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Study on Consumed Energy of Strenx 1100 Steel During MQL Assisted Hard Milling

Year 2021, Volume: 11 Issue: 4, 3003 - 3013, 15.12.2021
https://doi.org/10.21597/jist.939332

Abstract

Strenx 1100 is a structural steel utilized in the critical engineering applications such as marine, crane and transportation thanks to its high tensile and yield strength properties. Despite its advantages coming from mechanical properties, hard-to-cut structure of this material makes difficult the metal removing. Therefore, high amount of energy consumption reveals due to the requirement of high cutting forces. In order to overcome this problem, recently, minimum quantity lubrication (MQL) supported machining have been introduced from many authors. In this context, this study aims to measure and analyze the consumed energy in MQL assisted surface milling of Strenx 1100 steel. For this purpose, experimental results were evaluated by three methods: analysis on graphs, ANOVA based statistical evaluation and signal to noise (S/N) ratio based optimization. In the experiments, with the help of Taguchi, L9 orthogonal array design was adopted using three levels of cutting speed, feed rate and depth of cut. According to the obtained findings, cutting speed is the most effective parameter according to contribution rate (46.28%) and P value (0.048<0.05), meanwhile feed rate (23.6%) and depth of cut (27.8%) have important contributors on the consumed energy during milling. Seemingly, general trend for the consumed energy is about to increase with higher values of cutting speed and depth of cut and lower values of feed rate according to 3D plots. This situation is confirmed with optimal solutions as vC=75 m/min, f=0.25 mm, aP=0.225 mm/rev achieved by S/N ratios. Conducted experiments and further analysis provide an important guidance for the industrial applications in MQL reinforced machining of hard materials.

