Enhancing Machining Quality of EN24 Steel Through Multi-Parameter Optimization Using Taguchi Approach
DOI:
https://doi.org/10.31181/rme480Keywords:
Turning Process Optimization, EN24 Steel Machining, Taguchi Method, SR Analysis, MRRAbstract
This study optimises the EN24 steel turning process parameters using the Taguchi method to reduce SR, cutting force, and vibration, and to boost the material removal rate. The study examines how feed rate (FR), Depth of cut (DoC), Nose radius (NR), cutting condition, and tool type affect machining performance. The Taguchi approach was used to plan 16 L16 orthogonal array runs using S/N ratios for each response metric. Experimental results showed that NR significantly affects cutting force, improving parameter optimization by 164.95 N to 3.8 N. The best cutting force (20.45 N) was found at 0.2 mm/rev, 1 mm DoC, and 0.4 mm NR under MQL-II conditions using a coated tool. The minimum surface roughness (SR) was 1.08 µm with a S/N ratio of -0.668. Wet cutting with coated tools and 0.25 mm/rev FR produced the best surface finish. Under MQL-II settings, the greatest MRR attained was 20192 mm³/min with a FR of 0.3 mm/rev, a DoC of 2 mm, and a NR of 1.6 mm. The statistical regression models constructed showed significant predictive power, with R² of 97.9% for cutting force and 60.2% for SR. ANOVA confirmed the statistical relevance of NR and DoC in responses. This research provides a comprehensive optimization solution to improve EN24 steel turning efficiency and surface quality for cost-effective, high-performance manufacturing.
References
Abdelaoui, F. Z. E., Jabri, A., & Barkany, A. E. (2023). Optimization techniques for energy efficiency in machining processes—a review. The International Journal of Advanced Manufacturing Technology, 125(7), 2967-3001. https://doi.org/10.1007/s00170-023-10927-y
Abellán-Nebot, J. V., Vila Pastor, C., & Siller, H. R. (2024). A review of the factors influencing surface roughness in machining and their impact on sustainability. Sustainability, 16(5), 1917. https://doi.org/10.3390/su16051917
Banerjee, N., & Sharma, A. (2018). A comprehensive assessment of minimum quantity lubrication machining from quality, production, and sustainability perspectives. Sustainable materials and technologies, 17, e00070. https://doi.org/10.1016/j.susmat.2018.e00070
Bazaz, S. M., Ratava, J., Lohtander, M., & Varis, J. (2023). An investigation of factors influencing tool life in the metal cutting turning process by dimensional analysis. Machines, 11(3), 393. https://doi.org/10.3390/machines11030393
Castillejo-Cuberos, A., Boland, J., & Escobar, R. (2021). Short-Term Deterministic Solar Irradiance Forecasting Considering a Heuristics-Based, Operational Approach. Energies, 14(18), 6005. https://doi.org/10.3390/en14186005
Imran, M., Shuangfu, S., Yuzhu, B., Yuming, W., & Raheel, N. (2025). Optimising subsurface integrity and surface quality in mild steel turning: A multi-objective approach to tool wear and machining parameters. Journal of Materials Research and Technology, 35, 3440-3462. https://doi.org/10.1016/j.jmrt.2025.01.246
Kumar, M. V., Meignanamoorthy, M., Sakthivelu, S., Kumar, S. D., Chanakyan, C., & Alagarsamy, S. (2020). Optimization of material removal rate in CNC turning of AA2024 via Taguchi technique. Materials Today: Proceedings, 27, 1163-1167. https://doi.org/10.1016/j.matpr.2020.02.045
Lin, C.-M., & Lin, Y.-S. (2023). Utilizing a two-stage Taguchi method and artificial neural network for the precise forecasting of cardiovascular disease risk. Bioengineering, 10(11), 1286. https://doi.org/10.3390/bioengineering10111286
