Strenx 1100 Çeliğinin MMY Şartları Altında Frezelenmesinde Yüzey Pürüzlülüğü Değerlendirmesi

Öz

Strenx 1100 üstün mekanik özellikler ile nitelendirilen, gemi ve vinç gibi birçok mühendislik alanında genellikle yük taşıma uygulamalarında tercih edilen en önemli yapı çeliklerinden bir tanesidir. Minimum Miktarda Yağlama (MMY) kesme bölgesine pulverize olmuş yağ uygulanması ile sürdürülebilir imalatı sağlayan, geleneksel yaklaşımlarla kıyaslandığında kendisini daha iyi işlenebilirlik karakteristikleri ile ispatlamış bir yöntemdir. Yüzey pürüzlülüğü, endüstriyel ihtiyaçlara göre üretilmesi gereken, işlenen parçanın kalitesini belirli ölçüde yansıtan bir cevap parametresidir. Bu makale MMY şartları altında Strenx 1100 malzemenin frezelenmesi süresince yüzey pürüzlülüğünün değerlendirilmesi üzerine odaklanmıştır. Kesme hızı (vC), ilerleme (f) and talaş derinliğinin (aP) üç seviyesi birleştirilerek L9 ortogonal dizi oluşturulması için Taguchi deneysel tasarımından yararlanılmıştır. Bulgular, varyans analizi (ANOVA), sinyal-gürültü oranına (S/N) dayalı optimizasyon ve 3d yüzey grafikleri kullanılarak tartışılmıştır. Sonuçlara göre, ilerleme (66.9%), yüzey pürüzlülüğü üzerinde talaş derinliği (22.5%) ve kesme hızından (0.4%) daha etkili olurken, kesme parametrelerinin birinci seviyesinin, vC=75 m/min, f=0.075 mm/rev and aP=0.25 mm, cevap parametresini optimize etmek için seçilmesi gerektiği görülmektedir. İstenen cevap değeri için doğru kesme koşullarının seçimini sağlayan grafiksel gösterimler yüzey pürüzlülüğünün genel eğilimlerini yansıtmaktadır.

Anahtar Kelimeler

• Strenx 1100 Çeliği
• Yüzey Pürüzlülüğü
• Frezeleme
• Minimum Miktarda Yağlama

Surface Roughness Evaluation in Milling of Strenx 1100 Steel under MQL Conditions

Abstract

Strenx 1100 is one of the most important structural steel characterized by upmost mechanical properties, generally preferred for load-bearing applications at many engineering fields such as marine and crane. Minimum quantity lubrication (MQL) is a method that presents sustainable machining with applying pulverized oil into the cutting zone, proved it by obtaining better machinability characteristics compared to conventional approaches. Surface roughness is a response parameter reflects the quality of a machined part in a certain degree which should be produced as per the industrial requirements. This paper focuses on the surface roughness (Ra) evaluation of Strenx 1100 steel during milling under MQL conditions. Taguchi design of experiments were utilized with combining three levels of cutting speed (vC), feed rate (f) and depth of cut (aP) in order to create L9 orthogonal array. The findings are discussed using analysis of variance (ANOVA), signal-to-noise ratio (S/N) based optimization and 3d surface plots. According to the results, it is observed that the first level of cutting parameters namely vC=75 m/min, f=0.075 mm/rev and aP=0.25 mm need to be selected for optimization of response parameter while feed rate has more influence (66.9%) than depth of cut (22.5%) and cutting speed (0.4%) on surface roughness. Graphical representations exhibit the general trend of surface roughness which provides chance to selection of accurate cutting conditions for required response value.

