Mathematical Modelling and Multiresponse Optimization to Minimize Surface Roughness in Drilling Custom 450 Stainless Steel

Year 2023, Volume: 4 Issue: 1, 11 - 24, 30.04.2023

Hüseyin Gökçe , İbrahim Çiftçi

https://doi.org/10.52795/mateca.1238328

Abstract

In the present study, drilling tests were carried out on Custom 450 stainless steel workpieces. The influences of control factors (cutting speed-Vc, feed rate-f and drill bit geometry-D) on the drilled holes’ surface roughness (Ra) and on the size of adhering workpiece (AW) to the drill bit was examined. The results obtained from tests designed based on the Taguchi’s L16 orthogonal array were analysed using ANOVA and grey relational analyses (GRA). Therefore, the control factors and their levels were optimised simultaneously for the quality characteristics (Ra and AW). In addition, mathematical models were also developed using Response Surface Methodology (RSM) in order to estimate the quality characteristics. The used drill bits were examined under digital and scanning electron microscopes and EDX analysis was also carried out on the drill bits. The experimental results showed that the Ra and AW increased with increasing the f. It was also seen that increasing the Vc resulted in decrease in the size of adhering layer and that the drill bit wear became clear at the highest Vc of 60 m/min. According to the ANOVA results, the most effective control factor on Ra was f with 93.11% and Vc with 58.14% on AW. GRA analysis revealed that the most influential control factor was the f and that the optimum levels were 60 m/min Vc, 0.005 m/min f and drill bit 4.

Keywords

Stainless Steel, Drilling, Surface Roughness, Adhering Workpiece, Optimisation

Supporting Institution

Çankırı Karatekin University

Project Number

MYO801202B33

Thanks

The authors would like to thank Çankırı Karatekin University for provision of funding with the Project MYO801202B33.

Custom 450 Paslanmaz Çeliğinin Delinmesinde Yüzey Pürüzlülüğünü Minimize Etmek için Matematiksel Modelleme ve Çok Yanıtlı Optimizasyon

Abstract

Bu çalışmada, Custom 450 paslanmaz çelik iş parçaları üzerinde delme testleri yapılmıştır. Kontrol faktörlerinin (kesme hızı-Vc, ilerleme miktarı-f ve matkap ucu geometrisi-D) delinen deliklerin yüzey pürüzlülüğü (Ra) ve matkap ucuna yapışan iş parçasının boyutu (AW) üzerindeki etkileri incelenmiştir. Taguchi'nin L16 ortogonal dizisine dayalı olarak tasarlanan testlerden elde edilen sonuçlar, ANOVA ve Gri İlişkisel Analizler (GRA) kullanılarak analiz edilmiştir. Kalite karakteristikleri (Ra ve AW) kontrol faktörleri ve seviyelerine bağlı olarak eş zamanlı optimize edilmiştir. Ayrıca, kalite karakteristiklerini tahmin etmek için Tepki Yüzey Metodolojisi (RSM) kullanılarak matematiksel modeller geliştirilmiştir. Kullanılan matkap uçları dijital ve taramalı elektron mikroskoplarında incelenmiş ve EDX analizleri yapılmıştır. Deneysel sonuçlar, Ra ve AW'nin f arttıkça arttığını göstermiştir. Ayrıca Vc'nin artması AW boyutunda azalmaya neden olduğu ve matkap ucu aşınmasının en yüksek Vc olan 60 m/dak'da belirginleştiği görülmüştür. ANOVA sonuçlarına göre Ra üzerinde en etkili kontrol faktörü %93.11 ile f ve AW üzerinde ise %58.14 ile Vc olmuştur. GRA analizi, en etkili kontrol faktörünün f olduğunu ve optimum seviyelerin 60 m/dk kesme hızı, 0.005 m/dk ilerleme ve 4 numaralı matkap ucu olduğunu belirlenmiştir.

