Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2025, Sayı: UTIS 2024, 40 - 51, 03.10.2025
https://doi.org/10.56193/matim.1657815

Öz

Kaynakça

  • 1. Mesquita, R.A. ve Schneider, R.S.E., Tool Steel Quality and Surface Finishing of Plastic Molds, Exacta, 8 (2011) 3, 307-318.
  • 2. Dang, X.-P., General Frameworks for Optimization of Plastic Injection Molding Process Parameters, Simulation Modelling Practice and Theory, 41 (2014) 15-27.
  • 3. Chen, W.C., Nguyen, M.H., Chiu, W.H., et al., Optimization of the Plastic Injection Molding Process Using the Taguchi Method, RSM, and Hybrid GA-PSO, International Journal of Advanced Manufacturing Technology, 83 (2016) 1873-1886.
  • 4. Sofuoğlu, H. ve Gedikli, H., Physical and Numerical Analysis of Three Dimensional Extrusion Process, Computational Materials Science, 31 (2004) 1-2, 113-124
  • 5. Mu, Y., Zhao, G., Chen, A., Wu, X., Modeling and Simulation of Polymer Melts Flow in the Extrusion Process of Plastic Profile with Metal Insert, International Journal of Advanced Manufacturing Technology, 67 (2013) 629-646.
  • 6. O'Connor, C.P.J., Martin, P.J., Menary, G., Viscoelastic Material Models of Polypropylene for Thermoforming Applications, International Journal of Material Forming, 3 (2010) 599-602.
  • 7. Gaspar-Cunha, A., Costa, P., Galuppo, W.D.C., Duarte, F., Costa, L., Multi-Objective Optimization of Plastics Thermoforming, Mathematics, 9 (2021) 1760-1780.
  • 8. Schmidt, F., Agassant, J.-F., Bellet, M., Experimental Study and Numerical Simulation of the Injection Stretch/Blow Molding Process, Polymer Engineering and Science, 38 (1998) 9, 1399-1412.
  • 9. Laroche, D., Kabanemi, K.K., Pecora, L., Diraddo, R.W., Integrated Numerical Modeling of the Blow Molding Process, Polymer Engineering and Science, 39 (1999) 1223-1233.
  • 10. Hamdi, A., Furkan, Y., Alper, U., Hammoudi, U., Multi-Objective Analysis and Optimization of Energy Aspects During Dry and MQL Turning of Unreinforced Polypropylene (PP): An Approach Based on ANOVA, ANN, MOWCA, and MOALO, International Journal of Advanced Manufacturing Technology, 128 (2023) 4933-4950.
  • 11. Hamdi, A., Furkan, Y., Alper, Y., Sidi, U., Merghache, M., Investigation of MQL and CNC Turning Parameters on the Machinability of Unreinforced Polypropylene: Study of Surface Roughness, Temperature, and Specific Cutting Energy, International Journal of Advanced Manufacturing Technology, (2024) 717-730.
  • 12. Internet: Uddeholm. Impax Supreme. Retrieved from https://www.uddeholm.com/app/uploads/sites/41/2017/10/Impax-Supreme-eng.pdf, Erişim Tarihi: Temmuz 10, 2024.
  • 13. Internet: Uddeholm Nimax Technical Data Sheet. Retrieved from https://www.uddeholm.com/app/uploads/sites/46/2017/11/Uddeholm_nimax-eng_p_12_1612_e7.pdf, Erişim Tarihi: Temmuz 10, 2024.
  • 14. Abou-El-Hossein, K.A., Kadirgama, K., Hamdi, M., Benyounis, K.Y., Prediction of Cutting Force in End-Milling Operation of Modified AISI P20 Tool Steel, Journal of Materials Processing Technology, 182 (2007) 1-3, 241-247.
  • 15. Fuat, K., Öztürk, B., Comparison and Optimization of PVD and CVD Method on Surface Roughness and Flank Wear in Hard Machining of DIN 1.2738 Mold Steel, Sensor Review, 1 (2018) 24-33.
  • 16. Gupta, A., Singh, H., Aggarwal, A., Taguchi-Fuzzy Multi Output Optimization (MOO) in High-Speed CNC Turning of AISI P-20 Tool Steel, Expert Systems with Applications, 38 (2011) 6822-6828.
  • 17. Reddy, B.S., Kumar, J.S., Reddy, K.V.K., Optimization of Surface Roughness in CNC End Milling Using Response Surface Methodology and Genetic Algorithm, International Journal of Engineering, Science and Technology, 3 (2011) 102-109.
  • 18. Mukkoti, V.V., Mohanty, C.P., Gandla, S., Sarkar, P., P, S.R., Dhanraj, B., Optimization of Process Parameters in CNC Milling of P20 Steel by Cryo-Treated Tungsten Carbide Tools Using NSGA-II, Production & Manufacturing Research, 8 (2020) 291-312.
  • 19. Vardhan, M.V., Mohanty, C.P., Dhanraj, B., Experimental Study on Parameters of P-20 Steel in CNC Milling Machine, Journal of Physics: Conference Series, 1495 (2019) 1-9.
  • 20. Hamdi, A., Yapan, Y.F., Uysal, A., Merghache, S.M., The Effects of Minimum Quantity Lubrication Parameters on the Lubrication Efficiency in the Turning of Plastic Mold Steel, International Journal of Advanced Manufacturing Technology, 132 (2024) 5803-5821.
  • 21. Zhang, X., Li, C., Zhou, Z., Liu, B., Zhang, Y., Yang, M., Gao, T., Liu, M., Zhang, N., Said, Z., Sharma, S., Ali, H.M., Vegetable Oil-Based Nanolubricants in Machining: From Physicochemical Properties to Application, Chinese Journal of Mechanical Engineering, 36 (2023) 1-39
