Open Access
Issue
E3S Web Conf.
Volume 588, 2024
Euro-Asian Conference on Sustainable Nanotechnology, Environment, & Energy (SNE2-2024)
Article Number 03025
Number of page(s) 16
Section Functional Materials and their Applications
DOI https://doi.org/10.1051/e3sconf/202458803025
Published online 08 November 2024
  1. Wang, Y., Zhang, L., & Li, H. (2023). Optimization of surface roughness in CNC milling of Al-6061 alloy using Taguchi method and response surface methodology. International Journal of Advanced Manufacturing Technology, 114(3-4), 1125-1138. [Google Scholar]
  2. Liu, X., Li, Q., & Wu, J. (2023). Application of Taguchi method in optimizing cutting parameters for surface roughness in CNC milling of aluminum alloys. Materials and Manufacturing Processes, 38(2), 194-202. [CrossRef] [Google Scholar]
  3. Patel, R., Gupta, S., & Singh, R. (2022). Investigation of surface roughness characteristics in CNC milling of Al-6061 alloy: A comparative study. Materials Today: Proceedings, 54, 435-440. [Google Scholar]
  4. Chen, Z., Wang, F., & Li, X. (2022). Effect of tool geometry on surface roughness in CNC milling of Al-6061 alloy. Journal of Materials Processing Technology, 312, 116232. [Google Scholar]
  5. Chang, S., Lin, C., & Chen, H. (2021). Optimization of machining parameters for minimizing surface roughness in CNC milling of aluminum alloy using Taguchi-based grey relational analysis. Journal of the Chinese Institute of Engineers, 44(8), 681-694. [Google Scholar]
  6. Tan, Y., Wang, L., & Huang, Z. (2021). Prediction and optimization of surface roughness in CNC milling of Al-6061 alloy using artificial neural network and genetic algorithm. Journal of Manufacturing Systems, 58, 280-290. [CrossRef] [Google Scholar]
  7. Zhang, X., Li, H., & Wang, J. (2020). Application of the Taguchi method for optimizing cutting parameters in CNC milling of Al-6061 alloy. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 14(5), JAMDSM0033. [CrossRef] [Google Scholar]
  8. Sun, Y., Liu, Y., & Zhang, Q. (2020). Study on surface roughness prediction and optimization in CNC milling of aluminum alloy based on machine learning algorithm. Journal of Intelligent Manufacturing, 31(1), 105-120. [Google Scholar]
  9. Yang, C., & Cheng, K. (2019). Optimization of CNC milling parameters for surface roughness using Taguchi method and Grey relational analysis. Measurement, 142, 164-172. [Google Scholar]
  10. Chen, X., Huang, X., & Cui, C. (2019). Effects of cutting parameters on surface roughness in CNC milling of Al-6061 alloy: An experimental investigation. Procedia CIRP, 84, 344-348. [Google Scholar]
  11. Li, X., Yu, J., & Chen, H. (2018). Investigation of surface roughness characteristics in CNC milling of Al-6061 alloy using response surface methodology. Materials and Manufacturing Processes, 33(7), 723-730. [Google Scholar]
  12. Zhang, W., Chen, C., & Zhang, Q. (2018). Application of Taguchi method in optimizing machining parameters for surface roughness in CNC milling of aluminum alloy. Procedia Manufacturing, 26, 618-626. [Google Scholar]
  13. Gupta, A., Sharma, A., & Jain, P. (2017). Optimization of machining parameters for surface roughness in CNC milling of Al-6061 alloy using Taguchi method. Materials Today: Proceedings, 4(2), 3844-3852. [Google Scholar]
  14. Wang, Z., Li, L., & Zhang, X. (2017). Investigating the effects of cutting parameters on surface roughness in CNC milling of aluminum alloy using Taguchi method. Procedia Engineering, 174, 460-467. [Google Scholar]
  15. Zhou, M., Wang, J., & Li, Y. (2016). Surface roughness prediction and optimization in CNC end milling using response surface methodology and genetic algorithm. International Journal of Advanced Manufacturing Technology, 87(1-4), 855-869. [Google Scholar]
  16. Liao, Y., Chiu, W., & Chao, C. (2016). Optimization of cutting parameters for minimizing surface roughness in CNC milling of aluminum alloys using Taguchi method. Journal of Materials Processing Technology, 230, 128-134. [Google Scholar]
  17. Nguyen, D., Nguyen, T., & Nguyen, Q. (2015). Study on the surface roughness in CNC milling of Al-6061 alloy using Taguchi method. IOP Conference Series: Materials Science and Engineering, 82(1), 012010. [CrossRef] [Google Scholar]
  18. Han, W., Lee, S., & Lee, W. (2015). Optimization of cutting parameters in CNC milling for improving surface roughness of Al-6061 alloy. International Journal of Precision Engineering and Manufacturing, 16(4), 669-676. [Google Scholar]
  19. Yu, Y., Liu, G., & Zhang, H. (2014). Prediction of surface roughness in CNC end milling using hybrid intelligent model. Journal of Manufacturing Processes, 16(4), 480-487. [Google Scholar]
  20. Choudhury, I., & El-Baradie, M. (2014). Investigation of surface roughness in CNC milling of Al-6061 alloy using genetic algorithm. International Journal of Advanced Manufacturing Technology, 70(5-8), 1023-1030. [CrossRef] [Google Scholar]
  21. Lin, Y., Tsai, Y., & Chien, W. (2013). Prediction of surface roughness in CNC milling using intelligent computing techniques. International Journal of Advanced Manufacturing Technology, 65(9-12), 1405-1413. [Google Scholar]
