Binali , Hakan Gündoğmuş

Öz

Turning, the most widely used machining process in manufacturing, continues to maintain its popularity today. Given its ongoing relevance, evaluating machinability in turning operations remains critical. In this study, dry turning was applied to Toolox 33, a material commonly used due to its favorable machinability characteristics. In the experimental research, changes in surface roughness and cutting force (two of the most critical output parameters) were evaluated in the context of machinability by applying different values of machining parameters, including tool nose radius, cutting speed, feed, and cutting depth. The investigation was undertaken with consideration of integrating machine learning methods into the manufacturing process. The results of the study indicated that optimal cutting force values can be achieved by employing a larger tool nose radius, higher cutting speeds, and lower feed rates and depths of cut. Similarly, optimal surface roughness was obtained under conditions involving a larger nose radius tool, lower feed, and shallower cutting depth. However, variations in the cutting speed parameter led to differing results in surface roughness. For instance, while an increase in cutting speed led to lower surface roughness values in some experimental sets, an increase in surface roughness was observed in others. Graphical evaluations confirmed the suitability of machine learning techniques for this application. The optimum cutting force was recorded under experimental conditions involving a 0.8 mm nose radius tool, a feed rate of 0.2 mm/rev, a depth of cut of 0.2 mm, and a cutting speed of 60 m/min. The best surface roughness results were obtained in the same experiment that yielded the optimum cutting force values. Compared to the optimum result obtained with a 0.8 mm nose radius tool, reducing the nose radius to 0.4 mm increased the cutting force by 29.87%, increasing the feed rate to 0.4 mm/rev led to a 100% rise, and increasing the depth of cut to 0.4 mm resulted in a 62.33% increase. In contrast, increasing the cutting speed from 40 m/min to 60 m/min reduced the cutting force by 44.20%. Following the physical experiments, it was observed that increasing the cutting speed from 40 to 60 m/min reduced surface roughness (Ra) by approximately 5% to 22%, while increasing the cutting depth from 0.2 mm to 0.4 mm and the feed rate from 0.2 mm/rev to 0.4 mm/rev led to increases of 65.28% and 147.93% in Ra, respectively. Additionally, compared to the 0.4 mm nose radius tool, the use of a 0.8 mm nose radius tool, which yielded the optimum surface quality, resulted in a 34.80% improvement in surface roughness.

Anahtar Kelimeler

Toolox 33 Machine Learning Heat Map Decision Tree Machinability

Destekleyen Kurum

This research was supported by Hakkari University, Scientific Research Project Coordination Unit

Proje Numarası

Grant no. FM24BAP8

Teşekkür

This research was supported by Hakkari University, Scientific Research Project Coordination Unit (BAP; Grant no. FM24BAP8).

