Optimization of the Effect of Processing Parameters on Surface Roughness and Cutting Energy in CNC Milling of Al-7075 Material

Ali Pınarbaşı; Mustafa Kemal Külekçi; Cem Boğa; Uğur Eşme

doi:10.21605/cukurovaummfd.792420

Research Article

Optimization of the Effect of Processing Parameters on Surface Roughness and Cutting Energy in CNC Milling of Al-7075 Material

Year 2020, Volume: 35 Issue: 2, 345 - 356, 30.06.2020

Ali Pınarbaşı , Mustafa Kemal Külekçi , Cem Boğa , Uğur Eşme

https://doi.org/10.21605/cukurovaummfd.792420

Abstract

In this study, it is intended to determine the most suitable process parameters for surface roughness and cutting energy of the 7075 series aluminum material in dry environment conditions by using Taguchi method in CNC milling. The process parameters were determined as, cutting speed, feed rate and cutting depth and the effects of these parameters on surface roughness, current flow through the grid and the energy consumed were examined. As a result, optimum process parameters were determined and the results were analyzed by graphs. It was observed that the most effective parameter on the surface roughness was the feed rate, while the most effective parameter on the current drawn from the network was the cutting depth. As a result of the experiments, the best surface roughness value was found to be 0.39 µm and the lowest current value was 0.9 A. In addition, considering the determined current amount and processing time, the lowest energy amount consumed was calculated as 7089.28 J.

