Effect of Process Parameters in MIG Welding on Mild Steel IS 2062

Transcription

1 Effect of Process Parameters in MIG Welding on Mild Steel IS 262 R.SUDARSHAN 1, Dr. M.Devaiah 2, 1, 2 Geethanjali College of Engineering and Technology, Cheeryal (V), Keesara (M), Medchal, Telangana, India. Corresponding Author Abstract The main objective of this paper is to predict and optimize MIG welding of some economically important similar materials or dissimilar materials in industry through applying a statistical approach to SPSS software, develop mathematical models and optimize the welding operation. This was achieved by controlling selected welding parameters; V-butt angles, welding current and welding voltage position, to relate the ultimate tensile strength to the selected input welding parameters. The materials studied in this work are Mild steel IS262. The experimental results which are obtain corresponding to the effect of different V-angles such as 3, 45 and 6 at, different welding current such as 8 Amp, Amp and 11 Amp and various welding voltage 17 Volt, 19 Volt and 2 Volt on ultimate tensile strength of welding of Mild Steel IS262, are used to find out the significance of input parameter on output by using SPSS and ANSYS method, static analysis and thermal analysis. This result shows better ultimate tensile strength prediction capability and applicability to such industrial MIG welding leading to effective selection of machining parameter for better ultimate tensile strength. Keywords: Ultimate tensile strength, Analysis of object (ANSYS), ling in Pro-E, Metal inert gas (MIG) welding, SPSS Software. INTRODUCTION Welding is a manufacturing process of creating a permanent joint obtained by the fusion of the surface of the parts to be joined together, with or without the application of pressure and a filler material. The materials to be joined may be similar or dissimilar to each other. The heat required for the fusion of the material may be obtained by burning of gas or by an electric arc. The latter method is more extensively used because of greater welding speed. Welding is extensively used in fabrication as an alternative method for casting or forging and as a replacement for bolted and riveted joints. It is also used as a repair medium e.g. to reunite a metal at a crack or to build up a small part that has broken off such as a gear tooth or to repair a worn surface such as a bearing surface. MonikaK.et al. [1], analyzed the Mechanical Properties of MIG Welded Dissimilar Joints under the effect of heat input. Welding current, voltage and speed of wire determines the heat input. The IS262, IS45C8, IS13Cr1 were used as a base material. 1.2 mm diameter copper coated mild steel was used as a filler wire. The both joints (IS262 & IS45C8) and (IS262 & IS13Cr1) increased the tensile strength when increased with the heat input and also increased the hardness value when decreased with the heat input. M.Aghakhani et al. [2] have done work on optimization of gas metal arc welding process parameter for increase quality and productivity of weldment. In this research work for increasing quality and productivity of weldment they have considered weld dilution as output parameter and effect of input parameter wire feed rate (W), welding voltage (V), nozzle-to-plate distance (N), welding speed (S)and gas flow rate (G)was found on it. The base material use for experiment is ST-37 steel plate and the mixture of 8% argon and 2% CO 2 is use as shielding gas. The experiment was designed by Taguchi's L25 orthogonal array and analysis was carried out by ANOVA method also they develop mathematical model for weld dilution. From the experimental result they found that the wire feed rate has the most significant effect on the weld dilution while gas flow rate has no effect on weld dilution. C. N. Patel et al. [3] evaluated the parameters; welding current, wire diameter and wire feed rate to investigate their influence on weld bead hardness for MIG welding and TIG welding by Taguchi s method and Grey Relational Analysis (GRA). From the study it was concluded that the welding current was most significant parameter for MIG and TIG welding. By use of GRA optimization technique the optimal parameter combination was found to be welding current, Amp; wire diameter 1.2 mm and wire feed rate, 3m/min for MIG welding. EXPERIMENTAL WORK The machine use for performing the experiment is AUTOK6 SYNERGIC, manufactured by ESAB India Limited in Gujarat Apollo Equipments Ltd. Work piece material From the literature survey of past researchers it is show that the material selection in manufacturing process is most important think as per process availability and customer s requirement. There is number of material used in modern 246