References

  • Abbas AT, Anwar S, Abdelnasser E, Luqman M, Qudeiri JEA, Elkaseer A, 2021. Effect of Different Cooling Strategies on Surface Quality and Power Consumption in Finishing End Milling of Stainless Steel 316. Materials, 14(4): 903.
  • Abbas AT, Benyahia F, El Rayes MM, Pruncu C, Taha MA, Hegab H, 2019. Towards optimization of machining performance and sustainability aspects when turning AISI 1045 steel under different cooling and lubrication strategies. Materials, 12(18): 3023.
  • Aramcharoen A, Mativenga PT 2014. Critical factors in energy demand modelling for CNC milling and impact of toolpath strategy. Journal of cleaner production, 78: 63-74.
  • Benedicto E, Carou D, Rubio E, 2017. Technical, economic and environmental review of the lubrication/cooling systems used in machining processes. Procedia engineering, 184: 99-116.
  • Bilga PS, Singh S, Kumar R, 2016. Optimization of energy consumption response parameters for turning operation using Taguchi method. Journal of cleaner production, 137: 1406-1417.
  • Das A, Pradhan O, Patel SK, Das SR, Biswal BB, 2019. Performance appraisal of various nanofluids during hard machining of AISI 4340 steel. Journal of Manufacturing Processes, 46: 248-270.
  • Draganescu F, Gheorghe M, Doicin C, 2003. Models of machine tool efficiency and specific consumed energy. Journal of Materials Processing Technology, 141(1): 9-15.
  • Duflou JR, Sutherland JW, Dornfeld D, Herrmann C, Jeswiet J, Kara S, Kellens K, 2012. Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP annals, 61(2): 587-609.
  • Gupta MK, Pruncu CI, Mia M, Singh G, Singh S, Prakash C, Gill HS, 2018. Machinability investigations of Inconel-800 super alloy under sustainable cooling conditions. Materials, 11(11): 2088.
  • Gupta MK, Song Q, Liu Z, Sarikaya M, Mia M, Jamil M, Kuntoğlu M, 2021. Tribological Performance Based Machinability Investigations in Cryogenic Cooling Assisted Turning of α-β Titanium Alloy. Tribology International, 107032.
  • HIOKI, 2021. https://assets.testequity.com/te1/Documents/pdf/hioki/PW3198.pdf.
  • 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.
  • Imani Asrai R, Newman ST, Nassehi A, 2018. A mechanistic model of energy consumption in milling. International Journal of Production Research, 56(1-2): 642-659.
  • Jamil M, Khan AM, Hegab H, Gupta MK, Mia M, He N, Liu Z, 2020. Milling of Ti–6Al–4V under hybrid Al 2 O 3-MWCNT nanofluids considering energy consumption, surface quality, and tool wear: a sustainable machining. The International Journal of Advanced Manufacturing Technology, 107(9): 4141-4157.
  • Jamil M, Zhao W, He N, Gupta MK, Sarikaya M, Khan AM, Pimenov DY, 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.
  • Kene AP, Choudhury SK, 2019. Analytical modeling of tool health monitoring system using multiple sensor data fusion approach in hard machining. Measurement, 145: 118-129.
  • Khan AM, Jamil M, Salonitis K, Sarfraz S, Zhao W, He N, Zhao G, 2019. Multi-objective optimization of energy consumption and surface quality in nanofluid SQCL assisted face milling. Energies, 12(4): 710.
  • Kuntoğlu M, 2021a. Surface Roughness Evaluation in Milling of Strenx 1100 Steel under MQL Conditions. Avrupa Bilim ve Teknoloji Dergisi, (25): 509-516.
  • Kuntoğlu M, 2021b. Tool Flank Wear Analysis for MQL Assisted Milling of Strenx 1100 Structural Steel. Avrupa Bilim ve Teknoloji Dergisi.
  • Kuntoğlu M, Aslan A, Pimenov DY, 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.
  • Kuntoğlu M, Aslan A, Sağlam H, Pimenov DY, 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.
  • 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.
  • Li L, Yan J, Xing Z, 2013. Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling. Journal of cleaner production, 52: 113-121.
  • Liu N, Zhang Y, Lu W, 2015. A hybrid approach to energy consumption modelling based on cutting power: a milling case. Journal of cleaner production, 104: 264-272.
  • Liu Z, Guo Y, Sealy M, Liu Z, 2016. Energy consumption and process sustainability of hard milling with tool wear progression. Journal of Materials Processing Technology, 229: 305-312.
  • Liu Z, Sealy MP, Li W, Zhang D, Fang X, Guo Y, Liu Z, 2018. Energy consumption characteristics in finish hard milling. Journal of Manufacturing Processes, 35: 500-507.
  • Mia M, 2018. Mathematical modeling and optimization of MQL assisted end milling characteristics based on RSM and Taguchi method. Measurement, 121: 249-260.
  • Mulyadi IH, Balogun VA, Mativenga PT, 2015. Environmental performance evaluation of different cutting environments when milling H13 tool steel. Journal of cleaner production, 108: 110-120. Omer AM, 2008. Energy, environment and sustainable development. Renewable and sustainable energy reviews, 12(9): 2265-2300.
  • Park CW, Kwon KS, Kim WB, Min BK, Park SJ, Sung IH, Seok J, 2009. Energy consumption reduction technology in manufacturing—A selective review of policies, standards, and research. International journal of precision engineering and manufacturing, 10(5): 151-173.
  • Qun S, Weimin Z, 2012. Carbon footprint analysis in metal cutting process. Paper presented at the Proceedings of the 1st International Conference on Mechanical Engineering and Material Science.
  • Sealy MP, Liu Z, Guo Y, Liu Z, 2016. Energy based process signature for surface integrity in hard milling. Journal of Materials Processing Technology, 238: 284-289.
  • Sealy MP, Liu Z, Zhang D, Guo Y, Liu Z, 2016. Energy consumption and modeling in precision hard milling. Journal of cleaner production, 135: 1591-1601.
  • Shokrani A, Dhokia V, Newman ST, 2018. Energy conscious cryogenic machining of Ti-6Al-4V titanium alloy. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232(10): 1690-1706.
  • SSAB, (2021). https://www.ssab.com.tr/api/sitecore/Datasheet/GetDocument?productId=6A0A9E9 AF58C4AA2A29FC15CA0CE2590&language=en.
  • 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.
  • Vu NC, Dang XP, Huang SC, 2020. Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition. Measurement and Control, 0020294020919457.
  • Wang W, Tian G, Chen M, Tao F, Zhang C, Abdulraham AA, Jiang Z, 2020. Dual-objective program and improved artificial bee colony for the optimization of energy-conscious milling parameters subject to multiple constraints. Journal of cleaner production, 245: 118714.
  • Xu K, Luo M, Tang K, 2016. Machine based energy-saving tool path generation for five-axis end milling of freeform surfaces. Journal of cleaner production, 139: 1207-1223.
  • Yan J, Li L, 2013. Multi-objective optimization of milling parameters–the trade-offs between energy, production rate and cutting quality. Journal of cleaner production, 52: 462-471.
  • Yoon HS, Lee JY, Kim MS, Ahn SH, 2014. Empirical power-consumption model for material removal in three-axis milling. Journal of cleaner production. Journal of Cleaner Production, 78: 54-62.
  • Zhang T, Liu Z, Sun X, Xu J, Dong L, Zhu G, 2020. Investigation on specific milling energy and energy efficiency in high-speed milling based on energy flow theory. Energy, 192: 116596.
  • Zhao G, Liu Z, He Y, Cao H, Guo Y, 2017. Energy consumption in machining: Classification, prediction, and reduction strategy. Energy, 133: 142-157.

