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Strenx 1100 Yapısal Çeliğinin MMY Destekli Frezelenmesinde Serbest Yüzey Aşınması Analizi

Year 2021, Issue: 25, 629 - 635, 31.08.2021
https://doi.org/10.31590/ejosat.938234

Abstract

Strenx 1100 yapısal çeliği, deniz araçları ve ağır araçlar gibi önemli mühendislik uygulamalarında kullanılmasına olanak sağlayan benzersiz malzeme özelliklerine sahiptir. Bu malzemeden üretilen parçaların yüzey bütünlüğü büyük önem taşıdığından, takım aşınma durumunun da dikkate alınması gerekir. Uygulanabilir ve kolay ölçülebildiği için, takım serbest yüzey aşınması (VB) bu alanda yaygın olarak tercih edilir ve kalan takım ömrünü yansıtır. İşlenebilirlik özelliklerini iyileştirmek adına özellikle kesilmesi zor malzemeler için son zamanlarda işleme operasyonlarında minimum miktarda yağlama (MQL) uygulanmaktadır. Bu çalışma Strenx 1100 çeliğinin MMY destekli frezelemesi sırasında serbest yüzey aşınması analizini ve deneylerini içerir. Kesme hızı, ilerleme ve talaş derinliği deneysel plana dâhil edilmiş ve Taguchi L9 tasarımı benimsenmiştir. Ölçülen aşınma sonuçları 3 boyutlu yüzey grafikleri ile değerlendirilmiş, varyans analizi (ANOVA) ve parametrelerin optimizasyonu sinyal / gürültü (S / N) oranına bağlı olarak gerçekleştirilmiştir. Buna göre, kesme hızı serbest yüzey aşınmasını yaklaşık% 53.2 oranında etkileyen ilk parametredir ve bunu yaklaşık % 35.77 ile ilerleme izlemektedir. S / N oranına bağlı parametrik optimizasyon, minimum serbest yüzey aşınması için kesm parametrelerinin birinci sırasının seçilmesi gerektiğini göstermektedir. Bu kapsamlı analiz, çok çeşitli kesme parametrelerinin uygulanması sırasında sınırlamaları ortaya koyduğu için endüstrideki pratik uygulamalar için bir kılavuz niteliğindedir.

References

  • 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.
  • Aslan, A. (2020). Optimization and Analysis of Process Parameters for Flank Wear, Cutting Forces and Vibration in Turning of AISI 5140: A Comprehensive Study. Measurement, 107959.
  • Astakhov, V. P. (2007). Effects of the cutting feed, depth of cut, and workpiece (bore) diameter on the tool wear rate. The International Journal of Advanced Manufacturing Technology, 34(7), 631-640.
  • Bermingham, M., Sim, W., Kent, D., Gardiner, S., & Dargusch, M. (2015). Tool life and wear mechanisms in laser assisted milling Ti–6Al–4V. Wear, 322, 151-163.
  • Chandrasekaran, H., & M'Saoubi, R. (2006). Improved machinability in hard milling and strategies for steel development. CIRP annals, 55(1), 93-96.
  • Coromant, S. (1994). Modern metal cutting: a practical handbook: Sandvik Coromant.
  • 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.
  • Davim, J. P. (2011). Machining of hard materials: Springer Science & Business Media.
  • Dong, P. Q., & Duc, T. M. (2019). Performance evaluation of MQCL hard milling of SKD 11 tool steel using MoS2 nanofluid. Metals, 9(6), 658.
  • Grzesik, W. (2008). Machining of hard materials. In Machining (pp. 97-126): Springer.
  • Gupta, M. K., Song, Q., Liu, Z., Sarikaya, M., Jamil, M., Mia, M., . . . Pimenov, D. Y. (2021). Environment and economic burden of sustainable cooling/lubrication methods in machining of Inconel-800. Journal of Cleaner Production, 287, 125074.
  • 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.
  • 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.
  • ISO 3685-1993(E). Tool life testing with single point turning tools. (1993). In.
  • Jang, D.-y., Jung, J., & Seok, J. (2016). Modeling and parameter optimization for cutting energy reduction in MQL milling process. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 5-12.
  • Kechagias, J. D., Aslani, K.-E., Fountas, N. A., Vaxevanidis, N. M., & Manolakos, D. E. (2020). A comparative investigation of Taguchi and full factorial design for machinability prediction in turning of a titanium alloy. Measurement, 151, 107213.
  • Kıvak, T. (2014). Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts. Measurement, 50, 19-28.
  • Kuntoğlu, M., & Sağlam, H. (2019). Investigation of progressive tool wear for determining of optimized machining parameters in turning. Measurement, 140, 427-436.
  • 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.
  • Mia, M. (2018). Mathematical modeling and optimization of MQL assisted end milling characteristics based on RSM and Taguchi method. Measurement, 121, 249-260.
  • Muaz, M., & Choudhury, S. K. (2019). Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel. Measurement, 138, 557-569.
  • Najiha, M. S., & Rahman, M. (2016). Experimental investigation of flank wear in end milling of aluminum alloy with water-based TiO 2 nanofluid lubricant in minimum quantity lubrication technique. The International Journal of Advanced Manufacturing Technology, 86(9), 2527-2537.
  • Niaki, F. A., & Mears, L. (2017). A comprehensive study on the effects of tool wear on surface roughness, dimensional integrity and residual stress in turning IN718 hard-to-machine alloy. Journal of Manufacturing Processes, 30, 268-280.
  • Sen, B., Gupta, M. K., Mia, M., Pimenov, D. Y., & Mikołajczyk, T. (2021). Performance Assessment of Minimum Quantity Castor-Palm Oil Mixtures in Hard-Milling Operation. Materials, 14(1), 198.
  • Sen, B., Mia, M., Mandal, U. K., Dutta, B., & Mondal, S. P. (2019). Multi-objective optimization for MQL-assisted end milling operation: an intelligent hybrid strategy combining GEP and NTOPSIS. Neural Computing and Applications, 31(12), 8693-8717.
  • Sen, B., Mia, M., Mandal, U. K., & Mondal, S. P. (2019). GEP-and ANN-based tool wear monitoring: a virtually sensing predictive platform for MQL-assisted milling of Inconel 690. The International Journal of Advanced Manufacturing Technology, 105(1), 395-410.
  • Siddhpura, A., & Paurobally, R. (2013). A review of flank wear prediction methods for tool condition monitoring in a turning process. The International Journal of Advanced Manufacturing Technology, 65(1-4), 371-393.
  • Singh, G., Gupta, M. K., Mia, M., & Sharma, V. S. (2018). Modeling and optimization of tool wear in MQL-assisted milling of Inconel 718 superalloy using evolutionary techniques. The International Journal of Advanced Manufacturing Technology, 97(1), 481-494.
  • SSAB. (2021). https://www.ssab.com.tr/api/sitecore/Datasheet/GetDocument?productId=6A0A9E9AF58C4AA2A29FC15CA0CE2590&language=en. .
  • 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.
  • Taguchi, G. (1987). System of experimental design; engineering methods to optimize quality and minimize costs. Retrieved from New York, America:
  • Tönshoff, H., Arendt, C., & Amor, R. B. (2000). Cutting of hardened steel. CIRP annals, 49(2), 547-566.
  • Umbrello, D., Micari, F., & Jawahir, I. (2012). The effects of cryogenic cooling on surface integrity in hard machining: A comparison with dry machining. CIRP annals, 61(1), 103-106.
  • Zhang, S., Li, J., & Lv, H. (2014). Tool wear and formation mechanism of white layer when hard milling H13 steel under different cooling/lubrication conditions. Advances in Mechanical Engineering, 6, 949308.

