Sleipner Soğuk İş Takım Çeliğinin Yüzey Finish Modellemesinde Takım Yolu Stratejisinin ve Kesme Parametrelerinin MRR ve Ra Üzerine Etkisi

Öz

Bu çalışmada içerdiği alaşım elementi ve karbon oranına bağlı olarak yüksek sertlik, aşınma dayanımı ve tokluk özelliklerinden dolayı kalıpçılık endüstrisinde yaygın olarak kullanılan Sleipner soğuk iş takım çeliği kullanılmıştır. Frezeleme yöntemleri ile yapılan deneysel çalışmalarda kesme parametreleri ve takım yolu stratejisinin, yüzey pürüzlülüğü (Ra) ve malzeme kaldırma oranı (MRR) üzerindeki etkileri araştırılmıştır. Kesme parametreleri olarak, takım yolu hareketleri, kesici takım devri ve ilerleme hızı parametrelerinin farklı seviyeleri kullanılmıştır. Finish işleminde yapılan frezelemede talaş derinliği sabit tutulmuştur. Deneysel çalışmalar, Taguchi deney tasarım yöntemi kullanarak L16 ortogonal dizine göre yapılmıştır. Ayrıca yapılan deneysel çalışmalarda elde edilen sonuçlar, S/N oranlarına dönüştürülerek ideal parametre seviyeleri belirlenip, Minitab 17 paket programı kullanılarak ANOVA analizi yöntemi ile yüzey pürüzlülük ve malzeme kaldırma oranı sonuçları istatiksel olarak değerlendirilmiştir. Elde edilen sonuçlar ile ANOVA tabloları oluşturulmuş ve kesme parametrelerinin sonuçlar üzerindeki etkileri belirlenmiştir. Ayrıca yüzey yanıt yöntemi kullanılarak işleme parametrelerine bağlı Ra ve MRR sonuçlarının matematiksel modellemesi yapıldı ve Genetik algoritma yöntemi kullanılarak optimum işleme parametreleri belirlendi. Sonuçlar değerlendirildiğinde oluşturulan matematiksel modelin deney sonuçları ile uyumlu olduğu belirlendi.

Anahtar Kelimeler

• Yüzey Pürüzlülüğü
• Sleipner
• ANOVA
• Taguchi
• Takım Yolu Stratejisi
• MRR.

The Effect of Tool Path Strategy and Cutting Parameters on MRR and Ra in Surface Finish Modeling of Sleipner Cold Work Tool Steel

Abstract

In this study, Sleipner cold work tool steel, which is widely used in the molding industry, was used due to its high hardness, wear resistance and toughness properties depending on the alloying element and carbon ratio it contains. In experimental studies with milling methods, the effects of cutting parameters and toolpath strategy on surface roughness (Ra) and material removal rate (MRR) were investigated. Different levels of tool path movements, cutting tool speed and feed rate parameters were used as cutting parameters. The depth of cut was kept constant during milling in the finishing process. Experimental studies were carried out according to the L16 orthogonal array using the Taguchi experimental design method. In addition, the results obtained in the experimental studies were converted to S/N ratios and the ideal parameter levels were determined, and the results of the surface roughness and material removal rate were evaluated statistically by ANOVA analysis method using the Minitab 17 package program. ANOVA tables were created with the results obtained and the effects of cutting parameters on the results were determined. In addition, mathematical modeling of Ra and MRR results depending on processing parameters was performed using the surface response method, and optimum processing parameters were determined using the genetic algorithm method. When the results were evaluated, it was determined that the mathematical model created was compatible with the experimental results.

Keywords

• Surface Roughness
• Sleipner
• ANOVA
• Taguchi
• Tool Path Strategy
• MRR.

References

1. B. Özlü, “Investigation of the effect of cutting parameters on cutting force, surface roughness and chip shape in turning of Sleipner cold work tool steel,” J. Fac. Eng. Archit. Gazi Univ., vol. 36, no. 3, pp. 1241–1251, 2021, https://doi.org/10.17341/gazimmfd.668169
2. K. Aldaş, İ. Özkul, A. Akkurt, “An ANFIS-Based Approach for Predicting the Surface Roughness of Cold Work Tool Steel in,” TEM J., vol. 2, no. 3, pp. 234–240, 2013,
3. P. G. Benardos and G. C. Vosniakos, “Predicting surface roughness in machining: A review,” Int. J. Mach. Tools Manuf., vol. 43, no. 8, pp. 833–844, 2003, https://doi.org/10.1016/S0890-6955(03)00059-2
4. G. Quintana, J. De Ciurana, and J. Ribatallada, “Surface roughness generation and material removal rate in ball end milling operations,” Mater. Manuf. Process., vol. 25, no. 6, pp. 386–398, 2010, https://doi.org/10.1080/15394450902996601
5. Z. Yao and S. K. Gupta, “Cutter path generation for 2.5D milling by combining multiple different cutter path patterns,” Int. J. Prod. Res., vol. 42, no. 11, pp. 2141–2161, 2004, https://doi.org/10.1080/00207540310001652879
6. I. F. Edem, V. A. Balogun, B. D. Nkanang, and P. T. Mativenga, “Software analyses of optimum toolpath strategies from computer numerical control (CNC) codes,” Int. J. Adv. Manuf. Technol., vol. 103, no. 1–4, pp. 997–1007, 2019, https://doi.org/10.1007/s00170-019-03604-6
7. S. Moshat, S. Datta, A. Bandyopadhyay, and P. Pal, “Optimization of CNC end milling process parameters using PCA-based Taguchi method,” Int. J. Eng. Sci. Technol., vol. 2, no. 1, pp. 92–102, 2010, https://doi.org/10.4314/ijest.v2i1.59096
8. L. T. Tunc and D. Stoddart, “Tool path pattern and feed direction selection in robotic milling for increased chatter-free material removal rate,” Int. J. Adv. Manuf. Technol., vol. 89, no. 9–12, pp. 2907–2918, 2017, https://doi.org/10.1007/s00170-016-9896-2

