Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 29, Pages: 1-8
J. Santhakumar* , T. Rajasekaran and Einstein Johnson
Objective: This paper presents the research work done in Pocket milling operation using Stainless steel 316 materials. The most critical process parameters namely, Coating thickness (µm), Depth of Cut [DOC] (mm), Spindle Speed [SS] (rev/ min) and Feed Rate (mm/min) are taken into consideration to improve the quality of the output characteristics. The required output characteristics are Cycle time (min), Surface quality measurement, i.e., Roughness of the surface (Ra ), Wear of the Tool (mm), Microhardness (HV) and Material Removal Rate (mm3 /min). Methods/Analysis: The Taguchi method was used to tabulate the Design of experiments (DOE) of L9 orthogonal array. According to the study, the required Material Removal Rate is considered ‘higher the better’ and other required output characteristics are ‘lower the better.’ To determine to a most significant process parameter, Taguchi’s grey relation analysis is used. To determine the significance and shift of each parameter to the total variation observed, Signal to Noise Ratio (S/N) was used. The contributions of each process parameters to obtain the required output characteristics are studied. Findings: From the grey relation grade, the highest maximum – minimum value shows that the depth of cut is the most influential input parameter for the obtained output characteristics. The optimum input parameters are tool coating thickness of 10 µm, the speed of the spindle being 2500 rev/min, feed rate being 1000 mm/min and depth of cut being 0.7 mm. A verification test was performed conducive to determine the veritableness of the obtained optimal input parameters. The test proves that the optimum input process variables satisfy the required output characteristics for pocket milling operation in AISI Stainless Steel 316 material.
Keywords: Coating Thickness, Design of Experiments (DOE), Grey Relational Analysis, Material Removal Rate (MRR), Signal to Noise Ratio
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