Study on multi-objective optimization of X12M steel milling process by reference ideal method

Keywords: surface milling, multi-objective optimization, surface roughness, cutting force, Reference Ideal Method, RIM


For all machining cutting methods, surface roughness is a parameter that greatly affects the working ability and life of machine elements. Cutting force is a parameter that not only affects the quality of the machining surface but also affects the durability of cutter and the level of energy consumed during machining. Besides, material removal rate (MRR) is a parameter that reflects machining productivity. Workpiece surface machining with small surface roughness, small cutting force and large MRR is desirable of most machining methods. Milling is a popular machining method in the machine building industry. This is considered to be one of the most productive machining methods, capable of machining many different types of surfaces. With the development of the cutting tool and machine tool manufacturing industries, this method is increasingly guaranteed with high precision, sometimes used as the final finishing method. Milling using a face milling cutter is more productive than using a cylindrical cutter because there are multiple cutter s involved at the same time. This article presents a study of multi-objective optimization of milling process using a face milling cutter. The experimental material used in this study is X12M steel. Taguchi method has been applied to design an orthogonal experimental matrix with 27 experiments (L27). In which, five parameters have been selected as the input parameters of the experimental process including insert material, tool nose radius, cutting speed, feed rate and cutting depth. The Reference Ideal Method (RIM) is applied to determine the value of input parameters to ensure minimum surface roughness, minimum cutting force and maximum MRR. Influence of the input parameters on output parameters is also discussed in this study


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Author Biography

Khanh Nguyen Lam, University of Transport and Communications

Faculty of Mechanical Engineering


Panshetty, S. S., Bute, P. V., Patil, R., Satpute, J. B. (2016). Optimization of Process Parameters in Milling Operation by Taguchi’s Technique Using Regression Analysis. International Journal of Science Technology & Engineering, 2 (11), 130–136.

Pratyusha, J., Ashok Kumar, U., Laxminarayana, P. (2013). Optimization of process parameters for Milling Using Taguchi Methods. International Journal of Advanced Trends in Computer Science and Engineering, 2 (6), 129–135.

Yildirim, C. V., Kivak, T., Erzincanli, F., Uygur, I., Sarikaya, M. (2017). Optimization of MQL Parameters Using the Taguchi Method in Milling of Nickel Based Waspaloy. Gazi University - Journal of Science, 30 (2), 173–186.

Soni, S. K., Moulick, S. K. (2015). Optimization of Milling Process Parameter for Surface Roughness of Inconel 718 By Using Taguchi Method. International Journal for Scientific Research & Development, 2 (11), 57–63.

Zhou, J. W., Chen, Y., Fu, Y. C., Xu, J. H., Hu, A. D., Liu, S. Q. (2014). Optimization of Milling Parameters of CFRP for Surface Roughness Using Taguchi Design Method. Advanced Materials Research, 1027, 76–79. doi:

Thakre, A. A. (2013). Optimization of Milling Parameters for Minimizing Surface Roughness Using Taguchi’s Approach. International Journal of Emerging Technology and Advanced Engineering, 3 (6), 226–230.

Ahmad, T., Khan, N. Z., Khan, Z. A. (2017). Optimization of end Milling Process Parameters on Surface Roughness Using Taguchi Method. International Journal of Scientific & Engineering Research, 8 (7), 190–194.

Londhe/Chilwant, P. (2016). Optimization of cutting parameters in milling operation to improve surface finish of EN 31. International journal of engineering sciences & management research, 3 (9), 1–9. Available at:

Do, T.-V., Vu, N.-C., Nguyen, Q.-M. (2018). Optimization of cooling conditions and cutting parameters during hard milling of AISI H13 steel by using Taguchi method. 2018 IEEE International Conference on Advanced Manufacturing (ICAM). doi:

Lestari, W. D., Ismail, R., Jamari, J., Bayuseno, A. P. (2019). Optimization of CNC milling parameters through the Taguchi and RSM methods for surface roughness of UHMWPE acetabular cup. International Journal of Mechanical Engineering and Technology, 10 (2), 1762–1775.

Maurya, P., Sharma, P., Diwaker, B. (2012). Implementation of Taguchi methodology to Optimization of CNC end milling process parameters of AL6351–T6. International Journal of Modern Engineering Research, 2 (5), 3530–3533.

Zhang, S., Guo, Y. B. (2009). Design Optimization of Cutting Parameters Using Taguchi Method and ANOVA during High-Speed Machining Hardened H13 Steel. Materials Science Forum, 626-627, 129–134. doi:

Parashar, V., Purohit, R. (2017). Investigation of the effects of the Machining Parameters on Material Removal Rate using Taguchi method in end Milling of Steel Grade EN19. Materials Today: Proceedings, 4 (2), 336–341. doi:

Sequeira, A. A., Prabhu, R., Sriram, N. S., Bhat, T. (2012). Effect of Cutting Parameters on Cutting Force and Surface Roughness of Aluminium Components using Face Milling Process - a Taguchi Approach. IOSR Journal of Mechanical and Civil Engineering, 3 (4), 7–13. doi:

Giridhar Reddy, P., Gowthaman, S., Jagadeesha, T. (2020). Optimization of Cutting Parameters Based on Surface Roughness and Cutting Force During End Milling of Nimonic C-263 Alloy. IOP Conference Series: Materials Science and Engineering, 912, 032020. doi:

Günay, M., Kaçal, A., Turgut, Y. (2011). Optimization of machining parameters in milling of Ti-6Al-4V alloy using Taguchi method. Journal of New World Sciences Academy, 6 (1), 428–440.