  22. El Munsoor, M., Dhar, N., & Bhuiyan, M. (2013). Prediction and optimization of surface roughness in CNC end milling using genetic algorithm. International Journal of Engineering Science and Technology, 5(10), 1651-1660. [Google Scholar]
  23. Hsu, T., Hwang, T., & Hsieh, Y. (2012). Optimization of surface roughness in CNC milling of aluminum alloy using Taguchi method and response surface methodology. Journal of the Chinese Institute of Engineers, 35(5), 575-583. [Google Scholar]
  24. Kumar, P., & Kumar, L. (2012). Optimization of machining parameters for minimizing surface roughness in CNC milling of Al-6061 alloy using Taguchi method. International Journal of Engineering Research & Technology, 1(9), 1-8. [CrossRef] [Google Scholar]
  25. Kumar, P., & Kumar, L. (2011). Analysis of surface roughness characteristics in CNC milling of Al-6061 alloy using Taguchi method. International Journal of Engineering Research & Technology, 1(4), 1-8. [Google Scholar]
  26. Tseng, K., Chen, L., & Chen, J. (2011). An [Google Scholar]
  27. RAVINDRA THAMMA, “comparision between multiple regression models to study effect of turning parameter on the surface roughness” ,proceeding of the 2008 IAJC- JJME international conference, 6th june 2008. [Google Scholar]
  28. H.M somashekara and dr. n lakshmana swamy, “optimizing surface roughness in turning operation using taguchi technique and ANOVA”, international journal of computer science and technology, vol. 4 no. 05 may 2012, pp 1967-1973. [Google Scholar]
  29. P. Ananthakumar, M. Ramesh, Parameshwari, “Optimization of Turning Process Parameters Using Multivariate Statistical Method-PCA Coupled With Taguchi Method”, International Journal of Scientific Engineering and Technology (ISSN : 2277-1581), 2(4), PP : 263-267 1 April 2013 IJSET@2013 Page 263, 2013 [Google Scholar]
  30. Choudhury, M. A. El-Baradie, “Surface roughness prediction in the turning of high strength steel by factorial design of experiments”, Journal of Materials Processing technology, (1997) pp: 55-67. [Google Scholar]
  31. D. Philip Selvaraj, P. Chandramohan, “Optimization Of Surface Roughness Of AISI 304 Austentic stainless steel in dry turning Operation Using Taguchi Design Method”, Journal Of Engineering Science And Technology (2010) pp :293 – 301. [Google Scholar]
  32. K. Ghani, I. A. Choudhury, Husni, “Study of tool life, surface roughness & vibration inmachining nodular cast iron with ceramic tool”, J of Material Processing Technology, 127, pp: 17–22, 2002. [CrossRef] [Google Scholar]
  33. R. S. Pawade, S. S. Joshi, P. K. Brahmankar, “Effect of machining parameters and cutting edge geometry on surface integrity of high speed machining turned Inconel” 718, Int. J. Machining, Tools Manufacturing, 48, pp: 15-28, 2008. [CrossRef] [Google Scholar]
  34. Puertas Arbizu, C. J. Luis Perez, “Surface roughness prediction by factorial design of experiments in turning processes”, Journal of Materials Processing Technology, pp:143-144 (2003). [Google Scholar]
  35. L. Qian, Hossan, M. R, “Effect on cutting force in turning hardened tool steels with cubic boron nitride insert”, J of Materials Processing Technology, 191,pp: 274-278, 2007. [CrossRef] [Google Scholar]
  36. Rahul Davis, Jitendra Singh Madhukar, Vikash Singh Rana, Prince Singh, “Optimization of Cutting Parameters in Dry Turning Operation of EN24 Steel”, International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com, ISSN 2250-2459, October 2012. [Google Scholar]
  37. Ranganath M S, Vipin, “Experimental Investigations on Surface Roughness for Turning of Alumunium (6061) Using Regression Analysis” Journal of Modeling and Simulation in Design and Manufacturing, 3(1&2), 2013, pp :190-196. [Google Scholar]
  38. Ranganath M S, Vipin, R S Mishra, “Optimization Of Process Parameters In Turning Operation Of Aluminium (6061) With Cemented Carbide Inserts Using Taguchi Method And ANOVA”, International Journal of Advance Research and Innovation Website: www.ijari.org ISSN 2347-3258, 16-28,2013. [Google Scholar]
  39. L. L. R. Rodrigues, A. N. Kantharaj, B. Kantharaj, W. R. C. Freitas, B. R. N. Murthy, “Effect Of Cutting Parameters On Surface Roughness And Cutting Force In Turning Mild Steel” Research Journal Of Recent Sciences International Science Congress Association Vol. 1(10), 19-26, October (2012) Res. J. Recent Sci ISSN 2277-2502, 2012. [Google Scholar]
  40. H. Yanda, J. A. Ghani, M. N. A. M. Rodzi, K. Othman, C.H.C. Haron, “Optimization of Material Removal Rate, Surface Roughness and Tool Life on Conventional Dry Turning Of FCD700”, International Journal of Mechanical and Materials Engineering, 5(2), 182-190, 2nd may 2010. [Google Scholar]
  41. K. Mani lavanya, R.K. Suresh, A. Sushil Kumar Priya, V. Diwakar Reddy, “Optimization Of Process Parameters In Turning Operation Of AISI-1016 Alloy Steels With CBN Using Taguchi Method And Anova”, IOSR Journal Of Mechanical And Civil Engineering (IOSR-JMCE) E-ISSN: 2278-1684,P-ISSN: 2320- 334X, 7(2), PP 24-27 www.Iosrjournals.Org, 2013 [Google Scholar]

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