Toolpath Motion Strategy and Feed Rate in CNC Milling on Energy Consumption of Machining Process

Luqman Dwi Saputra, Eko Yudiyanto

Abstract


The use of CNC milling machines to produce components, especially aluminum brackets used for automotive, is one of the advances in the industrial field. The use of CNC milling machines has the advantage of producing processes with speed accuracy, and better workpiece quality than conventional machines. This research investigates energy consumption in the CNC milling process by varying the toolpath motion strategies—Zigzag, Constant Overlap Spiral, Parallel Spiral, and Parallel Spiral Clean Corners—as well as feed rates of 700 mm/min, 800 mm/min, 900 mm/min, and 1000 mm/min. The goal is to find out the best parameters for using energy in the machining process. The material used in this research is Aluminum 6061. The shape tested is a bracket. The simulation was conducted to determine the machining process time using Mastercam software. The simulation results indicate that the Zigzag toolpath motion strategy at a feed rate of 1000 mm/min produces the lowest energy consumption (307.620 Kilojoules) whereas the Parallel Spiral Clean Corners toolpath at a feed rate of 700 mm/min produce the highest energy consumption (457.142 Kilojoules). The selection of appropriate machining parameters has a significant influence on the efficiency of processing time and production costs. By selecting the right toolpath motion strategy and feeding parameters, the manufacturing industry can increase productivity and reduce production costs more effectively.

Keywords


CNC milling, energy consumption, feed rate, machining time, simulation, toolpath motion.

Full Text:

PDF

References


A. Pajaziti, O. Tafilaj, A. Gjelaj, and B. Berisha, “Optimization of toolpath planning and CNC machine performance in time-efficient machining,” Machines, vol. 13, no. 1, pp. 1–20, Jan. 2025, doi: 10.3390/machines13010065.

B. Kasim, A. Yunus, I. Yusuf, Mawardi, and Darmein, “Optimization of CNC machining parameters to improve surface roughness quality of the AL6061 material using the Taguchi method,” Jurnal Polimesin, vol. 21, no. 4, pp. 408–413, 2023, [Online]. Available: http://e-jurnal.pnl.ac.id/polimesin

W. Sumbodo, Kriswanto, Murdani, I. Suwanda, and T.S. Allam, “Optimization of CNC milling machining time through variation of machine parameters and toolpath strategy in various cross-sectional shape on tool steels and die steels materials,” in Proceedings of the 7th Engineering International Conference on Education, Scitepress, Apr. 2020, pp. 84–92. doi: 10.5220/0009006800840092.

A. Ambroziak, M.T. Solarczyk, and A. Biegus, “Numerical and analytical investigation of alumunium bracket strengthening,” Archives of Civil Engineering, vol. 64, no. 2, pp. 37–54, 2018, doi: 10.2478/ace-2018-0015.

R. Raharjo, T.D. Widodo, and R. Bintarto, “Desain manufaktur bracket alumunium,” Jurnal Rekayasa Mesin, vol. 9, no. 2, pp. 119–125, 2018, doi: 10.21776/ub.jrm.2018.009.02.8.

W.D. Lestari, A.T. Danaryanto, A. Nugroho, R. Ismail, J. Jamari, and A.P. Bayuseno, “Wear property of machined Ultra High Molecular Weight Polyethylene (UHMWPE) acetabular liner product with CNC milling,” Journal of Mechanical Engineering Science and Technology (JMEST), vol. 5, no. 2, pp. 110–122, 2021, doi: 10.17977/um016v5i22021p110.

C. Camposeco-Negrete and J. de Dios Calderón-Nájera, “Optimization of energy consumption and surface roughness in slot milling of AISI 6061 T6 using the Response Surface Method,” International Journal of Advanced Manufacturing Technology, vol. 103, no. 9–12, pp. 4063–4069, Aug. 2019, doi: 10.1007/s00170-019-03848-2.

R. Prasetya, A. Andoko, S. Suprayitno, R. Wulandari, P. Trihutomo, K. Mishima et al., “Simulation of the performance of Kevlar impregnated shear thickening fluid ballistic test results,” Journal of Mechanical Engineering Science and Technology (JMEST), vol. 8, no. 1, pp. 54–70, May 2024, doi: 10.17977/um016v8i12024p054.

