Yerriswamy Wooluru 1, Swamy D R 2, Jagadish Rangaswamy 3. JSS Academy of Technical Education, Bangalore , India.

Transcription

1 Development of Mathematical Model to Predict Weld Bead Geometry and Parameters Optimization for Pulsed MIG Welding Using Statistical Design of Experiments Yerriswamy Wooluru 1, Swamy D R 2, Jagadish Rangaswamy 3 1 JSS Academy of Technical Education, Bangalore , India. 2 JSS Academy of Technical Education, Bangalore , India Pitch Pine Lane West, Ypsilanti, Michigan 48197, USA Abstract Pulsed MIG welding process offers spray metal transfer at low average currents, high metal deposition rate and less distortion. Study the effect of Pulsed MIG welding process variables on the bead geometry is inevitable to enhance mechanical properties of weld bead geometry. This can be achieved by developing mathematical models to predict the weld bead dimensions in terms of process parameters. Design of experiments tool was used to develop models for pulsed MIG welding process. The models so developed can be used to predict the weld bead geometry. Keywords: Process parameter optimization, Design of Experiments, ANOVA, weld bead geometry. It is very important to evaluate the effect of MIG welding process variables upon weld bead geometry. The weld quality depends on number of independent variable factors. Their effects on dependent factors are evaluated through empirical investigation. In practice, a small number of controllable variables contribute to a vital share of the effect of the product quality. These variables do not necessarily produce a constant effect on the product. The question would therefore arise as to how efficiently and economically the contribution of each of these factors can be assessed individually and also collectively to produce the total effect on the weld quality. An approach that fulfills these requirements is available in the statistically designed experiments. INTRODUCTION Pulsed MIG welding maintains an arc at low current and superimposes short periodic pulses ( Hz) of high current in order to detach and transfer single drops of molten metal from the electrode to the weld pool. The pulsing of the current at such high and low levels results in mean current. Parameters in pulsed MIG welding are: Peak current (Ip), Peak duration (Tp), Base current (Ib), Base current duration (Tb) and Wire feed speed (Ws).In Pulsed MIG welding process a square wave current pulses are used as shown in Figure 1. Figure 1. Pulsed MIG welding current wave form LITERATURE REVIEW Christensen [1] formulated no dimensional factors to relate bead dimensions with the operating parameters. Chandel [2] demonstrated the theoretical predictions of the effect of current, electrode polarity, diameter, and electrode extension on the melting rate, bead height, and bead width and weld penetration, submerged arc welding (SAW). Markelj and Tusek [3] modeled the current and voltage in Tungsten inert Gas welding as quadratic polynomials of sheet thickness. The results were presented for algorithmic optimization in the case of T- joint with fillet weld. Kim [4] distinguished experimental data obtained for weld bead geometry with those obtained from empirical formulae in gas metal arc welding(gmaw).patel et al. [5], evaluated the welding parameters for Metallic inert Gas welding and Tungsten inert Gas welding by Taguchi s method. It was concluded that the welding current was most significant parameter for both the welding operation. Ghazvinloo H.R. et al.[6], analyzed robotic MIG welding of AA6061 sproperties like fatigue life, impact and bead penetration properties under the effect of welding speed, voltage and current. Pradip et al. [7], have investigate the effects of welding process parameters of Gas Metal Arc Welding (GMAW) on tensile strengths of SS 3Cr12 steel material specimen. Duhan et al. [8], have developed a response surface model to predict 10298