Kaynakça

  • Binali, R., Demir, H., & Çiftçi, İ. (2017). An investigation into the machinability of hot work tool steel (Toolox 44). 3rd Iron and Steel Symposium (UDCS’17), 441-444.
  • Persson, U., & Chandrasekaran, H. (2002). Machinability of martensitic steels in milling and the role of hardness. In Proc. 6th Int. Tooling Conf., Karlstad University, Sweden, 1225-1236.
  • Binali, R., Demirpolat, H., Kuntoğlu, M., & Sağlam, H. (2023). Machinability investigations based on tool wear, surface roughness, cutting temperature, chip morphology and material removal rate during dry and MQL-assisted milling of Nimax mold steel. Lubricants, 11(3), 101. https://doi.org/10.3390/lubricants11030101
  • Bayraktar, Ş., & Uzun, G. (2021). Ön sertleştirilmiş Toolox 44 ve Nimax kalıp çeliklerinin işlenebilirliği üzerine deneysel çalışma. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(4), 1939-1948. https://doi.org/10.17341/gazimmfd.641824
  • Kuram, E., & Ucuncu, N. (2024). Toolox 44 çeliğinin tornalanmasında kesme hızının, ilerlemenin ve kesici uç burun radyüsünün takım aşınmasına ve yüzey pürüzlülüğüne etkileri. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 12(4), 1006-1017. https://doi.org/10.29109/gujsc.1530456
  • Erdem, S., Özdemir, M., Rafighi, M., & Yavuz, M. (2023). 1.2367 sıcak iş takım çeliğinin sert tornalanmasında kesme parametrelerin yüzey pürüzlülüğü ve kesme kuvvetleri üzerinde etkisi. Politeknik Dergisi, 26(3), 1071-1077. https://doi.org/10.2339/politeknik.1059568
  • Özlü, B. (2022). Evaluation of energy consumption, cutting force, surface roughness and vibration in machining Toolox 44 steel using Taguchi-based gray relational analysis. Surface Review and Letters, 29(08). https://doi.org/10.1142/S0218625X22501037
  • Binali, R., Coşkun, M., & Neşeli, S. (2022). An investigation of power consumption in milling AISI P20 plastic mold steel by finite elements method. Avrupa Bilim ve Teknoloji Dergisi, (34), 513-518. https://doi.org/10.31590/ejosat.1083257
  • Plastike, M. (2018). Optimization of surface roughness in finish milling of AISI P20+ S plastic-mold steel. Optimization, 52(2), 195-200. https://doi.org/10.17222/mit.2017.088
  • Binali, R. (2023). Parametric optimization of cutting force and temperature in finite element milling of AISI P20 steel. Journal of Materials and Mechatronics: A, 4(1), 244-256. https://doi.org/10.55546/jmm.1257453
  • Banavase Shivalingappa, A., & Dhamal, A. C. (2019). Analysis of machining performance using high pressure minimum quantity lubrication (MQL) [Master’s thesis, KTH, School of Industrial Engineering and Management (ITM), Sweden].
  • S. K., T., Shankar, S., & K, D. (2020). Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 234(1), 329-342. https://doi.org/10.1177/0954406219873932
  • Elshaer, R. N., El-Aty, A. A., Sayed, E. M., Barakat, A. F., & Sobh, A. S. (2024). Optimization of machining parameters for turning operation of heat-treated Ti-6Al-3Mo-2Nb-2Sn-2Zr-1.5 Cr alloy by Taguchi method. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-65786-8
  • Adizue, U. L., & Takács, M. (2025). Exploring the correlation between design of experiments and machine learning prediction accuracy in ultra-precision hard turning of AISI D2 with CBN insert: A comparative study of Taguchi and full factorial designs. The International Journal of Advanced Manufacturing Technology, 137, 2061-2090. https://doi.org/10.1007/s00170-025-15186-7
  • Turan, İ., Özlü, B., Ulaş, H. B., & Demir, H. (2025). Prediction and modelling with Taguchi, ANN and ANFIS of optimum machining parameters in drilling of Al 6082-T6 alloy. Journal of Manufacturing and Materials Processing, 9(3), 92. https://doi.org/10.3390/jmmp9030092
  • Hernandez, S., Hardell, J., Winkelmann, H., Ripoll, M. R., & Prakash, B. (2015). Influence of temperature on abrasive wear of boron steel and hot forming tool steels. Wear, 338, 27-35. https://doi.org/10.1016/j.wear.2015.05.010
  • Bansal, M., Goyal, A., & Choudhary, A. (2022). A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decision Analytics Journal, 3, Article 100071. https://doi.org/10.1016/j.dajour.2022.100071
  • Jumasseitova, А. К., & Kaidarova, N. А. (2024). Understanding management challenges through heatmap analysis of online teaching experience correlations. Annali d’Italia, 60, 29-36.
  • Liu, M., Xie, H., Pan, W., Ding, S., & Li, G. (2025). Prediction of cutting force via machine learning: State of the art, challenges and potentials. Journal of Intelligent Manufacturing, 36(2), 703-764. https://doi.org/10.1007/s10845-023-02260-8
  • Li, G., Li, N., Wen, C., & Ding, S. (2018). Investigation and modeling of flank wear process of different PCD tools in cutting titanium alloy Ti6Al4V. The International Journal of Advanced Manufacturing Technology, 95, 719-733. https://doi.org/10.1007/s00170-017-1222-0