2 industry but steel have corrosion resistive property and IS 262 steel are shown in table 3.2 to 3.3 IS: 262 Specification of Structural Steel for Fabrication Chemical Composition of IS 262 steel Grade C % Mn % S % P % Si C. E. % Max. Max. Max. Max. Max. Max. A B C Mechanical Properties IS 262 steel Grade UTS (MPA) Y.S.(MPA) Min. EI. % Min Bend A Min. < 2 mm 2-4 mm > 4 mm 5.65 So Test B T C T & 3T* * 2T - Less than 25 mm. * 3T - More than 25 mm. Filler metal The filler material use for the experiment is copper coated MS material electrodes with size of 1.2 mm diameter. Shielding Gas A shielding gas mixture is selected for the experiments. It contains 2%CO2 and 8% Argon. Sample Preparation Mild steel IS 262 plates with the dimensions of 12 x x 8 mm are prepared with the bevel angle of 3, 45 and 6. These specimens are then welded with a root gap distance 1 mm. Figure shows the single V groove butt joint preparations. Material Grade Width Length Thickness Mild Steel IS262 mm 12 mm 8 mm Figure 1: Schematic view of samples used for testing Input Parameter Level and Value Parameter Level Gas (Psi) Pressure Current (Amp) (Volt) (A) (B) (C) (D) Parameter considered for experiment Input Parameters 1 Angles 3, 45 and 6 2 Current (Amp) 3 (V) Constant Parameters 1 Electrode size 2 Shielded Gas 3 Gas Pressure (Psi) Output Parameter 1 Ultimate strength (KN) 2 Yield strength (KN) Angles 247

3 UTM Machine frame analysis, and collaboration and deployment (batch and automated scoring services). SPSS is widely used program for statistical analysis in social science. It is also used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations, data miners, and others. The original SPSS manual has been described as one of sociology s most influential books for allowing ordinary researchers to do their own statistical analysis. In addition to statistical analysis, data management (case selection, file reshaping, creating derived data) and data documentation ( a metadata dictionary was stored in the data file) are features of the base software. Statistics included in the base software: Descriptive statistics: Cross tabulation, frequencies, descriptive, explore, descriptive ratio statistics Bi varieties statistics: Means, t-test, ANOVA, Correlation (bivariate, partial, distances), nonparametric tests Prediction for numerical outcomes: Linear regression Prediction for identifying Groups: Factor analysis, cluster analysis (two-step, k-means, hierarchical), and discriminate Figure 2: Schematic view of UTM and tensile test SPSS SOFTWARE: The SPSS software originally stood for statistical package for the social sciences, reflecting the original market, although the software is now popular in other fields as well, including the health sciences and marketing. SPSS STATISTICS is a software package used for statistical analysis. Long produced by SPSS inc., it was acquired by IBM in 29. The current versions (215) are officially named IBM SPSS STATISTICS. Companion products of same family are used for survey authoring and deployment (IBM SPSS DATA COLLECTION), data mining (IBM SPSS MODELER), text RESULTS AND DISCUSSIONS Mid welded steel IS262 samples which are prepared with different V-angles such as 3, 45 and 6 with variable current 248

4 and voltage by using Universal Testing Machine (UTM) model UTS -. The test results such as Ultimate Strength and Yield Strength values are listed in table 1 below S. No Sample Table. 1: Ultimate Strength and Yield Strength values at different angle, voltage and current Gas pressure V used Angle Current (A) (V) Yield Strength Ultimate Strength The test results of yield strength with different parameters such as V-angle, current and voltage are represented in the bar charts as shown in figure below 2 Yield Strength V-Angle Yield Strength Current 249

5 2 Yield Strength Figure 3: Test results of Yield strength with different V-angle, current and voltage The test results of ultimate strength with different parameters such as V-angle, current and voltage are represented in the bar charts as shown in figure below Ultimate Strength V- Angle Ultimate Strength Current 2