2 tensile strength of inert gas metal arc welding of AISI (EN 31) high carbon steel joint. M. Aghakhani et al. [9], have done work on optimization of gas metal arc welding process parameter for increase quality and productivity of weldment. Kumar [10] studied TIG parameters and pitting corrosion of Al-alloys using ANOVA, regression analysis and mathematical models. He found that Peak current (Ip) and frequency have direct and base current (Ib) and pulse on time have inverse relation to pitting. Ratnayake and Vik [11] suggested a methodology to recognize the most frequently appearing imperfect and defective welds by grouping based on welding procedure specifications (WPSs) that contribute to the highest level of quality deterioration. Joshi [12] studied the MIG and TIG welding. The authors have used the Design of Experiment method for conducting the experiments and data analysis. Palani [13] studying TIG parameters of Al using Taguchi and mathematical models found that welding speed impacts weld strength and percentage elongation more than other parameters. Statistical design of experiments (DOE) concept has been successfully applied to many welding situations This may be achieved by the development of mathematical expressions, which can be fed into a computer, relating the bead dimensions to the important welding parameter affecting these dimensions Also, optimization of the welding parameters to control and obtain the required shape and quality of weld beads is possible with these expressions [14, 15, and 16]. PLAN OF INVESTIGATION The work was planned to be carried out in the following steps: 1. Identifying important process control variables 2. Finding upper and lower limits of control variables viz., Pulse current (Ip), Pulse duration (Tp), Wire feed speed (Ws), Background current (Ib) and background current duration. 3. Selection of optimization parameter. 4. Development of design matrix. 5. Conducting experiment as per design matrix. 6. Recording responses viz Penetration (P), Width of the weld bead (W) and Dilution (D). 7. Develop mathematical model to predict weld bead geometry 8. Determining the co-efficient of the model using DOE software 9. Check the adequacy of the models 10. Optimize the welding process parameters IDENTIFYING THE IMPORTANT PROCESS CONTROL VARIABLES AND FINDING LIMITS In pulsed MIG welding process, there are many variables affecting the weld bead geometry.however it was planned to study the effects of those parameters which affect definitely the mode of metal transfer and the amount of heat input to the work piece and thus determine the bead geometry and shape relationships. Based on their importance the welding variables have been put in to two groups as follows. Primary variables: Pulse current, Pulse time, Wire feed rate, Welding speed, feed wire diameter, shielding composition. Secondary variables: Background current,back ground current duration, mean current, Volt-ampere ratio,shielding gas flow rate,torch angle,electrode polarity, Pre heat condition,burn back, rise time, Inductance level, short circuit current, Arc length and electrode stick out. Out of these parameters only important variables were chosen which has no dependence on each other.based on these consideration pulse current (IP), Pulse time (Tp), Welding speed (Ws), Background current (Ib), and background current duration (Tb) were chosen as the control variables. Factors and their levels are given in Table 1. WELDING FACTORS AND THEIR LIMITS Sl. No. Table 1. Factors and their levels Factors Unit Notations Lower Limit 1 pulse current 2 Welding speed Basic Level Upper Level Variation interval Amps A mm/min B Pulse time ms C Background current 5 Background current time period Amps D ms E The limiting values of the welding parameters and constants of the experiments were set based on welding knowledge and experience. SELECTION OF THE EXPERIMENTAL CONSTANTS The following welding operational parameters and conditions were made constant during the experiments. Plate thickness: 12 mm, V-groove with90 0 included angle Feed wire diameter: 1.2 mm Torch to work angle: 90 0 Gas flow rate: 20 lit/min 10299