  • 22. Iqbal, A., Dar, N.U., He, N., Khan, I., Li, L., Optimizing Cutting Parameters in Minimum Quantity of Lubrication Milling of Hardened Cold Work Tool Steel, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223 (2008) 43-54.
  • 23. Touggui, Y., Uysal, A., Emiroglu, U., et al., Evaluation of MQL Performances Using Various Nanofluids in Turning of AISI 304 Stainless Steel, International Journal of Advanced Manufacturing Technology, 115 (2021) 3983-3997.
  • 24. Saatçi, E., Yapan, Y.F., Uslu Uysal, M., & Uysal, A., Orthogonal Turning of AISI 310S Austenitic Stainless Steel Under Hybrid Nanofluid-Assisted MQL and a Sustainability Optimization Using NSGA-II and TOPSIS, Sustainable Materials and Technologies, 36 (2023) e00628.
  • 25. Zeng, X., Peng, Y., Lang, H., et al., Probing the Difference in Friction Performance Between Graphene and MoS2 by Manipulating the Silver Nanowires, Journal of Materials Science, 54 (2019) 540–551.
  • 26. Lou, M.S., Chen, J.F., Li, C.M., Liu, J.C.H., Surface Roughness Prediction Technique for CNC End-Milling, Journal of Industrial Technology, 15 (1999) 1-6.
  • 27. Kalpakjian, S., Schmid, S.R., Sekar, V., Manufacturing Engineering and Technology, 7th edition, Pearson Publications, Singapore, 2013.
  • 28. Sharma, A.K., Tiwari, A.K., Dixit, A.R., Effects of Minimum Quantity Lubrication (MQL) in machining processes using conventional and nanofluid based cutting fluids: A comprehensive review, J. of Cleaner Production, 127 (2016) 1–18.
  • 29. Sarıkaya, R., Güllü, H., Taguchi Design and Response Surface Methodology Based Analysis of Machining Parameters in CNC Turning Under MQL, J. of Cleaner Production, 65 (2014) 604–616.
  • 30. Dhar, N., Islam, S., Kamruzzaman, M., Effect of Minimum Quantity Lubrication (MQL) on Tool Wear, Surface Roughness and Dimensional Deviation in Turning AISI-4340 Steel, Gazi University Journal of Science, 20 (2010) 2, 23–32.
  • 31. Brinksmeier, E., Meyer, D., Huesmann-Cordes, A.G., Herrmann, C., Metalworking fluids—Mechanisms and performance, CIRP Annals, 57 (2015) 2, 605–628.
  • 32. Nalbant, M., Gökkaya, H., Sur, G., Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning, Materials & Design, 28 (2007) 4, 1379–1385.
  • 33. Risbood, K.A., Dixit, U.S., Sahasrabudhe, A.D., Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process, Journal of Materials Processing Technology, 132 (2003) 1–3, 203–214.
  • 34. Roy, R.K., A Primer on the Taguchi Method, Society of Manufacturing Engineers, Dearborn, 2010.
  • 35. Kurt, M., Hortomacıoğlu, S., Mutlu, B., Köklü, U., Minimization of the surface roughness and form error on the milling of free-form surfaces using a grey relational analysis, Materiali in Tehnologije / Materials and Technology, 46 (3) (2012) 205–213.
  • 36. Krishankant, J.T., Bector, M., Kumar, R., Application of Taguchi method for optimizing turning process by the effects of machining parameters, Int. J. Eng. Adv. Technol., 2 (1) (2012) 263–274.
  • 37. Yan, J., Li, L., Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality, J. Clean. Prod., 52 (2013) 462–471.
  • 38. Penn State University. (n.d.). Sequential (or Extra) Sums of Squares | STAT 501. Available at https://online.stat.psu.edu/stat501/lesson/6/6.3
  • 39. Minitab. (n.d.). Analysis of variance table for Fit Regression Model and Linear Regression. Available at https://support.minitab.com/en-us/minitab/help-and-how-to/statistical-modeling/regression/how-to/fit-regressionmodel/interpret-the-results/all-statistics-and-graphs/analysis-of-variance-table/
  • 40. Minitab. (n.d.). Analysis of Variance table for One-Way ANOVA. Available at https://support.minitab.com/en-us/minitab/help-and-how-to/statistical-modeling/anova/how-to/one-way-anova/interpret-the-results/all-statistics-and-graphs/analysis-of-variance-table/#:~:text=Sequential%20sums%20of%20squares%20are,the%20adjusted%20sums%20of%20squares
  • 41. Penn State University. (n.d.). 13.2 - The ANOVA Table. Available at https://online.stat.psu.edu/stat415/book/export/html/822
  • 42. Abdullah, T., Rao, P.S., Rao, P.S., Maqsood, Z., Optimization of Cutting Parameters in CNC Turning by using Taguchi Method: A Review, International Journal of Technical Innovation in Modern Engineering & Science, 5 (2019) 5, 1068-1072.
  • 43. Samtaş, G., Optimisation of cutting parameters during the face milling of AA5083-H111 with coated and uncoated inserts using Taguchi method, Int. J. Machining Machinability Mater., 17 (2015) 3, 211-232.
  • 44. Saini, S., Ahuja, I.S. & Sharma, V.S., Influence of cutting parameters on tool wear and surface roughness in hard turning of AISI H11 tool steel using ceramic tools, Int. J. Precis. Eng. Manuf., 13 (2012), 1295–1302.

