Optimal Selection of Cutting Parameters for Surface Roughness in Milling Machining of AA6061-T6

Document Type : Original Article


1 Department of Industrial Engineering, Faculty of Industry and Mine Khash, University of Sistan and Baluchestan, Zahedan, Iran

2 Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Malaysia


Due to its ability to remove material quickly while maintaining optimum surface quality, end milling is considered one of the most frequent metal cutting procedures in industry. The present study aimed to investigate the impacts of cutting parameters and tool geometry on milling of Aluminum Alloy 6061-T6 to examine the impact surface roughness by utilizing response surface methodology (RSM).  RSM was used to create a second-order mathematical model of surface roughness for this purpose. A multiple regression analysis used the analysis of variance to demonstrate the effect of machining settings on surface roughness and determine experiment performance. The trials for optimizing surface roughness were set up utilizing the central composite design (CCD) method and various cutting parameters such as spindle speed, feed rate and depth of cut. Also the parameters used in tool geometry are the radial rake angle (10, 13, 16, 19 and 22 degrees), and nose radius (0, 0.2, 0.4, 0.6 and 0.8 mm). The result shows that the nose radius has more significant effect on the surface roughness followed by the radial rake angle. Moreover, the effect of the depth of cut on surface roughness is more dominant than cutting speed. The optimum combinations of cutting and tool geometry parameters were cutting speed (60.53 m/min), feed rate (0.025 mm/tooth), depth of cut (0.84 mm), radial rake angle (12.72 degree) and nose radius (0.34 mm).