Keywords

Paslanmaz çelik, delme, yüzey pürüzlülüğü, yığıntı talaş, optimizasyon

Project Number

MYO801202B33

References

• 1. G. Basmacı, Optimization of processing parameters of AISI 316 Ti stainless steels, Academic Platform Journal of Engineering and Science, 6(3): 01-07, 2018.
• 2. J.D. Darwin, D.M. Lal, G. Nagarajan, Optimization of cryogenic treatment to maximize the wear resistance of 18%Cr martensitic stainless steel, Journal of Materials Processing Technology, 195: 241-247, 2008.
• 3. J.C. Outeiro, D. Umbrello, R. M'Saoubi, Experimental and numerical modelling of the residual stresses induced in orthogonal cutting of AISI 316L steel, International Journal of Machine Tools and Manufacture, 46: 1786-1794, 2006.
• 4. Ö. Tekaslan, N. Gerger, U. Şeker, AISI 304 östenitik paslanmaz çeliklerde kesme parametrelerine bağlı olarak yüzey pürüzlülüklerinin araştırılması, Balıkesir Üniversitesi FBE Dergisi, 10(2): 3-12, 2008.
• 5. H. Gökçe, Optimization of cutting tool and cutting parameters in face milling of Custom 450 through the Taguchi method, Advances in Materials Science and Engineering, 1-11, 2019.
• 6. Internet:https://www.spacematdb.com/spacemat/manudatasheets/custom%20450.pdf
• 7. A. Uysal, Investigation of cutting temperature in minimum quantity lubrication milling of ferritic stainless steel by using multi wall carbon nanotube reinforced cutting fluid, Journal of the Faculty of Engineering and Architecture of Gazi University, 32(3): 645-650, 2017.
• 8. N.A. Özbek, A. Çiçek, M. Gülesin, O. Özbek, AISI 304 ve AISI 316 östenitik paslanmaz çeliklerin işlenebilirliğinin değerlendirilmesi, Journal of Polytechnic, 20(1): 43-49, 2017.
• 9. S. Kalpakjian, S. Schmid, Manufacturing Engineering and Technology, 7th ed., Pearson Education Inc, Singapore, 625–665, 2014.
• 10. M. Yavuz, H. Gökçe, İ. Çiftçi, H. Gökçe, Ç. Yavaş, U. Şeker, Investigation of the effects of drill geometry on drilling performance and hole quality, The International Journal of Advanced Manufacturing Technology, 106(9): 4623-4633, 2020.
• 11. M. Kurt, Y. Kaynak, B. Bakır, U. Köklü, G. Atakök, L. Kutlu, Experimental investigation and Taguchi optimization for the effect of cutting parameters on the drilling of Al 2024-t4 alloy with diamond like carbon (DLC) coated drills, 5. Uluslararası İleri Teknolojiler Sempozyumu (IATS’09), 2009, Karabük.
• 12. H.L. Tonshoff, W. Spintig, W. Konig, A. Neises, Machining of holes developments in drilling techonolgy, Annals of the CIRP, 43(2): 551-560, 1994.
• 13. A. Çakır, O. Bahtiyar, U. Şeker, Farklı soğutma şartları ile farklı kesme parametrelerinin AA7075 ve AA2024 alüminyum alaşımlarında delik delme işlemlerine etkisinin deneysel olarak incelenmesi, 16. Uluslararası Makina Tasarım ve İmalat Kongresi, 30 Haziran – 03 Temmuz 2014, İzmir, Türkiye.
• 14. S. Yağmur, A. Acır, U. Şeker, M. Günay, An experimental investigation of effect of cutting parameters on cutting zone temperature in drilling, J. Fac. Eng. Archit. Gazi Univ, 28(1): 1-6, 2013.
• 15. M. Sekmen, M. Günay, U. Şeker, Effect on formations of built-up edge and built-up layer, surface roughness of cutting speed and rake angle in the machining of aluminum alloys, Journal of Polytechnic, 18(3): 141-148, 2015.
• 16. İ. Çiftçi, H. Gökçe, Ti6Al4V titanyum alaşımının delinmesinde delme yönteminin aşınmaya etkisinin incelenmesi, Journal of Polytechnic, 22:3, 627-631, 2019.