3 Nozzle to plate distance: 20 mm Pre heat: Nil SELECTION OF OPTIMIZATION PARAMETER Optimization parameter is the reaction (response) to the action of the factors (pulse current, Pulse time, Welding speed, Background current and Background current time period) determining the behavior of the system being studied. An optimization parameter should be: 1. Effective from the view point of reaching a goal 2. Universal in nature 3. Quantitative and expressed by a single number 4. Statistically effective 5. With a physical meaning, simple and easy to calculate. Based on the above considerations the following parameters are selected: Penetration (P): Penetration is the depth to which the base metal has been melted. It is normally a function of arc pressure. Arc pressure is decided from current density. Therefore, welding current is an important parameter deciding penetration besides the welding current also decides the deposition rate. Therefore the heat transferred through the droplets and deposited mass also has some influence on penetration. Depth of penetration is also influenced by the nature of the shielding gas.in pulsed MIG welding although the mean current decides the deposition rate and penetration the magnitude of the pulse current also has some influence on the penetration. Generally higher pulse current gives slightly higher penetration with larger pulse current can be attributed to the higher arc force and higher force due to the impinging droplet. The depth of penetration is also influenced by welding speed. Width of the weld bead (W): In consumable arc welding process such as MIG and FCAW the bead width is controlled by deposition rate welding speed and also the fluidity of the molten pool. in FCAW processes the thermo physical property of the molten flux also contribute to the bead formation. However in pulsed MIG welding using solid wire the bead width is largely controlled by the mean current decreased with increasing the welding speed. Bead wetting Angle: It is an important aspect of the deposited weld metal. Bead wetting angle is particularly important in case of multi pass welding and surfacing applications as larger bead wetting angle can lead to inter pass lack of fusion defects and slag entrapment. In fluxed process like FCAW and SAW the bead wetting angle can affect the slag detachability.therefore the bead wetting angle is an important aspect in V- groove wetting as well as surfacing.however the effect of current on bead wetting angle appears to be limited when compare to the effect of welding speed on wetting angle. Dilution: (D) Dilution can be defined as the ratio of the plate fusion area to the total area. It may also be used as rough measure of susceptibility to fusion defects and as a mean of comparing fusion characteristics with other processes. As a direct measure it is of interest in surfacing applications and also gives an idea about the inter pass refinement in the multilayer welding. FRACTIONAL FACTORIAL DESIGN MATRIX A five factor, two levels, fractional factorial design with interaction effect is as shown in Table 2 to conduct the experiments. Std Run Center Pt Table 2. Design Matrix Blocks A B C D E AB AC AD AE BC BD BE CD CE DE

4 Std Run Center Pt Blocks A B C D E AB AC AD AE BC BD BE CD CE DE EXPERIMENTAL DETAILS Steel plates of size 150x 75 mm were used for welding trials. The required number of plates was cut from 12 mm thick carbon steel (BMC-16) structural steel plate. V- Grooves with 90 0 included angle were cut into machined on the center of the specimen and depth of the V- groove was limited to 6mm only. The specimen was fixed on the carriage in vertical position. The M450 PS programmable MIG power source was used in this experiment. The weld beads were deposited using the welding conditions stipulated by the design matrix as shown in the Table 2.The weld runs were made at random to avoid systematic errors in the result. The experimental setup with gas flow meter and wire feeder used for bead on plate experiment in pulsed MIG is shown in figure 3, 4, and 5.It Consists of a traveling carriage with a table for supporting the test plate. The carriage speed is continuously varied from 1 mm/sec to 20 mm/sec. A frame held the welding torch above the table and it was provided with horizontal and vertical movements for setting the nozzle to job distance. The experimental set up used for welding in vertical position. Thought the experiment the test plates were kept stationary and the welding torch was moved. Vertical and horizontal movements were provided for the torch. The correct standoff distance and also for proper centering of the torch with reference to the V-Groove were made. The welding gun was allowed to cool to room temperature and spatter was cleaned from the nozzle after each weld run. Two samples were cut transverse to the weld bead from each welded plate then milling, surface grinding, polishing and etching for examination using 10% nital. Weld bead profiles were sketched with the help of a profile projector at a magnification of X10.Areas and dimensions of interest were measured using a planimeter and scale. The weld bead dimensions and shape relations were tabulated against their respective welding conditions in the coded forms as shown in Table 4.The tabulated values are averages from two samples from each specimen. The magnified bead profiles for different welding trials were made and a sample bead profile is shown in Figure 2. Electrode wire: Figure 2. Weld bead profile The copper coated electrode wire of diameter 1.2 mm and its nominal composition of the weld metal as supplied by the manufacturer used in this experiment are as shown in Table 3 Table 3. Chemical composition of electrode material Carbon ,08% Silicon % Manganese 0.03% (Max) Phosphorus 0.03% 10301

