Balancing Surface Quality and Energy Demand in Machining of Medium-Carbon Steel: A Pareto-Based Optimization Study
Submitted: 2026-01-15
|Accepted: 2026-02-01
|Published: 2026-06-22
Copyright (c) 2026 Aswin Karkadakattil
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Keywords:
Machining optimization, NSGA-II, Surface roughness, Cutting power, multi-objective optimization, Simulation-based study
Supporting agencies:
Abstract:
Machining performance is strongly influenced by the selection of cutting parameters, as cutting speed, feed rate, and depth of cut directly affect both surface quality and energy consumption during material removal. In practical manufacturing environments, improving surface finish often requires operating conditions that increase power demand, while efforts to reduce energy usage may compromise surface integrity. This inherent conflict transforms machining parameter selection into a multi-objective decision problem rather than a single-criterion optimization task. To systematically investigate this tradeoff, the present study develops a simulation-based multi-objective optimization framework that simultaneously minimizes surface roughness and cutting power. Literature-validated empirical models are employed to represent the influence of cutting parameters on process responses, enabling structured performance evaluation without the need for additional experimental trials. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is implemented to generate Pareto-optimal machining conditions that capture the compromise between surface quality and energy efficiency. The obtained Pareto front clearly illustrates the opposing behavior of the two objectives: improved surface finish is generally associated with higher power consumption, whereas reduced energy demand corresponds to moderately increased surface roughness. By explicitly visualizing and analysing these trade-offs, the proposed framework functions as a decision-support tool, allowing manufacturing engineers to select machining parameters based on specific production priorities. The methodology provides a transparent and adaptable approach for balancing quality and energy considerations in machining operations.
References:
Benardos, P. G., & Vosniakos, G. C. (2003). Predicting surface roughness in machining: a review. International Journal of Machine Tools and Manufacture, 43(8), 833–844. https://doi.org/10.1016/S0890-6955(03)00059-2
Bharti, P. S., Maheshwari, S., & Sharma, C. (2012). Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II. Journal of Mechanical Science and Technology, 26(6), 1875–1883. https://doi.org/10.1007/s12206-012-0411-x
Bhoskar, T., Kulkarni, O. K., Kulkarni, N. K., Patekar, S. L., Kakandikar, G. M., & Nandedkar, V. M. (2015). Genetic algorithmand its applications to mechanical engineering: a review. Materials Today, 2(4–5), 2624–2630. https://doi.org/10.1016/j.matpr.2015.07.219
Biermann, D., Amuth, R., Hess, S., & Tiffe, M. (2018). Simulation-based analysis and optimisation of the cutting edge micro shape for machining of nickel-base alloys. Procedia CIRP, 67, 284–289. https://doi.org/10.1016/j.procir.2017.12.214
Chen, J. (2009). Multi-objective optimization of cutting parameters with improved NSGA-II. International Conference on Management and Service Science. IEEE, 2009. https://doi.org/10.1109/ICMSS.2009.5302835
Chávez-García, H., & Castillo-Villar, K. K. (2018). Simulation-based model for the optimization of machining parameters in a metal-cutting operation. Simulation Modelling Practice and Theory, 84, 204–221. https://doi.org/10.1016/j.simpat.2018.02.008