Tool Flank Wear Analysis for MQL Assisted Milling of Strenx 1100 Structural Steel

Year 2021, Issue: 25, 629 - 635, 31.08.2021
https://doi.org/10.31590/ejosat.938234

Abstract

Strenx 1100 structural steel has unique material properties enables to utilize in important engineering applications such as marine and heavy vehicles. Due to the surface integrity of the parts produced by this material is of paramount importance, tool wear condition needs to be considered as well. As being applicable and easy to measure, tool flank wear (VB) is widely preferred in the field and reflects the remaining tool life. Minimum quantity lubrication (MQL) has been preferred most recently in machining operations especially for the hard-to-cut materials to improve the machinability characteristics. This paper contains experiments and further analysis of tool flank wear during MQL assisted milling of the Strenx 1100 steel. Cutting speed, feed rate and depth of cut were included in the experimental plan and Taguchi L9 design was adopted. Measured wear results were evaluated with 3d surface plots, analysis of variance (ANOVA) and optimization of parameters were carried out depend on signal to noise (S/N) ratio. Accordingly, cutting speed is the first parameter affecting tool flank wear about 53.2% and followed by feed rate about 35.77%. Parametric optimization depend on S/N ratio shows that first order of theall cutting parameters need to be selected for minimum tool flank wear which is observed on 3d graphs that the increase in the amount of wear with higher parameter levels. This comprehensive analysis is a guide for the practical applications in the industry as providing the limitations during applying a wide range of cutting parameters.

