# Experimental and Mathematical Studies for Optimality of GTAW Parameters on Similar and Dissimilar Steel Substrates

Authors: Bandaru Kiran; Dega Nagaraju; Senthil Kumaran Selvaraj; Baye Molla
Journal: Advances in Materials Science and Engineering
 (2022)

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## Abstract

Argon arc welding is a frequently used process, to assist various manufacturing processes. The paper highlights the postweld experimental results of similar (EN 10025 Steel-Grade S295) and dissimilar (D36 shipbuilding steel) materials, which possess a wide range of applications in manufacturing industries. In this work, experimental studies are carried out for optimality of gas tungsten arc welding (GTAW) parameters on a butt joint with single and double V 45° groove angles using Taguchi orthogonal L9 array design. The welding is carried out using the back hand technique, and the filler rod is taken as ER303L to obtain finer welded joints. After the weld, residual stresses, microhardness, and geometrical properties of weld are studied at varied zones, that is, weld zone, HAZ, and base metal to examine the weld efficiency with specific predefined weld parameters. Furthermore, tensile tests are carried out for the weldments with optimal predefined weld parameters, and subsequently, the welded specimens with better tensile strength are discussed in the results. Genetic algorithm and mathematical modelling are opted for selecting the final optimal welding parameters. As per the obtained results of residual stresses, hardness, and tensile tests, dissimilar double V groove butt joint is projected to have more effective weld joint.


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## Body

## 1. Introduction

Welding is a manufacturing process that is used to join the metals by the application of various fusion techniques. It is one of the most significant methods available for fabrication, maintenance, and repair of materials in industries. The welding method can use laser light or electric flames. For decades, during the welding process open flames are used to manufacture the intricate components. The welding process can be used to join many variants of metals and alloys with distinct dimensions. Welding plays a predominant role while manufacturing any machine or mechanical components such as pressure vessels, heavy vehicles, jet aircrafts, and many more. For any of the welding process, selection of proper joint design and effective welding parameters are necessary for attaining optimal final product. In this case study, single and double V groove angled butt welds were considered, to study the mechanical properties and deformations. Residual stresses are the remaining stresses, which remain in the manufactured component even without the application of any external load. Residual stresses are of two types in nature, tensile, and compressive. Compressive residual stresses enhance the fatigue life of the fabricated component, whereas tensile residual stresses induce crack initiation in the welded component and may lead to the failure of the component. Residual stresses can be generated in the welded components, by selecting inappropriate weld groove geometry, type of base materials, welding process, parameters, and techniques. There are a few mechanical techniques such as preheat treatments (annealing and normalizing), surface hardening (shoot peening, ball burnishing, and rolling with minimum load), welding sequence (back and front hand welding techniques), and vibratory stress relieving, which can be used to reduce the amount of residual stresses generated in the components. The amount of residual stresses in the component inversely varies with tensile strength, fatigue strength, flexural strength, and hardness, while it varies directly with the toughness of the component. In this work, the amount of residual stresses and microhardness at weld zone, HAZ, and parent metal for single V and double V groove butt weld are studied experimentally.The complete research article is divided into 5 major sections. In Section2, the detailed literature review is mentioned. In Section 3, the Taguchi orthogonal L9 array design is elaborated. In Section 4, mathematical modelling is explained. In Section 5, experimental methodology was listed out. Results and discussions of the research work are given in Section 6. Finally, conclusions are highlighted in Section 7.


## 2. Literature Review

In day-to-day scenarios, most of the mechanical components undergo a welding process, to either join or repair the specimens. Hence, investigating on the welds to obtain endurance and reliability becomes a priority for all manufacturing researchers. Li et al. [1] prepared NiCrBSi coatings on steel substrate by plasma spraying, and the remelted coating was done by TIG arc process. Baghel and Nagesh [2]examined surface roughness, hardness, wear resistance, microstructure, and phase composition and studied to select a best possible combination of consumable materials such as shielding gas, electrode, and filler rod for TIG welding. Huang et al. [3] developed a series of welding wires with ZrB2 particles by the in situ reaction and were successfully introduced into the spray-formed 7055 aluminium alloy welded joints by GTAW welding. Mechanical properties and microstructure were examined and analysed.Kiran et al. [4] performed simulation and mathematical studies on similar and dissimilar materials by employing single and double V groove angles by GTAW. Kumar et al. [5] analysed TIG welding process for joining aluminium alloy using grey relation analysis. Welding speed, gas flow rate, and current were identified as influencing parameters for TIG welding. Satish et al. [6] did weldability investigation and optimization on process parameters for TIG-welded AA8006 alloy. Sadeghi et al. [7] studied acoustic emission signals generated during tensile loading by measuring residual stresses. Jayakumar [8] studied transient thermal model for various friction welding tool geometries. Lukianenko et al. [9] investigated the mechanical properties such as tensile strength, fatigue strength, and hardness on aluminium lithium sample by opting TIG and MIG welding processes. Buyukyildirim et al. [10] studied the impact of shielding gas on the welded component during the gas tungsten arc welding process.Murugesan et al. [11] investigated the effect of residual stresses on microhardness during TIG welding of T91 tube. Vemaaboina et al. [12] studied distortion in the sample by opting Taguchi techniques while laser welding. Wang et al. [13] investigated the tensile behaviour of Mg/Al alloy, which was welded by cold metal transfer welding. Sateesh and Dhas [14] used Taguchi method for analysing the effect of submerged arc welding parameters on bead height, width, and dilution of bead.Deepali et al. [15] studied the effect of welding speed and groove geometry on the tensile strength and impact strength. They have considered 3 different groove angles for analysing the output results. Pasupathy and Ravisankar [16] studied the effect of welding parameters on AA1050 weld component. They studied the tensile strength by opting Taguchi optimization. Amin et al. [17] studied the effect of SAW parameters on the residual stresses. Taguchi optimization technique is used for carrying out the experimental process.Costa et al. [18] studied the effect of residual stresses on the welded component during cold-wire TIG welding. The researchers used the acoustic technique and X-ray diffraction method to measure the amount of residual stresses generated in the welded component. Skariya et al. [19] studied GTAW parameters by opting grey wolf optimization technique. 15CDVS Steel is considered as a workpiece material during welding. Schneidet et al. [20] investigated the effect of TIG hybrid welding process parameters on the weld bead geometry.Jurica et al. [21] performed A-TIG welding process on stainless steel materials. The welding robot is used for performing the TIG welding process. Jayashree et al. [22] investigated the effect of welding process parameters on microhardness and weld bead geometry. In their work, 6061Al alloy is considered as the workpiece. Srirangan and Paulraj [23] studied the effect of welding process parameters on the tensile strength of the welded component. Optimization technique-Taguchi grey relation analysis is used for carrying out the experimental work. Incoloy 800 HT is considered the sample for their research work. Sivaraman and Paulraj [24] performed MIG welding experimental analysis by using the multiobjective optimization technique. AA2219-T87 is used as the workpiece material for the metal inert gas welding process.Vasudevan et al. [25] studied the effect of A-TIG welding process parameters on weld bead geometry. They have considered modified 9Cr-1Mo Steel as the workpiece material. Greyjevo et al. [26] performed grey relation analysis and Taguchi optimization methods to optimize the weld groove parameters. Eazhil et al. [27] performed Taguchi optimization on TIG welding process parameters by considering 6063 aluminium alloy as a workpiece. Kiran and Nagaraju [28] applied Taguchi optimization for analysing process parameters on wear properties during machining of Hastelloy C-276.Unlike all before mentioned articles, in this article Taguchi orthogonal L9 array design is used for optimality of welding parameters and studies on the effect of residual stresses in mechanical properties of EN 10025 steel were carried out. The work uses the mathematical model for thermal and structural analysis of experimental results. As per Taguchi analysis, final optimal level is considered to be of level-5 parameters. In case, the genetic algorithm is used to find out the optimum level from all 9 levels [29–34]. Furthermore, the tensile test was carried out to identify more effective weld joint between single V and double V butt joints.