Keywords

Milling process, Taguchi method, Cutting parameters, Surface roughness, Energy

References

1. Bhushan, R.K., 2013. Optimization of Cutting Parameters for Minimizing Power Consumption and Maximizing Tool Life During Machining of Al Alloy SiC Particle Composites, Journal of Cleaner Production 32, 242-254. doi.org/10.1016/j.jclepro.2012.08.008.
2. Zhang, J.Z., Chen, J.C., Kirby, E.D., 2007. Surface Roughness Optimization in an End- milling Operation Using the Taguchi Design Method, Journal of Materials Processing Technology 184, 233-239. doi.org/10.1016/j.jmatprotec.2006.11.029.
3. Tsai, Y.H., Chen, J.C., Lou, S.J., 1999. In Process Surface Recognition System Based on Neural Networks in End Milling Cutting Operations, International Journal of Machine Tools and Manufacture 39, 583-605. doi.org/10.1016/S0890-6955(98)00053-4.
4. Vardhan, M.V., Sankaraiah, G., Yohan, M., Rao, H.J., 2017. Optimization of Parameters in CNC Milling of P20 Steel Using Response Surface Methodology and Taguchi Method, Materials Today:Proceedings 4, 9163-9169. doi.org/10.1016/j.matpr.2017.07.273.
5. Nian, C.Y., Yang, W.H., Tarng, Y.S., 1999. Optimization of Turning Operations with Multiple Performance Characteristics, Journal of Material Processing Technology 95, 90-96. doi.org/10.1016/S0924-0136(99)00271-X.
6. Gajjal, S.Y., Unkule, A.J., Gajjal, P.S., 2018. Taguchi Technique for Dry Sliding Wear Behavior of Peek Composite Materials, Materials Today: Proceedings 5, 950-957. doi.org/10.1016/j.matpr.2017.11.170.
7. Ghani, J.A., Choudhury, I.A., Hassan, H.H., 2004. Application of Taguchi Method in the Optimization of End Milling Parameters, Journal of Materials Processing Technology 145, 84-92. doi.org/10.1016/S0924- 0136(03)00865-3.
8. Park, S.H., 1996. Robust Design and Analysis for Quality Engineering, Chapman&Hall, London, England.
9. Ravi Kumar, M., Reddappa, H.N., Suresh, R., Gangadharappa, M., 2018. Investigation on Hardness of Al 7075/Al2O3/SiCp Hybrid Composite Using Taguchi Technique, Materials Today: Proceedings 5, 22447-22453. doi.org/10.1016/j.matpr.2018.06.614.
10. 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. doi.org/10.1016/j. measurement.2015.11.042.
11. Noordin, M.Y., Venkatesh, V.C., Chan, C.L., Abdullah, A., 2001. Performance Evaluation of Cemented Carbide Tools in Turning AISI 1010 Steel, Journal of Materials Processing Technology 116, 16-21. doi.org/10.1016/ S0924-0136(01)00838-X.
12. Ucun, İ., Aslantaş, K., 2009. Sertleştirilmiş 52100 Takım Çeliğinin Tornalanmasında Karbürlü Kesici Takımın Performansının Araştırılması, 5. Uluslararası İleri Teknolojiler Sempozyumu, Karabük.
13. Niranjan, D.B., Shivashankar, G.S., Sreenivas Rao, K.V., Praveen, R., 2017. Optimization of Cutting Process Parameters on Al6061 Using ANOVA and Taguchi Method, Materials Today: Proceedings 4, 10845-10849. doi.org/10.1016/j.matpr.2017.08.037.
14. Camposeco-Negrete, C., 2013. Optimization of Cutting Parameters for Minimizing Energy Consumption in Turning of AISI 6061 T6 Using Taguchi Methodology and ANOVA, Journal of Cleaner Production 53, 195-203. doi.org/10.1016/j.jclepro.2013.03.049.
15. Zhou, Z., Zhang, C., Tian, G., Xie, Y., Lin, W., Huang, Z., 2016. Energy Consumption Modeling and Prediction of the Milling Process: A Multistage Perspective, Journal of Engineering Manufacture 232, 1973-1985. doi.org/10.1177/0954405416682278.
16. Kant, G., Sangwan, K.S., 2014. Prediction and Optimization of Machining Parameters for Minimizing Power Consumption and Surface Roughness in Machining, Journal of Cleaner Production 83, 151-164. doi.org/10.1016/ j.jclepro.2014.07.073.
17. Hanafi, I., Khamlichi, A., Cabrera, F.M., Almansa, E., Jabbouri, A., 2012. Optimization of Cutting Conditions for Sustainable Machining of PEEK-CF30 Using TiN Tools, Journal of Cleaner Production 33, 1-9. doi.org/10.1016/j.jclepro.2012.05.005.
18. Fratila, D., Caizar, C., 2011. Application of Taguchi Method to Selection of Optimal Lubrication and Cutting Conditions in Face Milling of AlMg3, Journal of Cleaner Production 19, 640-645. doi.org/10.1016/ j.jclepro.2010.12.007.
19. Sarıkaya, M., Güllü, A., 2014. Taguchi Design and Response Surface Methodology Based Analysis of Machining Parameters in CNC Turning Under MQL, Journal of Cleaner Production 65, 604-616. doi.org/10.1016/ j.jclepro.2013.08.040.
20. Çetin, M.H., Özçelik, B., Kuram, E., Demirbas, E., 2011. Evaluation of Vegetable Based Cutting Fluids with Extreme Pressure and Cutting Parameters in Turning of AISI 304L by Taguchi Method, Journal of Cleaner Production 19, 2049-2056. doi.org/10.1016/ j.jclepro.2011.07.013.
21. Lee, W., Kim, S.H., Park, J., Min, B.K., 2017. Simulation-Based Machining Condition Optimization for Machine Tool Energy Consumption Reduction, Journal of Cleaner Production 150, 352-360. doi.org/10.1016/ j.jclepro.2017.02.178.
22. Okwudire, C., Rodgers, J., 2013. Design and Control of a Novel Hybrid Feed Drive for High Performance and Energy Efficient Machining, CIRP Annals, Manufacturing Technology. 62, 391-394. doi.org/10.1016/j.cirp.2013.03.139.
23. Abele, E., Eisele, C., Schrems, S., 2012. Simulation of the Energy Consumption of Machine Tools for a Specific Production Task, Leveraging Technology for a Sustainable World, 233-237. doi.org/10.1007/978-3-642- 29069-5_40.
24. Hazir, E., Koc, K.H., 2019. Optimization of Wood Machining Parameters in CNC Routers: Taguchi Orthogonal Array Based Simulated Angling Algorithm, Maderas. Ciencia y Tecnología 21, 493–510. doi: 10.4067/S0718- 221X2019005000406.
25. Bhosale, K.K., Patil, V.P., Chavan, R.R., Mane, V.V., Sakharkar, A.R., Pawar, C.K., Bhole, A.R., Shinde S.S., 2018. Parameters Optimization of CNC Machining using Taguchi Methodology, International Journal for Research in Applied Science & Engineering Technology (IJRASET), 6, 4594-4598. http://doi.org/10.22214/ijraset.2018.4753.
26. Kumar, M.V., Kumar, B.J.K., Rudresha, N., 2018. Optimization of Machining Parameters in CNC Turning of Stainless Steel (EN19) by TAGUCHI’S Orthogonal Array Experiments, Materials Today: Proceedings, 5, 11395-11407. https://doi.org/10.1016/j.matpr.2018.02.107.
27. Wu, Q., Li, D.P., Zhang, Y.D., 2016. Detecting Milling Deformation in 7075 Aluminum Alloy Aeronautical Monolithic Components Using the Quasi-Symmetric Machining Method, Metals 6, 1-14. doi.org/10.3390/met6040080.
28. Starke, E.A., Staley, J.T., 1996. Application of Modern Aluminium Alloys to Aircraft, Progress in Aerospace Sciences 32, 131-172. doi.org/10.1016/0376-0421(95)00004-6.
29. Senthil, K., Iqbal, M.A., Chandel, P.S., Gupta, N.K., 2017. Study of the Constitutive Behavior of 7075-T651 Aluminum Alloy, International Journal of Impact Engineering 108, 171-190. doi.org/10.1016/j.ijimpeng.2017.05.002.