Main Subjects

  1. Zhuang, K., Fu, C., Weng, J., Hu, C. "Cutting edge microgeometries in metal cutting: a review. " The International Journal of Advanced Manufacturing Technology, Vol. 116, 7, (2021), 2045-2092. https://doi.org/10.1007/s00170-021-07558-6
  2. Li, S., Sui, J., Ding, F., Wu, S., Chen, W., Wang, C. "Optimization of Milling Aluminum Alloy 6061-T6 using Modified Johnson-Cook Model" Simulation Modelling Practice and Theory, Vol. 111, (2021), 102330. https://doi.org/10.1016/j.simpat.2021.102330
  3. Jawahir, I., Brinksmeier, E., M'saoubi, R., Aspinwall, D., Outeiro, J., Meyer, D., Umbrello, D., Jayal, A. "Surface integrity in material removal processes: Recent advances" CIRP Annals-Manufacturing Technology, 60, No. 2, (2011), 603-626. https://doi.org/10.1016/j.cirp.2011.05.002
  4. Kim, Y. M., Shin, S. J., Cho, H. W. "Predictive modeling for machining power based on multi-source transfer learning in metal cutting" International Journal of Precision Engineering and Manufacturing-Green Technology, Vol. 9, 1, (2022), 107-125. https://doi.org/10.1007/s40684-021-00327-6
  5. Saligheh, A., Hajialimohammadi, A., Abedini, V. "Cutting Forces and Tool Wear Investigation for Face Milling of Bimetallic Composite Parts Made of Aluminum and Cast Iron Alloys" International Journal of Engineering, Transactions C: Aspects, 33, No. 6, (2020), 1142-1148. DOI: 10.5829/ije.2020.33.06c.12
  6. Zhu, K. “Modeling of the Machining Process”, In Smart Machining Systems Springer, Cham., (2022), 19-70, DOI: 10.1007/978-3-030-87878-8_2
  7. Kant, T., Pare, V. "Study of optimum process selection parameter in high speed cnc end milling of composite materials using meta heuristic optimization" International Journal of Science & Technology, 1, No. 2, (2022), 9-18.
  8. Prasanth, I. S. N. V. R., Ravishankar, D. V., Manzoor Hussain M., "Analysis of Milling Process Parameters and their Influence on Glass Fiber Reinforced Polymer Composites (RESEARCH NOTE)" International Journal of Engineering, Transactions A: Basics, 30, No. 7, (2017), 1074-1080 DOI: 10.5829/ije.2017.30.07a.17
  9. Zailani, Z. A., Rusli, N. S. N., Shuaib, N. A., "Effect of Cutting Environment and Swept Angle Selection in Milling Operation" International Journal of Engineering, Transactions C: Aspects, Vol. 34, No. 11, (2021), 2578-2584 DOI: 10.5829/ije.2021.34.11bc.02
  10. Gao, X., Cheng, X., Ling, S., Zheng, G., Li, Y., Liu, H. "Research on optimization of micro-milling process for curved thin wall structure" Precision Engineering, 73, (2022), 296-312. https://doi.org/10.1016/j.precisioneng.2021.09.015
  11. Venkata Vishnu, A., Sudhakar Babu, S., "Mathematical Modeling and Multi Response Optimization for Improving Machinability of Alloy Steel using RSM, Grey Relational Analysis and Jaya Algorithm" International Journal of Engineering, Transactions C: Aspects, 34, No. 09, (2021), 2157-2166. DOI: 10.5829/IJE.2021.34.09C.13
  12. Reddy, N. S. K., Rao, P. V. "Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling", The International Journal of Advanced Manufacturing Technology, 26, No. (11-12), (2005), 1202-1210. https://doi.org/10.1007/s00170-004-2110-y
  13. Esfandiari, A. "Cuckoo Optimization Algorithm in Cutting Conditions During Machining" Journal of Advances in Computer Research 5, No. 2, (2014), 45-57.
  14. Hassan, A., El-Hamid, A., Wagih, A., Fathy, A. "Effect of mechanical milling on the morphologyand structural evaluation of Al-Al2O3 nanocomposite powders" International Journal of Engineering, Transactions A: Basics, 27, No. 4, (2014), 625-632. DOI: 10.5829/idosi.ije.2014.27.04a.14
  15. Mahesh, G., Muthu, S., Devadasan, S. "Prediction of surface roughness of end milling operation using genetic algorithm" The International Journal of Advanced Manufacturing Technology 77, No. (1-4), (2015), 369-381. https://doi.org/10.1007/s00170-014-6425-z
  16. Nakhaei, M. R., Naderi, G. "Modeling and Optimization of Mechanical Properties of PA6/NBR/Graphene Nanocomposite Using Central Composite Design" International Journal of Engineering, Transactions C: Aspects,33, No. 9, (2020), 1803-1810. DOI: 10.5829/ije.2020.33.09c.15
  17. Lauro, C. H., Pereira, R. B., Brandão, L. C., Davim, J. P. "Design of Experiments—Statistical and artificial intelligence analysis for the improvement of machining processes: A review" Design of Experiments in Production Engineerin, (2016), 89-107. DOI: 10.1007/978-3-319-23838-8_3
  18. Edem, I. F., Balogun, V. A. "Energy efficiency analyses of toolpaths in a pocket milling process" International Journal of Engineering, Transactions B: Application, 31, No. 5, (2018), 847-855. DOI: 10.5829/ije.2018.31.05b.22
  19. Sahith Reddy, S., Achyutha Kumar Reddy, M. "Optimization of Calcined Bentonite Clay Utilization in Cement Mortar using Response Surface Methodology" International Journal of Engineering, Transactions A: Basics 34, No. 7, (2021), 1623-1631. DOI: 10.5829/ije.2021.34.07a.07
  20. Vahdani, M., Ghazavi, M., Roustaei, M. "Prediction of Mechanical Properties of Frozen Soils Using Response Surface Method: An Optimization Approach" International Journal of Engineering, Transactions A: Basics 33, No. 10, (2020), 1826-1841. DOI: 10.5829/ije.2020.33.10a.02
  21. Ozdemir, F., Witharamage, C. S., Darwish, A. A., Okuyucu, H., Gupta, R. K. "Corrosion behavior of age hardening aluminum alloys produced by high-energy ball milling" Journal of Alloys and Compounds, 900, (2022), 163488. https://doi.org/10.1016/j.jallcom.2021.163488
  22. Kadirgama, K., Noor, M. M., Rahman, M. M., Rejab, M. R. M., Haron, C. H. C., Abou-El-Hossein, K. A. "Surface roughness prediction model of 6061-T6 aluminium alloy machining using statistical method" European Journal of Scientific Research, 25, No. 2, (2009): 250-256.
  23. Reddy, N. S. K., Rao, P. V. "Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms." The International Journal of Advanced Manufacturing Technology, 28, No. (5-6), (2006): 463-473. https://doi.org/10.1007/s00170-004-2381-3
  24. Kumar, M. S., Prasad, J., Krishna, D. A., Narayana, M. V., Anusha, M., Saravanakumar, A. "Parametric optimization of aluminium alloy milling using Taguchi method for surface roughness" International Journal of Scientific Research and Review, Vol. 7, No. 3, (2018), 516-522.
  25. Raja, S. B., Baskar, N. "Application of particle swarm optimization technique for achieving desired milled surface roughness in minimum machining time" Expert Systems with Applications 39, No. 5, (2012), 5982-5989. https://doi.org/10.1016/j.eswa.2011.11.110
  26. Li, B., Tian, X., Zhang, M. "Modeling and multi-objective optimization of cutting parameters in the high-speed milling using RSM and improved TLBO algorithm" The International Journal of Advanced Manufacturing Technology, 111, No. 7, (2020), 2323-2335. https://doi.org/10.1007/s00170-020-06284-9
  27. Zain, A. M., Haron, H. Sharif, S. "Simulated annealing to estimate the optimal cutting conditions for minimizing surface roughness in end milling Ti-6Al-4V" Machining Science and Technology, 14, No. 1, (2010), 43-62. https://doi.org/10.1080/10910340903586558