Strenx 1100 Çeliğinin MMY Yardımıyla Sert Frezelenmesinde Enerji Tüketimi Üzerine Çalışma

Year 2021, Volume: 11 Issue: 4, 3003 - 3013, 15.12.2021
https://doi.org/10.21597/jist.939332

Abstract

Strenx 1100, yüksek çekme ve akma dayanımı özellikleri sayesinde denizcilik, vinç ve nakliye gibi kritik mühendislik uygulamalarında kullanılan bir yapısal çeliktir. Mekanik özelliklerinden gelen avantajlarına rağmen, yine bu özellikler malzemeden talaş kaldırmayı güçleştirmektedir. Bu sebeple yüksek kesme kuvvetlerine ihtiyaç duyulması nedeniyle yüksek miktarda enerji tüketimi ortaya çıkar. Bu sorunun üstesinden gelmek için, son zamanlarda, birçok yazar minimum miktarda yağlama (MMY) destekli işleme öne sürülmüştür. Bu bağlamda, bu çalışma Strenx 1100 çeliğinin MMY destekli yüzey frezelemesinde tüketilen enerjiyi ölçerek analiz etmeyi amaçlamaktadır. Bu amaçla elde edilen deneysel sonuçlar 3 şekilde değerlendirilmiştir: grafikler üzerinden analiz, ANOVA tabanlı istatistiksel değerlendirme ve sinyal / gürültü (S / N) oranına dayalı optimizasyon. Deneylerde, Taguchi yardımıyla L9 ortogonal dizi tasarımı, üç seviyede kesme hızı, ilerleme ve talaş derinliği kullanılarak uygulanmıştır. Elde edilen bulgulara göre kesme hızı, katkı oranı (% 46.28) ve P değerine (0.048 <0.05) göre enerji tüketiminde en etkili parametredir, bu arada ilerleme hızı (% 23.6) ve kesme derinliğinin (% 27.8) tüketilen enerji üzerinde önemli katkıları vardır. Görünüşe göre, 3D grafikler incelendiğinde, tüketilen enerji için genel eğilimin, daha yüksek kesme hızı ve talaş derinliği değerleri ve daha düşük ilerleme değerleri için arttığı gözlenmiştir. Bu durum, S / N oranlarıyla elde edilen vC=75 m/dak, f=0.25 mm, aP=0.225 mm/dev optimal çözümlerle doğrulanmıştır. Gerçekleştirilen deneyler ve daha ileri analizler, sert malzemelerin MMY ile destekli işlemesinde endüstriyel uygulamalar için önemli bir rehberlik sağlar.