Keywords

• Strenx 1100 Steel
• Surface Roughness
• Milling
• Minimum Quantity Lubrication

Kaynakça

1. Akıncıoğlu, S., Gökkaya, H., & Uygur, İ. (2016). The effects of cryogenic-treated carbide tools on tool wear and surface roughness of turning of Hastelloy C22 based on Taguchi method. The International Journal of Advanced Manufacturing Technology, 82(1-4), 303-314.
2. Al Bashir, M., Mia, M., & Dhar, N. R. (2018). Investigations on surface milling of hardened AISI 4140 steel with pulse jet MQL applicator. Journal of The Institution of Engineers (India): Series C, 99(3), 301-314.
3. Anand, K., & Mathew, J. (2020). Evaluation of size effect and improvement in surface characteristics using sunflower oil-based MQL for sustainable micro-endmilling of Inconel 718. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42(4), 1-13.
4. Bensouilah, H., Aouici, H., Meddour, I., Yallese, M. A., Mabrouki, T., & Girardin, F. (2016). Performance of coated and uncoated mixed ceramic tools in hard turning process. Measurement, 82, 1-18.
5. Boswell, B., Islam, M. N., Davies, I. J., Ginting, Y., & Ong, A. K. (2017). A review identifying the effectiveness of minimum quantity lubrication (MQL) during conventional machining. The International Journal of Advanced Manufacturing Technology, 92(1), 321-340.
6. Cui, X., Zhao, J., Jia, C., & Zhou, Y. (2012). Surface roughness and chip formation in high-speed face milling AISI H13 steel. The International Journal of Advanced Manufacturing Technology, 61(1), 1-13.
7. Çetindağ, H. A., Çiçek, A., & Uçak, N. (2020). The effects of CryoMQL conditions on tool wear and surface integrity in hard turning of AISI 52100 bearing steel. Journal of Manufacturing Processes, 56, 463-473.
8. Das, A., Pradhan, O., Patel, S. K., Das, S. R., & Biswal, B. B. (2019). Performance appraisal of various nanofluids during hard machining of AISI 4340 steel. Journal of Manufacturing Processes, 46, 248-270.