  • Ma, J., Gao, Y., Jia, Z., Song, D., & Si, L. (2018). Influence of spindle speed on tool wear in high-speed milling of Inconel 718 curved surface parts. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232(8), 1331-1341. https://doi.org/10.1177/0954405416668925
  • Arnaud, L., Gonzalo, O., Seguy, S., Jauregi, H., & Peigné, G. (2011). Simulation of low rigidity part machining applied to thin-walled structures. The International Journal of Advanced Manufacturing Technology, 54, 479-488. https://doi.org/10.1007/s00170-010-2976-9
  • Li, G., Yi, S., Li, N., Pan, W., Wen, C., & Ding, S. (2019). Quantitative analysis of cooling and lubricating effects of graphene oxide nanofluids in machining titanium alloy Ti6Al4V. Journal of Materials Processing Technology, 271, 584-598. https://doi.org/10.1016/j.jmatprotec.2019.04.035
  • Gökkaya, H., & Nalbant, M. (2007). Kesme hızının yığıntı katmanı ve yığıntı talaş oluşumu üzerindeki etkilerinin SEM ile incelenmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 22(3), 481-488.
  • Hekimoğlu, A. P., Bayraktar, Ş., & Turgut, Y. (2018). Kesme hızı ve ilerlemenin Al-35Zn alaşımının işlenebilirliğine etkisinin incelenmesi. In SETSci-Conference Proceedings (Vol. 3, pp. 77-83).
  • Korkut, I., & Donertas, M. A. (2007). The influence of feed rate and cutting speed on the cutting forces, surface roughness and tool–chip contact length during face milling. Materials & Design, 28(1), 308-312. https://doi.org/10.1016/j.matdes.2005.06.002
  • Binali, R., Demirpolat, H., Kuntoğlu, M., & Kaya, K. (2024). Exploring the tribological performance of mist lubrication technique on machinability characteristics during turning S235JR steel. Manufacturing Technologies and Applications, 5(3), 276-283. https://doi.org/10.52795/mateca.1541090
  • Aydın, M. (2024). Ti6Al4V alaşımının ortogonal tornalanmasında ilerleme hızının kesme kuvveti ve talaş morfolojisi üzerindeki etkilerinin sonlu elemanlar analizi. Gazi University Journal of Science Part C: Design and Technology, 12(2), 567-576. https://doi.org/10.29109/gujsc.1420233
  • Yılmaz, V., Dilipak, H., Sarıkaya, M., Yılmaz, C. Y., & Özdemir, M. (2014). Frezeleme işlemlerinde kesme kuvveti, titreşim ve yüzey pürüzlülüğü sonuçlarının modellenmesi. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 30(4), 220-226.
  • Şeker, U., Kurt, A., & Ciftci, I. (2004). The effect of feed rate on the cutting forces when machining with linear motion. Journal of Materials Processing Technology, 146(3), 403–407. https://doi.org/10.1016/j.jmatprotec.2003.12.001
  • Demirpolat, H., Binali, R., Patange, A. D., Pardeshi, S. S., & Gnanasekaran, S. (2023). Comparison of tool wear, surface roughness, cutting forces, tool tip temperature, and chip shape during sustainable turning of bearing steel. Materials, 16(12), 4408. https://doi.org/10.3390/ma16124408
  • Demirpolat, H., Kaya, K., Binali, R., & Kuntoğlu, M. (2023). AISI 52100 rulman çeliğinin tornalanmasında işleme parametrelerinin yüzey pürüzlülüğü, kesme sıcaklığı ve kesme kuvveti üzerindeki etkilerinin incelenmesi. İmalat Teknolojileri ve Uygulamaları, 4(3), 179-189. https://doi.org/10.52795/mateca.1393430
  • Memiş, F. (2015). AISI 2205 (EN 1.4462) paslanmaz çeliğin CNC torna tezgahında işlenmesinde yüzey pürüzlülüğü ve kesme kuvvetlerinin deneysel araştırılması [Master’s thesis, Fen Bilimleri Enstitüsü].
  • Trent, E. M., & Wright, P. K. (1991). Metal cutting (3rd ed.). Butterworth-Heinemann.
  • Kul, B. S., & Yamaner, A. S. Comparative evaluation of dry-MQL turning applications for AISI 5115 steel. Manufacturing Technologies and Applications, 6(1), 23-32.
  • Akgün, M., Özger, G., & Ulaş, H. B. (2014). Döküm yöntemiyle üretilmiş AZ91 magnezyum alaşımının işlenebilirliğinin yüzey pürüzlülüğü açısından değerlendirilmesi. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 30(5), 323-328.
  • Kuntoğlu, M., Kaya, K., & Binali, R. (2023). Investigation of surface roughness changes in the machining of carbon steel under sustainable conditions. In 1st International Conference on Pioneer and Innovative Studies (pp. 163-167).
  • Yamaner, A. S., & Kul, B. S. (2025). Evaluation of tool radius and machining parameters on cutting forces and surface roughness for AA 6082 aluminum alloy. European Mechanical Science, 9(2), 125-138. https://doi.org/10.26701/ems.1666294
  • Demir, H., Ulaş, H. B., & Binali, R. (2018). Toolox 44 malzemesinde talaş kaldırma miktarının yüzey pürüzlülüğü ve takım aşınması üzerindeki etkilerinin incelenmesi. Technological Applied Sciences, 13(1), 19-28. https://doi.org/10.12739/NWSA.2018.13.1.2A0132

