ESTABLISHING RELATIONSHIPS TO PREDICT WELD BEAD PROFILES OF ROBOTIC ARM GAS METAL ARC (GMA) WELDED BUTT JOINTS

Generally, the robotic arm Gas Metal Arc (GMA) welding process involves sophisticated sensing and control techniques applied to various process variables. This research work has been carried out to develop a response surface model to establish a relationship between process parameters and weld bead penetration in the robotic arm GMA welding process. To achieve the objectives, partial-penetration and single-pass welds were fabricated in 14 mm thick “Y” grooved modified SAE 1022 low carbon steel plate using three different process parameters such as welding current, welding speed, and land height. The experiments were conducted based on a three factor, five-level, face-centered composite design matrix. After welding, the joints were sectioned and bead profiles were scanned using stereo-zoom macroscope. Empirical relationships were developed to predict bead profiles such as penetration, weld width and reinforcement height incorporating the significant main and interaction welding parameters alone. The developed empirical relationships can be effectively used to predict the weld bead profile of the robotic arm GMA welded modified SAE 1022 low carbon steel plate joints.

KEYWORDS: Robotic arm GMA Welding, Weld bead profiles, Design of Experiments, Response surface methodology, Optimization

INTRODUCTION

Welding is inherently complex due to the simultaneous occurrence of chemical, physical, and mechanical phenomena involving solid, liquid, gas, and plasma states within a small volume, over short distances and time, and at high temperatures [1]. This complexity makes sensing, measuring, and controlling welding parameters challenging in conventional welding processes. To manage this, automatic or robotic welding is widely adopted in industries like automotive for better process control [2]. Although several studies have modeled the relationship between process parameters and bead geometry in semi-automatic arc welding [3], limited work has focused on automatic or robotic arc welding systems. Gas Metal Arc Welding (GMAW), commonly known as MIG welding when using argon, helium, or their mixtures, is also termed Metal Active Gas (MAG) welding when using pure 36 CO2 as the shielding gas [4]. A constant voltage direct current power source is typically used in GMAW and Robotic GMAW (R-GMAW), though constant current or alternating current can also be employed as needed [5]. There are four main metal transfer modes in GMAW globular, short-circuiting, spray, and pulsed-spray each offering unique benefits and limitations [6]. This study employs Response Surface Methodology to optimize welding parameters for partial penetration, bead width, reinforcement height, and dilution in robotic GMAW of Y-grooved SAE 1022 low-carbon steel joints using 2.0 mm ER70S-6 filler wire. ANOVA was conducted to identify significant main and interaction effects. Optimizing these parameters improves weld quality and reduces experimental costs [7].

METHODOLOGY

Two housing halves of a modified SAE 1022 low carbon steel, with the chemical composition and electrode wire are shown in Table 1. The specification of the welding wire depends upon the material that is required to be welded. So, AWS A5.18ER 70S-6 welding wire of 2.0 mm diameter was selected. As the thickness of the base metal was 14 mm, welding wire with a diameter of 2.0 mm was selected. The significant welding input parameters that can affect the output response were identified and their range of operation was selected as shown in Table 2.

Fig. 1 Robotic GMA Welded Modified SAE 1022 Low Carbon Steel

This study used an inverter-based robotic power source LINCOLN Power Wave® S700 Advanced Process Welder (K3279-1) paired with a FANUC ROBOT M201A/121 to join 14 mm "Y" grooved SAE 1022 low carbon steel housing halves. This setup was chosen for its fast arc start, stick-out and crater control, and Fresh Tip Treatment Technology (FTTT), which prevents globule formation at the wire tip during weld stop [8]. Weld bead geometry was examined using a stereo-zoom macroscope. The welded “Y” groove butt joint is shown in Figure 1. In the Robotic GMAW process, process parameters were categorized into two groups. The first group, consisting of variable parameters, includes welding current, arc voltage, torch travel speed, and groove land height. The second group, held constant during the process, includes shielding gas flow and composition, torch angle, stick-out, electrode extension, and electrode diameter (Table 1). The constant parameters and their limits are listed in Table 3.With this value (Table 2 & 3) a full design of experiment was performed using three factors and five levels central composite design matrix, thus the model consists of 20 treatments.

RESULTS AND DISCUSSION

1. Development of an empirical relationship

The input parameters (variables) that were selected using the four-factor and three levels face-centered composite design matrix. Analysis of variance (ANOVA) method was used to identify the significant main and interaction between welding parameters and their level of significance (Table 6). Response Surface Methodology has used to optimize the robotic arm GMA welding process parameters to attain desired weld bead characteristics such as optimum partial - penetration, weld bead width, reinforcement height, and dilution. The levels of the significant process variables are shown in Table 4.

