This research investigates the intricate relationship between cutting parameterscutting speed, feed rate, and depth of cut and surface roughness in machining processes. Surface
roughness is a key determinant of machined surface quality, and optimizing cutting parameters is
crucial for achieving superior finishes. Employing advanced visualization techniques, including
contour plots and 3D surface profiles, the study offers a comprehensive exploration of surface
topography dynamics. Statistical analyses and regression modeling enhance the quantitative
understanding of how cutting parameters interact to shape surface roughness. The research affirms
the significant influence of cutting speed, feed rate, and depth of cut, providing practical insights for
industries seeking to balance efficiency and quality in manufacturing. This study contributes not only
to academic knowledge but also directly informs manufacturing practices. Practical guidelines
derived from the analysis offer actionable insights, and regression models provide predictive
capabilities for optimizing surface finishes under specific machining conditions. The integration of
theoretical insights and practical implications positions this research as a valuable resource for
researchers and practitioners in precision machining.
This research investigates the intricate relationship between cutting parameterscutting speed, feed rate, and depth of cut and surface roughness in machining processes. Surface
roughness is a key determinant of machined surface quality, and optimizing cutting parameters is
crucial for achieving superior finishes. Employing advanced visualization techniques, including
contour plots and 3D surface profiles, the study offers a comprehensive exploration of surface
topography dynamics. Statistical analyses and regression modeling enhance the quantitative
understanding of how cutting parameters interact to shape surface roughness. The research affirms
the significant influence of cutting speed, feed rate, and depth of cut, providing practical insights for
industries seeking to balance efficiency and quality in manufacturing. This study contributes not only
to academic knowledge but also directly informs manufacturing practices. Practical guidelines
derived from the analysis offer actionable insights, and regression models provide predictive
capabilities for optimizing surface finishes under specific machining conditions. The integration of
theoretical insights and practical implications positions this research as a valuable resource for
researchers and practitioners in precision machining.
| № | Author name | position | Name of organisation |
|---|---|---|---|
| 1 | Muxiddinov .N. | PhD, Associate Professor | Tashkent State Technical University, Tashkent cit |
| № | Name of reference |
|---|---|
| 1 | 1. He, C. L., Zong, W. J., & Zhang, J. J. (2018). Influencing factors and theoretical modeling methods of surface roughness in turning process: State-of-theart. International Journal of Machine Tools and Manufacture, 129, 15-26. 2. Motorcu, A. R. (2010). The optimization of machining parameters using the Taguchi method for surface roughness of AISI 8660 hardened alloy steel. Journal of mechanical Engineering, 56(6), 391- 401. 3. Shah, D., & Bhavsar, S. (2020). Effect of tool nose radius and machining parameters on cutting force, cutting temperature and surface roughness–an experimental study of Ti-6Al-4V (ELI). Materials Today: Proceedings, 22, 1977-1986. 4. Korkut, I., & Donertas, M. A. (2007). The influence of feed rate and cutting speed on the cutting forces, surface roughness and tool–chip contact length during face milling. Materials & design, 28(1), 308-312. |
| 2 | 5. Kiswanto, G., Zariatin, D. L., & Ko, T. J. (2014). The effect of spindle speed, feed-rate and machining time to the surface roughness and burr formation of Aluminum Alloy 1100 in micro-milling operation. Journal of Manufacturing Processes, 16(4), 435-450. 6. Strawn, N. (2022). Filament plots for data visualization. Applied and Computational Harmonic Analysis, 60, 205-241. 7. Parnin, C., & Rugaber, S. (2012, June). Programmer information needs after memory failure. In 2012 20th IEEE International Conference on Program Comprehension (ICPC) (pp. 123-132). IEEE. |
| 3 | 8. Brandt, L. E., & Freeman, W. T. (2021). Toward Automatic Interpretation of 3D Plots. In Document Analysis and Recognition–ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5–10, 2021, Proceedings, Part II 16 (pp. 35- 50). Springer International Publishing. 9. Yasir, M., Ginta, T. L., Ariwahjoedi, B., Alkali, A. U., & Danish, M. (2016). Effect of cutting speed and feed rate on surface roughness of AISI 316l SS using endmilling. ARPN Journal of Engineering and Applied Sciences, 11(4), 2496-2500. 10. Asiltürk, I., & Akkuş, H. (2011). Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method. Measurement, 44(9), 1697-1704. |
| 4 | 11. Özel, T., Hsu, T. K., & Zeren, E. (2005). Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened AISI H13 steel. The International Journal of Advanced Manufacturing Technology, 25, 262-269. 12. Kiswanto, G., Zariatin, D. L., & Ko, T. J. (2014). The effect of spindle speed, feed-rate and machining time to the surface roughness and burr formation of Aluminum Alloy 1100 in micro-milling operation. Journal of Manufacturing Processes, 16(4), 435-450. 13. Kurt, M., Bagci, E., & Kaynak, Y. (2009). Application of Taguchi methods in the optimization of cutting parameters for surface finish and hole diameter accuracy in dry drilling processes. The International Journal of Advanced Manufacturing Technology, 40, 458-469. |
| 5 | 14. Panjvani, K., Dinh, A. V., & Wahid, K. A. (2019). LiDARPheno–A low-cost lidar-based 3D scanning system for leaf morphological trait extraction. Frontiers in plant science, 10, 147. 15. Bar-Kochba, E., Toyjanova, J., Andrews, E., Kim, K. S., & Franck, C. (2015). A fast iterative digital volume correlation algorithm for large deformations. Experimental Mechanics, 55(1), 261-274. 16. Bruno, L., Fransos, D., Coste, N., & Bosco, A. (2010). 3D flow around a rectangular cylinder: a |
| 6 | computational study. Journal of Wind Engineering and Industrial Aerodynamics, 98(6-7), 263-276. 17. Ding, K., Teng, Q., Wang, Z., He, X., & Feng, J. (2018). Improved multipoint statistics method for reconstructing three-dimensional porous media from a two-dimensional image via porosity matching. Physical Review E, 97(6), 063304. 18. Teti, R., Jemielniak, K., O’Donnell, G., & Dornfeld, D. (2010). Advanced monitoring of machining operations. CIRP annals, 59(2), 717-739. 19. Bartos, M., Vida, I., Frotscher, M., Meyer, A., Monyer, H., Geiger, J. R., & Jonas, P. (2002). Fast |
| 7 | synaptic inhibition promotes synchronized gamma oscillations in hippocampal interneuron networks. Proceedings of the National Academy of Sciences, 99(20), 13222-13227. 20. Sharif, S., Safari, H., Izman, S., & Kurniawan, D. (2014). Effect of high speed dry end milling on surface roughness and cutting forces of Ti-6Al-4V ELI. Applied Mechanics and Materials, 493, 546-551. 21. Bhardwaj, B., Kumar, R., & Singh, P. K. (2014). Surface roughness (Ra) prediction model for turning of AISI 1019 steel using response surface methodology and Box–Cox transformation. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 228(2), 223-232 |