Analisis Pengaruh Variasi Kecepatan Spindel Terhadap Kekasaran Permukaan Material Scr420h Hasil Pemesinan CNC Turning Mazak Quick Turn Smart 250
DOI:
https://doi.org/10.55681/sentri.v5i7.6974Keywords:
spindle speed, surface roughness, CNC turning, SCr420H, finishing machiningAbstract
Surface quality is an important parameter in machining processes because it affects component performance, dimensional accuracy, and service life. In CNC turning processes, spindle speed is one of the cutting parameters that significantly influences the surface characteristics of machined components. SCr420H steel is widely used in automotive components requiring high wear resistance and dimensional precision; however, the effect of spindle speed variation on its surface roughness using a Mazak Quick Turn Smart 250 CNC turning machine requires further experimental investigation. This study aims to analyze the effect of spindle speed variations on the surface roughness of SCr420H material and determine the optimum spindle speed based on the lowest roughness value. A quantitative experimental method with a one-factor design was conducted by varying spindle speeds of 1200, 1400, 1600, 1800, and 2000 rpm during the finishing CNC turning process. Other machining parameters were maintained constant, including a feed rate of 0.20 mm/rev, depth of cut of 0.4 mm, VNMG 160404-HQ insert with a 0.4 mm nose radius, and dry machining conditions. Surface roughness measurements were performed using a Surfcom 1800G Surface Roughness Tester to obtain Ra and Rz values. The results showed that spindle speed variations produced a non-linear change in surface roughness. The average Ra values at spindle speeds of 1200–2000 rpm were 3.679 µm, 5.258 µm, 5.942 µm, 5.512 µm, and 3.411 µm, respectively, while the average Rz values were 24.015 µm, 33.640 µm, 36.811 µm, 34.812 µm, and 22.071 µm, respectively. The optimum machining condition was achieved at a spindle speed of 2000 rpm, which resulted in the lowest Ra and Rz values. These findings indicate that proper spindle speed selection can improve the surface quality of SCr420H during CNC turning and provide a reference for determining machining parameters in precision manufacturing processes.
Downloads
References
Akgün, M., & Kara, F. (2021). Analysis and optimization of cutting tool coating effects on surface roughness and cutting forces on turning of AA 6061 alloy. Advances in Materials Science and Engineering, 2021, 6498261. https://doi.org/10.1155/2021/6498261
Elbah, M., Yallese, M. A., & Khellaf, A. (2022). Modeling and optimization of machining parameters during turning using carbide insert tools. Measurement, 189, 110492. https://doi.org/10.1016/j.measurement.2022.110492
El Rayes, M. M., Abbas, A. T., Al-Abduljabbar, A. H., Ragab, A. E., Benyahia, F., & Elkaseer, A. (2023). Investigation and statistical analysis for optimizing surface roughness, cutting forces, temperature, and productivity in turning process. Metals, 13(6), 1098. https://doi.org/10.3390/met13061098
Hantang, S., Khanthirat, W., Lawong, A., Warorot, W., & Sudsuansee, T. (2023). Optimizing surface roughness of steel material using CNC turning and Taguchi technique. Journal of Engineering and Industrial Technology, 43(2), 145–154. https://doi.org/10.14456/jeit.2023.22
Kumar, R., Singh, J., & Kumar, P. (2022). Optimization of machining parameters for surface roughness and material removal rate in CNC turning using response surface methodology. Materials Today: Proceedings, 56, 2156–2162. https://doi.org/10.1016/j.matpr.2022.02.315
Liu, X., Chen, M., & Zhao, J. (2023). Surface integrity evaluation in precision machining of alloy steels: Effects of cutting parameters and tool geometry. Journal of Materials Research and Technology, 24, 1123–1138. https://doi.org/10.1016/j.jmrt.2023.03.091
Mohan, N. S., Ramachandra, A., & Kulkarni, S. M. (2021). Effect of cutting parameters on surface roughness during CNC turning of alloy steel. Journal of Manufacturing Processes, 64, 1324–1333. https://doi.org/10.1016/j.jmapro.2021.03.046
Patel, V., Patel, K., & Desai, K. (2023). Experimental investigation and optimization of CNC turning parameters for improving surface quality. The International Journal of Advanced Manufacturing Technology, 126, 2845–2860. https://doi.org/10.1007/s00170-023-11245-8
Putra, Y. M., Timuda, G. E., Darsono, N., Chollacoop, N., & Khaerudini, D. S. (2023). Optimization of machining parameters on the surface roughness of aluminum in CNC turning process using Taguchi method. International Journal of Innovation in Mechanical Engineering and Advanced Materials, 5(2), 56–62. https://doi.org/10.22441/ijimeam.v5i2.21679
Rahman, M., Asad, A., & Islam, M. (2024). Assessment of surface roughness parameters in precision machining processes using Ra and Rz evaluation. Measurement: Sensors, 33, 101121. https://doi.org/10.1016/j.measen.2024.101121
Ramesh, S., Karunamoorthy, L., & Palanikumar, K. (2021). Influence of machining parameters on surface roughness during turning of hardened steel using coated carbide inserts. Materials and Manufacturing Processes, 36(8), 921–930. https://doi.org/10.1080/10426914.2021.1875307
Singh, R., Sharma, V., & Kumar, A. (2021). Analysis of cutting speed effects on surface roughness and tool performance during turning operation. Engineering Science and Technology, an International Journal, 24(6), 1450–1458. https://doi.org/10.1016/j.jestch.2021.03.012
Vignesh, S. H. (2023). Surface roughness optimization of EN24 steel in CNC turning using Taguchi method. International Journal of Vehicle Structures and Systems, 15(5). https://doi.org/10.4273/ijvss.15.5.08
Wang, H., Li, B., & Zhang, S. (2022). Mechanical behavior and machinability characteristics of chromium alloy steels for automotive applications. Materials Characterization, 190, 112031. https://doi.org/10.1016/j.matchar.2022.112031
Zhang, Y., Liu, Z., & Wang, X. (2022). Investigation of cutting temperature and tool wear mechanisms in turning of alloy steels. Wear, 504–505, 204414. https://doi.org/10.1016/j.wear.2022.204414
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Syahrizal Rafli Ferdian, Jenni Ria Rajagukguk

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.