• 17. S. Kalpakjian, S.R. Schmid, Manufacturing Engineering and Technology 6th ed., Pearson Education, 2009.
• 18. H. Gökçe, Investigation of drilling performance of copper material in terms of cutting force and tool temperature, El-Cezerî Journal of Science and Engineering, 7(3): 1039-1053, 2020.
• 19. H. Gökçe, M. Yavuz, İ. Çiftçi, An investigation into the performance of HSS drills when drilling commercially pure molybdenum, Sigma Journal of Engineering and Natural Sciences, 38(1): 61-70, 2020.
• 20. M. Günay, T. Meral, Modelling and multiresponse optimization for minimizing burr height, thrust force and surface roughness in drilling of ferritic stainless steel, Indian Academy of Sciences - Sadhana, 45, 275, 2020.
• 21. V.N. Gaitonde, S.R. Karnik, B.T. Achyutha, B. Siddeswarappa, GA applications to RSM based models for burr size reduction in drilling, J. Sci. Ind. Res. (India) 64, 347–353, 2005.
• 22. S. Kumar, Y. Rizvi, R. Kumar, A review of modelling and optimization techniques in turning processes, Int. J. Mech. Eng. Technol., 9, 1146–56, 2018.
• 23. N. Mondal, S. Mandal, M.C. Mandal, FPA based optimization of drilling burr using regression analysis and ANN model, Measurement, 152, 1-10, 2020.
• 24. M. Dörterler, İ. Şahin, H. Gökçe, A grey wolf optimizer approach for optimal weight design problem of the spur gear, Engineering Optimization, 51(6): 1013-1027, 2019.
• 25. D. Özyürek, A. Kalyon, M. Yıldırım, T. Tuncay, İ. Çiftçi, Experimental investigation and prediction of wear properties of Al/SiC metal matrix composites produced by thixomoulding method using Artificial Neural Networks, Materials & Design, 63, 270-277, 2014.
• 26. H. Öktem, T. Erzurumlu, H. Kurtaran, Application of response surface methodology in the optimization of cutting conditions for surface roughness, Journal of Materials Processing Technology, 170(1–2): 1-16, 2005.
• 27. P.V.S. Suresh, P.V. Rao, S.G. Deshmukh, A genetic algorithmic approach for optimization of surface roughness prediction model, International Journal of Machine Tools and Manufacture, 42(6): 675-680, 2002.
• 28. J.Z. Zhang, J.C. Chen, Surface roughness optimization in a drilling operation using the taguchi design method, Materials and Manufacturing Processes, 24(4): 459-467, 2009.
• 29. A.T. Abbas, D.Y. Pimenov, I.N. Erdakov, M.A. Taha, M.S. Soliman, M.M. El Rayes, ANN surface roughness optimization of AZ61 magnesium alloy finish turning: minimum machining times at prime machining costs, Materials, 11(5): 808, 2018.
• 30. A.I. Toulfatzis, G.A. Pantazopoulos, C.N. David, D.S. Sagris, A.S. Paipetis, Machinability of eco-friendly lead-free brass alloys: cutting-force and surface-roughness optimization, Metals, 8(4): 250, 2018.
• 31. U. Çaydaş, A. Hasçalık, Ö. Buytoz, A. Meyveci, Performance evaluation of different twist drills in dry drilling of AISI 304 austenitic stainless steel, Mater. Manuf. Process. 26, 951–960, 2011.
• 32. A. Çiçek, T. Kıvak, E. Ekici, Optimization of drilling parameters using Taguchi technique and response surface methodology (RSM) in drilling of AISI 304 steel with cryogenically treated HSS drills, Journal of Intelligent Manufacturing, 26, 295-305, 2015.