## 3. Taguchi Optimization Design

Taguchi optimization design is adopted to optimize the welding parameters. Generally, the technique involves and increases in the number of levels and time taken for experimental analysis. It also requires financial and time extensions to complete the multiple levels for experimental outputs. Hence, the research was particularly carried out for orthogonal L9. It is a standard optimization design that needs minimum number of levels to identify the influence of welding parameters on final weldment. The required number of experiments to be done is given by the expression(1)NTaguchi=1+NvL−1,NTaguchi=Number of experiments to be performed,Nv=Number of variables,L=Number of level.Dr. Taguchi introduced unique metrics, named as S/N ratios, to evaluate system’s vigorousness. These metrics guide technicians for making decisions in optimizing process. The product performance may vary and can be viable to various reasons. The reasons for the variability are named as noise factors. Noise factors may cause deviation of performance measures or functionality measures from its triggered value. Basically, S/N ratios restrain the changes in a system due to noise factors. S/N ratio characteristics can be defined by various variables, and choosing under this variety for optimal results is a challenging task. Preferably, adopted features of S/N ratio in quality control engineering are as follows:Larger-the-better characteristics:(2)SNRatio=−10log101n1∑i=1n1yij2.Smaller-the-better characteristics:(3)SNRatio=−10log101n1∑i=1n1yij2.Nominal-the-better characteristics:(4)SNRatio=−10log10y¯Sy2,where yi is the observed value through experimentation and n is the number of trails of each experiment. y¯ is the mean of experimentally observed data and Sy2 is the variance of observed data. For each response, with S/N transformation, the larger the S/N ratio is, the better the result is. Taguchi’s method is used to get the optimal set of process parameters, for each output response individually by considering each production measure as unit response. The S/N ratio is employed to characterize quality response, and the highest S/N is considered. Higher-the-better and lower-the-better processes of S/N are applied for all output parameters, respectively. Table 1 shows the Taguchi table that was considered for optimization. Figure 1 illustrates the root gap, land, groove angle, and thickness of a workpiece considered.Table 1
Parameters for modelling and experimentation.
LevelsRoot land (mm)Welding current (Amps)Gas flow rate (Lit/min)10.51001220.51251430.51501641100145112516611501271.51001681.51251291.515014Figure 1
(A-root gap, B-land, C-groove angle, t-material thickness).


## 4. Mathematical Modelling

To optimize the mathematical model, variables had to be categorized. Two classes of variables are identified as primary variables that included the material parameters that are collected from previous research articles and the secondary variables that constituted the fluid parameters of the ambient surroundings.The primary variables are then discretized into temperature-dependent functions based on the crystal structure of the material. Tables2 and 3 categorize the primary variables (material properties) with alpha structure up to 720°C and beta structure up to 5000°C. The optimization was mainly done by comparing the thermal properties of the weld for single and double V grooves and the structural properties of the weld for similar and dissimilar materials.Table 2
Primary variables of low carbon steel (material properties).
TemperatureThermal conductivitySpecific heatYoung’s modulusThermal coefficientPoisson ratioYield stress051.94502001.00Ee − 050.278629010051.1499.22001.10E − 050.309526030046.1565.52001.20E − 050.33120045041.05630.51501.30E − 050.33815055037.5705.51101.40E − 050.357512060035.6773.3881.40E − 050.373811072030.641080.4201.40E − 050.37389.880026931201.40E − 050.42389.8145029.45437.9321.50E − 050.47380.2151029.74000.21.50E − 050.4940.1158029.7735.250.000021.50E − 050.4940.0098500042.24000.000021.55E − 050.4940.0098Table 3
Primary variables of shipbuilding steel (material properties).
TemperatureSpecific heatThermal conductivityYoung’s modulusPoisson’s ratioYield stress2042735.1199.670.3371.920050236.8199.30.32362.130054836.8198.040.33134740060236.2192.90.34317.550066534.61740.353267.960074132.0125.30.364201.8700148128.561.70.375139.876068625.826.30.38694.8990065326.614.90.39769.2100065327.212.020.456.3110065327.811.320.41950.35120065328.411.10.4347.65130065329.011.10.44146.4140065329.611.10.45245.9Secondary variables are chosen as those parameters in which temperature has a significant effect. This narrowed it down to density and viscosity functions of the air. The curve fitting technique is employed for each property variable against the temperature values. The comparison of various curve fitting techniques is done, and error function is also generated. Selection of the best technique involved having the least mean-squared-error (MSE) value.Density of Air:Gaussian curve fitting technique is employed because it had MSE of 8.9%. The residual function given in equation (5) is applicable if the induced temperature is up to 10,000 K. Figure 2 gives a plot of the density function(5)ρ=0.6752e−0.005281T+0.5946e−0.0007602T.Figure 2
Density of air v/s temperature.Viscosity of Air:Smoothed cubic spline curve fitting technique was employed because it had MSE of 6.2%. The residual function given in equation (5) is applicable if the induced temperature is up to 10,000 K. Figure 3 gives a plot of the viscosity function(6)η=−1.323×10−14T3+8.289×10−11T2+9.145×10−8T+1.315×10−5.Figure 3
Viscosity of air v/s temperature.