5 Gas supply and wire feeder: The smith s gas unit used in the study is a pressure flow device capable of proportionately mixing Argon and CO 2ininfinitely varying proportions. The gas flow rate and gas mixing ratio were set using separate knobs provided in the front panel. In this study the gas flow rate was kept at 20 lit/min. The mixing accuracy as reported by the manufacturer is about + or 2% of the full scale. Figure 5. Experimental setup The wire feeder unit employed is a millermatic four roll drive feed unit.it provides a continuously variable wire feed speed from about 1.0 m/mm to 18 m/min. Figure 3. Gas flow meter CONDUCTION OF EXPERIMENT AS PER DESIGN MATRIX Sixteen experimental runs were conducted as per the fractional factorial design matrix at random to avoid any systematic error creeping into the system. The surface plates were cross-sectioned at their midpoints to obtain test specimens. The values of the response are given in Table 4. Figure 4. Wire feeder Std Run Center Pt Table 4. Design matrix with responses Blocks A B C D E Penetration mm Weld bead width in mm Dilution in %

6 Std Run Center Pt Blocks A B C D E Penetration mm Weld bead width in mm Dilution in % MATHEMATICAL MODEL The study attempts relate the important welding process parameters to process output characteristics, through developing empirical regression models for various target parameters. Linear Regression function is fitted to the data and the coefficient values are found using regression analysis with the help of MINITAB statistical software. The developed mathematical models are accurately representing the actual pulsed MIG welding process. NORMAL PROBABILITY PLOT FOR PENETRATION, WELD BEAD WIDTH AND DILUTION Validating ANOVA Assumptions It is necessary to check the assumptions of ANOVA before draw conclusions. There are three assumptions in ANOVA analysis: normality, constant variance, and independence. Normality assumptions have been checked. Figure 6. Normality plot for Penetration, weld bead width and Dilution 10303

7 The normality plot of the residuals above shows that the residuals follow a normal distribution in all the three cases. It indicates significant and insignificant factors and residuals follow a straight line. According to the above normal plots C and DE are significant for penetration, C, E, BC, CD, are insignificant for weld bead width and B, BE, CD are insignificant for Dilution case. PARETO PLOT FOR PENETRATION, WELD BEAD WIDTH AND DILUTION MODEL Pareto chart shows the same results. Since some of the terms are insignificant, we can drop these terms in the model. Figure 7. Pareto chart for Penetration, weld bead width and Dilution Table 5. ANOVA for penetration Source DF Adj SS Adj MS F-Value P-Value Model Linear A B C D E Way Interactions A*B A*C A*D A*E B*C B*D B*E C*D C*E D*E Error Total Table 6. Regression coefficients with P-values Term Effect Coef SE Coef T-Value P-Value VIF Constant A B C D E A*B A*C A*D A*E B*C B*D B*E C*D C*E D*E Table 7. Model summary Penetration S R-sq R-sq(adj) R-sq(pred) % 45.98% 0.00% Discussion: Table 6 shows the estimation coefficient (Coef.) of each variable term in a regression model for penetration along with the corresponding standard deviation (SD coef), t-statistics (t-stat) and probability (P) values determined at 5% significance level. Variable terms with P < 0.05, are C, and DE which are considered statistically significant for penetration of weld bead geometry. Therefore, 10304