IMPAX ve NIMAX Takım Çeliklerinin Parmak Freze ile İşlenmesinde Yüzey Pürüzlülüğünün Karşılaştırılması

Yıl 2025, Sayı: UTIS 2024, 40 - 51, 03.10.2025
https://doi.org/10.56193/matim.1657815

Öz

Bu çalışmada talaşlı işlemede kullanılan kesme parametrelerinin NIMAX ve IMPAX (DIN 1.2738) kalıp çeliklerinin yüzey pürüzlülüğü üzerindeki etkileri araştırılmıştır. Değerlendirilen kesme parametreleri kesme hızı (75, 100 ve 125 m/dak), ilerleme hızı (0,05, 0,10 ve 0,15 mm/dev) ve kesme koşuludur (Kuru ve Hibrit Nano Minimum Miktar Yağlama/ Hybrid Nano Minimum Quantity Lubrication – HNMQL). Taguchi’nin L18 (2¹x3²) ortogonal dizisi ve Varyans Analizi (ANOVA) kullanılarak bu parametreler sistematik olarak incelenmiş ve optimize edilmiştir. Deneysel sonuçlar, HNMQL koşulları altında yüzey pürüzlülüğünde önemli gelişmeler ve iyileşmeler olduğunu ortaya koymuştur. NIMAX çeliği için HNMQL koşulları yüzey pürüzlülüğünde %51,35’e varan bir iyileşme sağlarken kesme koşulu %81,6’lık bir oranla en etkili parametre olmuştur. IMPAX çeliği için ise HNMQL koşulu yüzey pürüzlülüğünde %40,2’ye varan bir iyileşme sağlarken, kesme koşulu %72 oranında bir katkı sağlayarak, NIMAX çeliğinde olduğu gibi en yüzey pürüzlülüğüne en çok katkı sağlayan parametre olmuştur. Deney sonuçları incelendiğinde genel olarak NIMAX malzemesi, aynı kesme parametreleri altında IMPAX malzemesine kıyasla %43,68’e kadar daha iyi yüzey pürüzlülüğü sonuçları sergilemiştir.