References

  • 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.
  • Aslan, A. (2020). Optimization and Analysis of Process Parameters for Flank Wear, Cutting Forces and Vibration in Turning of AISI 5140: A Comprehensive Study. Measurement, 107959.
  • Astakhov, V. P. (2007). Effects of the cutting feed, depth of cut, and workpiece (bore) diameter on the tool wear rate. The International Journal of Advanced Manufacturing Technology, 34(7), 631-640.
  • Bermingham, M., Sim, W., Kent, D., Gardiner, S., & Dargusch, M. (2015). Tool life and wear mechanisms in laser assisted milling Ti–6Al–4V. Wear, 322, 151-163.
  • Chandrasekaran, H., & M'Saoubi, R. (2006). Improved machinability in hard milling and strategies for steel development. CIRP annals, 55(1), 93-96.
  • Coromant, S. (1994). Modern metal cutting: a practical handbook: Sandvik Coromant.
  • 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.
  • Davim, J. P. (2011). Machining of hard materials: Springer Science & Business Media.
  • Dong, P. Q., & Duc, T. M. (2019). Performance evaluation of MQCL hard milling of SKD 11 tool steel using MoS2 nanofluid. Metals, 9(6), 658.
  • Grzesik, W. (2008). Machining of hard materials. In Machining (pp. 97-126): Springer.
  • Gupta, M. K., Song, Q., Liu, Z., Sarikaya, M., Jamil, M., Mia, M., . . . Pimenov, D. Y. (2021). Environment and economic burden of sustainable cooling/lubrication methods in machining of Inconel-800. Journal of Cleaner Production, 287, 125074.
  • 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.
  • 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.
  • ISO 3685-1993(E). Tool life testing with single point turning tools. (1993). In.
  • Jang, D.-y., Jung, J., & Seok, J. (2016). Modeling and parameter optimization for cutting energy reduction in MQL milling process. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 5-12.
  • Kechagias, J. D., Aslani, K.-E., Fountas, N. A., Vaxevanidis, N. M., & Manolakos, D. E. (2020). A comparative investigation of Taguchi and full factorial design for machinability prediction in turning of a titanium alloy. Measurement, 151, 107213.
  • Kıvak, T. (2014). Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts. Measurement, 50, 19-28.
  • Kuntoğlu, M., & Sağlam, H. (2019). Investigation of progressive tool wear for determining of optimized machining parameters in turning. Measurement, 140, 427-436.
  • 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.
  • Mia, M. (2018). Mathematical modeling and optimization of MQL assisted end milling characteristics based on RSM and Taguchi method. Measurement, 121, 249-260.
  • Muaz, M., & Choudhury, S. K. (2019). Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel. Measurement, 138, 557-569.
  • Najiha, M. S., & Rahman, M. (2016). Experimental investigation of flank wear in end milling of aluminum alloy with water-based TiO 2 nanofluid lubricant in minimum quantity lubrication technique. The International Journal of Advanced Manufacturing Technology, 86(9), 2527-2537.
  • Niaki, F. A., & Mears, L. (2017). A comprehensive study on the effects of tool wear on surface roughness, dimensional integrity and residual stress in turning IN718 hard-to-machine alloy. Journal of Manufacturing Processes, 30, 268-280.
  • Sen, B., Gupta, M. K., Mia, M., Pimenov, D. Y., & Mikołajczyk, T. (2021). Performance Assessment of Minimum Quantity Castor-Palm Oil Mixtures in Hard-Milling Operation. Materials, 14(1), 198.
  • Sen, B., Mia, M., Mandal, U. K., Dutta, B., & Mondal, S. P. (2019). Multi-objective optimization for MQL-assisted end milling operation: an intelligent hybrid strategy combining GEP and NTOPSIS. Neural Computing and Applications, 31(12), 8693-8717.
  • Sen, B., Mia, M., Mandal, U. K., & Mondal, S. P. (2019). GEP-and ANN-based tool wear monitoring: a virtually sensing predictive platform for MQL-assisted milling of Inconel 690. The International Journal of Advanced Manufacturing Technology, 105(1), 395-410.
  • Siddhpura, A., & Paurobally, R. (2013). A review of flank wear prediction methods for tool condition monitoring in a turning process. The International Journal of Advanced Manufacturing Technology, 65(1-4), 371-393.
  • Singh, G., Gupta, M. K., Mia, M., & Sharma, V. S. (2018). Modeling and optimization of tool wear in MQL-assisted milling of Inconel 718 superalloy using evolutionary techniques. The International Journal of Advanced Manufacturing Technology, 97(1), 481-494.
  • SSAB. (2021). https://www.ssab.com.tr/api/sitecore/Datasheet/GetDocument?productId=6A0A9E9AF58C4AA2A29FC15CA0CE2590&language=en. .
  • 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.
  • Taguchi, G. (1987). System of experimental design; engineering methods to optimize quality and minimize costs. Retrieved from New York, America:
  • Tönshoff, H., Arendt, C., & Amor, R. B. (2000). Cutting of hardened steel. CIRP annals, 49(2), 547-566.
  • Umbrello, D., Micari, F., & Jawahir, I. (2012). The effects of cryogenic cooling on surface integrity in hard machining: A comparison with dry machining. CIRP annals, 61(1), 103-106.
  • Zhang, S., Li, J., & Lv, H. (2014). Tool wear and formation mechanism of white layer when hard milling H13 steel under different cooling/lubrication conditions. Advances in Mechanical Engineering, 6, 949308.
There are 34 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Mustafa Kuntoğlu 0000-0002-7291-9468

Publication Date August 31, 2021
Published in Issue Year 2021 Issue: 25

Cite

APA Kuntoğlu, M. (2021). Tool Flank Wear Analysis for MQL Assisted Milling of Strenx 1100 Structural Steel. Avrupa Bilim Ve Teknoloji Dergisi(25), 629-635. https://doi.org/10.31590/ejosat.938234