Yıl 2025, Cilt: 9 Sayı: 3, 234 - 245

Kübra Kaya , Tayfun Çetin , Rüstem Binali , Hakan Gündoğmuş

Öz

Proje Numarası

Grant no. FM24BAP8

Kaynakça

  • Binali, R., Demir, H., & Çiftçi, İ. (2017). An investigation into the machinability of hot work tool steel (Toolox 44). 3rd Iron and Steel Symposium (UDCS’17), 441-444.
  • Persson, U., & Chandrasekaran, H. (2002). Machinability of martensitic steels in milling and the role of hardness. In Proc. 6th Int. Tooling Conf., Karlstad University, Sweden, 1225-1236.
  • Binali, R., Demirpolat, H., Kuntoğlu, M., & Sağlam, H. (2023). Machinability investigations based on tool wear, surface roughness, cutting temperature, chip morphology and material removal rate during dry and MQL-assisted milling of Nimax mold steel. Lubricants, 11(3), 101. https://doi.org/10.3390/lubricants11030101
  • Bayraktar, Ş., & Uzun, G. (2021). Ön sertleştirilmiş Toolox 44 ve Nimax kalıp çeliklerinin işlenebilirliği üzerine deneysel çalışma. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(4), 1939-1948. https://doi.org/10.17341/gazimmfd.641824
  • Kuram, E., & Ucuncu, N. (2024). Toolox 44 çeliğinin tornalanmasında kesme hızının, ilerlemenin ve kesici uç burun radyüsünün takım aşınmasına ve yüzey pürüzlülüğüne etkileri. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 12(4), 1006-1017. https://doi.org/10.29109/gujsc.1530456
  • Erdem, S., Özdemir, M., Rafighi, M., & Yavuz, M. (2023). 1.2367 sıcak iş takım çeliğinin sert tornalanmasında kesme parametrelerin yüzey pürüzlülüğü ve kesme kuvvetleri üzerinde etkisi. Politeknik Dergisi, 26(3), 1071-1077. https://doi.org/10.2339/politeknik.1059568
  • Özlü, B. (2022). Evaluation of energy consumption, cutting force, surface roughness and vibration in machining Toolox 44 steel using Taguchi-based gray relational analysis. Surface Review and Letters, 29(08). https://doi.org/10.1142/S0218625X22501037
  • Binali, R., Coşkun, M., & Neşeli, S. (2022). An investigation of power consumption in milling AISI P20 plastic mold steel by finite elements method. Avrupa Bilim ve Teknoloji Dergisi, (34), 513-518. https://doi.org/10.31590/ejosat.1083257
  • Plastike, M. (2018). Optimization of surface roughness in finish milling of AISI P20+ S plastic-mold steel. Optimization, 52(2), 195-200. https://doi.org/10.17222/mit.2017.088
  • Binali, R. (2023). Parametric optimization of cutting force and temperature in finite element milling of AISI P20 steel. Journal of Materials and Mechatronics: A, 4(1), 244-256. https://doi.org/10.55546/jmm.1257453
  • Banavase Shivalingappa, A., & Dhamal, A. C. (2019). Analysis of machining performance using high pressure minimum quantity lubrication (MQL) [Master’s thesis, KTH, School of Industrial Engineering and Management (ITM), Sweden].
  • S. K., T., Shankar, S., & K, D. (2020). Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 234(1), 329-342. https://doi.org/10.1177/0954406219873932
  • Elshaer, R. N., El-Aty, A. A., Sayed, E. M., Barakat, A. F., & Sobh, A. S. (2024). Optimization of machining parameters for turning operation of heat-treated Ti-6Al-3Mo-2Nb-2Sn-2Zr-1.5 Cr alloy by Taguchi method. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-65786-8
  • Adizue, U. L., & Takács, M. (2025). Exploring the correlation between design of experiments and machine learning prediction accuracy in ultra-precision hard turning of AISI D2 with CBN insert: A comparative study of Taguchi and full factorial designs. The International Journal of Advanced Manufacturing Technology, 137, 2061-2090. https://doi.org/10.1007/s00170-025-15186-7