• 33. A. Mavi, Determination of optimum cutting parameters affecting the surface form properties in the ductile stainless steels with gray relational analysis method, Gazi University Journal of Science Part C: Design and Technology, 6, 634–643, 2018.
• 34. S. Orak, R.A. Arapoğlu, M.A. Sofuoğlu, Development of an ANN-based decision-making method for determining optimum parameters in turning operation. Soft Comput 22, 6157–6170, 2018.
• 35. E. Yarar, A.T. Ertürk, F.G. Koç, Comparative Analysis in Drilling Performance of AA7075 in Different Temper Conditions. J. of Materi Eng and Perform, 2022.
• 36. https://www.ulbrich.com/uploads/data-sheets/Custom-450-Stainless-Steel-Wire-UNS-S45000.pdf
• 37. Y. Kuo, T. Yang, G.W. Huang, The use of grey relational analysis in solving multiple attribute decision-making problems, Computers & Industrial Engineering, 55(1): 80-93, 2008.
• 38. E. Yılmaz, F. Güngör, S. Hartomacıoğlu, Determining the appropriate tool holder selection by using grey relational analysis on machining process of AISI 4340 steel, Beykent Üniversitesi Fen ve Mühendislik Bilimleri Dergisi,12(2): 7-13, 2019.
• 39. S. Gurgen, M.A. Sofuoglu, F.H. Cakir, S. Orak, M.C. Kushan, Multi response optimization of turning operation with self-propelled rotary tool, Procedia - Social and Behavioral Sciences, 195: 2592-2600, 2015.
• 40. N. Yaşar, Thrust force modelling and surface roughness optimization in drilling of AA-7075: FEM and GRA, J. Mech. Sci. Technol., 33: 4771–4781, 2019.
• 41. M.A. Amran, S. Salmah, N.I.S. Hussein, R. Izamshah, M. Hadzley, M.S. Kasim, M.A. Sulaiman, Effects of machine parameters on surface roughness using response surface method in drilling process, Procedia Engineering, 68: 24–29, 2013.
• 42. İ. Çiftçi, Tool wear during machining of aisi 304 austenitic stainless steel using a coated cemented carbide tool, Teknoloji, 7(3): 489-495, 2004.
• 43. E.O. Ezugwu, S.K. Kim, The performance of cermet cutting tools when machining an Ni-Cr-Mo (En 24) steel, Lubrication Engineering, 51(2): 139-145, 1995.
• 44. Material-Removal Process and Machine Tools, Mark Standart Handbook for Mechanical Engineers, 9th ed., New York, Mc Graw Hill, 1996.
• 45. Z. Tekiner, S. Yeşilyurt, Investigation of the cutting parameters depending on process sound during turning of AISI 304 austenitic stainless steel, Materials & Desing, 25(6): 507-513, 2004.
• 46. E.M. Trent, Metal Cutting, Butterworths Pres, London, 1989.
• 47. İ. Çiftçi, M. Turker, U. Şeker, CBN cutting tool wear during machining of particulate reinforced MMCs, Wear, 257: 1041-1046, 2004.
• 48. H. Gökçe, Investigation of the effect of the tap geometry on the Al 5083 aluminium material in tapping, Gazi Journal of Engineering Sciences (GJES), 6(3): 242-247, 2020.
• 49. C. Relvas, A. Ramos, New methodology for product development process using structured tools, Proceedings of the Institution of Mechanical Engineers Part B-journal of Engineering Manufacture, 235(3): 378-393, 2021.
• 50. H. Gökçe, M.A. Biberci, Mathematical modeling and multiresponse optimization to reduce surface roughness and adhesion in Al 5083 H116 alloys used in ammunition propulsion actuators, Multidiscipline Modeling in Materials and Structures, 19(2): 341-359, 2023.
• 51. N. Bhople, S. Mastud, R. Mittal, Metallurgical and machining performance aspects of cryotreated tungsten carbide micro-end mill cutters, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 237(3):492-502, 2023.