Okolie, J. A., Epelle, E. I., Nanda, S., Castello, D., Dalai, A. K., & Kozinski, J. A. (2021). Modeling and process optimization of hydrothermal gasification for hydrogen production: A comprehensive review. The Journal of Supercritical Fluids, 173, 105199. https://doi.org/10.1016/j.supflu.2021.105199
Patil, S., Sathish, T., Rao, P., Prabhudev, M., Vijayan, V., Rajkumar, S., Sharma, S., Kumar, A., Abbas, M., & Makki, E. (2024). Optimization of surface roughness in milling of EN 24 steel with WC-Coated inserts using response surface methodology: analysis using surface integrity microstructural characterizations. Frontiers in Materials, 11, 1269608. https://doi.org/10.3389/fmats.2024.1269608
Pimenov, D. Y., da Silva, L. R. R., Machado, A. R., Franca, P. H. P., Pintaude, G., Unune, D. R., Kuntoğlu, M., & Krolczyk, G. M. (2024). A comprehensive review of the machinability of difficult-to-machine alloys with advanced lubricating and cooling techniques. Tribology International, 196, 109677. https://doi.org/10.1016/j.triboint.2024.109677
Pimenov, D. Y., Mia, M., Gupta, M. K., Machado, Á. R., Pintaude, G., Unune, D. R., Khanna, N., Khan, A. M., Tomaz, Í., & Wojciechowski, S. (2022). Resource saving by optimization and machining environments for sustainable manufacturing: A review and prospects. Renewable and Sustainable Energy Reviews, 166, 112660. https://doi.org/10.1016/j.rser.2022.112660
Praveen, N., NG, S. K., Prasad, C. D., Soni, H., Prasad, M., TC, S. K., Mallik, U., & Aden, A. A. (2025). Effect of CNC Turning Parameters on MRR, Cutting Force and Surface Roughness for Ternary Shape Memory Alloys (SMAs). Results in Engineering, 104876. https://doi.org/10.1016/j.rineng.2025.104876
Ruzova, Т., & Haddadi, B. . (2025). SR AND ITS MEASUREMENT METHODS - ANALYTICAL REVIEW. . Results in Surfaces and Interfaces,, 100441. https://doi.org/10.1016/j.rsurfi.2025.100441
Subhedar, D. G., Chauhan, K. V., & Patel, D. A. (2022). An experimental investigation of TiN coating on cutting force and surface finish in milling of aluminium. Materials Today: Proceedings, 59, 161-165. https://doi.org/10.1016/j.matpr.2021.10.384
Tu, L., Zhang, Y., Yu, Q., Wang, X., Wang, C., Zhang, Z., Huang, L., Geng, L., Chen, Y., & Zhang, J. (2025). Machinability and tool wear mechanism in the high-speed milling of TiAl alloys with different microstructures. journal of materials research and technology. https://doi.org/10.1016/j.jmrt.2025.04.248
Tzeng, C.-J., Lin, Y.-H., Yang, Y.-K., & Jeng, M.-C. (2009). Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis. Journal of materials processing technology, 209(6), 2753-2759. https://doi.org/10.1016/j.jmatprotec.2008.06.046
Tzotzis, A., García-Hernández, C., Huertas-Talón, J.-L., & Kyratsis, P. (2020). Influence of the nose radius on the machining forces induced during AISI-4140 hard turning: A CAD-based and 3D FEM approach. Micromachines, 11(9), 798. https://doi.org/10.3390/mi11090798
Veza, I., Spraggon, M., Fattah, I. R., & Idris, M. (2023). Response surface methodology (RSM) for optimizing engine performance and emissions fueled with biofuel: Review of RSM for sustainability energy transition. Results in Engineering, 18, 101213. https://doi.org/10.1016/j.rineng.2023.101213
Wadhwa, A. S., Abbass, M., Akhai, S., Kumar, D., & Kumar, P. . (2024). Integrating Taguchi Optimization for Multi-Criteria decision making in engineering applications. . In Advances in computational intelligence and robotics book series., pp (125–150). https://doi.org/10.4018/979-8-3693-6502-1.ch005
Zaidan, M. A., Dada, L., Alghamdi, M. A., Al-Jeelani, H., Lihavainen, H., Hyvärinen, A., & Hussein, T. (2019). Mutual information input selector and probabilistic machine learning utilisation for air pollution proxies. Applied Sciences, 9(20), 4475. https://doi.org/10.3390/app9204475
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