Multi-response Optimization using TGRA for End Milling of AISI H11 Steel Alloy Using Carbide End Mill

The AISI H11 steel is an important material used for making tools & dies. Machining is a very important activity in manufacture of tools & dies where the surface finish and metal removal rate play a very vital role. This paper presents the influence of the cutting speed, feed rate and depth of cut in end milling onto the surface roughness (SR) and metal removal rate (MRR). The machining experiments have been carried out on CNC vertical milling machine. Taguchi grey relational analysis (TGRA) with standard L27 orthogonal array has been selected to investigate the connection for studying surface roughness and metal removal rate (MRR). Both the responses viz. surface roughness and material removal rate are assumed to have equal weightage (W1 = W2 = 0.5) considering general machining conditions. The model significance tests have been conducted using ANOVA to find out which factors are statistically significant. The percentage contribution of cutting speed, feed rate and depth of cut are 29.13 %, 40.93 % and 17.4 % respectively. Optimization has been carried out to get optimum combination of SR and MRR.

[1]  A. Haq,et al.  Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method , 2008 .

[2]  P. Sahoo,et al.  Tribological testing and optimisation of electroless Ni‐P coatings based on Taguchi method and grey relational analysis , 2008 .

[3]  George-Christopher Vosniakos,et al.  Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments , 2002 .

[4]  Suhas S. Joshi,et al.  Multi-objective optimization of surface roughness and cutting forces in high-speed turning of Inconel 718 using Taguchi grey relational analysis (TGRA) , 2011 .

[5]  Suha K. Shihab,et al.  RSM based Study of Cutting Temperature During Hard Turning with Multilayer Coated Carbide Insert , 2014 .

[6]  Ahmed A. D. Sarhan,et al.  Optimizing the cutting parameters for better surface quality in 2.5D cutting utilizing titanium coated carbide ball end mill , 2012 .

[7]  R. Jeyapaul,et al.  Multi Objective Optimization in Turning of EN25 Steel Using Taguchi Based Utility Concept Coupled With Principal Component Analysis , 2014 .

[8]  N. C. Hwang,et al.  Grey relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling , 2009 .

[9]  M. V. Kini,et al.  Effect of machining parameters on surface roughness and material removal rate in finish turning of ±30° glass fibre reinforced polymer pipes , 2010 .

[10]  D. Chakradhar,et al.  Performance improvement of cryogenic turning process during machining of 17-4 PH stainless steel using multi objective optimization techniques , 2019, Measurement.

[11]  Saurav Datta,et al.  Parametric optimization of CNC end milling using entropy measurement technique combined with grey-Taguchi method , 2010 .

[12]  T. Senthilvelan,et al.  Multi-response optimization of machining parameters in hot turning using grey analysis , 2011 .

[13]  Nihat Tosun,et al.  Gray relational analysis of performance characteristics in MQL milling of 7075 Al alloy , 2010 .

[14]  Junxue Ren,et al.  Optimization of Cutter Geometric Parameters in End Milling of Titanium Alloy Using the Grey-Taguchi Method , 2015 .

[15]  G. Abdou,et al.  Analysis of force patterns and tool life in milling operations , 1995 .

[16]  Ranjit K. Roy,et al.  Design of Experiments Using The Taguchi Approach: 16 Steps to Product and Process Improvement , 2001 .

[17]  Lohithaksha M. Maiyar,et al.  Optimization of Machining Parameters for end Milling of Inconel 718 Super Alloy Using Taguchi based Grey Relational Analysis , 2013 .

[18]  I. Jawahir,et al.  Tribological behavior of PCD tools during superfinishing turning of the Ti6Al4V alloy using cryogenic, hybrid and flood as lubri-coolant environments , 2017 .

[19]  Satnam Singh,et al.  Optimization of mechanical properties of polyester hybrid composite laminate using Taguchi methodology – Part 1 , 2015 .

[20]  R. N. Rai,et al.  Optimization of machining process parameters in conventional turning operation of Al5083/B4C composite under dry condition , 2018 .

[21]  N. Sivakumaran,et al.  Multi-objective optimisation of CNC turning process using grey based fuzzy logic , 2009 .

[22]  Kalyanmoy Deb,et al.  Multi-objective Optimization , 2014 .

[23]  Fu-Chen Chen,et al.  Multi-objective optimisation of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach , 2007 .

[24]  İlhan Asiltürk,et al.  Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods , 2016 .

[25]  Türkay Dereli,et al.  Dynamic optimization of multipass milling operations via geometric programming , 1999 .

[26]  Tzeng Yih-Fong,et al.  Dimensional quality optimisation of high-speed CNC milling process with dynamic quality characteristic , 2005 .

[27]  Rajesh Kumar,et al.  Surface roughness (Ra) prediction model for turning of AISI 1019 steel using response surface methodology and Box–Cox transformation , 2014 .

[28]  Rajesh Kumar,et al.  Effect of machining parameters on surface roughness in end milling of AISI 1019 steel , 2014 .

[29]  Rajesh Kumar,et al.  An improved surface roughness prediction model using Box-Cox transformation with RSM in end milling of EN 353 , 2014 .

[30]  B. Mondal,et al.  TAGUCHI METHOD AND ANOVA: AN APPROACH FOR PROCESS PARAMETERS OPTIMIZATION OF HARD MACHINING WHILE MACHINING HARDENED STEEL , 2009 .