### 4.1. Analysing the Heat Flow during Welding

The supplied heat is channelled into three segments, namely, material conduction, medium convection, and radiation. The conduction heat is again categorized into two segments that are heat utilized and heat dispersed. To calculate these heat coefficients, it needs to have the thermal conductivity coefficient, thermal expansion coefficient, and specific heat of the material.From equations (2) and (3), it can be calculated the convection coefficient and radiation coefficient of air. Equation (7) gives the Prandtl number of air as a function of temperature(7)Pr=0.0985×e−0.007716T+0.673×e0.000726T.Conductivity Coefficients:The thermal conductivity, which is categorized into primary variable, is employed in quadric smoothened spline due to its MSE being 8.1%. Equation (8) gives the residual equation for thermal conductivity of base material with temperature as independent variable. Figure 4 gives a plot of the conductivity coefficient function of the base metal(8)K=52.24287−0.005975399T−0.00006551554T2+5.05125×10−8T3−7.450146×10−12T4.Figure 4
Conductive heat transfer coefficient of base material v/s temperature.


### 4.2. Thermal Expansion Coefficients

The thermal expansion coefficient, which is categorized into primary variable, is employed in quadric smoothened spline due to its MSE being 9.0%. Equation (9) gives the residual equation for thermal expansion coefficient of base material with temperature as independent variable. Figure 5 gives a plot of the thermal expansion coefficient function of the base metal(9)α=0.0000993794+1.015936×10−8T−7.869899×10−12T2+2.660096×10−15T3−2.8959889999999996×10−19T4.Figure 5
Thermal expansion coefficient of base material v/s temperature.After calculating the Rayleigh number and the Nusselt number using the above equations, combined convection and radiation heat transfer coefficients are deduced. The combined coefficient equation is then sorted according to the heat values, that is, temperature and voltage values. Tables1, 4 and 5 give the data regarding the current, heat input, and combined heat transfer coefficient for both single and double grooved welds. The mathematical equations employed to calculate the heat chart are given from equations (9) to (12).Table 4
Thermal results of single V 45°.
LevelHeat loss (Q) -WattHeat generated rate (Q1)-WattHeat utilized (Q2)Heat flux (W/m2)Internal heat generation (q)-W/m3Cooling timeConvective and radiative heat tr. Coeff.Thermal conductivity of plateThermal diffusivityArc efficiency (%) = Q2/Q11336.7215001163.283862010.352643668391.9728481.30E − 05782343.420001656.65499799.144916633190.7937521.33E − 05833354.127002345.97788228.18612980380311148571.37E − 05874331137010393449409.218574901536.4826451.28E − 05765342.521937.515955295291.341882548556.81035501.32E − 05826353.626552301.47640491.21712734152031345551.35E − 0587733412509163041057.597506842932.91021421.27E − 05738346.221712.51366.284535956.522755992753.71231481.31E − 05809349.321451795.75961601.6689936002781442531.33E − 0584Table 5
Thermal results of double V 45°.
LevelHeat loss (Q) -WattHeat generated rate (Q1)-WattHeat utilized (Q2)Heat flux (W/m2)Internal heat generation (q)- W/m3Cooling timeConvective and radiative heat tr. Coeff.Thermal conductivity of plateThermal diffusivityArc efficiency (%) = Q2/Q11158.6412501091.361811620.426301936737.61324481.30E − 05872161.291662.51501.212491957.465415326244.21541521.33E − 05903163.522202056.53413719.95568953325.11756571.37E − 05934158.2313501191.771978297.605329716267.61427451.28E − 05885159.717251565.32598344.6824330574471630501.32E − 05916161222020593417869.865569644977.61839551.35E − 05937158.212501091.81812350.811302058468.41624421.27E − 05878159.71662.51502.82494596.811415766135.11737481.31E − 05909161.5222502088.483466805.661577800943.51939531.33E − 0593An increase in temperature ensures that the molecules in the material gain kinetic energy. Due to this increase in molecular activity, the properties of material such as thermal expansion coefficient (e.g., for an equal amount of expansion of the same material at say 100°C and at 300°C, the temperature increase for 300°C is lower than that of 100°C), specific heat capacity (by virtue of the material and also due to the conversion to internal energy because of temperature rise, so we expect a linear trend initially), Young’s modulus (because of increase in thermal expansion coefficient, the material will more likely to deform at higher temperature than at a lower temperature for any given value of stress. It is similar to the Poisson ratio).The heat generated is calculated as given in equation (9).(10)Q1=V×I.To calculate weld area, weld bead dimensions are calculated that are given in Table6. The weld bead calculations are represented in Tables 7 and 8 for single and double grooved welds, respectively.Table 6
Parameters required to calculate bead area.
ParameterValue in mm unitsDiameter of tungsten tip2.4Optimum standoff distance2Area of plate10000 sq.mmVolume of plate60000 cu.mmProjected area of arc301.211 sq.mmTable 7
Structural results of single V 45°.
LevelsBead width (mm)Bead height (mm)Angle made by beadVol of weld bead (mm3)Vol of groove (mm3)Width of groove (mm)Deformation at the centre (mm)15.5119.981478.751251.254.550.2261.323.4215471251.254.550.336.41.525.111592.51251.254.550.545.7119.33124210354.140.156.11.424.651324.810354.140.266.61.727.25138710354.140.475.81.120.771041.68373.720.186.31.525.4611168373.720.296.81.827.891171.88373.720.3Table 8
Structural results of double V 45°.
LevelsBead width (mm)Bead height (mm)Angle made by beadVol of weld bead (mm3)Vol of groove (mm3)Width of groove (mm)Deformation at the centre (mm)15.51.121.8877.86272.28025.91.425.38946.26272.280.136.41.626.56991.86272.280.245.71.324.5786.6517.52.070.156.21.525.82828517.52.070.166.71.726.9869.4517.52.070.275.91.323.78706.84651.860.186.21.728.73781.24651.860.296.81.929.2818.44651.860.3The heat lost is calculated as given in equation (11)(11)Q=8kθmh15+vw4a,where v is the welding speed (obtained from Table 9), w=2t×tanθ- is the weld width, and t is the weld thickness.Table 9
Weld speed calculation.
S. No.Welding current (Amps)Single V grooveDouble V grooveArc on time (min)Speed mm/secVoltageArc on time (min)Speed mm/secVoltage110030.551504 : 300.3712.5212502 : 230.71603 : 250.4813.3315001 : 470.931802 : 400.62514.8410003 : 310.4713.704 : 270.3713.5512502 : 150.7415.503 : 100.5213.8615001 : 40117.702 : 350.64514.8710002 : 450.612.504 : 000.4212.5812501 : 480.9213.702 : 510.5813.3915001 : 251.1714.302 : 100.7715Net heat utilized is calculated as per equation (12).(12)Q2=Q1−Q.To calculate the heat flux value, area considered is only the projected area of the arc cone generated(13)A=83πr2.