  • Turan, İ., Özlü, B., Ulaş, H. B., & Demir, H. (2025). Prediction and modelling with Taguchi, ANN and ANFIS of optimum machining parameters in drilling of Al 6082-T6 alloy. Journal of Manufacturing and Materials Processing, 9(3), 92. https://doi.org/10.3390/jmmp9030092
  • Hernandez, S., Hardell, J., Winkelmann, H., Ripoll, M. R., & Prakash, B. (2015). Influence of temperature on abrasive wear of boron steel and hot forming tool steels. Wear, 338, 27-35. https://doi.org/10.1016/j.wear.2015.05.010
  • Bansal, M., Goyal, A., & Choudhary, A. (2022). A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decision Analytics Journal, 3, Article 100071. https://doi.org/10.1016/j.dajour.2022.100071
  • Jumasseitova, А. К., & Kaidarova, N. А. (2024). Understanding management challenges through heatmap analysis of online teaching experience correlations. Annali d’Italia, 60, 29-36.
  • Liu, M., Xie, H., Pan, W., Ding, S., & Li, G. (2025). Prediction of cutting force via machine learning: State of the art, challenges and potentials. Journal of Intelligent Manufacturing, 36(2), 703-764. https://doi.org/10.1007/s10845-023-02260-8
  • Li, G., Li, N., Wen, C., & Ding, S. (2018). Investigation and modeling of flank wear process of different PCD tools in cutting titanium alloy Ti6Al4V. The International Journal of Advanced Manufacturing Technology, 95, 719-733. https://doi.org/10.1007/s00170-017-1222-0
  • Ma, J., Gao, Y., Jia, Z., Song, D., & Si, L. (2018). Influence of spindle speed on tool wear in high-speed milling of Inconel 718 curved surface parts. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232(8), 1331-1341. https://doi.org/10.1177/0954405416668925
  • Arnaud, L., Gonzalo, O., Seguy, S., Jauregi, H., & Peigné, G. (2011). Simulation of low rigidity part machining applied to thin-walled structures. The International Journal of Advanced Manufacturing Technology, 54, 479-488. https://doi.org/10.1007/s00170-010-2976-9
  • Li, G., Yi, S., Li, N., Pan, W., Wen, C., & Ding, S. (2019). Quantitative analysis of cooling and lubricating effects of graphene oxide nanofluids in machining titanium alloy Ti6Al4V. Journal of Materials Processing Technology, 271, 584-598. https://doi.org/10.1016/j.jmatprotec.2019.04.035
  • Gökkaya, H., & Nalbant, M. (2007). Kesme hızının yığıntı katmanı ve yığıntı talaş oluşumu üzerindeki etkilerinin SEM ile incelenmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 22(3), 481-488.
  • Hekimoğlu, A. P., Bayraktar, Ş., & Turgut, Y. (2018). Kesme hızı ve ilerlemenin Al-35Zn alaşımının işlenebilirliğine etkisinin incelenmesi. In SETSci-Conference Proceedings (Vol. 3, pp. 77-83).
  • Korkut, I., & Donertas, M. A. (2007). The influence of feed rate and cutting speed on the cutting forces, surface roughness and tool–chip contact length during face milling. Materials & Design, 28(1), 308-312. https://doi.org/10.1016/j.matdes.2005.06.002
  • Binali, R., Demirpolat, H., Kuntoğlu, M., & Kaya, K. (2024). Exploring the tribological performance of mist lubrication technique on machinability characteristics during turning S235JR steel. Manufacturing Technologies and Applications, 5(3), 276-283. https://doi.org/10.52795/mateca.1541090