8 a second-order model was built to describe the behavior of each response, followed by the optimization stage to find the best setting for each factor. The second-order models for penetration of weld bead geometry in terms of coded variables with all significant terms are given in Equation (1).The value of R 2 was 72.12%. This means that regression model provided an explanation of the relationship between independent factors and the response. The associated p-value for the model was lower than 0.05 (i.e. α = 0.05, or 95% confidence) which indicated that the model was considered to be moderately statistically significant. Table 8. ANOVA for weld bead width Source DF Adj SS Adj MS F-Value P-Value Model Linear A B C D E Way Interactions Table 9. Regression coefficients with P-values (Weld bead width) Term Effect Coef SE Coef T-Value P-Value VIF Constant A B C D E A*B A*C A*D A*E B*C B*D B*E C*D C*E D*E Table 10. Model summary weld bead width S R-sq R-sq(adj) R-sq (pred) % 93.76% 87.11% A*B A*C A*D A*E B*C B*D B*E C*D C*E D*E Error Total Discussion: Table 9 shows the estimation coefficient (Coef) of each variable term in a regression model for weld bead width along with the corresponding standard deviation (SD coef), t-statistics (t-stat) and probability (P) values determined at 5% significance level. Variable terms with P < 0.05, A, B, D, AB, AC, AD, AE, BD, BE, CE, DE are considered statistically significant for weld bead width of weld bead geometry. The second-order models for weld bead width of weld bead geometry in terms of coded variables with all significant terms are given in Equation (2).The value of R 2 was 93.76%. This means that regression model provided an explanation of the relationship between independent factors and the response. The associated p-value for the model was lower than 0.05 (i.e. α = 0.05, or 95% confidence) which indicated that the model was considered to be statistically significant

9 Table 11. ANOVA for Dilution Source DF Adj SS Adj MS F-Value P-Value Model Linear A B C D E Way Interactions A*B A*C A*D A*E B*C B*D B*E C*D C*E D*E Error Total Discussion :Table 12 shows the estimation coefficient (Coef) of each variable term in are regression model for weld bead width along with the corresponding standard deviation (SD coef), t-statistics (t-stat) and probability (P) values determined at 5% significance level. Variable terms with P < 0.05, A, C,D, E,AB, AC, AD, AE, BC,BD,CE, DE are considered statistically significant for dilution for weld bead geometry. The second-order models for weld bead width of weld bead geometry in terms of coded variables with all significant terms are given in Equation (3).The value of R 2 was 96.78%. This means that regression model provided an explanation of the relationship between independent factors and the response. The associated p-value for the model was lower than 0.05 (i.e. α = 0.05, or 95% confidence) which indicated that the model was considered to be statistically significant. EFFECTS AND INTERACTION OF PROCESS VARIABLES: MAIN EFFECTS AND INTERACTION OF PROCESS VARIABLES ON DEPTH OF PENETRATION : Table 12. Regression coefficients with P-values (Dilution) Term Effect Coef SE Coef T-Value P-Value VIF Constant A B C D E A*B A*C A*D A*E B*C B*D B*E C*D C*E D*E Table 13. Model summary Dilution S R-sq R-sq(adj) R-sq(pred) % 93.76% 87.11% Figure 8. Illustration of main and interaction effect on Penetration 10306

10 MAIN EFFECTS AND INTERACTION OF PROCESS VARIABLES ON PENETRATION From Figure 8, it is evident that Penetration (P) increases with an increase in pulse time (C) and as the Back ground current (D) increases, penetration (P) decreases considerably. Factors A, B and C will not enhance the penetration much as they are insignificant. There is an interaction effect between background current (D) and background current time period (E), as the background current (D) increases,penetration (P) decreases. Welding speed and background current period (BE), Pulse time and background current period (CE),background current and background current period(de). MAIN EFFECTS AND INTERACTION OF PROCESS VARIABLES ON DILUTION Figure 10. Illustration of main and interaction effect on Dilution Figure 9. Illustration of main and interaction effect on weld bead width MAIN EFFECTS AND INTERACTION OF PROCESS VARIABLES ON WELD BEAD WIDTH (W) From Figure 9, it is evident that weld bead width (W) increases with an increase in pulse current (A ) and back ground current (D) and as the Welding speed (B) increases, weld bead width (W) decreases considerably. Increase the Factors Pulse time (C)and background current (D) will not enhance the weld bead width at all. There is an interaction effect between factors Pulse current and pulse time (AC), Pulse current and background current time period (AE), From Figure 10, it is evident that Dilution (D) increases with an increase in pulse time (C).As Pulse time (A),background current (D) background current time period (E) increases, the dilution decreases. There is no effect of welding speed (B) on Dilution when welding speed varies from lower level to higher level. There is an interaction effect between factors Pulse current and background current time period (AE), Pulse time and background current period (CE). MATHEMATICAL MODELS IN CODED FORM After testing adequacy and significance of each coefficient of the regression equations we obtained the linear mathematical models for penetration, weld bead width and dilution in the coaded form as follows