### 4.3. Analysing the Structural Properties of the Weld

For developing the model for tensile strength and fatigue strength, primary variables are taken into consideration. Equation (14) depicts the theoretical relation between tensile strength (σb) and fatigue strength (σw)(14)σw=0.7−1.85×10−4σbσb.Figures6 to 11 gives a comparison of plots for tensile and fatigue strengths. The regression factor R, which is the ratio of σw and σb, is almost a straight line as seen in Figures 10 and 11.Figure 6
Theoretical UTS v/s temperature for similar materials.Figure 7
Theoretical UTS v/s temperature for dissimilar materials.Figure 8
Theoretical fatigue strength v/s temperature for similar materials.Figure 9
Theoretical fatigue strength v/s temperature for dissimilar materials.Figure 10
Theoretical fatigue strength v/s theoretical UTSe for similar materials.Figure 11
Theoretical fatigue strength v/s theoretical UTSe for dissimilar materials.The theoretical UTS is calculated based on the strain ratio equation of the material as given in equation (15)(15)dσdxx=0=dσdεx=0×dεdxx=0=Edεdxx=0,where σ is the stress applied, “E” is Young’s modulus of the material, ϵ is the strain, and “x” is the displacement of the material. In equation (15), “x” is the elongation experienced by the material (if we fix a point, the displacement it experiences by virtue of tension).The strainɛ can be related to displacement by equation (16)(16)ε=xa0.The relationship between stress and displacement gives a derivative as given in equation (17)(17)dσdxx=0=2πλσthcos2πxλ=2πσλx=0.Clubbing the terms ofdσ/dx together, the theoretical strength becomes as given in equation (18)(18)σth=λE2πa0.We assume the theoretical strength to take the form of a wave like nature where the centre of the weld has a maximum value and gradually reducing with distance.σ=Reσtheikx−ωtSo, equations (14) and (16) by making use of (15), we get dσ/dxx=0=E/a0=2πσth/λ. This gives the result in (17).


### 4.4. Optimization of Parameters

A general regression equation is developed using R programme. A linear model having degree of freedom as 2 is chosen as this work only deals with two-way optimization. Equation (19) gives the regression model of the two-way variate analysis(19)P=Q−1S.Here, the objective is minimization of modular functionP where Q is the structural integration and S is the thermal diffusion. A six factored minimization is done so as to find out the optimality conditions for the weld. Table 10 describes the parameters along with their conditions and supportive arguments.Table 10
Parameter characterization.
S. NoParameterConditionArgument1Tensile strengthMaximumThe weld strength and stress carrying capacity should be higher2Fatigue strengthMaximumThe life of weld should be longer3Residual stress distributionMinimumResidual stress has a negative impact on tensile and fatigue strength. It can be minimized by ball burnishing4Temperature distributionMaximumTemperature distribution is proportional to thermal efficiency that should be higher5HAZ spread distanceMinimumThermal stress that should be minimum is directly related to HAZ6Thermal diffusivityMinimumBecause the high temperature should not reach to the inner core and spoil the binderThe model parameters are kept as per the experimental model carried out. The parameters are given as per Table11. Regression equation for hardness optimization is given in equation (20)(20)h=0.7667−0.04967B+0.2627I+0.9750G−0.000047B2−0.000747I2−0.04583G2−0.000107B×I+0.005000B×G.Table 11
Hardness (HV).
LevelsAt centre of weldmentAt HAZAt base plateSingle VDouble VSingle VDouble VSingle VDouble V1138144.6127.21261321322151.8150.6132.6130.8139.2138.63157.8143.6138136.2140.4140.44139.2138121.8120132.6132.65153156125.4123.6135134.46155.4144126126137.4137.47142.8137120118.21321328144141121.2120132132.69156.6143.4126121.2132.6132.6Regression equation for residual stresses optimization is given in equation (21)(21)R=−148.3−1.813B+1.640I+41.08G+0.007067B2+0.003733I2−1.458−0.002400B×I−0.02333B×G.


