Material Strength Optimization of Dissimilar MIG Welding between Carbon and Stainless Steels

Abstract

This study examines the effects of stick-out, welding current, welding speed, and voltage on the mechanical characteristics and microstructure of MIG welding on SUS 304 stainless steel and S20C steel. The Taguchi method was used to maximize the experiment’s outcomes. Fine columnar dendrites formed at fusion sites, and δ-ferrite phases with dark lines and shapes accumulated between the fusion line and the austenite phases. A welding current of 110 A, voltage of 15 V, welding speed of 500 mm/min, and stick-out of 12 mm were the optimal settings for the ultimate tensile strength (UTS). The UTS value confirmation was 469.4 MPa, which agrees with the estimated value determined using the Taguchi technique. The tensile test revealed that welding current had a far greater impact on mechanical qualities than welding voltage, speed, and stick-out distance. The ideal welding parameters for flexural strength were as follows: stick-out of 12 mm, arc voltage of 15 V, welding speed of 450 mm/min, and welding current of 110 amp. The Taguchi method is useful, as evidenced by the validation of the flexure strength of 1937.45 MPa, which is much greater than the other samples. The impact of the thermal annealing process on the mechanical characteristics of the dissimilar weld joints could be the subject of future research. The investigation results may offer more insightful information about the dissimilar welding field.

1. Introduction

Dissimilar welding is a widely used welding technique that can be used to fuse two different metals, including carbon steel, alloyed steel, steel and aluminum, copper and steel, and steel and copper [1,2,3,4]. Dissimilar welding could be applied when the users consider saving costs and improving the component quality. This welding process is widely used in petroleum, boilers, automobiles, shipbuilding, aerospace, and electronics [5,6,7]. Fusion welding techniques, including Gas Metal Arc Welding (GMAW), Gas Tungsten Arc Welding (GTAW), Shielded Metal Arc Welding (SMAW), high-energy EBW welding (Electron Beam Welding), plasma PAW (plasma welding), and Submerged Arc Welding (SAW), are recommended because of their strong welding capabilities and readily available equipment, which allows for the formation of dissimilar joints [8].

Among numerous dissimilar materials, low-carbon and stainless steel have remarkable properties, making them the primary materials used throughout the industry, such as power generation, nuclear reactors, petrochemical, and chemical sectors, mostly to obtain tailor-made characteristics in a component while lowering costs [9]. Dissimilar welding can join the components, increasing their strength and economy. Kim et al. [10] evaluated the microstructure and thermomechanical parameters of dissimilar welding between carbon steel grade SS400 and ferritic stainless steel STS441. They discovered that martensite phase development occurs at the weld joint due to the fall in Cr and Nb content. The dissimilar weld joint has equivalent tensile strength to the STS441 base metal, but its thermal fatigue strength is more muscular. Ranjbarnodeh et al. [11] evaluated the residual stress of dissimilar welding of low-carbon steel and ferritic stainless steel using a finite element program called ANSYS and an experiment analysis.

Using the X-ray diffraction method, the residual stress in the experimental specimens is quantified and shown to be similar to the simulation results. The residual stress decreases as the welding current increases. Singh et al. [12] reported the dissimilar welding process between SS 304 stainless steel and medium-carbon steel grade EN 8. The welding current, welding voltage, and welding speed were examined. The study pointed out the presence of large-grain austenite, small-grain austenite, and δ-ferrite. The impact strength was strongly affected by the in-grain misorientation of the austenite phase and the ratio of δ-ferrite. Thang et al. [13] examined the dissimilar welding process of SS 304 stainless steel pipe and A 106B carbon steel pipe with the GTAW technique, using ER309L and E 309L-16 filling rods. The non-destructive testing results indicated no significant defects, while the tensile strength was higher than the ASME section IX standard. In addition, there is a presence of δ-ferrite in the weld joints. An et al. [14] studied the dissimilar welding joints of Q235 carbon steel and SS 304 stainless steel created via tungsten inert gas welding (TIG) and GMAG. The microstructure of the weld joints consisted of austenite, δ-ferrite, and some partial martensite phases. The weld joint’s impact strength was comparable to the base metal’s. The fracture surface displayed numerous dimples and cleavage fractures, indicating both brittle and ductile mechanisms. Besides the mechanical characteristics, Abioye et al. [15] also considered the weld-bead shape of the dissimilar welding joint between low-carbon steel and AISI 304 stainless steel. The impacts of wire feed rate, welding speed, and welding voltage were studied. The results showed that the maximum ultimate tensile strength and hardness were 422 MPa and 112 HB, respectively. Noga et al. [16] investigated the welding process of austenite stainless steel 316Ti with TIG, MIG, and EBW welding methods and plasma PAW welding methods. The results revealed the presence of various delta ferrite contents depending on the technology utilized. The TIG and PAW connectors had the most significant delta ferrite content at about 5%, while the EBW connector had the lowest at about 2%.

