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Investigation on the Effect of Tool Coating Thickness in Pocket Milling using Austenite Ss316

Affiliations

  • Department of Mechanical Engineering, SRM University, Kattankulathur - 603203, Tamil Nadu, India

Abstract


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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References


  • Gaurav S, Sargade VG. Comparative performance evaluation of uncoated and coated carbide inserts in dry end milling of stainless steel (SS316L). International Journal of Computer Applications (IJCA). 2011; 206:7–15
  • Soni HG, Patel KP. An Experimental Analysis of End Mill Cutter for AISI 316 using Regression. International Journal of Scientific Research and Development. 3(02):2321–0613.
  • Gopalsamy BM, Mondal B, Ghosh S. Optimization of machining parameters for hard machining: grey relational theory approach and ANOVA. International Journal of Advanced Manufacturing Technology. 2009; 45:1068–86.
  • Pinar AM. Optimization of Process Parameters with Minimum Surface Roughness in the Pocket Machining of AA5083 Aluminum Alloy via Taguchi Method. Arab Journal of Science and Engineering. 2013; 38:705–14
  • Gologlu C, Sakarya N. The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method. Journal of Materials Processing Technology. 2008; 206:7–15
  • Muthuramalingam T, Mohan B. Application of Taguchigrey multi-responses optimization on process parameters in electro erosion. Measurement. 2014; 58:495–502
  • Romero PE, Dorado R, Diaz FA, Rubio EM. Influence of pocket geometry and tool path strategy in pocket milling of UNS A96063 alloy. Procedia Engineering. 2013; 63:523–31.
  • Zhao W, Wang S, Han Z. Cutting Performance Evaluation of End Mills for Titanium Aircraft Components, CIRP. 2015; 35:1–7.
  • Rawangwong S, Chatthong J, Boonchouytan W, Burapa R. Influence of Cutting Parameters in Face Milling SemiSolid AA 7075 using Carbide Tool Affected the Surface Roughness and Tool Wear. Energy Procedia. 2014; 56:448– 57.
  • Chua MS, Rahman M, Wong YS, Loh HT. Determination of optimal cutting conditions using design of experiments and optimization techniques. International Journal of Machachine and Tools Manufacturing. 1993; 33:2297–305
  • Chahal M, Singh V, Garg R, Kumar S. Surface Roughness Optimization Techniques of CNC Milling: A Review. International Journal of Scientific Engineering Research. 2012 D; 3(12):2229–5518
  • Khan AR, Shahzad M, Ahmed MA. Quality Improvement of End Milled Slots using Titanium Nitride Coated Tools. Journal of Multidisciplinary Engineering Science and Technology. 2010; 2.
  • Zhang JZ, Chen JC, Kirby D. Surface roughness optimization in an end-milling operation using the Taguchi design method. Journal of Materials Processing Technology. 2007; 184:233–9.
  • Tsao CC. Grey–Taguchi method to optimize the milling parameters of aluminum alloy. International Journal of Advanced Manufacturing Technology. 2009; 4041–48
  • Lin TR. Optimisation Technique for Face Milling Stainless Steel with Multiple Performance Characteristics. International Journal of Advanced Manufacturing Technology. 2002; 19:330–5.
  • Rajyalakshmi G, Venkata Ramaiah P. Application of Taguchi, Fuzzy-Grey Relational Analysis for Process Parameters Optimization of WEDM on Inconel-825. Indian Journal of Science and Technology. 2015 Dec; 8(35),
  • Nair A, Govindan P, Ganeshan H. A comparison between different optimization techniques for CNC end milling process. Procedia Engineering. 2014; 97, 36–46.

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