Optimization of CNC Turning Parameters for Surface Roughness of Brass 36000 Using the Taguchi Method

Agus Noviana, Muhamad Fitri, Dedik Romahadi

Abstract


Brass is widely used in industrial applications due to its excellent machinability and durability, making it well suited for CNC turning operations. Although numerous studies have investigated the optimization of turning parameters, variations in machine tools and cutting conditions often lead to differing conclusions. This study aims to optimize surface roughness in the CNC turning of Brass 36000 using the Taguchi method. An L9 orthogonal array was employed to evaluate the effects of spindle speed, feed rate, depth of cut, and coolant type. Experimental data were analyzed using signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) to identify the most influential parameters and optimal cutting conditions. The results indicate that feed rate is the dominant factor affecting surface roughness, contributing 95.54% of the total variation, followed by spindle speed (1.88%), depth of cut (0.33%), and coolant type (0.18%). The optimal machining parameters were determined as a spindle speed of 1700 rpm, feed rate of 0.1 mm/rev, depth of cut of 1.0 mm, and the use of synthetic coolant (GT41), resulting in a minimum surface roughness of 0.67 µm. These findings demonstrate that precise control of feed rate is critical for achieving improved surface quality in CNC turning of brass.

Keywords


CNC turning; surface roughness; Taguchi method; Brass 36000; machining parameter optimization

References


O. G. Ehibor and B. N. G. Aliemeke, “Optimization of Process Parameters of Surface Roughness and Material Re-moval Rate in Orthogonal Turning of AISI 1045 Carbon Steel Using Taguchi Technique,” Industrial Engineering Letters, vol. 11, no. 1, pp. 16–25, May 2021, doi: 10.7176/IEL/11-1-03.

T. Ghosh, Y. Wang, K. Martinsen, and K. Wang, “A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105,” The International Journal of Advanced Manufacturing Technology, vol. 111, no. 9–10, pp. 2419–2439, Dec. 2020, doi: 10.1007/s00170-020-06209-6.

A. I. Alateyah, Y. El-Taybany, S. El-Sanabary, W. H. El-Garaihy, and H. Kouta, “Experimental Investigation and Optimization of Turning Polymers Using RSM, GA, Hybrid FFD-GA, and MOGA Methods,” Polymers (Basel), vol. 14, no. 17, p. 3585, Aug. 2022, doi: 10.3390/polym14173585.

R. Surendran and A. Kumaravel, “Assessment of machinability behaviour of LM24 – nano Al 2 O 3 – gr hybrid composites through stir casting technique,” Mater Res Express, vol. 11, no. 3, p. 036525, Mar. 2024, doi: 10.1088/2053-1591/ad30a9.

V. R. Pathapalli, S. R. P, V. R. Basam, and M. K. Doni, “Multi Response Optimization of Turning Process by Consid-ering its Cutting Parameters Implementing Grey Relational Analysis,” International Journal of Integrated Engi-neering, vol. 11, no. 8, pp. 110–118, Dec. 2019, doi: 10.30880/ijie.2019.11.08.011.

S. Abdulkareem, U. J. Rumah, and A. Adaokoma, “Optimizing Machining Parameters during Turning Process,” International Journal of Integrated Engineering, vol. 3, no. 1, pp. 23–27, Sep. 2011, Accessed: Jan. 11, 2026. [Online]. Available: https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/116

M. H. Helmi, M. Azuddin, and W. Abdullah, “Investigation of Surface Roughness and Material Removal Rate (MRR) on Tool Steel Using Brass and Copper Electrode for Electrical Discharge Grinding (EDG) Process,” International Journal of Integrated Engineering, vol. 1, no. 1, 2009, Accessed: Jan. 11, 2026. [Online]. Available: https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/74

Cong Chi Tran, Van Tuan Luu, Van Tuu Nguyen, Van Tung Tran, Van Tuong Tran, and Huy Dai Vu, “Mul-ti-objective Optimization of CNC Milling Parameters of 7075 Aluminium Alloy Using Response Surface Methodol-ogy,” Jordan Journal of Mechanical and Industrial Engineering, vol. 17, no. 03, pp. 393–402, Sep. 2023, doi: 10.59038/jjmie/170308.

A. Kumar, C. Upadhyay, and S. Shashikant, “Experimental investigation on WEDM performance analysis using grey-fuzzy integrated with TLBO algorithm for Inconel 625: comparison with GA and SA,” World Journal of Engi-neering, vol. 18, no. 5, pp. 720–733, Sep. 2021, doi: 10.1108/WJE-12-2020-0643.