Al-7075 Malzemenin CNC Frezelenmesinde İşlem Parametrelerinin Yüzey Pürüzlülüğüne ve Kesme Enerjisine Etkisinin Optimizasyonu

Year 2020, Volume: 35 Issue: 2, 345 - 356, 30.06.2020

Ali Pınarbaşı , Mustafa Kemal Külekçi , Cem Boğa , Uğur Eşme

https://doi.org/10.21605/cukurovaummfd.792420

Abstract

Bu çalışmada, CNC frezeleme prosesinde 7075 serisi alüminyum malzemenin kuru ortam şartlarında yüzey pürüzlülüğü ve kesme enerjisi için en uygun işlem parametrelerinin Taguchi yöntemi kullanılarak belirlenmesi amaçlanmıştır. İşlem parametreleri ilerleme hızı, kesme hızı ve kesme derinliği olarak belirlenmiş olup bu parametrelerin yüzey pürüzlülüğüne, şebeke üzerinden çekilen akım miktarına ve tüketilen enerji üzerindeki etkileri incelenmiştir. Sonuçta optimum işlem parametreleri belirlenmiş ve sonuçlar grafiklerle analiz edilmiştir. Yüzey pürüzlülüğüne en çok etki eden parametre ilerleme hızı iken, şebekeden çekilen akıma en çok etki eden parametrenin kesme derinliği olduğu gözlemlenmiştir.
Deneyler sonucunda en iyi yüzey pürüzlülük değeri 0,39 µm, en düşük akım değeri 0,9 A olarak bulunmuştur. Ayrıca, belirlenen akım miktarı ve işlem süresi göz önüne alınarak tüketilen en düşük enerji miktarı 7089,28 J olarak hesaplanmıştır.