Taguchi Method is a statistical and optimization method designed to improve product quality and performance while lowering manufacturing costs [17,18,19]. For instance, Gajbhiye et al. [20] applied the Taguchi method via Minitab 18 software to investigate the optimization process of welding between En8D and SAE1018 materials for the tractor pedal shaft. The results demonstrated that welding voltage had the most significant influence on the tensile strength of the shaft, followed by welding current, gas pressure, and nozzle-to-work distance. Meseguer-Valdenebro et al. [21] also applied the Taguchi method to explore the welding process of the 6063-T alloy. They conducted the ASME section IX standard to indicate the passed sample with a good penetration depth. The welding speed is the most critical parameter that affects the weld bead’s geometry, followed by power and edge spacing between profiles.

Despite the many dissimilar studies, applying optimization to find optimal characteristics is still rare and needs more investigation. In this study, the impacts of welding current, welding voltage, welding speed, and stick-out distance on the tensile strength of dissimilar welding between SUS 304 and low-carbon steel S20C were evaluated using the GMAG technique. The microstructure of the dissimilar weld joints was also analyzed. The experiment parameters were designed via the Taguchi method to optimize the results. The study’s findings could provide helpful insight into the dissimilar welding field.

2. Materials and Methods

To create a SUS304 and S20C dissimilar welding joint, a steel sheet was prepared with the dimensions of 110 mm × 105 mm × 2 mm, as shown in Figure 1c. Figure 1a,b shows the flexural sample shape, which follows standard ASTM E290 [22], and the tensile sample shape, following standard ASTM E8/E8M-13 [23]. The selected welding wire is ER 70S-6, having low carbon and high manganese content. The nominal chemical compositions of these steel sheets and the welding wire are presented in

Table 1. Chemical composition of stainless steel 304 (%), S20C steel, and welding wire ER 70S-6 (%).

Standard	C	Si	Mn	P	S	Cr	Ni	Cu
ASTM A276/A276M [24]	≤0.08	1.00	≤2.00	≤0.045	≤0.030	18.0–20.0	8.0–11.0	-
JIS G 4051 [25]	0.18–0.23	0.15–0.35	0.3–0.6	≤0.03	≤0.035	≤0.2	≤0.2	0.3
AWS A5.18/A5.18M [26]	0.06–0.15	0.80–1.15	1.40–1.85	≤0.025	≤0.035	-	-	-

. Before welding, these sheets were fixed with a jig, as shown in Figure 1c. The welding 6-arm axis Robot Panasonic named TA-1400G2 series YA-1NA (Panasonic, Osaka, Japan), tensile test samples, and flexural test samples are shown in Figure 2.

Before welding, the parameters were surveyed to ensure good welding quality, avoiding significant defects. After that, the initial welding parameters were selected, as shown in

Table 2. The welding parameters after the initial survey.

Welding Parameters (Factors)	Level 1	Level 2	Level 3	Level 4
Welding Current, I (Ampere)	80	90	100	110
Welding Voltage, U (Voltage)	15	16	17	18
Welding Speed, v (mm/min)	450	500	550	600
Electrical Stick out, d (mm)	8	12	16	20

. The welding parameters were designed based on four types: welding current, welding voltage, welding speed, and electrical stick out. The gas flow rate was fixed at 14 L/min to ensure the well-protective weld joints.

Table 3. The input parameters designed via the Taguchi method and output results of experiment samples.