S. J. Juliyana et al., “Taguchi optimization of Wire EDM process parameters for machining LM5 aluminium alloy,” PLoS One, vol. 19, no. 10, p. e0308203, Oct. 2024, doi: 10.1371/journal.pone.0308203.

Neerav Nishant, N. Rathore, Vinay Kumar Nassa, V. K. Dwivedi, Thulasimani T, and Surrya Prakash Dillibabu, “Integrating machine learning and mathematical programming for efficient optimization of electric discharge machining technique,” The Scientific Temper, vol. 14, no. 03, pp. 859–863, Sep. 2023, doi: 10.58414/SCIENTIFICTEMPER.2023.14.3.46.

A.-T. Nguyen, V.-H. Nguyen, T.-T. Le, and N.-T. Nguyen, “Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II,” Advances in Materials Science and Engineering, vol. 2022, pp. 1–21, May 2022, doi: 10.1155/2022/5406570.

A. Indaka and B. Wahyudi, “Optimization of CNC milling parameters using the response surface method for alu-minum 6061,” Jurnal Polimesin, vol. 22, no. 3, p. 343, Jul. 2024, doi: 10.30811/jpl.v22i3.4909.

H. Javid et al., “Parametric analysis of turning HSLA steel under minimum quantity lubrication (MQL) and nanofluids-based minimum quantity lubrication (NF-MQL): a concept of one-step sustainable machining,” The In-ternational Journal of Advanced Manufacturing Technology, vol. 117, no. 5–6, pp. 1915–1934, Nov. 2021, doi: 10.1007/s00170-021-07776-y.

Rajhans Metals Private Limited, “C36000: Free Machining Brass.” Accessed: Jan. 11, 2026. [Online]. Available: https://www.rajhans.com/storage/alloy/ExVsSusOgPIjo30jFsNe.pdf

M. P. Groover, Fundamentals of modern manufacturing : materials, processes, and systems, Seventh edition. NJ: John Wiley & Sons, Inc., 2020. Accessed: Jan. 11, 2026. [Online]. Available: https://www.wiley.com/en-us/Fundamentals+of+Modern+Manufacturing%3A+Materials%2C+Processes%2C+and+Systems%2C+7th+Edition-p-9781119475217R150

Serope Kalpakjian and Steven R. Schmid, Manufacturing Engineering & Technology. Singapore: Pearson Education, 2015.

S. Tamang and M. Chandrasekaran, “Multi-objective Optimization of Turning Performance Characteristics using GA Coupled with AHP based Approach,” International Journal of Integrated Engineering, vol. 13, no. 6, pp. 126–136, Aug. 2021, doi: 10.30880/ijie.2021.13.06.012.

M. Nalbant, H. Gökkaya, and G. Sur, “Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning,” Mater Des, vol. 28, no. 4, pp. 1379–1385, Jan. 2007, doi: 10.1016/J.MATDES.2006.01.008.

Charan Shivesh, Kamaljit Singh, and Surjeet Singh, “An experimental investigation of material removal rate and surface roughness on naval brass (C360) by considering different parameters on CNC turning,” Int. J. Eng. Sci. Res. Technol., vol. 6, no. 7, pp. 35–43, Aug. 2017, doi: 10.5281/ZENODO.839111.

Y. Putra, Y. M. Putra, G. E. Timuda, N. Darsono, N. Chollacoop, and D. S. Khaerudini, “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, vol. 5, no. 2, pp. 56–62, Dec. 2023, doi: 10.22441/ijimeam.v5i2.21679.

Nur Izaaqila M Mazlan and Badaruddin Ibrahim, “Optimization of CNC Turning Parameters for Surface Rough-ness in Brass Using Response Surface Methodology (RSM),” Research and Innovation in Technical and Vocational Education and Training, vol. 5, no. 1, pp. 171–180, Jul. 2025, doi: 10.30880/ritvet.2025.05.01.017.

V. R. Pathapalli, S. R. P, V. R. Basam, and M. K. Doni, “Multi Response Optimization of Turning Process by Consid-ering its Cutting Parameters Implementing Grey Relational Analysis,” International Journal of Integrated Engi-neering, vol. 11, no. 8, pp. 110–118, Dec. 2019, doi: 10.30880/ijie.2019.11.08.011.




DOI: http://dx.doi.org/10.22441/ijimeam.v7i3.37302

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