References

  • Abbas AT, Anwar S, Abdelnasser E, Luqman M, Qudeiri JEA, Elkaseer A, 2021. Effect of Different Cooling Strategies on Surface Quality and Power Consumption in Finishing End Milling of Stainless Steel 316. Materials, 14(4): 903.
  • Abbas AT, Benyahia F, El Rayes MM, Pruncu C, Taha MA, Hegab H, 2019. Towards optimization of machining performance and sustainability aspects when turning AISI 1045 steel under different cooling and lubrication strategies. Materials, 12(18): 3023.
  • Aramcharoen A, Mativenga PT 2014. Critical factors in energy demand modelling for CNC milling and impact of toolpath strategy. Journal of cleaner production, 78: 63-74.
  • Benedicto E, Carou D, Rubio E, 2017. Technical, economic and environmental review of the lubrication/cooling systems used in machining processes. Procedia engineering, 184: 99-116.
  • Bilga PS, Singh S, Kumar R, 2016. Optimization of energy consumption response parameters for turning operation using Taguchi method. Journal of cleaner production, 137: 1406-1417.
  • Das A, Pradhan O, Patel SK, Das SR, Biswal BB, 2019. Performance appraisal of various nanofluids during hard machining of AISI 4340 steel. Journal of Manufacturing Processes, 46: 248-270.
  • Draganescu F, Gheorghe M, Doicin C, 2003. Models of machine tool efficiency and specific consumed energy. Journal of Materials Processing Technology, 141(1): 9-15.
  • Duflou JR, Sutherland JW, Dornfeld D, Herrmann C, Jeswiet J, Kara S, Kellens K, 2012. Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP annals, 61(2): 587-609.
  • Gupta MK, Pruncu CI, Mia M, Singh G, Singh S, Prakash C, Gill HS, 2018. Machinability investigations of Inconel-800 super alloy under sustainable cooling conditions. Materials, 11(11): 2088.
  • Gupta MK, Song Q, Liu Z, Sarikaya M, Mia M, Jamil M, Kuntoğlu M, 2021. Tribological Performance Based Machinability Investigations in Cryogenic Cooling Assisted Turning of α-β Titanium Alloy. Tribology International, 107032.
  • HIOKI, 2021. https://assets.testequity.com/te1/Documents/pdf/hioki/PW3198.pdf.
  • 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.
  • Imani Asrai R, Newman ST, Nassehi A, 2018. A mechanistic model of energy consumption in milling. International Journal of Production Research, 56(1-2): 642-659.
  • Jamil M, Khan AM, Hegab H, Gupta MK, Mia M, He N, Liu Z, 2020. Milling of Ti–6Al–4V under hybrid Al 2 O 3-MWCNT nanofluids considering energy consumption, surface quality, and tool wear: a sustainable machining. The International Journal of Advanced Manufacturing Technology, 107(9): 4141-4157.
  • Jamil M, Zhao W, He N, Gupta MK, Sarikaya M, Khan AM, Pimenov DY, 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.
  • Kene AP, Choudhury SK, 2019. Analytical modeling of tool health monitoring system using multiple sensor data fusion approach in hard machining. Measurement, 145: 118-129.
  • Khan AM, Jamil M, Salonitis K, Sarfraz S, Zhao W, He N, Zhao G, 2019. Multi-objective optimization of energy consumption and surface quality in nanofluid SQCL assisted face milling. Energies, 12(4): 710.
  • Kuntoğlu M, 2021a. Surface Roughness Evaluation in Milling of Strenx 1100 Steel under MQL Conditions. Avrupa Bilim ve Teknoloji Dergisi, (25): 509-516.
  • Kuntoğlu M, 2021b. Tool Flank Wear Analysis for MQL Assisted Milling of Strenx 1100 Structural Steel. Avrupa Bilim ve Teknoloji Dergisi.
  • Kuntoğlu M, Aslan A, Pimenov DY, 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.
  • Kuntoğlu M, Aslan A, Sağlam H, Pimenov DY, 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.
  • 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.
  • Li L, Yan J, Xing Z, 2013. Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling. Journal of cleaner production, 52: 113-121.
  • Liu N, Zhang Y, Lu W, 2015. A hybrid approach to energy consumption modelling based on cutting power: a milling case. Journal of cleaner production, 104: 264-272.
  • Liu Z, Guo Y, Sealy M, Liu Z, 2016. Energy consumption and process sustainability of hard milling with tool wear progression. Journal of Materials Processing Technology, 229: 305-312.
  • Liu Z, Sealy MP, Li W, Zhang D, Fang X, Guo Y, Liu Z, 2018. Energy consumption characteristics in finish hard milling. Journal of Manufacturing Processes, 35: 500-507.
  • Mia M, 2018. Mathematical modeling and optimization of MQL assisted end milling characteristics based on RSM and Taguchi method. Measurement, 121: 249-260.
  • Mulyadi IH, Balogun VA, Mativenga PT, 2015. Environmental performance evaluation of different cutting environments when milling H13 tool steel. Journal of cleaner production, 108: 110-120. Omer AM, 2008. Energy, environment and sustainable development. Renewable and sustainable energy reviews, 12(9): 2265-2300.
  • Park CW, Kwon KS, Kim WB, Min BK, Park SJ, Sung IH, Seok J, 2009. Energy consumption reduction technology in manufacturing—A selective review of policies, standards, and research. International journal of precision engineering and manufacturing, 10(5): 151-173.
  • Qun S, Weimin Z, 2012. Carbon footprint analysis in metal cutting process. Paper presented at the Proceedings of the 1st International Conference on Mechanical Engineering and Material Science.
  • Sealy MP, Liu Z, Guo Y, Liu Z, 2016. Energy based process signature for surface integrity in hard milling. Journal of Materials Processing Technology, 238: 284-289.
  • Sealy MP, Liu Z, Zhang D, Guo Y, Liu Z, 2016. Energy consumption and modeling in precision hard milling. Journal of cleaner production, 135: 1591-1601.
  • Shokrani A, Dhokia V, Newman ST, 2018. Energy conscious cryogenic machining of Ti-6Al-4V titanium alloy. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232(10): 1690-1706.
  • SSAB, (2021). https://www.ssab.com.tr/api/sitecore/Datasheet/GetDocument?productId=6A0A9E9 AF58C4AA2A29FC15CA0CE2590&language=en.
  • 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.
  • Vu NC, Dang XP, Huang SC, 2020. Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition. Measurement and Control, 0020294020919457.
  • Wang W, Tian G, Chen M, Tao F, Zhang C, Abdulraham AA, Jiang Z, 2020. Dual-objective program and improved artificial bee colony for the optimization of energy-conscious milling parameters subject to multiple constraints. Journal of cleaner production, 245: 118714.
  • Xu K, Luo M, Tang K, 2016. Machine based energy-saving tool path generation for five-axis end milling of freeform surfaces. Journal of cleaner production, 139: 1207-1223.
  • Yan J, Li L, 2013. Multi-objective optimization of milling parameters–the trade-offs between energy, production rate and cutting quality. Journal of cleaner production, 52: 462-471.
  • Yoon HS, Lee JY, Kim MS, Ahn SH, 2014. Empirical power-consumption model for material removal in three-axis milling. Journal of cleaner production. Journal of Cleaner Production, 78: 54-62.
  • Zhang T, Liu Z, Sun X, Xu J, Dong L, Zhu G, 2020. Investigation on specific milling energy and energy efficiency in high-speed milling based on energy flow theory. Energy, 192: 116596.
  • Zhao G, Liu Z, He Y, Cao H, Guo Y, 2017. Energy consumption in machining: Classification, prediction, and reduction strategy. Energy, 133: 142-157.
There are 43 citations in total.