Keywords

Frezeleme işlemi, Taguchi metodu, Kesme parametreleri, Yüzey pürüzlülüğü, Enerji

References

1. Bhushan, R.K., 2013. Optimization of Cutting Parameters for Minimizing Power Consumption and Maximizing Tool Life During Machining of Al Alloy SiC Particle Composites, Journal of Cleaner Production 32, 242-254. doi.org/10.1016/j.jclepro.2012.08.008.
2. Zhang, J.Z., Chen, J.C., Kirby, E.D., 2007. Surface Roughness Optimization in an End- milling Operation Using the Taguchi Design Method, Journal of Materials Processing Technology 184, 233-239. doi.org/10.1016/j.jmatprotec.2006.11.029.
3. Tsai, Y.H., Chen, J.C., Lou, S.J., 1999. In Process Surface Recognition System Based on Neural Networks in End Milling Cutting Operations, International Journal of Machine Tools and Manufacture 39, 583-605. doi.org/10.1016/S0890-6955(98)00053-4.
4. Vardhan, M.V., Sankaraiah, G., Yohan, M., Rao, H.J., 2017. Optimization of Parameters in CNC Milling of P20 Steel Using Response Surface Methodology and Taguchi Method, Materials Today:Proceedings 4, 9163-9169. doi.org/10.1016/j.matpr.2017.07.273.
5. Nian, C.Y., Yang, W.H., Tarng, Y.S., 1999. Optimization of Turning Operations with Multiple Performance Characteristics, Journal of Material Processing Technology 95, 90-96. doi.org/10.1016/S0924-0136(99)00271-X.
6. Gajjal, S.Y., Unkule, A.J., Gajjal, P.S., 2018. Taguchi Technique for Dry Sliding Wear Behavior of Peek Composite Materials, Materials Today: Proceedings 5, 950-957. doi.org/10.1016/j.matpr.2017.11.170.
7. Ghani, J.A., Choudhury, I.A., Hassan, H.H., 2004. Application of Taguchi Method in the Optimization of End Milling Parameters, Journal of Materials Processing Technology 145, 84-92. doi.org/10.1016/S0924- 0136(03)00865-3.
8. Park, S.H., 1996. Robust Design and Analysis for Quality Engineering, Chapman&Hall, London, England.
9. Ravi Kumar, M., Reddappa, H.N., Suresh, R., Gangadharappa, M., 2018. Investigation on Hardness of Al 7075/Al2O3/SiCp Hybrid Composite Using Taguchi Technique, Materials Today: Proceedings 5, 22447-22453. doi.org/10.1016/j.matpr.2018.06.614.
10. 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. doi.org/10.1016/j. measurement.2015.11.042.
11. Noordin, M.Y., Venkatesh, V.C., Chan, C.L., Abdullah, A., 2001. Performance Evaluation of Cemented Carbide Tools in Turning AISI 1010 Steel, Journal of Materials Processing Technology 116, 16-21. doi.org/10.1016/ S0924-0136(01)00838-X.
12. Ucun, İ., Aslantaş, K., 2009. Sertleştirilmiş 52100 Takım Çeliğinin Tornalanmasında Karbürlü Kesici Takımın Performansının Araştırılması, 5. Uluslararası İleri Teknolojiler Sempozyumu, Karabük.
13. Niranjan, D.B., Shivashankar, G.S., Sreenivas Rao, K.V., Praveen, R., 2017. Optimization of Cutting Process Parameters on Al6061 Using ANOVA and Taguchi Method, Materials Today: Proceedings 4, 10845-10849. doi.org/10.1016/j.matpr.2017.08.037.
14. Camposeco-Negrete, C., 2013. Optimization of Cutting Parameters for Minimizing Energy Consumption in Turning of AISI 6061 T6 Using Taguchi Methodology and ANOVA, Journal of Cleaner Production 53, 195-203. doi.org/10.1016/j.jclepro.2013.03.049.
15. Zhou, Z., Zhang, C., Tian, G., Xie, Y., Lin, W., Huang, Z., 2016. Energy Consumption Modeling and Prediction of the Milling Process: A Multistage Perspective, Journal of Engineering Manufacture 232, 1973-1985. doi.org/10.1177/0954405416682278.
16. Kant, G., Sangwan, K.S., 2014. Prediction and Optimization of Machining Parameters for Minimizing Power Consumption and Surface Roughness in Machining, Journal of Cleaner Production 83, 151-164. doi.org/10.1016/ j.jclepro.2014.07.073.
17. Hanafi, I., Khamlichi, A., Cabrera, F.M., Almansa, E., Jabbouri, A., 2012. Optimization of Cutting Conditions for Sustainable Machining of PEEK-CF30 Using TiN Tools, Journal of Cleaner Production 33, 1-9. doi.org/10.1016/j.jclepro.2012.05.005.
18. Fratila, D., Caizar, C., 2011. Application of Taguchi Method to Selection of Optimal Lubrication and Cutting Conditions in Face Milling of AlMg3, Journal of Cleaner Production 19, 640-645. doi.org/10.1016/ j.jclepro.2010.12.007.
19. Sarıkaya, M., Güllü, A., 2014. Taguchi Design and Response Surface Methodology Based Analysis of Machining Parameters in CNC Turning Under MQL, Journal of Cleaner Production 65, 604-616. doi.org/10.1016/ j.jclepro.2013.08.040.
20. Çetin, M.H., Özçelik, B., Kuram, E., Demirbas, E., 2011. Evaluation of Vegetable Based Cutting Fluids with Extreme Pressure and Cutting Parameters in Turning of AISI 304L by Taguchi Method, Journal of Cleaner Production 19, 2049-2056. doi.org/10.1016/ j.jclepro.2011.07.013.
21. Lee, W., Kim, S.H., Park, J., Min, B.K., 2017. Simulation-Based Machining Condition Optimization for Machine Tool Energy Consumption Reduction, Journal of Cleaner Production 150, 352-360. doi.org/10.1016/ j.jclepro.2017.02.178.
22. Okwudire, C., Rodgers, J., 2013. Design and Control of a Novel Hybrid Feed Drive for High Performance and Energy Efficient Machining, CIRP Annals, Manufacturing Technology. 62, 391-394. doi.org/10.1016/j.cirp.2013.03.139.
23. Abele, E., Eisele, C., Schrems, S., 2012. Simulation of the Energy Consumption of Machine Tools for a Specific Production Task, Leveraging Technology for a Sustainable World, 233-237. doi.org/10.1007/978-3-642- 29069-5_40.
24. Hazir, E., Koc, K.H., 2019. Optimization of Wood Machining Parameters in CNC Routers: Taguchi Orthogonal Array Based Simulated Angling Algorithm, Maderas. Ciencia y Tecnología 21, 493–510. doi: 10.4067/S0718- 221X2019005000406.
25. Bhosale, K.K., Patil, V.P., Chavan, R.R., Mane, V.V., Sakharkar, A.R., Pawar, C.K., Bhole, A.R., Shinde S.S., 2018. Parameters Optimization of CNC Machining using Taguchi Methodology, International Journal for Research in Applied Science & Engineering Technology (IJRASET), 6, 4594-4598. http://doi.org/10.22214/ijraset.2018.4753.
26. Kumar, M.V., Kumar, B.J.K., Rudresha, N., 2018. Optimization of Machining Parameters in CNC Turning of Stainless Steel (EN19) by TAGUCHI’S Orthogonal Array Experiments, Materials Today: Proceedings, 5, 11395-11407. https://doi.org/10.1016/j.matpr.2018.02.107.
27. Wu, Q., Li, D.P., Zhang, Y.D., 2016. Detecting Milling Deformation in 7075 Aluminum Alloy Aeronautical Monolithic Components Using the Quasi-Symmetric Machining Method, Metals 6, 1-14. doi.org/10.3390/met6040080.
28. Starke, E.A., Staley, J.T., 1996. Application of Modern Aluminium Alloys to Aircraft, Progress in Aerospace Sciences 32, 131-172. doi.org/10.1016/0376-0421(95)00004-6.
29. Senthil, K., Iqbal, M.A., Chandel, P.S., Gupta, N.K., 2017. Study of the Constitutive Behavior of 7075-T651 Aluminum Alloy, International Journal of Impact Engineering 108, 171-190. doi.org/10.1016/j.ijimpeng.2017.05.002.

