Authors : Sekiteri Patience Sametu; Ogunobo Romanus; Umar Adam Isah

Volume/Issue : Volume 11 - 2026, Issue 9 - September

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Google Scholar : https://tinyurl.com/5cx4mwhk

DOI : https://doi.org/10.38124/ijisrt/26sep1393

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This research uses theoretical and machine learning approach to evaluate depth of cut. Comparison shows that when heat is high it increases axial stresses and percentage error increment value from 2.20 (µm) to predicted surface at 36.50 (µm).

Keywords : Artificial Intelligence, Axes Detection, Depth of Cut, CNC Processes.

References :

1. Cao, S., & Guan, J., (2018). Research on CNC machining process and programming technology of complex parts based on power mill. In 8th International Conference on Mechatronics, Computer and Education Informationization (MCEI) (pp. 309-312). Atlantis Press. https://doi.org/10.2991/mcei-18.2018.62
2. He, Y., Wu, P., Li, Y., Wang, Y., Tao, F., & Wang, Y. (2020). A generic energy prediction model of machine tools using deep learning algorithms. Applied Energy, 275, 115402. https://doi.org/10.1016/j.apenergy.2020.115402
3. Han, J., Jiang, Y., Tian, X. et al. (2018). A local smoothing interpolation method for short line segments to realize continuous motion of tool axis acceleration. Int J Adv Manuf Technol 95, 1729–1742 https://doi.org/10.1007/s00170-017-1264-3
4. Javidikia, M., Sadeghifar, M., Songmene, V., & Jahazi, M. (2020). On the impacts of tool geometry and cutting conditions in straight turning of aluminum alloys 6061-T6: An experimentally validated numerical study. The International Journal of Advanced Manufacturing Technology, 106(9), 4547-4565. https://doi.org/10.1007/s00170-020-04945-3
5. Li F., Liu J., Hu B., Jin H., (2023). Three-dimensional stability lobe diagram construction in milling process with considering multiple modes //Research scuare,, P. 1 - 15 https://doi.org/10.21203/rs.3.rs-2998903/v1
6. Liu, G., Liu, F., Liu, P., & Wu, P. (2016). On-line detection method and detection system for multi-source energy consumption status of machine tools. Comput. Integr. Manuf. Syst, 22, 1550-1557.
7. Okokpujie, I. P., Bolu, C. A., Ohunakin, O. S., Akinlabi, E. T., & Adelekan, D. S. (2019). A review of recent application of machining techniques, based on the phenomena of CNC machining operations. Procedia Manufacturing, 35, 1054-1060. https://doi.org/10.1016/j.promfg.2019.06.056
8. Shin, S. J., Woo, J., Rachuri, S., & Meilanitasari, P. (2018). Standard data-based predictive modeling for power consumption in turning machining. Sustainability, 10(3), 598. https://doi.org/10.3390/su10030598
9. Sihimbaev M.R., Sherov K.T., Zharkevich O.M., Alimbaev S.T., Mekhtiev A.D., (2014). The development and analysis of the machining attachments for the decrement of oscillations of a boring bar in the process of the refinement of high-accuracy apertures. Journal of Vibroengineering,16(2), pp, 1022–1032.
10. Wu C., Chen F., & Long, X., (2020) The self-excited vibration induced by friction of the shaft-hull coupled system with the water-lubricated rubber bearing and its stick-slip phenomenon //Ocean Engineering,198,107002.
11. Zhao, L., Fang, Y., Lou, P., Yan, J., & Xiao, A. (2021). Cutting parameter optimization for reducing carbon emissions using digital twin. International Journal of Precision Engineering and Manufacturing, 22(5), 933-949. https://doi.org/10.1007/s12541-021-00486-1
12. Zhong, Q., Tang, R., & Peng, T. (2017). Decision rules for energy consumption minimization during material removal process in turning. Journal of Cleaner Production, 140, 1819-1827. https://doi.org/10.1016/j.jclepro.2016.07.084