Details

Primary Language English
Subjects Mechanical Engineering
Journal Section Makina Mühendisliği / Mechanical Engineering
Authors

Mustafa Kuntoğlu 0000-0002-7291-9468

Publication Date December 15, 2021
Submission Date May 19, 2021
Acceptance Date August 13, 2021
Published in Issue Year 2021 Volume: 11 Issue: 4

Cite

APA Kuntoğlu, M. (2021). Study on Consumed Energy of Strenx 1100 Steel During MQL Assisted Hard Milling. Journal of the Institute of Science and Technology, 11(4), 3003-3013. https://doi.org/10.21597/jist.939332
AMA Kuntoğlu M. Study on Consumed Energy of Strenx 1100 Steel During MQL Assisted Hard Milling. J. Inst. Sci. and Tech. December 2021;11(4):3003-3013. doi:10.21597/jist.939332
Chicago Kuntoğlu, Mustafa. “Study on Consumed Energy of Strenx 1100 Steel During MQL Assisted Hard Milling”. Journal of the Institute of Science and Technology 11, no. 4 (December 2021): 3003-13. https://doi.org/10.21597/jist.939332.
EndNote Kuntoğlu M (December 1, 2021) Study on Consumed Energy of Strenx 1100 Steel During MQL Assisted Hard Milling. Journal of the Institute of Science and Technology 11 4 3003–3013.
IEEE M. Kuntoğlu, “Study on Consumed Energy of Strenx 1100 Steel During MQL Assisted Hard Milling”, J. Inst. Sci. and Tech., vol. 11, no. 4, pp. 3003–3013, 2021, doi: 10.21597/jist.939332.
ISNAD Kuntoğlu, Mustafa. “Study on Consumed Energy of Strenx 1100 Steel During MQL Assisted Hard Milling”. Journal of the Institute of Science and Technology 11/4 (December 2021), 3003-3013. https://doi.org/10.21597/jist.939332.
JAMA Kuntoğlu M. Study on Consumed Energy of Strenx 1100 Steel During MQL Assisted Hard Milling. J. Inst. Sci. and Tech. 2021;11:3003–3013.
MLA Kuntoğlu, Mustafa. “Study on Consumed Energy of Strenx 1100 Steel During MQL Assisted Hard Milling”. Journal of the Institute of Science and Technology, vol. 11, no. 4, 2021, pp. 3003-1, doi:10.21597/jist.939332.
Vancouver Kuntoğlu M. Study on Consumed Energy of Strenx 1100 Steel During MQL Assisted Hard Milling. J. Inst. Sci. and Tech. 2021;11(4):3003-1.