No.	I (A)	U (V)	Stick-Out (mm)	Speed (mm/min)	UTS (MPa)	Elongation (%)	Yield Strength (MPa)
1	80	15	8	450	304.5	17.5	225.2
2	80	16	12	500	239.8	8.0	208.9
3	80	17	16	550	195.5	7.5	169.2
4	80	18	20	600	141.7	6.8	88.8
5	90	15	12	550	419.4	48.7	308.9
6	90	16	8	600	312.6	12.8	246.0
7	90	17	20	450	269.2	9.83	220.7
8	90	18	16	500	336.6	18.8	246.8
9	100	15	16	600	323.6	10.0	256.9
10	100	16	20	550	286.5	25.7	230.8
11	100	17	8	500	373.2	55.8	286.9
12	100	18	12	450	311.9	47.8	236.2
13	110	15	20	500	399.0	44.2	304.2
14	110	16	16	450	397.3	42.5	329.7
15	110	17	12	600	440.7	49.6	348.0
16	110	18	8	550	347.4	38.0	275.1

shows the welding parameters that were designed by the Taguchi method. Each sample number has 3 test samples, and the mechanical properties are the average value of 3 samples. After welding, the dissimilar weld sheet was cut by an electrical discharge wire cutting machine to accurately obtain the tensile test samples. The tensile samples were tested with the WE1000B universal testing machine (Jinan, Shandong, China). The microstructure of the samples was observed via metallurgical microscope Oxion OX.2153-PLM EUROMEX (Euromex Microscopen bv, Arnhem, The Netherlands). The samples for microstructure observation were cut, ground, polished, and etched with 4% Nital or HCl/HNO₃ solution for carbon or stainless steel parts.

3. Results and Discussion

3.1. Macro and Microstructure Results

Firstly, the macrostructure of the welding joints was analyzed, as shown in Figure 3. According to the ASME IX standard, the welding joint’s depth of penetration (DOP) should equal or be more significant than 0.9t (t is the thickness of the sheet). Therefore, the DOP value should be higher than 1.8 mm. The sample passed the evaluation with this condition, as shown in

Table 4. The DOP values of dissimilar weld joints between SUS 304 and S20C.

No.	DOP (mm)	Pass/Fail	UTS (MPa)	Elongation (%)
1	0.95	Failed	304.5	17.5
2	0.45	Failed	239.8	8.0
3	0.36	Failed	195.5	7.5
4	0.33	Failed	141.7	6.75
5	1.83	Passed	419.4	48.67
6	1.76	Failed	312.6	12.83
7	0.38	Failed	269.2	9.83
8	2.23	Passed	336.6	18.8
9	1.09	Failed	323.6	10.0
10	1.23	Failed	286.5	25.66
11	2.58	Passed	373.2	55.83
12	2.00	Passed	311.9	47.83
13	1.97	Passed	399.0	44.16
14	2.57	Passed	397.3	42.5
15	2.77	Passed	440.7	49.6
16	2.38	Passed	347.4	38.0

. Six sample numbers, including 5, 8, 11 12, 13, 14, 15, and 16, passed the DOP standards, while other samples had DOP values lower than 1.8 mm. According to Table 3, the UTS values of these passed samples were more significant than 310 MPa, and the elongation was more excellent than 18%. The highest UTS value among these samples was sample No. 15, with 440.7 MPa, with a good elongation at a break of 49.6%. At the same time, the failed samples had lower UTS and elongation values.

Figure 4 presents the tensile test diagrams and UTS comparison between weld joints and base metals. Figure 4a shows the tensile test curve of sample No. 5, which had a good UTS value and passed the DOP standard, as shown in Table 4. In addition, Figure 4b illustrates the UTS comparison between S20C, SUS304, and sample No. 5. The base metals had UTS values of 443.1 MPa and 520.5 MPa, corresponding to S20C and SUS340 steels, respectively. These UTS values were higher than sample No. 5 and all other samples presented in Table 3. The microstructures and Taguchi analyses are discussed in the following results.

