Optimization of CNC Machining Parameters for Improved Surface Finish

  • Authors

    • Michael Anderson University of Pinar del Río, Cuba. Author
    • Fami Sarah University of Pinar del Río, Cuba. Author

    Published 2026-01-02

  • CNC Machining, Surface Roughness, Process Optimization, Taguchi Method, ANOVA, Machining Parameters

    Issue

    Vol. 1 No. 1 (2026)

    Section

    Articles

    How to Cite

    Optimization of CNC Machining Parameters for Improved Surface Finish. (2026). International Journal of Modern Research in Science & Engineering, 1(1), 13-25. https://worldcometresearchgroup.com/index.php/ijmrse/article/view/54

  • Abstract

    Surface finish Surface finish is one of the most important quality traits in Computer Numerical Control (CNC) machining, which has a direct effect on functional performance, fatigue life, corrosion resistance and aesthetic values of machined parts. In current manufacturing businesses like aerospace, automobile, biomedical and precision engineering, getting high-quality surface finish without compromising productivity and cost is still a significant challenge. Many process parameters such as cutting speed, feed rate, depth of cut, tool geometry, and coolant conditions have a very strong influence on the performance of CNC machining. Unacceptable selection of these parameters usually results in low surface integrity, more wear on the tools and unnecessary machining time. In this paper, the work will provide an in-depth research on how CNC machining parameters can be optimized in order to enhance the level of surface finish. Some of the classical and modern methods of optimization reviewed by the study are the Taguchi method, Response Surface Methodology (RSM), Grey Relational Analysis (GRA), Genetic Algorithms (GA), and the combination of artificial intelligence (hybrid). The experimental methodology is offered, the parameters of which are designed, it is machined, measurement of surface roughness is conducted, and statistical analysis through the analysis of variance (ANOVA). Mathematical models that relate machining parameters and the surface roughness are elaborated and optimization strategies dwelled on. The findings show that the most controlling parameter that influences the roughness of the surface is the feed rate, the next is cutting speed and depth of cut. The parameter combinations which are optimized have a lot of reduction in surface roughness than conventional settings do. The results can be useful to manufacturing engineers and researchers who aim to promote the quality of surfaces, productivity, and sustainability of CNC machining processes.

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