## 4.1. Analysing the Heat Flow during Welding

The supplied heat is channelled into three segments, namely, material conduction, medium convection, and radiation. The conduction heat is again categorized into two segments that are heat utilized and heat dispersed. To calculate these heat coefficients, it needs to have the thermal conductivity coefficient, thermal expansion coefficient, and specific heat of the material.From equations (2) and (3), it can be calculated the convection coefficient and radiation coefficient of air. Equation (7) gives the Prandtl number of air as a function of temperature(7)Pr=0.0985×e−0.007716T+0.673×e0.000726T.Conductivity Coefficients:The thermal conductivity, which is categorized into primary variable, is employed in quadric smoothened spline due to its MSE being 8.1%. Equation (8) gives the residual equation for thermal conductivity of base material with temperature as independent variable. Figure 4 gives a plot of the conductivity coefficient function of the base metal(8)K=52.24287−0.005975399T−0.00006551554T2+5.05125×10−8T3−7.450146×10−12T4.Figure 4
Conductive heat transfer coefficient of base material v/s temperature.


## 4.2. Thermal Expansion Coefficients

The thermal expansion coefficient, which is categorized into primary variable, is employed in quadric smoothened spline due to its MSE being 9.0%. Equation (9) gives the residual equation for thermal expansion coefficient of base material with temperature as independent variable. Figure 5 gives a plot of the thermal expansion coefficient function of the base metal(9)α=0.0000993794+1.015936×10−8T−7.869899×10−12T2+2.660096×10−15T3−2.8959889999999996×10−19T4.Figure 5
Thermal expansion coefficient of base material v/s temperature.After calculating the Rayleigh number and the Nusselt number using the above equations, combined convection and radiation heat transfer coefficients are deduced. The combined coefficient equation is then sorted according to the heat values, that is, temperature and voltage values. Tables1, 4 and 5 give the data regarding the current, heat input, and combined heat transfer coefficient for both single and double grooved welds. The mathematical equations employed to calculate the heat chart are given from equations (9) to (12).Table 4
Thermal results of single V 45°.
LevelHeat loss (Q) -WattHeat generated rate (Q1)-WattHeat utilized (Q2)Heat flux (W/m2)Internal heat generation (q)-W/m3Cooling timeConvective and radiative heat tr. Coeff.Thermal conductivity of plateThermal diffusivityArc efficiency (%) = Q2/Q11336.7215001163.283862010.352643668391.9728481.30E − 05782343.420001656.65499799.144916633190.7937521.33E − 05833354.127002345.97788228.18612980380311148571.37E − 05874331137010393449409.218574901536.4826451.28E − 05765342.521937.515955295291.341882548556.81035501.32E − 05826353.626552301.47640491.21712734152031345551.35E − 0587733412509163041057.597506842932.91021421.27E − 05738346.221712.51366.284535956.522755992753.71231481.31E − 05809349.321451795.75961601.6689936002781442531.33E − 0584Table 5
Thermal results of double V 45°.
LevelHeat loss (Q) -WattHeat generated rate (Q1)-WattHeat utilized (Q2)Heat flux (W/m2)Internal heat generation (q)- W/m3Cooling timeConvective and radiative heat tr. Coeff.Thermal conductivity of plateThermal diffusivityArc efficiency (%) = Q2/Q11158.6412501091.361811620.426301936737.61324481.30E − 05872161.291662.51501.212491957.465415326244.21541521.33E − 05903163.522202056.53413719.95568953325.11756571.37E − 05934158.2313501191.771978297.605329716267.61427451.28E − 05885159.717251565.32598344.6824330574471630501.32E − 05916161222020593417869.865569644977.61839551.35E − 05937158.212501091.81812350.811302058468.41624421.27E − 05878159.71662.51502.82494596.811415766135.11737481.31E − 05909161.5222502088.483466805.661577800943.51939531.33E − 0593An increase in temperature ensures that the molecules in the material gain kinetic energy. Due to this increase in molecular activity, the properties of material such as thermal expansion coefficient (e.g., for an equal amount of expansion of the same material at say 100°C and at 300°C, the temperature increase for 300°C is lower than that of 100°C), specific heat capacity (by virtue of the material and also due to the conversion to internal energy because of temperature rise, so we expect a linear trend initially), Young’s modulus (because of increase in thermal expansion coefficient, the material will more likely to deform at higher temperature than at a lower temperature for any given value of stress. It is similar to the Poisson ratio).The heat generated is calculated as given in equation (9).(10)Q1=V×I.To calculate weld area, weld bead dimensions are calculated that are given in Table6. The weld bead calculations are represented in Tables 7 and 8 for single and double grooved welds, respectively.Table 6
Parameters required to calculate bead area.
ParameterValue in mm unitsDiameter of tungsten tip2.4Optimum standoff distance2Area of plate10000 sq.mmVolume of plate60000 cu.mmProjected area of arc301.211 sq.mmTable 7
Structural results of single V 45°.
LevelsBead width (mm)Bead height (mm)Angle made by beadVol of weld bead (mm3)Vol of groove (mm3)Width of groove (mm)Deformation at the centre (mm)15.5119.981478.751251.254.550.2261.323.4215471251.254.550.336.41.525.111592.51251.254.550.545.7119.33124210354.140.156.11.424.651324.810354.140.266.61.727.25138710354.140.475.81.120.771041.68373.720.186.31.525.4611168373.720.296.81.827.891171.88373.720.3Table 8
Structural results of double V 45°.
LevelsBead width (mm)Bead height (mm)Angle made by beadVol of weld bead (mm3)Vol of groove (mm3)Width of groove (mm)Deformation at the centre (mm)15.51.121.8877.86272.28025.91.425.38946.26272.280.136.41.626.56991.86272.280.245.71.324.5786.6517.52.070.156.21.525.82828517.52.070.166.71.726.9869.4517.52.070.275.91.323.78706.84651.860.186.21.728.73781.24651.860.296.81.929.2818.44651.860.3The heat lost is calculated as given in equation (11)(11)Q=8kθmh15+vw4a,where v is the welding speed (obtained from Table 9), w=2t×tanθ- is the weld width, and t is the weld thickness.Table 9
Weld speed calculation.
S. No.Welding current (Amps)Single V grooveDouble V grooveArc on time (min)Speed mm/secVoltageArc on time (min)Speed mm/secVoltage110030.551504 : 300.3712.5212502 : 230.71603 : 250.4813.3315001 : 470.931802 : 400.62514.8410003 : 310.4713.704 : 270.3713.5512502 : 150.7415.503 : 100.5213.8615001 : 40117.702 : 350.64514.8710002 : 450.612.504 : 000.4212.5812501 : 480.9213.702 : 510.5813.3915001 : 251.1714.302 : 100.7715Net heat utilized is calculated as per equation (12).(12)Q2=Q1−Q.To calculate the heat flux value, area considered is only the projected area of the arc cone generated(13)A=83πr2.