6 Ultimate Strenth Figure 4 Test results of Ultimate Strength with different V-angle, current and voltage From figure 3 and 4 shows that the Ultimate Strength and Tensile Strength variation with respect to V-Angle, Current and voltage. Summary Regression R R Square Adjusted R Square a a. Predictors: (Constant), angles, voltage Std. Error of the Estimate MODEL: SPSS allows you to specify multiple models in a single regression command. This tells you the number of model being reported R: R is the square root of R-square and is the correlation between the observed and predicted values of dependent variable. R-SQUARE: This is the proportional of variance in the dependent variable (yield) which can be explained by the independent variables (angles, voltage). This is an overall measure of the strength of association and does not reflect the extent to which any particular independent variable is associated with the dependent variable. ADJUSTED R-SQUARE: This is an adjustment of the R- squared that penalizes the addition of extraneous predictors to the model. Adjusted R-squared is computed using the formula 1-((1-R sq) (N-1) / (N-K-1)), Where K is the number of predictors. STD.ERROR OF THE ESTIMATE: This is also referred to as the root mean squared error. It is the standard deviation of the error term and the square root of the mean square for the residual in the ANOVA table ANOVA a 1 Sum of Squares df Mean Square F Sig Regression b Residual Total a. Dependent Variable: yield, b. Predictors: (Constant), angles, voltage MODEL: SPSS allows you to specify multiple models in a single regression command. This tells you the number of model being reported. REGRESSION, RESIDUAL, TOTAL: Looking at the breakdown of variance in the Outcome variable, these are the categories we will examine: Regression, Residual and Total. The Total variance is partitioned into the variance which can be explained by the independent variables () and the variance which is not explained by the independent variables (Error). SUM OF SQUARES: These are the sum of squares associated with the three sources of variance, Total model and residual. The total variance is partitioned into the variance which can be explained by the independent variables (Regression) and the variance which is not explained by the independent variables (Residual). Df: These are the degrees of freedom associated with the sources of variance. The total variance has N-1 degree of freedom. The Regression degree of freedom corresponds to the no. of coefficients estimated minus 1. Including the intercept, these are three coefficients, so the model has 3-1=2 251

7 D.o.f. The error d.o.f is the Df total minus the DFmodel, 6. F AND SIG. : This is the F-statistic the P-value associated with it. The f-statistic is the mean square (Regression ) divided by the mean square (Residual ).The P-value is compared to some alpha level in testing the null hypothesis that all of model coefficients are.492 Coefficients 1 Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) voltage angles a. Dependent Variable: yield, b. Predictors in the : (Constant), angles, voltage MODEL: SPSS allows you to specify multiple models in a single regression command. This tells you the number of model being reported. B: These are the value for the regression equation for predicting the dependent variable from the independent variable. The regression equation is presented in many different ways. Y predicted = b+b1 * *1+b2 * *2+b3 * *3+b4 * *4. : The coefficient of voltage is.762 Angles: The coefficient of angles is STD.ERROR: These are the standard errors associated with the coefficients. BETA: These are the standardized coefficients. These are the coefficients that you would obtain if you standardized all of the variables in the regression, including the dependent and all of the independent variables and ran the regression. By standardizing the variables before running the regression, you have put all of the variables on the same scale, and you can compare the magnitude of the coefficients to see which one has more of an effect. You will also notice that the larger betas are associated with the larger t-values and lower p- values. T AND SIG.: These are the t-statistics and their associated 2- tailed p-values used in testing whether a given coefficients is significantly different from zero. y= A+B*x1+c*x2 Summary Regression R R Square Adjusted R Square a a. Predictors: (Constant), angles, voltage ANOVA a 1 Sum of Squares df Mean Square Std. Error of the Estimate F Sig Regression b Residual Total a. Dependent Variable: ultimate, b. Predictors: (Constant), angles, voltage Coefficients Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) voltage angles a. Dependent Variable: ultimate, Excluded Variables a Beta In t Sig. Partial Correlation Collinearity Statistics Tolerance 1 current. b.... a. Dependent Variable: ultimate, b. Predictors in the : (Constant), angles, voltage MODEL: SPSS allows you to specify multiple models in a single regression command. This tells you the number of model being reported. R: R is the square root of R-square and is the correlation between the observed and predicted values of dependent 252