Teşekkür

Yazarlar, bu çalışmaya sağladığı destek ve kesici takımların temini için Fatih Çakıroğlu’na (CKR Kesici Takımlar Sanayi ve Dış Ticaret Ltd. Şirketi – İstanbul/Türkiye) teşekkür ederler.

Kaynakça

  • 1. Mesquita, R.A. ve Schneider, R.S.E., Tool Steel Quality and Surface Finishing of Plastic Molds, Exacta, 8 (2011) 3, 307-318.
  • 2. Dang, X.-P., General Frameworks for Optimization of Plastic Injection Molding Process Parameters, Simulation Modelling Practice and Theory, 41 (2014) 15-27.
  • 3. Chen, W.C., Nguyen, M.H., Chiu, W.H., et al., Optimization of the Plastic Injection Molding Process Using the Taguchi Method, RSM, and Hybrid GA-PSO, International Journal of Advanced Manufacturing Technology, 83 (2016) 1873-1886.
  • 4. Sofuoğlu, H. ve Gedikli, H., Physical and Numerical Analysis of Three Dimensional Extrusion Process, Computational Materials Science, 31 (2004) 1-2, 113-124
  • 5. Mu, Y., Zhao, G., Chen, A., Wu, X., Modeling and Simulation of Polymer Melts Flow in the Extrusion Process of Plastic Profile with Metal Insert, International Journal of Advanced Manufacturing Technology, 67 (2013) 629-646.
  • 6. O'Connor, C.P.J., Martin, P.J., Menary, G., Viscoelastic Material Models of Polypropylene for Thermoforming Applications, International Journal of Material Forming, 3 (2010) 599-602.
  • 7. Gaspar-Cunha, A., Costa, P., Galuppo, W.D.C., Duarte, F., Costa, L., Multi-Objective Optimization of Plastics Thermoforming, Mathematics, 9 (2021) 1760-1780.
  • 8. Schmidt, F., Agassant, J.-F., Bellet, M., Experimental Study and Numerical Simulation of the Injection Stretch/Blow Molding Process, Polymer Engineering and Science, 38 (1998) 9, 1399-1412.
  • 9. Laroche, D., Kabanemi, K.K., Pecora, L., Diraddo, R.W., Integrated Numerical Modeling of the Blow Molding Process, Polymer Engineering and Science, 39 (1999) 1223-1233.
  • 10. Hamdi, A., Furkan, Y., Alper, U., Hammoudi, U., Multi-Objective Analysis and Optimization of Energy Aspects During Dry and MQL Turning of Unreinforced Polypropylene (PP): An Approach Based on ANOVA, ANN, MOWCA, and MOALO, International Journal of Advanced Manufacturing Technology, 128 (2023) 4933-4950.
  • 11. Hamdi, A., Furkan, Y., Alper, Y., Sidi, U., Merghache, M., Investigation of MQL and CNC Turning Parameters on the Machinability of Unreinforced Polypropylene: Study of Surface Roughness, Temperature, and Specific Cutting Energy, International Journal of Advanced Manufacturing Technology, (2024) 717-730.
  • 12. Internet: Uddeholm. Impax Supreme. Retrieved from https://www.uddeholm.com/app/uploads/sites/41/2017/10/Impax-Supreme-eng.pdf, Erişim Tarihi: Temmuz 10, 2024.
  • 13. Internet: Uddeholm Nimax Technical Data Sheet. Retrieved from https://www.uddeholm.com/app/uploads/sites/46/2017/11/Uddeholm_nimax-eng_p_12_1612_e7.pdf, Erişim Tarihi: Temmuz 10, 2024.
  • 14. Abou-El-Hossein, K.A., Kadirgama, K., Hamdi, M., Benyounis, K.Y., Prediction of Cutting Force in End-Milling Operation of Modified AISI P20 Tool Steel, Journal of Materials Processing Technology, 182 (2007) 1-3, 241-247.
  • 15. Fuat, K., Öztürk, B., Comparison and Optimization of PVD and CVD Method on Surface Roughness and Flank Wear in Hard Machining of DIN 1.2738 Mold Steel, Sensor Review, 1 (2018) 24-33.
  • 16. Gupta, A., Singh, H., Aggarwal, A., Taguchi-Fuzzy Multi Output Optimization (MOO) in High-Speed CNC Turning of AISI P-20 Tool Steel, Expert Systems with Applications, 38 (2011) 6822-6828.