## 4.3. Analysing the Structural Properties of the Weld

For developing the model for tensile strength and fatigue strength, primary variables are taken into consideration. Equation (14) depicts the theoretical relation between tensile strength (σb) and fatigue strength (σw)(14)σw=0.7−1.85×10−4σbσb.Figures6 to 11 gives a comparison of plots for tensile and fatigue strengths. The regression factor R, which is the ratio of σw and σb, is almost a straight line as seen in Figures 10 and 11.Figure 6
Theoretical UTS v/s temperature for similar materials.Figure 7
Theoretical UTS v/s temperature for dissimilar materials.Figure 8
Theoretical fatigue strength v/s temperature for similar materials.Figure 9
Theoretical fatigue strength v/s temperature for dissimilar materials.Figure 10
Theoretical fatigue strength v/s theoretical UTSe for similar materials.Figure 11
Theoretical fatigue strength v/s theoretical UTSe for dissimilar materials.The theoretical UTS is calculated based on the strain ratio equation of the material as given in equation (15)(15)dσdxx=0=dσdεx=0×dεdxx=0=Edεdxx=0,where σ is the stress applied, “E” is Young’s modulus of the material, ϵ is the strain, and “x” is the displacement of the material. In equation (15), “x” is the elongation experienced by the material (if we fix a point, the displacement it experiences by virtue of tension).The strainɛ can be related to displacement by equation (16)(16)ε=xa0.The relationship between stress and displacement gives a derivative as given in equation (17)(17)dσdxx=0=2πλσthcos2πxλ=2πσλx=0.Clubbing the terms ofdσ/dx together, the theoretical strength becomes as given in equation (18)(18)σth=λE2πa0.We assume the theoretical strength to take the form of a wave like nature where the centre of the weld has a maximum value and gradually reducing with distance.σ=Reσtheikx−ωtSo, equations (14) and (16) by making use of (15), we get dσ/dxx=0=E/a0=2πσth/λ. This gives the result in (17).


## 4.4. Optimization of Parameters

A general regression equation is developed using R programme. A linear model having degree of freedom as 2 is chosen as this work only deals with two-way optimization. Equation (19) gives the regression model of the two-way variate analysis(19)P=Q−1S.Here, the objective is minimization of modular functionP where Q is the structural integration and S is the thermal diffusion. A six factored minimization is done so as to find out the optimality conditions for the weld. Table 10 describes the parameters along with their conditions and supportive arguments.Table 10
Parameter characterization.
S. NoParameterConditionArgument1Tensile strengthMaximumThe weld strength and stress carrying capacity should be higher2Fatigue strengthMaximumThe life of weld should be longer3Residual stress distributionMinimumResidual stress has a negative impact on tensile and fatigue strength. It can be minimized by ball burnishing4Temperature distributionMaximumTemperature distribution is proportional to thermal efficiency that should be higher5HAZ spread distanceMinimumThermal stress that should be minimum is directly related to HAZ6Thermal diffusivityMinimumBecause the high temperature should not reach to the inner core and spoil the binderThe model parameters are kept as per the experimental model carried out. The parameters are given as per Table11. Regression equation for hardness optimization is given in equation (20)(20)h=0.7667−0.04967B+0.2627I+0.9750G−0.000047B2−0.000747I2−0.04583G2−0.000107B×I+0.005000B×G.Table 11
Hardness (HV).
LevelsAt centre of weldmentAt HAZAt base plateSingle VDouble VSingle VDouble VSingle VDouble V1138144.6127.21261321322151.8150.6132.6130.8139.2138.63157.8143.6138136.2140.4140.44139.2138121.8120132.6132.65153156125.4123.6135134.46155.4144126126137.4137.47142.8137120118.21321328144141121.2120132132.69156.6143.4126121.2132.6132.6Regression equation for residual stresses optimization is given in equation (21)(21)R=−148.3−1.813B+1.640I+41.08G+0.007067B2+0.003733I2−1.458−0.002400B×I−0.02333B×G.


## 5. Experimental Methodology

Detailed methodology of optimization and experimental procedure is highlighted in Figure6.EN 10025 Steel of dimensions 50 × 100 × 6 mm was chosen for the welding experiment and clearly mentioned the flowchart of experiment methodology in Figure12. 36 such plates were purchased to obtain 18 weld joints for the analysis. All the samples were then immersed in 0.02% HNO3 solution to remove the top oxide layer from the metal, which generally has a thickness of 6 microns. In steel, the passivation process uses HNO3 to remove free iron from the surface. The chemical treatment leads to a protective oxide layer that is less likely to chemically react with air and cause corrosion. HNO3 is oxidizing in character, whereas the others are reducing. It tends to promote and preserve the corrosion-resistant qualities of steels; that is, it does not act to destroy the microscopically thin oxide surface film that gives stainless steels their corrosion resistance. Its oxidizing behaviour accounts for the fact that when alone or in solution with H2O, HNO3 will not dissolve and remove oxide scale from steels. Material preparation was initiated by outsourcing the specimens for cutting it to particular angles to prepare the grooves for the welds, before the commencement of welding on the base materials. The cutting edge was 22.5 degrees with a cut thickness of 0.5 mm with a tolerance up to 0.02 mm. The plates were characterized into single V and double V grooved types. The input parameters of weld were varied with gas flow rate, weld current, and land. The electrode used was tungsten carbide, and the filler was stainless steel. All the samples were clamped during the welding procedure, to obtain minimum metal deformations and residual stresses. The secondary varying parameters were edge start distance offset (this value was taken to minimize the loss of weld pool exceeding out of the groove at the start and end edges), electrode height from the plate, and the volume of electrode consumed for each weld pass. The welds were carried out under the combination values of the root land at 0.5, 1, and 1.5 mm; gas flow rates at 12, 14, and 16 lit/min; and welding currents of 100, 125, and 150 Amps. Nine sets of each were welded to know the effect of single V and double V grooves irrespective of material thickness on hardness, tensile strength, and residual stresses induced. Optimum standoff distance 2 mm is selected because it shows reasonable results, and the worst case was recorded for the 0 mm standoff distance condition [35]. After the welding was completed, it was kept at ambient temperature to cool down. The cooldown time was noted (to calculate the material diffusivity value). From the results obtained by considering optimal parameters, dissimilar materials are welded.Figure 12
Experimental flow chart.