8 variable. R-SQUARE: This is the proportional of variance in the dependent variable (yield) which can be explained by the independent variables (angles, voltage). This is an overall measure of the strength of association and does not reflect the extent to which any particular independent variable is associated with the dependent variable. ADJUSTED R-SQUARE: This is an adjustment of the R- squared that penalizes the addition of extraneous predictors to the model. Adjusted R-squared is computed using the formula 1-((1-R sq) (N-1) / (N-K-1)), Where K is the number of predictors. Correlations Table 2. Correlations Obtained from SPSS Software Current Angles Yield Ultimate Current Angles Yield Ultimate Pearson Correlation 1 1. ** Sig. (2-tailed) Pearson Correlation 1. ** Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation ** Sig. (2-tailed) Pearson Correlation ** 1 Sig. (2-tailed) **. Correlation is significant at the.1 level (2-tailed). CONCLUSION In this project work, experiments are carried out for Yield strength and Ultimate Tensile Strength with respect to variation of current, voltage and angles of V-butt joint, keeping the gas constant. There are 9 experimental readings taken for all variation of input parameter and they are use for conduct the parametric study for optimization of welding process parameter during welding of MS IS 262 material. The experimental result shows that the Yield strength and Ultimate Tensile Strength ultimate tensile strength will increase or decrease for the different angles. It is show that the Yield strength and Ultimate Tensile Strength ultimate tensile strength is increase with increase of current and voltage. Also from the experimental result it show that the ultimate tensile strength is increase initially with increase of voltage up to 2 Volt and then it decrease as increase the value of voltage. From SPSS software we can observe that angles will make influence and have significant in1st level interference than voltage and current, in 2 nd level interference we can observe that the voltage, angles angles will have significance, in angle and current angle will have influence i.e, least value in coefficients table. where as in 3 rd level interference among voltage, current, angles we can observe from the correlation table that angles have more significance over ultimate and yield strength but in opposite direction. References [1] Monika K., Bala Chennaiah M., Nanda Kumar P. and Prahalada Rao P., "The Effect of Heat input on the Mechanical Properties of MIG Welded Dissimilar Joints".InternationalJournalofEngineeringResearch&T echnology,(213),vol2, pp [2] M.Aghakhani, E.Mehrdad, and E.Hayati, Parametric optimization of gas metal arc welding process by Taguchi method on weld dilution, International Journal of ing and Optimization, August (211), Vol.1, No. 3, pp [3] C.N.Patel and Chaudhary.S, Parametric Optimization of Weld Strength of Metal Inert Gas Welding and Tungsten Inert Gas Welding by using Analysis of Variance and Grey Relational Analysis, International Journal of Research in Modern Engineering and Emerging Technology, (213), Vol. 1, No

9 [4] Sheikh Irfan and Prof. Vishal Achwal, An experimental study on the effect of MIG welding parameters on the weldability of galvenize steel, International Journal on Emerging Technologies 5 (1), June (214), pp [5] Ghazvin loo H.R., Honarbakhsh-Raouf A.and Shadfar N.," Effect of arc voltage, welding current and welding speed on fatigue life, impact energy and bead penetrationofaa661jointsproducedbyroboticmigwe lding".indianjournal of Science and Technology, (21), Vol 3. [6] Pawan Kumar, Dr.B.K.Roy and Nishant, Parameters optimization for gas metal arc welding of austenitic stainless steel (AISI34) & low carbon steel using Taguchi s technique, International journal of engineering and management research, August (213),pp [7] S. W. Campbell, A. M. Galloway, and N. A. Mcpherson, Artificial neural network prediction of weld geometry performed using GMAW with alternating shielding gases, Welding journal, June (212), Vol.91, pp [8] Tewari, S.P.,Gupta, A. and Prakash,J., Effect of Welding Parameters on the Weldability of Material,International Journal of Engineering Science and Technology, (21),Vol. 2, No. 4, pp