Experimental Analysis of TIG Welding of Stainless Steel 304 using Grey Taguchi Method

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1 Experimental Analysis of TIG Welding of Stainless Steel 304 using Grey Taguchi Method Surender Singh 1, Mandeep Singh 2, Vinod Kumar 3 1 M. Tech Scholar, 2 Assistant Prof. Dept. of M E OITM Juglan (Hisar), Haryana, India 3 Assistant Prof. Department of Mechanical Engineering, JMIETI Radaur (Yamunanagar) Haryana Abstract: TIG welding is one of the most widely used welding technique due to its versatility and ease that makes it suitable in almost any kind of working conditions. In this study, Taguchi s Design of Experiment approach is used to study the effect of welding process parameters on deposition rate and hardness of weld bead of SS304. Three input parameters Current, gas flow rate and number of passes are selected to ascertain their effects on the deposition rate and hardness of weld bead. The parameters are analyzed at three different levels. Optimal combination of parameters is obtained by using grey taguchi technique & minitab software. Keywords-- Stainless Steel (SS 304), Taguchi method, Grey Taguchi Method, Deposition rate, 1. INTRODUCTION Tungsten inert gas (TIG) welding is an arc-welding process that produces coalescence of metals by heating them with an arc between a nonconsumable tungsten electrode and the base metal. This process was originally developed for hard-toweld lightweight metals such as aluminum, magnesium and titanium. Many delicate components in aircraft and nuclear reactors are TIG welded and therefore TIG weld quality is of extreme importance. Basically, TIG weld quality is strongly characterized by the weld pool geometry; this is because the weld pool geometry plays an important role in determining the mechanical properties of the weld. TIG weld quality is strongly characterized by the weld pool geometry. This is because the weld pool geometry plays an important role in determining the mechanical properties of the weld. 2. LITERATURE REVIEW S.C. Juang and Y.S. Tarng (2002) presented the selection of process parameters for obtaining optimal weld pool geometry in the tungsten inert gas (TIG) welding of stainless steel. Taguchi method was adopted to analyze the effect of each welding process parameter on the weld pool geometry, and then to determine the process parameters with the optimal weld pool geometry. T. Senthil Kumar et al (2007) made investigation to study the influence of pulsed current TIG welding parameters on tensile properties of AA 6061 aluminium alloy weldments. M. Balasubramanian et al (2008) made investigation to develop mathematical models to predict tensile properties of pulsed current gas tungsten arc (GTA) welded titanium alloy weldments. The mathematical models were developed by response surface method (RSM). Shanping Lu et al (2009) systematically investigated the influences of argon and oxygen in helium base shielded gas tungsten arc (GTA) welding on the arc ignitability, bead protection and weld penetration by bead-on-plate welding on SUS304 stainless steel. Dheeraj Singh et al (2013) studied the optimum process parameters for Tungsten inert gas welding (TIG). The optimization of TIG welding operating parameters for stainless steel work piece using grey relation analysis method was done. Imran A. Shaikh and M. Veerabhadra Rao (2014) made investigation to study TIG welding parameters. In Grey relational analysis, an optimized process parameter of TIG welding was obtained, by analyzing grey relational grade we found the degree of influence of each parameter on quality target. Prashant et al (2015) studied the flux coated tungsten inert gas welding mainly focusing on increasing depth of penetration of weld and reducing width of weld bead. Er. Shipra kapoor and Er. Sanjeev Verma (2016) studied on the optimization of machining parameters in drilling process of EN-31 steel alloy by Taguchi based Grey relational analysis. 3. EXPERIENTATION The material used in this study was stainless steel (SS 304). The composition of the base material is shown in table 3.1. Table 3.1 Chemical Composition of SS304 Cr Ni C Mn Si P S N ISSN Page 51