Figure 5 depicts the microstructure of SUS 304 vs. S20C dissimilar steel welding joints. The microstructure of the dissimilar welding between SUS 304 and S20C shows the diversity of microstructures in different weld regions. Remarkably, in the macrostructure of the sample, there was a groove in the middle position, indicating the DOP issue, which is previously mentioned in Table 4. These microstructural changes can affect the mechanical properties of the weld, including strength, ductility, hardness, and crack resistance. Therefore, microstructure analysis is crucial for assessing the quality of the weld and ensuring its performance. The microstructure of SUS 304 and S20C’s base metals are presented in zones A and D, respectively. Zone A shows the ferrite in a bright color and pearlite in the dark color of S20C steel. Ferrite is a soft and ductile phase, while pearlite is a harder and more brittle phase. Moreover, the ferrite phase dominates the microstructure, indicating the low-carbon steel grade. Zone D, which is the microstructure of SUS 304 steel, has austenite phases with twin boundaries. The heat affect zone (HAZ) is shown in zone E and consisted of the austenite and δ-ferrite phases, which is similar to Hsieh et al. study [27]. Due to the heat input from the welding process, the microstructure of S20C steel in the HAZ changed. Ferrite became the dominant structure in this region. This change can affect the strength and ductility of the weld. Finally, the weld bead in zone C exhibited the microstructure of martensite and bainite, which are the results of rapid heating and cooling during the welding process, a result consistent with Khan et al. [28]. This zone shows a mixture of microstructures from both base materials, S20C steel, and SUS304 steel. This mixing can affect the strength and ductility of the weld. The hardness test was conducted in several zones, indicating that the base metals had a hardness of 124 HV and 129 HV, corresponding to S20C and SUS304, respectively. The weld metal had the highest hardness of 399 HV due to bainite and martensite structure. The HAZs had a hardness of 146 HV and 200 HV, corresponding to the S20C and SUS304 sides, respectively. This result is consistent with those of Ogedengbe et al. [29], who also indicated the highest hardness value of the weld metal zone.

Figure 6 presents the microstructure of the heat-affected zone in the stainless steel area of the dissimilar weld joint between SUS 304 and S20C and the microstructure of the weld metal zone. The heat-affected zone is the sensitivity region in the welding product. The weld metal (WM) presents the microstructure of the low-carbon steel of the original ER 70S-6 welding wire with about 0.1% carbon. Interestingly, the microstructure around the fusion line had a columnar dendrite shape, indicating the heat transfer direction during the welding process. The columnar dendritic structure was fine, indicating the good mechanical properties of the weld joints. Moreover, there were δ-ferrite phases with dark lines, and shapes gathered between the fusion line and the austenite phases. The existence of δ-ferrite phases is the result of the phase transformation from the austenite phase, indicating the specific phase of austenite stainless steel welding joints [18]. Finally, the twin boundaries’ shape and bright color illustrate the base metal (BM) area with austenite phases. Figure 6b shows that the weld metal zone of the weld bead had a martensite and bainite microstructure due to the welding process’s fast heating and cooling. This zone contained a variety of microstructures from both base materials, including S20C and SUS304 steel. This mixture can affect the weld’s strength and flexibility in the weld joints. Figure 6c shows the microstructure of the heat-affected zone of S20C. This microstructure had a larger ferrite grain size than the base metal of S20C because the heat from the welding process led to the grain growth phenomenon of the HAZ.

3.2. Taguchi Analysis of Tensile Strength and Confirmation Test

The Taguchi method emphasizes analyzing response variation using the signal-to-noise (S/N) ratio. The S/N ratio is the mean ratio to the standard deviation or noise. This ratio depends on the quality characteristics of the product and the process to be optimized. The S/N ratio helps analyze the output variability relative to the desired signal, which is the target or ideal value. The main goal is minimizing unpredictability and maximizing the S/N ratio, ultimately resulting in higher quality. Higher-the-better (HB), lower-the-better (LB), and nominal-is-best (NB) are the three commonly used standard S/N ratios. This approach aims to minimize quality characteristic variation caused by uncontrollable parameters. In other words, it helps optimize process parameters for better outcomes. The UTS is considered as the quality characteristic with the concept of “the larger-the-better” S/N ratio given by the following equation:

$$\mathrm{S}/\mathrm{N}=-10\log {\displaystyle \frac{1}{\mathrm{n}}}\left({\mathsf{\Sigma }\mathrm{y}}^2\right)$$

(1)

where S/N is the signal-to-noise ratio, y is the response for the given factor level combination, and n is the number of reactions in the factor level combination.

The calculated results and analysis of the S/N using Minitab 18 software are presented in

Table 5. Response table for the signal-to-noise ratios of the tensile strength value.

