Date of Publication :8th May 2024

Abstract:The manufacturing industry employs various metal cutting processes for part machining, with end milling being a prevalent method. Surface roughness significantly impacts the quality of machined parts, influencing properties such as wear resistance, ductility, tensile strength, and fatigue strength. This dissertation investigates the influence of cutting process parameters—cutting speed, feed rate, and depth of cut—on response variables including cutting force, surface roughness, and material removal rate (MRR). The objective is to optimize these parameters to achieve lower cutting force, reduced surface roughness, and higher MRR. Experiments were conducted on 316L stainless steel using a CNC milling machine equipped with TiSiN-coated solid carbide end mills. The Taguchi L9 design and ANOVA methods were utilized to identify the optimal parameter settings. Grey Relation Analysis (GRA) was also applied to evaluate the multiple performance characteristics and determine the optimal combination of parameters. Analysis of Variance (ANOVA) determined the significant influence of each parameter on the response variables. The findings indicate that depth of cut predominantly affects cutting force and surface roughness, while both depth of cut and feed rate significantly impact MRR. Optimal parameter settings are recommended based on varying priorities of MRR and surface roughness. This study provides valuable insights for enhancing machining efficiency and quality in industrial applications of 316L stainless steel.

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