2 Fig. 3.1Welded Samples Stainless Steel 304 Deposition The deposition rate of a welding consumable (electrode, wire or rod) is the rate at which weld metal is deposited (melted) onto a metal surface. Deposition rate is expressed in kilograms per hour (kg/hr). Deposition rates are calculated by the following mathematical formula (1) The hardness of steel is generally determined by testing its resistance to deformation. Rockwell hardness testing machine has been used to measure the hardness with C scales. Fig. 3.3 TIG Welding Machine 4. TAGUCHI TECHNIQUE Taguchi technique is a powerful design of experiment tool for acquiring the data in a controlled way & to analyze influence of process variable over some specific variable which is unknown function of these process variables and for the design of high quality systems. Taguchi creates a standard orthogonal array to accommodate the effect of several factors on the target value and defines the plan of experiment. The experimental results are analyzed using analysis of means and variance to study the influence of parameters. But Taguchi wanted to find a useful way of representing them statistically. Larger the better condition is chosen for deposition rate and hardness. 4.1GREY TAGUCHI TECHNIQUE Grey data processing must be performed before calculating the grey correlation coefficients. In this study, a linear normalization of the experimental results for effective deposition rate and hardness is performed in the range of 0 and 1, which is also called the grey relational generating. Hence target value of original sequence is larger is better for both deposition rate and hardness so original sequence ( ) is normalized as, (0 1) by the following formula to avoid the effect of adopting different units and to reduce the variability. (2) Fig. 3.2 Rockwell Testing Machine Welding Machine Semiautomatic TIGs equipment with direct current electrode negative, power source with a capacity of 300amp was used to join the stainless steel plates (SS304) of size 1000mm 48mm 5mm. The rated output current range is A and rated output voltage is 22 V. frequency of the machine is 50/60 Hz and duty cycle is 60%. Efficiency is 85% and power factor is Where ; is number of experimental data items & k ; is number of responses. The grey relational coefficient is calculated to express the relationship between the ideal and actual normalized experimental results. The grey relational coefficient can be expressed as, Where (3) ISSN Page 52

3 The parameters are equally weighted and therefore ξ is 0.5.The grey relational grade is determined by averaging the grey relational coefficient corresponding to each performance characteristic. The overall performance characteristic of multiple response process depends on the calculated grey relational grade. This approach converts a multiple response process optimization problem into a single response optimization situation with the objective function of an overall grey relational grade. The higher grey relational grade reveals that the corresponding experimental result is closer to the ideally normalized value. 5. RESULTS AND DISCUSSION Experimental values of deposition rate & hardness are processed by using design of experiment, taguchi method in minitab. Then results are analysed by grey taguchi technique and overall grey relational grade is obtained. Table: 5.1 Standard L9 Orthogonal Array Current(amp) Factors Gas flow rate(lt./min) of passes Table: 5.2 Experimental Results for Deposition and C u r r e n t Amp (A) (HRC) Gas Flow Liter/min (B) No of Passes (C) Deposition (kg/hr) Table: 5.3 Grey relational Coefficients of Experimental data of Deposition Deposition Norm. Value GRC Data Value Table: 5.4 Grey relational Coefficients of Experimental data of Norm. Value GRC Data Value Overall Grey Relational Grade & S/N ratio Grey rational grade (GRG) value is obtained by average value of Grey Relational Coefficient for Deposition and. S/N ratio obtained by using Grey Taguchi Technique is used. Here the term SIGNAL represent the desirable value (mean) and NOISE represent the undesirable value. Table: 5.5 S/N Ratio Table for Overall Grey Relational Grade ISSN Page 53