  • Aydın, M. (2024). Ti6Al4V alaşımının ortogonal tornalanmasında ilerleme hızının kesme kuvveti ve talaş morfolojisi üzerindeki etkilerinin sonlu elemanlar analizi. Gazi University Journal of Science Part C: Design and Technology, 12(2), 567-576. https://doi.org/10.29109/gujsc.1420233
  • Yılmaz, V., Dilipak, H., Sarıkaya, M., Yılmaz, C. Y., & Özdemir, M. (2014). Frezeleme işlemlerinde kesme kuvveti, titreşim ve yüzey pürüzlülüğü sonuçlarının modellenmesi. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 30(4), 220-226.
  • Şeker, U., Kurt, A., & Ciftci, I. (2004). The effect of feed rate on the cutting forces when machining with linear motion. Journal of Materials Processing Technology, 146(3), 403–407. https://doi.org/10.1016/j.jmatprotec.2003.12.001
  • Demirpolat, H., Binali, R., Patange, A. D., Pardeshi, S. S., & Gnanasekaran, S. (2023). Comparison of tool wear, surface roughness, cutting forces, tool tip temperature, and chip shape during sustainable turning of bearing steel. Materials, 16(12), 4408. https://doi.org/10.3390/ma16124408
  • Demirpolat, H., Kaya, K., Binali, R., & Kuntoğlu, M. (2023). AISI 52100 rulman çeliğinin tornalanmasında işleme parametrelerinin yüzey pürüzlülüğü, kesme sıcaklığı ve kesme kuvveti üzerindeki etkilerinin incelenmesi. İmalat Teknolojileri ve Uygulamaları, 4(3), 179-189. https://doi.org/10.52795/mateca.1393430
  • Memiş, F. (2015). AISI 2205 (EN 1.4462) paslanmaz çeliğin CNC torna tezgahında işlenmesinde yüzey pürüzlülüğü ve kesme kuvvetlerinin deneysel araştırılması [Master’s thesis, Fen Bilimleri Enstitüsü].
  • Trent, E. M., & Wright, P. K. (1991). Metal cutting (3rd ed.). Butterworth-Heinemann.
  • Kul, B. S., & Yamaner, A. S. Comparative evaluation of dry-MQL turning applications for AISI 5115 steel. Manufacturing Technologies and Applications, 6(1), 23-32.
  • Akgün, M., Özger, G., & Ulaş, H. B. (2014). Döküm yöntemiyle üretilmiş AZ91 magnezyum alaşımının işlenebilirliğinin yüzey pürüzlülüğü açısından değerlendirilmesi. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 30(5), 323-328.
  • Kuntoğlu, M., Kaya, K., & Binali, R. (2023). Investigation of surface roughness changes in the machining of carbon steel under sustainable conditions. In 1st International Conference on Pioneer and Innovative Studies (pp. 163-167).
  • Yamaner, A. S., & Kul, B. S. (2025). Evaluation of tool radius and machining parameters on cutting forces and surface roughness for AA 6082 aluminum alloy. European Mechanical Science, 9(2), 125-138. https://doi.org/10.26701/ems.1666294
  • Demir, H., Ulaş, H. B., & Binali, R. (2018). Toolox 44 malzemesinde talaş kaldırma miktarının yüzey pürüzlülüğü ve takım aşınması üzerindeki etkilerinin incelenmesi. Technological Applied Sciences, 13(1), 19-28. https://doi.org/10.12739/NWSA.2018.13.1.2A0132
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Mühendisliğinde Optimizasyon Teknikleri, Makine İle İşleme
Bölüm Research Article
Yazarlar

Kübra Kaya 0000-0002-9971-8826

Tayfun Çetin 0009-0003-3089-0489

Rüstem Binali 0000-0003-0775-3817

Hakan Gündoğmuş 0000-0003-4118-0207

Proje Numarası Grant no. FM24BAP8
Yayımlanma Tarihi
Gönderilme Tarihi 21 Haziran 2025
Kabul Tarihi 27 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 9 Sayı: 3

Kaynak Göster

APA Kaya, K., Çetin, T., Binali, R., Gündoğmuş, H. (t.y.). Finish turning of toolox 33 to improve machining parameters with different nose radius tools. European Mechanical Science, 9(3), 234-245.

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