Y. Novrialdy, K. Arwizet, A. Yufrizal, and F. Prasetya, “Pengaruh variasi feed rate terhadap kekasaran permukaan polyethylene menggunakan mesin CNC milling,” Jurnal Vokasi Mekanika, vol. 3, no. 2, pp. 25–33, 2021, [Online]. Available: http://vomek.ppj.unp.ac.id

H.B. Harja, E. Suherlan, N. Rusmana, and D.K. Nugraha, “Experimental study of geometric error of CNC turning machine tools based on ISO 13041-6,” Jurnal Polimesin, vol. 21, no. 4, pp. 395–402, 2023, [Online]. Available: http://e-jurnal.pnl.ac.id/polimesin

R. Prajapati, A. Rajurkar, and V. Chaudhary, “Tool path optimization of contouring operation and machining strategies for turbo machinery blades,” International Journal of Engineering Trends and Technology (IJETT), vol. 4, no. 5, pp. 1731–1737, 2013, [Online]. Available: http://www.ijettjournal.org

K.A. Shamsuddin, A.R. Ab-Kadir, and M.H. Osman, “A Comparison of milling cutting path strategies for thin-walled aluminium alloys fabrication,” The International Journal Of Engineering And Science (IJES), vol. 2, no. 3, pp. 01–08, 2013, [Online]. Available: www.theijes.com

A. Indaka and B. Wahyudi, “Optimization of CNC milling parameters using The Response Surface Method for Aluminum 6061,” Jurnal Polimesin, vol. 22, no. 3, 2024, [Online]. Available: http://e-jurnal.pnl.ac.id/polimesin

G. Musca, A. Mihalache, and L. Tabacaru, “Increase productivity and cost optimization in CNC manufacturing,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Dec. 2016, pp. 1–6. doi: 10.1088/1757-899X/161/1/012019.

S. Daneshmand, M. Mirabdolhosayni, and C. Aghanajafi, “Sifting through the optimal strategies of time-based tools path machining in software CAD-CAM,” Middle East J Sci Res, vol. 13, no. 7, pp. 844–849, 2013, doi: 10.5829/idosi.mejsr.2013.13.7.2658.

R.N. Amrullah, S. Hadi, and M.A. Rizza, “Simulation-based methodology to investigate the impact of material type and compressive speed variation on effective strain rate and springback,” Journal of Mechanical Engineering Science and Technology (JMEST), vol. 8, no. 2, p. 229, Sep. 2024, doi: 10.17977/um016v8i22024p229.

H.S. Haryadi, P. Moengin, and P. Astuti, “Designing system production to increase production capacity using simulation methods,” Jurnal Teknik Industri, vol. 13, no. 3, pp. 223–230, 2023, Accessed: Apr. 19, 2025. [Online]. Available: https://e-journal.trisakti.ac.id/index.php/tekin/article/view/19144

G. Ramavat, O. Beedalannagari, S. Patil, F. Romero, F. Ajila, A. Singhal, et al., “A Comprehensive review on optimization of process variables for CNC milling,” Nano World Journal, vol. 9, no. 3, pp. 786–791, Nov. 2023, doi: 10.17756/nwj.2023-s3-138.

H. Shagwira, T.O. Mbuya, F.M. Mwema, M. Herzog, and E.T. Akinlabi, “Taguchi optimization of surface roughness and material removal rate in cnc milling of polypropylene + 5wt.% quarry dust composites,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing, Apr. 2021, pp. 1–9. doi: 10.1088/1757-899x/1107/1/012040.

R. Suryadi, D. Riana, and Kangen, “Pengaruh parameter proses CNC milling terhadap surface roughness dan toleransi bidang pada inlet outer valve,” Jurnal Teknik Mesin, vol. 6, no. 2, pp. 53–62, 2022, Accessed: Apr. 19, 2025. [Online]. Available: http://repository.iti.ac.id/handle/123456789/2096

L.D. Saputra and E. Yudiyanto, “Analisis performa mesin CNC milling mini 3 sumbu terhadap akurasi gerak pemotongan,” Journal of Mechanical Engineering, vol. 1, no. 3, pp. 1–11, 2024, [Online]. Available: https://journal.pubmedia.id/index.php/jme

I. Sztankovics, “The analytical and experimental analysis of the machined surface roughness in high-feed tangential turning,” MDPI (Eng), vol. 5, pp. 1768–1784, Aug. 2024, doi: 10.3390/eng5030093.

W. Sumbodo, Kriswanto, and J. Jamari, “Simulation and optimization of machining time during milling AISI P20 steel,” in IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd, Mar. 2021, pp. 1–8. doi: 10.1088/1755-1315/700/1/012002.




DOI: http://dx.doi.org/10.17977/um016v9i12025p114

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Journal of Mechanical Engineering Science and Technology (JMEST)

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

View My Stats