9. Davim, J. P. (2011). Machining of hard materials: Springer Science & Business Media. Debnath, S., Reddy, M. M., & Yi, Q. S. (2016). Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method. Measurement, 78, 111-119.
10. Do, T.-V., & Le, N.-A.-V. (2019). Optimization of surface roughness and cutting force in MQL hard-milling of AISI H13 steel. Paper presented at the Advances in Engineering Research and Application: Proceedings of the International Conference, ICERA 2018.
11. Goindi, G. S., & Sarkar, P. (2017). Dry machining: a step towards sustainable machining–challenges and future directions. Journal of Cleaner Production, 165, 1557-1571.
12. Gupta, M. K., Pruncu, C. I., Mia, M., Singh, G., Singh, S., Prakash, C., . . . Gill, H. S. (2018). Machinability investigations of Inconel-800 super alloy under sustainable cooling conditions. Materials, 11(11), 2088.
13. Günan, F., Kıvak, T., Yıldırım, Ç. V., & Sarıkaya, M. (2020). Performance evaluation of MQL with AL2O3 mixed nanofluids prepared at different concentrations in milling of Hastelloy C276 alloy. Journal of Materials Research and Technology, 9(5), 10386-10400.
14. Hassanpour, H., Sadeghi, M. H., Rasti, A., & Shajari, S. (2016). Investigation of surface roughness, microhardness and white layer thickness in hard milling of AISI 4340 using minimum quantity lubrication. Journal of Cleaner Production, 120, 124-134.
15. Hosseini, S., Beno, T., Klement, U., Kaminski, J., & Ryttberg, K. (2014). Cutting temperatures during hard turning—Measurements and effects on white layer formation in AISI 52100. Journal of Materials Processing Technology, 214(6), 1293-1300.
16. Hsu, Q.-C. (2016). Optimization of minimum quantity lubricant conditions and cutting parameters in hard milling of AISI H13 steel. Applied Sciences, 6(3), 83.
17. Iqbal, A., Ning, H., Khan, I., Liang, L., & Dar, N. U. (2008). Modeling the effects of cutting parameters in MQL-employed finish hard-milling process using D-optimal method. Journal of materials processing technology, 199(1-3), 379-390.
18. Jamil, M., Zhao, W., He, N., Gupta, M. K., Sarikaya, M., Khan, A. M., . . . Pimenov, D. Y. (2021). Sustainable milling of Ti–6Al–4V: A trade-off between energy efficiency, carbon emissions and machining characteristics under MQL and cryogenic environment. Journal of Cleaner Production, 281, 125374.
19. Kene, A. P., & Choudhury, S. K. (2019). Analytical modeling of tool health monitoring system using multiple sensor data fusion approach in hard machining. Measurement, 145, 118-129.
20. Khaliq, W., Zhang, C., Jamil, M., & Khan, A. M. (2020). Tool wear, surface quality, and residual stresses analysis of micro-machined additive manufactured Ti–6Al–4V under dry and MQL conditions. Tribology International, 151, 106408.
21. Koklu, U., & Çoban, H. (2020). Effect of dipped cryogenic approach on thrust force, temperature, tool wear and chip formation in drilling of AZ31 magnesium alloy. Journal of Materials Research and Technology, 9(3), 2870-2880.
22. Kuntoğlu, M., Aslan, A., Pimenov, D. Y., Giasin, K., Mikolajczyk, T., & Sharma, S. (2020). Modeling of cutting parameters and tool geometry for multi-criteria optimization of surface roughness and vibration via response surface methodology in turning of AISI 5140 steel. Materials, 13(19), 4242.
23. Kuntoğlu, M., Aslan, A., Sağlam, H., Pimenov, D. Y., Giasin, K., & Mikolajczyk, T. (2020). Optimization and analysis of surface roughness, flank wear and 5 different sensorial data via tool condition monitoring system in turning of aisi 5140. Sensors, 20(16), 4377.
24. Kuntoğlu, M., & Sağlam, H. (2019). Investigation of progressive tool wear for determining of optimized machining parameters in turning. Measurement, 140, 427-436.
25. Kurc-Lisiecka, A., Piwnik, J., & Lisiecki, A. (2017). Laser welding of new grade of advanced high strength steel STRENX 1100 MC. Archives of Metallurgy and Materials, 62.
26. Mia, M. (2018). Mathematical modeling and optimization of MQL assisted end milling characteristics based on RSM and Taguchi method. Measurement, 121, 249-260.
27. Mia, M., & Dhar, N. R. (2018). Modeling of surface roughness using RSM, FL and SA in dry hard turning. Arabian Journal for Science and Engineering, 43(3), 1125-1136.
28. Ming, W., Shen, F., Zhang, G., Liu, G., Du, J., & Chen, Z. (2021). Green machining: A framework for optimization of cutting parameters to minimize energy consumption and exhaust emissions during electrical discharge machining of Al 6061 and SKD 11. Journal of Cleaner Production, 285, 124889.
29. Minh, D. T., The, L. T., & Bao, N. T. (2017). Performance of Al2O3 nanofluids in minimum quantity lubrication in hard milling of 60Si2Mn steel using cemented carbide tools. Advances in Mechanical Engineering, 9(7), 1687814017710618.
30. Shokoohi, Y., Khosrojerdi, E., & Shiadhi, B. R. (2015). Machining and ecological effects of a new developed cutting fluid in combination with different cooling techniques on turning operation. Journal of Cleaner Production, 94, 330-339.
31. SSAB. (2021). https://www.ssab.com.tr/api/sitecore/Datasheet/GetDocument?productId=6A0A9E9AF58C4AA2A29FC15CA0CE2590&language=en.
32. Sun, S., Brandt, M., & Dargusch, M. (2010). Thermally enhanced machining of hard-to-machine materials—a review. International Journal of Machine Tools and Manufacture, 50(8), 663-680.
33. Şahinoğullari, E., & Luş, H. M. (2021). Effect of Machining on the Surface Roughness of 31CrMoV9 and 34CrAIMo5 Steels After Nitriding. Avrupa Bilim ve Teknoloji Dergisi(21), 410-415.
34. Wang, C., Li, K., Chen, M., & Liu, Z. (2015). Evaluation of minimum quantity lubrication effects by cutting force signals in face milling of Inconel 182 overlays. Journal of Cleaner Production, 108, 145-157.
35. Wojciechowski, S., Maruda, R. W., Krolczyk, G. M., & Niesłony, P. (2018). Application of signal to noise ratio and grey relational analysis to minimize forces and vibrations during precise ball end milling. Precision Engineering, 51, 582-596.
36. Wu, Q., Xie, D.-J., Si, Y., Zhang, Y.-D., Li, L., & Zhao, Y.-X. (2018). Simulation analysis and experimental study of milling surface residual stress of Ti-10V-2Fe-3Al. Journal of Manufacturing Processes, 32, 530-537.