Deng, Z., Zhang, H., Fu, Y., Wan, L., & Liu, W. (2017). Optimization of process parameters for minimum energy consumption based on cutting specific energy consumption. Journal of Cleaner Production, 166, 1407–1414. https://doi.org/10.1016/j.jclepro.2017.08.022
Dweiri, F., Al-Jarrah, M., & Al-Wedyan, H. (2003). Fuzzy surface roughness modeling of CNC down milling of Alumic-79. Journal of Materials Processing Technology, 133(3), 266–275. https://doi.org/10.1016/S0924-0136(02)00847-6
Jain, N. K., & Jain, V. K. (2007). Optimization of electro-chemical machining process parameters using genetic algorithms. Machining Science and Technology, 11(2), 235–258. https://doi.org/10.1080/10910340701350108
Jain, R. K., Jain, V. K., & Dixit, P. M. (1999). Modeling of material removal and surface roughness in abrasive flow machining process. International Journal of Machine Tools and Manufacture, 39(12), 1903–1923. https://doi.org/10.1016/S0890-6955(99)00038-3
Jiao, Y., et al. (2004). Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations. International Journal of Machine Tools and Manufacture, 44(15), 1643–1651. https://doi.org/10.1016/j.ijmachtools.2004.06.004
Jin, Y., & Sendhoff, B. (2008). Pareto-based multiobjective machine learning: an overview and case studies. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 38(3), 397–415. https://doi.org/10.1109/TSMCC.2008.919172
Joshi, M., et al. (2021). Comparison of NSGA-II, MOALO and MODA for multi-objective optimization of micro-machining processes. Materials, 14(17), 5109. https://doi.org/10.3390/ma14175109
Kaya, N. (2006). Machining fixture locating and clamping position optimization using genetic algorithms. Computers in Industry, 57(2), 112–120. https://doi.org/10.1016/j.compind.2005.05.001
Khorasani, A. M., Yazdi, M. R. S., & Safizadeh, M. S. (2012). Analysis of machining parameters effects on surface roughness: a review. International Journal of Computational Materials Science and Surface Engineering, 5(1), 68–84. https://doi.org/10.1504/IJCMSSE.2012.049055
Kodali, S. P., Kudikala, R., & Kalyanmoy, D. (2008). Multi-objective optimization of surface grinding process using NSGA II. 2008 First International Conference on Emerging Trends in Engineering and Technology IEEE, 2008. https://doi.org/10.1109/ICETET.2008.139
Kumar, A., et al. (2017). NSGA-II approach for multi-objective optimization of wire electrical discharge machining process parameter on Inconel 718. Materials Today: Proceedings, 4(2), 2194–2202. https://doi.org/10.1016/j.matpr.2017.02.066
Lee, W., et al. (2017). Simulation-based machining condition optimization for machine tool energy consumption reduction. Journal of Cleaner Production, 150, 352–360. https://doi.org/10.1016/j.jclepro.2017.02.178
Li, M., Chen, T., & Yao, X. (2020). How to evaluate solutions in pareto-based search-based software engineering: a critical review and methodological guidance. IEEE Transactions on Software Engineering, 48(5), 1771–1799. https://doi.org/10.1109/TSE.2020.3036108
Liu, N., Zhang, Y. F., & Lu, W. F. (2015). A hybrid approach to energy consumption modelling based on cutting power: a milling case. Journal of Cleaner Production, 104, 264–272. https://doi.org/10.1016/j.jclepro.2015.05.049
Miko, E., & Nowakowski, Ł. (2012). Analysis and verification of surface roughness constitution model after machining process. Procedia Engineering, 39, 395–404. https://doi.org/10.1016/j.proeng.2012.07.043
Ngatchou, P., Zarei, A., & El-Sharkawi, A. (2005). Pareto multi objective optimization. Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems. IEEE, 2005.