11 Penetration = A B C D E A*B A*C A*D A*E B*C B*D B*E C*D C*E D*E (1) Weld bead width = A B C D E A*B A*C A*D A*E B*C B*D B*E C*D C*E D*E (2) Dilution = A B C D E A*B A*C A*D A*E B*C B*D B*E C*D C*E D*E (3) In calculating the predicted value of the optimization parameters, the coaded values of the factors are inserted in to the mathematical model. Rule 1.If an interaction effect has a positive sign, then for increasing the optimization parameter, what is required is a simultaneous increase or decrease of the value of the factors, for example the combination X 1= +1 and X 2 = +1 or X 1= -1 and X 2 = -1. Rule 2.If the interaction effect has a negative sign, then to increase the optimization parameter the factors should simultaneously change in different directions for example the combination X 1= +1 and X 2 = -1 or X 1= -1 and X 2 = +1. Table 14. Prediction of weld bead geometry for a treatment using the above model Sl. no. A B C D E Dilution in % Weld bead width in mm Penetration in mm CONCLUSION Fractional factorial technique can be effectively utilized to design the experiments for pulsed MIG welding. Mathematical models based on regression and analysis of variance technique can be an effective tool for prediction of weld bead geometry and shape relations. The developed models can also be utilized for predicting the values of controlled variables for achieving desired weld bead profile. These equations can be usefully employed for control of weld bead contours in mechanized and robotic welding system. The models can provide a picture of interaction between control factors and response factors. The suitable ranges of pulse current, pulse time, welding speed,background current and back ground current time period used for the experiment provide a useful data,which can be utilized for making bead in V- groove plate of medium thickness(10-15 mm).the established welding parameters for 12 mm thick plate with 6 mm depth V- groove (90 0 included angle) can be employed to get good quality of weld bead. REFERENCES [1] N.Christensen, V. Davies, & K. Gjermundsen, 1965 "Distribution of temperature in arc welding", Br Weld J vol. 12 (2), pp.54 75,. [2] R.S. Chandel, H.P. Seow, F.L. Cheong, 1997 "Effect of increasing deposition rate on the bead geometry of submerged arc welds", J Mater Process Technol, vol.72, pp ,. [3] F. Markelj, J. Tusek, 2001"Algorithmic optimization of parameters in tungsten inert gas welding of stainless-steel sheet", Sci Technol Weld Join vol.6 (6), pp ,. [4] I.S. Kim, K.J. Son, Y.S. Yang, P.K. Yarlagadda, 2003"Sensitivity analysis for process parameters in GMA welding processes using a factorial design method", Int. J Mach Tools Manuf. vol.43, pp ,. [5] C. N. Patel and Chaudhary, S., (2013), 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, Vol. 1, No.3. [6] Ghazvinloo H.R., Honarbakhsh-Raouf A. and Shadfar N., 2010, " Effect of arc voltage, welding current and welding speed on fatigue life, impact energy and bead penetration of AA6061 joints produced by robotic MIG welding". Indian Journal of Science and Technology, Vol.3. [7] Pradip D. Chaudhari and Nitin N. More, (2014), Effect of welding process parameters on tensile strength, IOSR journal of engineering, Vol. 04, Issue 05, pp [8] Rajkumar Duhan and Rajesh Nandal, 2013 Maximizing tensile strength in AISI (En 31) welded joints using gas metal arc (GMAW) welding, International journal of engineering 10308

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