2. Selection of a mathematical model for empirical relation

A convenient way to evaluate the performance of the mathematical equation in presenting the system under investigation is through the concept of response surface methodology [9]. It is assumed that there exist some functional relationship such as: - Y = f (I, S, H)

This defines the dependence of the response Y on the welding parameters I, S, H where these parameters have already defined in Table 4. The response Y may be any of the bead parameters i.e. penetration (P), bead width (W), and bead height (R) etc. Assuming a quadratic relationship in the first instance and taking into account all possible interactions, Equation could be written in the form of the following polynomial.

Y = b0 + b1 I + b2 S + b3 H + b12 IS + b13 IH + b23 SH + b11 I2 + b22 S2 + b33 H2

Or it can be also written as,

Y = b0 + b1 I + b2 S + b3 H + b4 IS + b5 IH + b6 SH+ b7 I2 + b8 S2 + b9 H2

These relations for predicting different responses mentioned above are given below.

Penetration (P) = {+ 9.50 + 1.24I - 0.29S - 1.0H - 0.14IS - 0.39IH + 0.36SH + 0.69I2 - 0.069S2 - 0.39H2} mm

Width (W) = {+17.67 + 0.78I - 0.70S + 0.27H - 0.087IS - 0.037IH - 0.087SH + 0.51I2 + 0.036S2 - 0.017H2} mm

Reinforcement height (R) = {+2.74 + 0.046I - 0.28S + 0.30H + 0.063IS - 0.012IH + 0.063SH - 0.28I2 + 0.19S2 + 0.035H2} mm

The response Y may be any of the bead parameters i.e.

penetration (P), bead width (W), and bead height (R) etc. Assuming a quadratic relationship in the first instance and taking into account all possible interactions, Equation could be written in the form of the following polynomial.

Y = b0 + b1 I + b2 S + b3 H + b12 IS + b13 IH + b23 SH + b11 I2 + b22 S2 + b33 H

Or it can be also written as,

Y = b0 + b1 I + b2 S + b3 H + b4 IS + b5 IH + b6 SH+ b7 I2 + b8 S2 + b9 H2

These relations for predicting different responses mentioned above are given below.

Penetration (P) = {+ 9.50 + 1.24I - 0.29S - 1.0H - 0.14IS - 0.39IH + 0.36SH + 0.69I2 - 0.069S2 - 0.39H2} mm

Width (W) = {+17.67 + 0.78I - 0.70S + 0.27H - 0.087IS - 0.037IH - 0.087SH + 0.51I2 + 0.036S2 - 0.017H2} mm

Reinforcement height (R) = {+2.74 + 0.046I - 0.28S + 0.30H + 0.063IS - 0.012IH + 0.063SH - 0.28I2 + 0.19S2 + 0.035H2} mm

 3. Analysis of Variance (ANOVA) for Robotic GMA welding parameters

The following Table-6 represents the ANOVA results for penetration, bead width, and reinforcement height.

                                                                                    Table 6 Analysis of Variance (ANOVA) Test Results

 The analysis of variance for the robotic arm arc welding process parameter has done to find out the significant mains and interaction of process parameters. From the results, it is observed that the welding current is the most significant parameter of having high F-value. It means that welding current is the most effective in controlling penetration and similarly, the weld bead width is mostly affected by the welding current and welding speed. This may be due to the change in the metal deposition rate. From the results, it is also observed that the land height (H) is the most significant parameter which is having higher F value. It means that land height is the most effective parameter in controlling the dilution. The most effective parameter of the designed matrix can be easily understood from the perturbation graphs as shown below in Figure 2.

Where, A = Welding current (I) in Amps; B = Welding speed (S) in mm/min; D = Land height (H) in mm 

4. Effect of process parameters on bead geometry

 • Effect of Welding current (I) The table and Figure 3 illustrate how welding current (I) affects weld bead geometry penetration (P), bead width (W), and reinforcement height (R). As shown, penetration and bead width increase by 55.5% and 25%, respectively, likely due to higher heat input and metal deposition with increased current. Reinforcement height may decrease as deeper arcs cause molten metal to spread sideways in the weld pool [10].

         Fig. 3 Effect of welding current on weld bead geometry

 • Effect of Welding speed (S)

 The table and Figure 4 show the effect of welding speed (S) on weld bead geometry penetration (P), bead width (W), and reinforcement height (R). As welding speed increases from 350 to 450 mm/min, reinforcement height drops by 40.47%, while bead width and penetration decrease by 17.5% and 15%, respectively. These reductions are likely due to decreased metal deposition rate at higher welding speeds.

