The objective of the current study is to enhance the overall process efficiency throughout the milling process and to improve the accuracy of the input parameters for better product quality during the high-speed milling process of 16MnCr5. A series of cutting tests were undertaken while carrying out this work in an attempt to optimize the final surface finish quality and to discover the fundamental relationship between the cutting constraints and surface roughness, as well as obtaining the surface roughness values. To begin with, the processing parameters that significantly influenced the surface roughness values were supplied by employing the analysis of variance (ANOVA) of factorial trials. The mathematical simulations of surface roughness, which were reliant on cutting factors, were generated with the help of partial least squares regression. The experiments are devised as well as conducted to verify the suggested prediction model's accuracy. The results of the research study suggested that the depth of cut was a better contributor and more sensitive to the response values than the other factors for the determination of both Surface Roughness and Material Removal Rate.

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