Level	I (A)	U (V)	Stick-Out (mm)	Speed (mm/min)
1	46.53	51.09	50.46	50.03
2	50.37	49.66	50.7	50.4
3	50.17	49.69	49.64	49.56
4	51.93	48.57	48.2	49
Delta of factor	5.4	2.52	2.51	1.4
Ranking of factor	1st	2nd	3rd	4th

. The results showed that the welding current had the strongest effect on the UTS value, followed by the welding voltage, stick-out, and speed. Therefore, controlling the welding current could lead to the desired UTS value of the dissimilar weld joint between SUS 304 and S20C. The reason for this phenomenon is that increasing the welding current directly results in improving the heat input rate. Therefore, the dissimilar weld joints melt differently when changing the welding current. In reverse, the welding speed contributes less to the UTS value than other parameters such as welding current, welding voltage, and stick-out.

In addition, the weld heat input Q could be calculated as

$$Q={\displaystyle \frac{U\times I}{v}}\times \eta$$

(2)

where Q is the weld heat input (Joule/mm); U is the voltage (V); I is the welding current (A); v is welding speed (mm/s); and ƞ is the weld thermal efficiency, which ranges from 69% to 91% for the GMAW technique. Besides the strong impact of welding current, the other parameters, such as welding voltage and welding speed, also greatly influence the heat input and welding quality, as shown in Equation (1). However, the surveyed ranges in this study, as shown in Table 2, are not set in equal and linear steps; therefore, the impact levels of these factors are different.

Figure 7 shows the main effect plot for the UTS value of the dissimilar weld joint between SUS 304 and S20C. The figure indicates that the optimal parameters for the UTS value with the “larger is better” option are a welding current of 110 A, a welding voltage of 15 V, a welding speed of 500 mm/min, and a stick-out of 12 mm.

Table 6. Analysis of variance for the UTS value.

Source	DF	Adj SS	Adj MS	F-Value	p-Value
I (A)	3	63,734	21,244.8	10.59	0.042
U (V)	3	12,454	4151.4	2.07	0.283
Stick-out (mm)	3	13,750	4583.2	2.29	0.257
Speed (mm/min)	3	2337	779	0.39	0.771
Error	3	6017	2005.6
Total	15	98,292
S = 44.78; R-sq = 93.88%; R-sq (adj) = 69.39%

presents the analysis of variance (ANOVA) for the UTS value of the dissimilar weld joint between SUS 304 and S20C. ANOVA evaluates the hypothesis that the means of two or more populations are equivalent. ANOVA determines the importance of one or more factors by comparing the response variable means at various factor levels. To ascertain the statistical significance of any differences between the means, it is necessary to compare the p-value with a predetermined significance level α. A significance level of 0.05 signifies a 5% probability of erroneously concluding the presence of a difference when, in reality, there is no difference. At the same time, S is a measure of the response variable’s units that indicates the deviation of the data values from the fitted values. The better the model captures the response, the lower the value of S. Moreover, R-sq represents the model’s explanation of the response’s variation. The greater the R-sq number, the more accurately the model matches the data. However, a high R-squared does not mean the model fits its assumptions. The degrees of freedom (DF) measure the amount of information in the data. The DF is calculated using the number of observations in the experiment. Adjusted sums of squares (Adj SS) are variance measurements for different model components. The modified sum of squares is calculated regardless of the order of the predictors in the model. Adjusted mean squares (Adj MS) calculate how much variation a term or model explains, given that all other variables are in the model, regardless of their order of entry. The F-value is a test statistic determining if a phrase relates to a response. A high F-value suggests that the term or model is noteworthy. The R-squared value is 93.88%, greater than 50% of the statistical significance. From this result, ignoring the error amount, the percentage of the parameters’ influence on the weld’s quality in a general way can be calculated in the following figure. Moreover, the p-value of the welding current was less than 0.05, indicating a good probability value with 95% confidence. From the optimal parameters set, including a welding current of 110 A, a welding voltage of 15 V, a welding speed of 500 mm/min, and a stick-out of 12 mm, the predicted UTS value was 491.8 MPa with a deviation of 40.4 MPa.