        Fig. 4 Effect of welding speed on weld bead geometry

• Effect of Land height (H)

Figure 5 illustrates how land height (H) influences weld bead geometry, including penetration (P), bead width (W), and reinforcement height (R). An increase in land height significantly raises the reinforcement height by 63.6%, likely due to a larger groove volume. However, penetration decreases by around 29.6%, possibly because the heat input is insufficient to fully fuse the increased land height with the deposited metal. As a result, the molten metal, while filling the groove, fails to entirely bond with the base metal and instead spreads outward, increasing the overall bead width across the surface

         Fig. 5 Effect of land height on weld bead geometry

• Microstructure

The Robotic GMA welded specimen of modified SAE 1022 low carbon steel joints were sectioned to the required size from the welded joints transverse to the welding direction and taken for metallographic examination. These specimens were cut transversely as required size and macro - polished with different grades (100, 320,400 and 600, 800 and 1000 grit size) of emery papers. Final polishing was done using the alumina compound (3μm and 0.1μm particle size) on the disc polishing machine for 5 minutes and then etched with a Nital with 5 ml Nitric acid solution applied for 10 to 30 seconds as per the ASTM specification E-381. The macrostructure of the welds was studied and macro images are recorded using stereo zoom macroscope as shown in figure 6. The microstructures of various weld regions for optimized highest penetration are recorded at 200X magnification using an optical microscope as shown in figure 6.

Fig. 6 Macro and microstructure of R-GMA Welded

              specimens on highest Penetration

CONCLUSIONS

In this paper, the effect of the process parameters for Robotic Arm Gas Metal Arc Welding in the prediction of bead geometry has been reported.

• Experimental results have shown that the process parameters such as the welding current having more effect on penetration i.e. the penetration increase with welding current by 55.5% and bead width increased by 25 % simultaneously.

• The results show that on increasing the welding speed, decreases reinforcement height (R) by 40.47 % and on increasing the land height, there is an increase in reinforcement height (R) by 63.6% i.e. the land height having a most significant effect on reinforcement height (R).

REFERENCES

1. I.S. Kim, K.J. Son, Y.S. Yang, and P.K.D.V. Yaragada, Sensitivity Analysis for Process Parameters in GMA Welding Processes Using a Factorial Design Method, Int. J. Mach. Tools Manuf. 43 (2003)763–769.

2. I. Kim, J. Son, S. Lee, and P.K.D. V Yarlagadda, Optimal Design of Neural Networks for Control in Robotic Arc Welding, Robot CIM-Int Manuf 20 (2004) 57–63.

3. D. Ramos-jaime, I. López-juárez, and P. Perez, Effect of Process Parameters on Robotic GMAW Bead Area Estimation, J. Mater. Process. Tech. 7 (2013) 398–405.

4. I.S. Kim, J.S. Son, I.G. Kim, J.Y. Kim, O.S. Kim, A study on relationship between process variables and bead penetration for robotic CO2 arc welding, J. Mater. Process. Tech 136 (2003) 139–145.

5. S. Srivastava and R.K. Garg, Process Parameter Optimization of Gas Metal Arc Welding on IS: 2062 Mild Steel Using Response Surface Methodology, J. Manuf. Process. 25 (2017) 296–305,

6. M.A. Wahab, M.J. Painter, and M.H. Davies, The Prediction of the Temperature Distribution and Weld Pool Geometry in the Gas Metal Arc Welding Process, J. Mater. Process. Tech 77 (1998) 233–239.

7. J. Xiong, G. Zhang, H. Gao, and L. Wu, Robotics and Computer-Integrated Manufacturing Modeling of Bead Section Profile and Overlapping Beads with Experimental Validation for Robotic GMAW-Based Rapid Manufacturing, Robot. Comput. Integr. Manuf. 29 (2013) 417–423.

8. M. Chaisri and P. Peasura, Effect of Gas Metal Arc Welding Parameters on Mechanical Properties for Carbon Steel ASTM A285 Grade A, Adv. Mat. Res. 342 (2012)16–20.

9. I.A. Ibrahim, S.A. Mohamat, A. Amir, and A. Ghalib, The Effect of Gas Metal Arc Welding (GMAW) Processes on Different Welding Parameters, Procedia Eng 41 (2012) 1502–1506.

10. Z. Yang and T. Debroy, Modeling Macro-and Microstructures of Gas-Metal-Arc, Metall Mater Trans B 30 (1999) 483-493.