### 5.1. Residual Stresses Measurement

All the weld samples were marked at three points for obtaining the values of residual stresses, that is, HAZ, weld zone, and the parent material. However, the weldment with optimal parameters (i.e., level-5) was studied for more varying points for the better understanding of residual stresses in the specimen. After conducting the tests, it was found that the residual stresses at the weld zone are higher and tensile in nature, whereas in the HAZ they are lower and compressive in nature. The equipment used was Pulstecµ X-360 X-ray scanner. The average time taken for each point was approximately 4 mins.


### 5.2. Microhardness Measurement

The Vickers hardness values at base metal zone, weld zone, and HAZ were found for all the 18 weld samples in order to estimate the theoretical tensile strength. Diamond tip indentation was made onto the target specimen, and a microscopic image was obtained that gave the dimensions of two diagonals for estimating the hardness. Two samples were chosen for further tests that had higher hardness and lower residual stresses.


### 5.3. Tensile Strength Measurement

The tensile test was performed for selected welds with optimal input parameters, for both single V and double V groove angles. The specimen was designed according to ASTM E08 standards, and it was shown in Figure13. The tensile specimen was prepared using a wire arc discharge matching process. Based on the results obtained, the double V groove butt joint sustained to be the better joint.Figure 13
Tensile specimen according to ASTM E08.


## 5.1. Residual Stresses Measurement

All the weld samples were marked at three points for obtaining the values of residual stresses, that is, HAZ, weld zone, and the parent material. However, the weldment with optimal parameters (i.e., level-5) was studied for more varying points for the better understanding of residual stresses in the specimen. After conducting the tests, it was found that the residual stresses at the weld zone are higher and tensile in nature, whereas in the HAZ they are lower and compressive in nature. The equipment used was Pulstecµ X-360 X-ray scanner. The average time taken for each point was approximately 4 mins.


## 5.2. Microhardness Measurement

The Vickers hardness values at base metal zone, weld zone, and HAZ were found for all the 18 weld samples in order to estimate the theoretical tensile strength. Diamond tip indentation was made onto the target specimen, and a microscopic image was obtained that gave the dimensions of two diagonals for estimating the hardness. Two samples were chosen for further tests that had higher hardness and lower residual stresses.


## 5.3. Tensile Strength Measurement

The tensile test was performed for selected welds with optimal input parameters, for both single V and double V groove angles. The specimen was designed according to ASTM E08 standards, and it was shown in Figure13. The tensile specimen was prepared using a wire arc discharge matching process. Based on the results obtained, the double V groove butt joint sustained to be the better joint.Figure 13
Tensile specimen according to ASTM E08.