  • 17. Reddy, B.S., Kumar, J.S., Reddy, K.V.K., Optimization of Surface Roughness in CNC End Milling Using Response Surface Methodology and Genetic Algorithm, International Journal of Engineering, Science and Technology, 3 (2011) 102-109.
  • 18. Mukkoti, V.V., Mohanty, C.P., Gandla, S., Sarkar, P., P, S.R., Dhanraj, B., Optimization of Process Parameters in CNC Milling of P20 Steel by Cryo-Treated Tungsten Carbide Tools Using NSGA-II, Production & Manufacturing Research, 8 (2020) 291-312.
  • 19. Vardhan, M.V., Mohanty, C.P., Dhanraj, B., Experimental Study on Parameters of P-20 Steel in CNC Milling Machine, Journal of Physics: Conference Series, 1495 (2019) 1-9.
  • 20. Hamdi, A., Yapan, Y.F., Uysal, A., Merghache, S.M., The Effects of Minimum Quantity Lubrication Parameters on the Lubrication Efficiency in the Turning of Plastic Mold Steel, International Journal of Advanced Manufacturing Technology, 132 (2024) 5803-5821.
  • 21. Zhang, X., Li, C., Zhou, Z., Liu, B., Zhang, Y., Yang, M., Gao, T., Liu, M., Zhang, N., Said, Z., Sharma, S., Ali, H.M., Vegetable Oil-Based Nanolubricants in Machining: From Physicochemical Properties to Application, Chinese Journal of Mechanical Engineering, 36 (2023) 1-39
  • 22. Iqbal, A., Dar, N.U., He, N., Khan, I., Li, L., Optimizing Cutting Parameters in Minimum Quantity of Lubrication Milling of Hardened Cold Work Tool Steel, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223 (2008) 43-54.
  • 23. Touggui, Y., Uysal, A., Emiroglu, U., et al., Evaluation of MQL Performances Using Various Nanofluids in Turning of AISI 304 Stainless Steel, International Journal of Advanced Manufacturing Technology, 115 (2021) 3983-3997.
  • 24. Saatçi, E., Yapan, Y.F., Uslu Uysal, M., & Uysal, A., Orthogonal Turning of AISI 310S Austenitic Stainless Steel Under Hybrid Nanofluid-Assisted MQL and a Sustainability Optimization Using NSGA-II and TOPSIS, Sustainable Materials and Technologies, 36 (2023) e00628.
  • 25. Zeng, X., Peng, Y., Lang, H., et al., Probing the Difference in Friction Performance Between Graphene and MoS2 by Manipulating the Silver Nanowires, Journal of Materials Science, 54 (2019) 540–551.
  • 26. Lou, M.S., Chen, J.F., Li, C.M., Liu, J.C.H., Surface Roughness Prediction Technique for CNC End-Milling, Journal of Industrial Technology, 15 (1999) 1-6.
  • 27. Kalpakjian, S., Schmid, S.R., Sekar, V., Manufacturing Engineering and Technology, 7th edition, Pearson Publications, Singapore, 2013.
  • 28. Sharma, A.K., Tiwari, A.K., Dixit, A.R., Effects of Minimum Quantity Lubrication (MQL) in machining processes using conventional and nanofluid based cutting fluids: A comprehensive review, J. of Cleaner Production, 127 (2016) 1–18.
  • 29. Sarıkaya, R., Güllü, H., Taguchi Design and Response Surface Methodology Based Analysis of Machining Parameters in CNC Turning Under MQL, J. of Cleaner Production, 65 (2014) 604–616.
  • 30. Dhar, N., Islam, S., Kamruzzaman, M., Effect of Minimum Quantity Lubrication (MQL) on Tool Wear, Surface Roughness and Dimensional Deviation in Turning AISI-4340 Steel, Gazi University Journal of Science, 20 (2010) 2, 23–32.
  • 31. Brinksmeier, E., Meyer, D., Huesmann-Cordes, A.G., Herrmann, C., Metalworking fluids—Mechanisms and performance, CIRP Annals, 57 (2015) 2, 605–628.
