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.
| № | Имя автора | Должность | Наименование организации |
|---|---|---|---|
| 1 | Muxiddinov .N. | PhD, Associate Professor | Tashkent State Technical University, Tashkent cit |
| № | Название ссылки |
|---|---|
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| 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 |