4 Table: 5.6 Response Table for Means of GRG Number Gas flow Level Current(A) of rate(b) passes(c) Delta Rank Total mean grey relational grade = ANALYSIS OF VARIANCE (ANOVA) Analysis of variance technique is used to test the adequacy of the model. The purpose of Analysis of variance is to investigate which control parameter significantly affects the effect on performance characteristics. Table: 5.5 Analysis of Variance of GRG Source DF Seq. SS Adj. SS Adj. MS F P % GRC GRC Overall S/N Deposition GRG Ratio Current Gas flow rate of passes Residual Error Total Figure 5.2: Main Effect Plot for S/N Ratio The percent contribution of these parameters (Current, gas flow rate, number of passes) on response variable (deposition rate and hardness) is 7.79, and respectively. The order of significance is gas flow rate, number of passes & current, on the basis of the observed p value, which is less than the significance level (0.05) for one of these parameters which is limiting condition. In Grey Taguchi method only significant performance parameter is required grey relational grade which established & effect whole result of this study. A 2 B 3 C 2 has been identified as the optimal input parametric setting for deposition rate and hardness obtained from response table for mean and main effect plot analysis. A 2 = 120 Amp B 3 =15 liter/min C 2 = 2 A=Current, B=Gas flow rate, C=Number of passes. Main aim of study should be to achieve such optimal contribution of control parameters to get highest grey relational grade for maximum value of deposition rate and hardness. The optimal value of grey relational grade is calculated as Figure 5.1: Main Effect Plot for Means Where η m is the total mean Grey relational grade, i is the mean Grey relational grade at optimal level and O is the number of main design process ISSN Page 54

5 parameters that significantly affect the deposition rate and hardness. The optimal value of grey relational grade, deposition rate and hardness for SS304 using TIG welding by Grey Taguchi method is , 0.585kg/hr and HRC respectively. 6. CONCLUSION Gas flow rate and Number of passes affect the deposition rate and hardness significantly while welding the stainless steel 304 on TIG welding machine. Gas flow rate is the most significant 1. S.C. Juang and Y.S. Tarng, (2002) Process parameter selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel, Journal of Materials Processing Technology, Volume 122, Page T Senthil Kumar, V. Balasubramanianand and M.Y Sanavullah, (2007) Influences of pulsed current tungsten inert gas welding parameters on the tensile properties of AA 6061 aluminium alloy, Journal of Materials & Design, Volume 28, Page M. Balasubramanian, V. Jayabalan and V. Balasubramanian, (2008) Developing mathematical models to predict tensile properties of pulsed current gas tungsten arc welded Ti 6Al 4V alloy, Journal of Materials and Design, Volume 29, Page Shanping Lu, Hidetoshi Fujii and Kiyoshi Nogi, (2009) Arc ignitability, bead protection and weld shape variations for He Ar O2 shielded GTA welding on SS304 stainless steel, Journal of materials processing technology, Volume 209, Page Dheeraj Singh, Vedansh Chaturvedi and Jyoti Vimal, (2013) Parametric optimization of TIG process parameters using Taguchi and Grey Taguchi analysis, International Journal of Emerging Trends in Engineering and Development, Volume 4, Page Imran A. Shaikh and M. Veerabhadra Rao, (2015) A Review on Optimizing Process Parameters for TIG Welding using Taguchi Method & Grey Relational Analysis, Volume 4, Page Er. Shipra kapoor and Er. Sanjeev Verma (2016) Optimization of Machining Parameters in Drilling of EN-31 Steel Alloy by Taguchi Based Grey Relational Analysis International Journal of Advance Engineering and Research Development, Volume 3, Page. 8. Prashant S Lugade and Manish J Deshmukh, (2015) Optimization of Process Parameters of Activated Tungsten Inert Gas (A-TIG) Welding for Stainless Steel 304L using Taguchi Method International Journal of parameter affecting deposition rate and hardness, followed by Number of passes and current. The contributions of gas flow rate, Number of passes and current towards the variation in deposition rate and hardness have been found to be 79.53%, 11.32% and 7.79% respectively. A 2 B 3 C 2 has been identified as the optimal input parametric setting for deposition rate and hardness obtained from response table for means and main effect plot analysis. A 2 = 120 Amp B 3 =15 liter/min C 2 = 2 A= Current, B = Gas flow rate, C = Number of passes. REFERENCES Engineering Research and General Science, Volume 3, Page ISSN Page 55