Teja, B., Naresh, N., Rajasekhar, K. (2013). Multi-Response Optimization of Milling Parameters on AISI 304 Stainless Steel using Grey-Taguchi Method. International Journal of Engineering Research & Technology, 2 (8), 2335–2341.

Cica, D., Caliskan, H., Panjan, P., Kramar, D. (2020). Multi-objective optimization of hard milling using Taguchi based Grey Relational Analysis. Tehnički vjesnik, 27 (2), 513–519. doi:

Norcahyo, R., Soepangkat, B. O. (2017). Optimization of Multi Response in End Milling Process of ASSAB XW-42 Tool Steel with Liquid Nitrogen Cooling Using Taguchi-Grey Relational Analysis. AIP Conference Proceedings, 1855 (1), 020011. doi:

Kanchana, J., Prasath, V., Krishnaraj, V., Geetha Priyadharshini, B. (2019). Multi response optimization of process parameters using grey relational analysis for milling of hardened Custom 465 steel. Procedia Manufacturing, 30, 451–458. doi:

Jenarthanan, M. P., Jeyapaul, R. (2013). Optimisation of machining parameters on milling of GFRP composites by desirability function analysis using Taguchi method. International Journal of Engineering, Science and Technology, 5 (4), 22–36. doi:

Jenarthanan, M. P., Gokulakrishnan, R., Jagannaath, B., Ganesh Raj, P. (2017). Multi-objective optimization in end milling of GFRP composites using Taguchi techniques with principal component analysis. Multidiscipline Modeling in Materials and Structures, 13 (1), 58–70. doi:

Fedai, Y., Kahraman, F., Akin, H. K., Basar, G. (2018). Optimization of machining parameters in face milling using multi-objective Taguchi technique. Technical Journal, 12 (2), 104–108. doi:

Tran, M. D., Pham, Q. D., Tran, T. L., Dang, V. T. (2019). Evaluation of MQCL Technique Using MoS2 Nanofluids During Hard Milling Process of SKD 11 Tool Steel. International Journal of Mechanical Engineering and Applications, 7 (4), 91–100. doi:

Mac, T.-B., Long, B. T., Nguyen, D.-T. (2020). Optimization of Surface Roughness and Vibration During Thermal-Assisted Milling SKD11 Steel Using Taguchi Method. Springer Proceedings in Materials. doi:

Duc, T. M., Long, T. T. (2020). Effects of MQL and MQCL Parameters on Surface Roughness in Hard Milling of SKD 11 Tool Steel. International Journal of Mechanical Engineering, 7 (10), 28–31. doi:

Xavierarockiaraj, S., Kuppan, P. (2014). Investigation of Cutting Forces, Surface Roughness and Tool Wear during Laser Assisted Machining of SKD11 Tool Steel. Procedia Engineering, 97, 1657–1666. doi:

Dong, P. Q., Duc, T. M., Long, T. T. (2019). Performance Evaluation of MQCL Hard Milling of SKD 11 Tool Steel Using MoS2 Nanofluid. Metals, 9 (6), 658. doi:

Inkhamnoi, A., Jirapattarasilp, K. (2013). Effect of Milling Parameters and Coolants on Surface Hardness of Tool Steel: SKD 11. Advanced Materials Research, 650, 596–601. doi:

Trung, D. D., Thien, N. V., Nguyen, N.-T. (2021). Application of TOPSIS Method in Multi-Objective Optimization of the Grinding Process Using Segmented Grinding Wheel. Tribology in Industry, 43 (1), 12–22. doi:

Nguyen Hong, S., Vo Thi Nhu, U. (2021). Multi-objective Optimization in Turning Operation of AISI 1055 Steel Using DEAR Method. Tribology in Industry, 43 (1), 57–65. doi:

Nguyen, N.-T., Trung, D. D. (2021). Combination of Taguchi method, Moora and Copras techniques in multi-objective optimization of surface grinding process. Journal of Applied Engineering Science. doi:

Cables, E., Lamata, M. T., Verdegay, J. L. (2016). RIM-reference ideal method in multicriteria decision making. Information Sciences, 337-338, 1–10. doi:

Sánchez-Lozano, J. M., Rodríguez, O. N. (2020). Application of Fuzzy Reference Ideal Method (FRIM) to the military advanced training aircraft selection. Applied Soft Computing, 88, 106061. doi:

Sofuoğlu, M. A., Arapoğlu, R. A., Orak, S. (2017). Multi Objective Optimization of Turning Operation Using Hybrid Decision Making Analysis. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, 18 (3), 595–610. doi:

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How to Cite
Nguyen Lam, K. (2021). Study on multi-objective optimization of X12M steel milling process by reference ideal method. EUREKA: Physics and Engineering, (4), 89-104.

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