## 6. Results and Discussions

All the experimental results are listed in Tables12 to 13. Bead height, width, and deformation of the welded component are measured using Vernier calliper. Failure of tensile specimens can be observed from Figure 14. Load vs. displacement graphs for single V butt joint, double V butt joint, and dissimilar double V butt joint can be observed from Figures 15–17 respectively.Table 12
Residual stresses (MPa).
LevelsAt centre of weldmentAt HAZAt base plateSingle VDouble VSingle VDouble VSingle VDouble V1237211200189−83−912300281262243−91−983356304301292−97−1074175152102104−120−1215221175187162−101−1326290199207183−160−156713212111098−131−1238198163150121−98−1349255197201176−155−146Table 13
Tensile strength for optimal levels.
Tensile strength (MPa)Fatigue strength (MPa)Fatigue ratioSingle VLevel 5421 (failed at HAZ)214.80.51Double VLevel 5426 (failed at HAZ)216.90.09Figure 14
Failure of tensile specimens.Figure 15
Load vs. displacement graph for single V butt joint.Figure 16
Load vs. displacement graph for double V butt joint.Figure 17
Load vs. displacement graph for dissimilar double V butt joint.Residual stresses (residue or remaining stress) are the secondary stresses induced in a component, which are generated by either thermal or mechanical constraints, without any external load. Residual stresses (RS) generated in the weld component are due to the nonuniform expansion and compression of the weld, whereas in the base material it is due to the nonuniform heat distribution while welding. Further, tensile and compressive type of residual stresses can be identified in weld sample. Due to the cycles of expansion and contraction, some percentage of the strain is induced in the weld zone [36, 37]. If the remaining percentage of strain, after cooling is multiplied by the elastic modulus of the material, the percentage of hardening strains (RS) generated in the weld component can be identified. Surface tensile residual stresses can cause brittle fracture, and surface compressive residual stress tends to increase fatigue strength. In this work, crowns were not grinded, to obtain untempered residual stresses in the weldment because grinding can cause additional surface tensile residual stresses. Tensile residual stresses may lead to crack propagation, whereas compressive residual stress will enhance the mechanical properties of the component. By studying the percentage of residual stress (compressive or tensile) in component, one can predict and analyse the actual lifetime of a welded component. From the Taguchi response tables, represented in Tables 14 and 15, it can be observed that residual stresses are directly proportional to current and inversely proportional to weld offset. It was also observed that the tensile residual stresses were induced at weld and HAZ zone while compressive residual stresses at base metal. The observations can be decrypted from Figures 18 and 19. The weld zone shows a complete rigid joint, and hence, obtaining longitudinal shrinkage is minimal. If the longitudinal shrinkage propagates, it can result in reduction of the length of the workpiece. Since this reduction in the weldment is minimal, it results in the tensile residual stresses across the weld length.Table 14
Taguchi analysis (residual stresses: smaller is better)—response table for signal-to-noise ratio.
LevelLandCurrentGas flow1−49.36−44.92−47.562−47.00−47.45−47.513−45.49−49.47−46.78Delta3.864.550.78Rank213Table 15
Taguchi analysis (residual stresses: smaller is better)—response table for means.
LevelLandCurrentGas flow1297.7181.3241.72228.7239.7243.33195.0300.3236.3Delta102.7119.07.0Rank213Figure 18
Residual stresses vs. length of plate in double V groove (level-5) weld specimen.Figure 19
Vickers hardness vs. length of plate in double V groove weld specimen.Predominant reasons for residual stress generation are as follows: size of weld bead (length, breadth, and height), mechanical and thermal properties of the workpiece, type of welding process, method of welding (back and front hand techniques), and preinduced RS in the component. In this work, back hand welding technique (postheating of the weld bead) is preferred. Obtained weld bead is uniform and smoother in back hand welding technique because the force of flame pushes out molten slag particles from the weld pool. So, there is none to minimum slag inclusion in the weld zone, avoiding subsequent defects in the weld bead. Residual stresses can be decreased by opting heat treatment or surface hardening (ball burnishing, shoot peening, and surface rolling) process.From the Taguchi response tables, represented in Tables16 and 17, it can be observed that high microhardness can be observed in the weld zone, as hardness of the stainless steel filler rod is comparatively higher than the mild carbon steel plate or base plate, whereas lower microhardness is found at HAZ and base plate. The observations can be graphically observed in Figure 19. As it is known that, above recrystallization temperature, the material varies its grain size from fine to coarse, which tends to increase brittleness and decrease toughness. From Table 12, it can be observed that hardness directly varies with gas flow rate, whereas inversely varies with weld offset.Table 16
Taguchi analysis (hardness: larger is better)—response table for signal-to-noise ratio.
LevelLandCurrentGas flow143.2642.9243.46243.4643.5043.47343.5843.9043.39Delta0.320.970.08Rank213Table 17
Taguchi analysis (hardness: larger is better)—response table for means.
LevelLandCurrentGas flow1145.8140.0149.22149.2149.6149.23151.2156.6147.8Delta5.416.61.4Rank213From Table12, it can also be identified that all the tensile specimens will break at HAZ due to its characteristic hardness. As stainless steel is used as a filler rod and being stronger than mild steel, the failure occurs at HAZ because it is more brittle in nature than the base plate area. Tensile specimen with double V 45° butt joint possesses high tensile strength than single V 45°.From Figure20 we can conclude that current of 125 Amps, gas flow of 16 lit/min, and weld offset of 1 mm are suitable for maximum hardness. From the above figures, it can be concluded that current of 100 Amps, gas flow of 16 lit/min, and weld offset of 1.5 mm are suitable for residual stress minimization. Based on the genetic algorithm optimization of the general regression equation obtained in MATLAB, the subsequent results of the mathematical model are given in Table 18. In Figures 21 and 20, X-axis units of weld offset are in (50, 100, 150) × 10−2. Figure 13 shows the optimal process parameters by considering the output results. Finally, S/N (signal-to-noise) ratio and mean tables for hardness and residual stresses are highlighted in Tables 14 to 17.Figure 20
Taguchi analysis for hardness v/s land, current, and gas flow rate.Figure 21
Taguchi analysis for residual stress v/s land, current, and gas flow rate.Table 18
Optimized values obtained from the mathematical model.
S. NoParameterValue1Ideal current125 Amps2Ideal voltage17.25 Volts3Arc efficiency0.84Ideal cooling time2751 seconds5Ideal diffusivity1.3727E − 056Ideal combined heat transfer coefficient38.771 W/m2K7Ideal gas flow rate16 lit/min8Ideal land1 mmFigure22 indicates that the hardness values are affected majorly when the current changes and are affected minorly with change in weld offset distance. Figure 22 also shows that weld offset has a high magnitude of effect and gas flow rate has a subsequent lower effect on the residual stresses.Figure 22
Optimal parameters—Taguchi analysis.By considering the optimal parameters, dissimilar materials are welded and the results of weld groove geometry, tensile test, hardness, and residual stresses tests are shown in Tables19 to 22.Table 19
Structural results of dissimilar double V 45°.
LevelBead width (mm)Bead height (mm)Angle made by beadVol of weld bead (mm3)Vol of groove (mm3)Width of groove (mm)Deformation at the centre (mm)56.21.424.3828517.52.070Table 20
Residual stresses (MPa) of dissimilar double V 45°
LevelAt centre of weldmentAt HAZAt base plateLCSSBSLCSSBS5175162170−132−97Table 21
Hardness (HV) of dissimilar double V 45°.
LevelAt centre of weldmentAt HAZAt base plateLCSSBSLCSSBS5156123135134142Table 22
Tensile and impact strength for dissimilar double V 45°.
LevelTensile strength (MPa)Similar5426 (failed at HAZ)Dissimilar5661 (failed at HAZ in ship building steel)


## 7. Conclusion

In this research article, argon arc welding was performed for the material EN 10025 Steel (grade S295) and D36 shipbuilding steel. After it, Taguchi orthogonal L9 array design was used to optimize the residual stresses and microhardness for particular input parameters. The genetic algorithm was used to select the optimal parameters to reduce residual stresses and increase microhardness.(1)
Tensile residual stresses are induced over the weld length and HAZ, whereas compressive residual stresses are induced at the base plate.(2)
It was observed that the amount of induced residual stresses in the component is directly proportional to current and gas flow rate due to large heat input, whereas it inversely varies with weld offset due to reduction in groove volume.(3)
Hardness is high in the weld bead zone as stainless steel is used as a filler rod, which is comparatively harder than EN 10025 Steel.(4)
Brittle nature can be observed in the HAZ (enlargement of grains) because temperature at HAZ is higher than the recrystallization temperature of EN 10025 Steel. Hence, hardness and tensile strength at HAZ are comparatively lower than the base plate and weld zone.(5)
Both the single and double V groove welded tensile specimens failed at the HAZ, whereas the double V groove showed higher tensile strength.(6)
Dissimilar material possesses high tensile and fatigue strength compared with the similar material welded sample.



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*Source: 5118566-2022-04-13.xml*