9. X. Lu, F. R. Wang, L. Xue, Y. Feng, and S. Y. Liang, “Investigation of material removal rate and surface roughness using multi-objective optimization for micro-milling of inconel 718,” Ind. Lubr. Tribol., vol. 71, no. 6, pp. 787–794, 2019, https://doi.org/10.1108/ILT-07-2018-0259
10. E. Bagci and E. U. Yüncüoğlu, “The effects of milling strategies on forces, material removal rate, tool deflection, and surface errors for the rough machining of complex surfaces,” Stroj. Vestnik/Journal Mech. Eng., vol. 63, no. 11, pp. 643–656, 2017, https://doi.org/10.5545/sv-jme.2017.4450
11. M. Tolouei-Rad, “Efficient CNC Milling by Adjusting Material Removal Rate,” Int. J. Mech. Mechatronics Eng., vol. 5, no. 10, pp. 342–346, 2011, https:// doi.org/10.5281/zenodo.1079140
12. Ç. Özay, “Investigating the surface roughness after tangential cylindrical grinding by the Taguchi method,” Mater. Test., vol. 56, no. 4, pp. 306–311, 2014, https://doi.org/10.3139/120.110561
13. A. K. Gür, T. Yıldız, and B. İçen, “Theoretical evaluation of abrasive wear behavior of B 4C/FeCrC coating layer evaluated by a Taguchi approach,” Mater. Test., vol. 62, no. 7, pp. 733–738, 2020, https://doi.org/10.3139/120.111540
14. A. K. Gür, Ç. Özay, and B. İçen, “Evaluation Of B4C/Ti Coating Layer, Investigation Of Abrasive Wear Behaviors Using Taguchi Technique And Response Surface Methodology. Surf. Rev. Lett., vol. 1950225, pp. 1–17, 2020, https://doi.org/10.1142/S0218625X19502251
15. C. Gologlu and N. Sakarya, “The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method,” J. Mater. Process. Technol., vol. 206, no. 1–3, pp. 7–15, 2008, https://doi.org/10.1016/j.jmatprotec.2007.11.300
16. S. Kumar, I. Saravanan, and L. Patnaik, “Optimization of surface roughness and material removal rate in milling of AISI 1005 carbon steel using Taguchi approach,” Mater. Today Proc., vol. 22, pp. 654–658, 2020, https://doi.org/10.1016/j.matpr.2019.09.039
17. A. M. Zain, H. Haron, and S. Sharif, “An overview of GA technique for surface roughness optimization in milling process,” Proc. - Int. Symp. Inf. Technol. 2008, ITSim, vol. 3, 2008, https://doi.org/10.1109/ITSIM.2008.4631925
18. Ç. Özay and Z. Küçük, “AISI 1040’ ın tornalama-frezeleme ile işlenmesinde yüzey pürüzlülüğünün genetik algoritma yöntemi ile optimizasyonu,” DÜMF Mühendislik Derg., vol. 11, no. 3, pp. 1081–1091, 2020, https://doi.org/10.24012/dumf.685119
19. A. M. Zain, H. Haron, and S. Sharif, “Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process,” Expert Syst. Appl., vol. 37, no. 6, pp. 4650–4659, 2010, https://doi.org/10.1016/j.eswa.2009.12.043
20. P. Palanisamy, I. Rajendran, and S. Shanmugasundaram, “Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations,” Int. J. Adv. Manuf. Technol., vol. 32, no. 7–8, pp. 644–655, 2007, https://doi.org/10.1007/s00170-005-0384-3
21. L. Imani, A. Rahmani Henzaki, R. Hamzeloo, and B. Davoodi, “Modeling and optimizing of cutting force and surface roughness in milling process of Inconel 738 using hybrid ANN and GA,” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 234, no. 5, pp. 920–932, 2020, https://doi.org/10.1177/0954405419889204
22. V. Savas, C. Ozay, and H. Ballikaya, “Experimental investigation of cutting parameters in machining of 100Cr6 with tangential turn-milling method,” Adv. Manuf., vol. 4, no. 1, pp. 97–104, 2016, https://doi.org/10.1007/s40436-016-0134-9