There are 29 citations in total.

Details

Primary Language	English
Journal Section	Articles
Authors	Ali Pınarbaşı Mustafa Kemal Külekçi Cem Boğa Uğur Eşme
Publication Date	June 30, 2020
Published in Issue	Year 2020 Volume: 35 Issue: 2

Cite

APA	Pınarbaşı, A., Külekçi, M. K., Boğa, C., Eşme, U. (2020). Optimization of the Effect of Processing Parameters on Surface Roughness and Cutting Energy in CNC Milling of Al-7075 Material. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 35(2), 345-356. https://doi.org/10.21605/cukurovaummfd.792420
AMA	Pınarbaşı A, Külekçi MK, Boğa C, Eşme U. Optimization of the Effect of Processing Parameters on Surface Roughness and Cutting Energy in CNC Milling of Al-7075 Material. cukurovaummfd. June 2020;35(2):345-356. doi:10.21605/cukurovaummfd.792420
Chicago	Pınarbaşı, Ali, Mustafa Kemal Külekçi, Cem Boğa, and Uğur Eşme. “Optimization of the Effect of Processing Parameters on Surface Roughness and Cutting Energy in CNC Milling of Al-7075 Material”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 35, no. 2 (June 2020): 345-56. https://doi.org/10.21605/cukurovaummfd.792420.
EndNote	Pınarbaşı A, Külekçi MK, Boğa C, Eşme U (June 1, 2020) Optimization of the Effect of Processing Parameters on Surface Roughness and Cutting Energy in CNC Milling of Al-7075 Material. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 35 2 345–356.
IEEE	A. Pınarbaşı, M. K. Külekçi, C. Boğa, and U. Eşme, “Optimization of the Effect of Processing Parameters on Surface Roughness and Cutting Energy in CNC Milling of Al-7075 Material”, cukurovaummfd, vol. 35, no. 2, pp. 345–356, 2020, doi: 10.21605/cukurovaummfd.792420.
ISNAD	Pınarbaşı, Ali et al. “Optimization of the Effect of Processing Parameters on Surface Roughness and Cutting Energy in CNC Milling of Al-7075 Material”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 35/2 (June 2020), 345-356. https://doi.org/10.21605/cukurovaummfd.792420.
JAMA	Pınarbaşı A, Külekçi MK, Boğa C, Eşme U. Optimization of the Effect of Processing Parameters on Surface Roughness and Cutting Energy in CNC Milling of Al-7075 Material. cukurovaummfd. 2020;35:345–356.
MLA	Pınarbaşı, Ali et al. “Optimization of the Effect of Processing Parameters on Surface Roughness and Cutting Energy in CNC Milling of Al-7075 Material”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, vol. 35, no. 2, 2020, pp. 345-56, doi:10.21605/cukurovaummfd.792420.
Vancouver	Pınarbaşı A, Külekçi MK, Boğa C, Eşme U. Optimization of the Effect of Processing Parameters on Surface Roughness and Cutting Energy in CNC Milling of Al-7075 Material. cukurovaummfd. 2020;35(2):345-56.

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