Nguyen, A. T., et al. (2023). A hybridization of machine learning and NSGA-II for multi-objective optimization of surface roughness and cutting force in AISI 4340 alloy steel turning. Journal of Machine Engineering, 23. https://doi.org/10.36897/jme/160172
Palanisamy, P., Rajendran, I., & Shanmugasundaram, S. (2007). Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. International Journal of Advanced Manufacturing Technology, 32(7), 644–655. https://doi.org/10.1007/s00170-005-0384-3
Pontes, F. J., et al. (2010). Artificial neural networks for machining processes surface roughness modeling. The International Journal of Advanced Manufacturing Technology, 49(9), 879–902. https://doi.org/10.1007/s00170-009-2456-2
Prakash, C., et al. (2016). Multi-objective optimization of powder mixed electric discharge machining parameters for fabrication of biocompatible layer on β-Ti alloy using NSGA-II coupled with Taguchi based response surface methodology. Journal of Mechanical Science and Technology, 30(9), 4195–4204. https://doi.org/10.1007/s12206-016-0831-0
Ramos-Frutos, J., et al. (2025). Improving the response surface methodology optimization with metaheuristics: A practical approach. Expert Systems with Applications, 129195. https://doi.org/10.1016/j.eswa.2025.129195
Sadeghifar, M., et al. (2020). Finite element simulation-based predictive regression modeling and optimum solution for grain size in machining of Ti-6Al-4V alloy: influence of tool geometry and cutting conditions. Simulation Modelling Practice and Theory, 104, 102141. https://doi.org/10.1016/j.simpat.2020.102141
Sealy, M. P., et al. (2016). Energy consumption and modeling in precision hard milling. Journal of Cleaner Production, 135, 1591–1601. https://doi.org/10.1016/j.jclepro.2015.10.094
Shao, W., Pi, D., & Shao, Z. (2019). A Pareto-based estimation of distribution algorithm for solving multiobjective distributed no-wait flow-shop scheduling problem with sequence-dependent setup time. IEEE Transactions on Automation Science and Engineering, 16(3), 1344–1360. https://doi.org/10.1109/TASE.2018.2886303
Shi, K. N., et al. (2019). An improved cutting power-based model for evaluating total energy consumption in general end milling process. Journal of Cleaner Production, 231, 1330–1341. https://doi.org/10.1016/j.jclepro.2019.05.323
Shin, S. J., Woo, J., & Rachuri, S. (2017). Energy efficiency of milling machining: component modeling and online optimization of cutting parameters. Journal of Cleaner Production, 161, 12–29. https://doi.org/10.1016/j.jclepro.2017.05.013
Venkatesan, D., Kannan, K., & Saravanan, R. (2009). A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Computing and Applications, 18(2), 135–140. https://doi.org/10.1007/s00521-007-0166-y
Wang, Q., et al. (2019). Energy consumption model for drilling processes based on cutting force. Applied Sciences, 9(22), 4801. https://doi.org/10.3390/app9224801
Wei, L., & Yuying, Y. (2008). Multi-objective optimization of sheet metal forming process using Pareto-based genetic algorithm. Journal of Materials Processing Technology, 208(1–3), 499–506. https://doi.org/10.1016/j.jmatprotec.2008.01.014
Yusoff, Y., Ngadiman, M. S., & Zain, A. M. (2011). Overview of NSGA-II for optimizing machining process parameters. Procedia Engineering, 15, 3978–3983. https://doi.org/10.1016/j.proeng.2011.08.745
Zain, A. M., Haron, H., & Sharif, S. (2009). Review of ANN technique for modeling surface roughness performance measure in machining process. 2009 Third Asia International Conference on Modelling & Simulation. https://doi.org/10.1109/AMS.2009.78
Zan, X., et al. (2020). A Pareto-based genetic algorithm for multi-objective scheduling of automated manufacturing systems. Advances in Mechanical Engineering, 12(1), 1687814019885294. https://doi.org/10.1177/1687814019885294
Zhang, Z., Wu, F., & Wu, A. (2024). Research on multi-objective process parameter optimization method in hard turning based on an improved NSGA-II algorithm. Processes, 12(5), 950. https://doi.org/10.3390/pr12050950
Zhou, L., et al. (2016). Energy consumption model and energy efficiency of machine tools: a comprehensive literature review. Journal of Cleaner Production, 112, 3721–3734. https://doi.org/10.1016/j.jclepro.2015.05.093
Zhou, W., et al. (2022). A Pareto-based discrete particle swarm optimization for parallel casting workshop scheduling problem with fuzzy processing time. Knowledge-Based Systems, 256, 109872. https://doi.org/10.1016/j.knosys.2022.109872
Zolpakar, N. A., et al. (2019). Application of multi-objective genetic algorithm (MOGA) optimization in machining processes. In Optimization of Manufacturing Processes (Springer), 185–199. https://doi.org/10.1007/978-3-030-19638-7_8