  • 32. Nalbant, M., Gökkaya, H., Sur, G., Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning, Materials & Design, 28 (2007) 4, 1379–1385.
  • 33. Risbood, K.A., Dixit, U.S., Sahasrabudhe, A.D., Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process, Journal of Materials Processing Technology, 132 (2003) 1–3, 203–214.
  • 34. Roy, R.K., A Primer on the Taguchi Method, Society of Manufacturing Engineers, Dearborn, 2010.
  • 35. Kurt, M., Hortomacıoğlu, S., Mutlu, B., Köklü, U., Minimization of the surface roughness and form error on the milling of free-form surfaces using a grey relational analysis, Materiali in Tehnologije / Materials and Technology, 46 (3) (2012) 205–213.
  • 36. Krishankant, J.T., Bector, M., Kumar, R., Application of Taguchi method for optimizing turning process by the effects of machining parameters, Int. J. Eng. Adv. Technol., 2 (1) (2012) 263–274.
  • 37. Yan, J., Li, L., Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality, J. Clean. Prod., 52 (2013) 462–471.
  • 38. Penn State University. (n.d.). Sequential (or Extra) Sums of Squares | STAT 501. Available at https://online.stat.psu.edu/stat501/lesson/6/6.3
  • 39. Minitab. (n.d.). Analysis of variance table for Fit Regression Model and Linear Regression. Available at https://support.minitab.com/en-us/minitab/help-and-how-to/statistical-modeling/regression/how-to/fit-regressionmodel/interpret-the-results/all-statistics-and-graphs/analysis-of-variance-table/
  • 40. Minitab. (n.d.). Analysis of Variance table for One-Way ANOVA. Available at https://support.minitab.com/en-us/minitab/help-and-how-to/statistical-modeling/anova/how-to/one-way-anova/interpret-the-results/all-statistics-and-graphs/analysis-of-variance-table/#:~:text=Sequential%20sums%20of%20squares%20are,the%20adjusted%20sums%20of%20squares
  • 41. Penn State University. (n.d.). 13.2 - The ANOVA Table. Available at https://online.stat.psu.edu/stat415/book/export/html/822
  • 42. Abdullah, T., Rao, P.S., Rao, P.S., Maqsood, Z., Optimization of Cutting Parameters in CNC Turning by using Taguchi Method: A Review, International Journal of Technical Innovation in Modern Engineering & Science, 5 (2019) 5, 1068-1072.
  • 43. Samtaş, G., Optimisation of cutting parameters during the face milling of AA5083-H111 with coated and uncoated inserts using Taguchi method, Int. J. Machining Machinability Mater., 17 (2015) 3, 211-232.
  • 44. Saini, S., Ahuja, I.S. & Sharma, V.S., Influence of cutting parameters on tool wear and surface roughness in hard turning of AISI H11 tool steel using ceramic tools, Int. J. Precis. Eng. Manuf., 13 (2012), 1295–1302.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Makine Mühendisliği (Diğer)
Bölüm Araştırma, Geliştirme ve Uygulama Makaleleri
Yazarlar

Buse Güney 0009-0002-4521-9826

Ege Güvenir 0009-0009-4145-2605

Hüseyin Emre Çakırca 0009-0001-3376-5080

Yaşar Emre Ünal 0009-0007-3690-7906

Ahmet Şimşek 0009-0008-2598-5244

Yusuf Furkan Yapan 0000-0001-9684-4117

Orhan Çakır 0000-0002-4169-2408

Yayımlanma Tarihi 3 Ekim 2025
Gönderilme Tarihi 14 Mart 2025
Kabul Tarihi 2 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Sayı: UTIS 2024

Kaynak Göster

Vancouver Güney B, Güvenir E, Çakırca HE, Ünal YE, Şimşek A, Yapan YF, vd. IMPAX ve NIMAX Takım Çeliklerinin Parmak Freze ile İşlenmesinde Yüzey Pürüzlülüğünün Karşılaştırılması. MATİM. 2025(UTIS 2024):40-51.