Figure 8 presents the influence of parameters on the UTS value of the dissimilar weld joint between SUS 304 and S20C. The results revealed that welding current was the most influential parameter, representing 69.04%. For welding voltage and stick-out, they accounted for 13.49% and 14.93%, respectively. The speed had the lowest impact, accounting for only 2.54%. In addition, to find the relationship between factors and UTS value follows a linear regression function equation:

UTS = 413 + 5.17 I − 22.1 U − 11.03 d − 0.146 v (MPa)

(3)

where I is the welding current (A), U is the welding voltage (V), d is the stick-out (mm), and v is the welding speed (mm/min). Based on the ratio values, this equation indicates the positive impact of improving the welding current and the negative effects of the other factors. The welding voltage had a high ratio of 22.1. Gajbhiye et al. [20] also pinpointed the strong impact of welding voltage on the tensile strength of dissimilar welding joints between En8D and SAE1018 steel.

The experimental validation step is crucial, and it is recommended that it be conducted to conclude the entire experiment. The optimal UTS value was 491.8 ± 40.4 MPa with the parameters condition of a welding current of 110 A, a welding voltage of 15 V, a welding speed of 500 mm/min, and a stick-out of 12 mm.

Similar to the previous sample number, the confirmation test was conducted with three samples to verify the results. The UTS values of the optimal samples are shown in

Table 7. The tensile strength results of the verification experiment.

No.	UTS (MPa)
1	482.5
2	469.3
3	456.4
Average	469.4

. Interestingly, the average UTS value of the confirmation sample was 469.4 ± 10.7 MPa, which falls within the predicted value range, indicating the accuracy of the expected value via the Taguchi method. Moreover, this result is slightly higher than that of Abioye et al. [15], who also investigated the dissimilar welding between 304 stainless steel and low-carbon steel. They reported an optimal UTS value of 422 MPa. The reason could be that the application of robot welding in this study improves the welding quality compared to the traditional manual welding technique in that research. This result is slightly lower than that of Ogbonna et al. [30], indicating an optimal UTS value of 559.25 MPa of the dissimilar AISI 1008 and AISI 316 welding joints. The reason could be the higher strength of ER309LSi austenitic stainless steel welding wire used in that report. Most importantly, this UTS value of 469.4 MPa is higher than all sample number values in Table 5, which is only 440.7 MPa. In other words, Taguchi’s optimal parameters successfully increase the mechanical properties of the dissimilar SUS 304 stainless steel and S20C steel.

3.3. Taguchi Analysis of Flexural Strength and Confirmation Test

Previous studies mainly focused on the tensile strength of the dissimilar welding joints. For example, Ogedengbe et al. [29] examined the effects of current, speed, and gas flow rate on the microstructure and mechanical properties of dissimilar AISI 304 stainless steel and low-carbon steel weldments using GTAW. In that study range, UTS increased when the welding current and speed decreased. Ogbonna et al. [30] studied the optimization of gas metal arc dissimilar joining of mild steel and 316 stainless steel. The best settings for MIG dissimilar welding of AISI 1008 and AISI 316 were 180 A welding current, 14 V voltage, and 19 L/min gas flow. However, flexural strength is also a critical mechanical property. In this study, the flexural strength and the tensile strength were investigated to thoroughly evaluate the weld joints’ quality.

The input parameters and flexural strength are shown in

Table 8. Input parameters designed via the Taguchi method and flexural strength values.

No.	I (A)	U (V)	Stick-Out (mm)	Speed (mm/min)	Flexural Strength (MPa)
1	80	15	8	450	918.2
2	80	16	12	500	995.0
3	80	17	16	550	320.0
4	80	18	20	600	143.7
5	90	15	12	550	1041.5
6	90	16	8	600	224.3
7	90	17	20	450	767.3
8	90	18	16	500	291.1
9	100	15	16	600	433.4
10	100	16	20	550	731.1
11	100	17	8	500	1401.9
12	100	18	12	450	1534.8
13	110	15	20	500	1564.8
14	110	16	16	450	1397.4
15	110	17	12	600	1694.1
16	110	18	8	550	1381.5

. Most samples that passed the DOP standards also had good flexural strength, including sample numbers 5, 11, 12, 13, 14, 15, 16. However, sample number 8 had a low flexural strength of 291.1 MPa. This low flexural strength could be the low elongation of this sample, as shown in Table 3. The elongation of sample number 8 was only 18.8%, which is significantly lower than the other samples. Sample number 8 presented more brittle characteristics under flexural tests than other passing samples.

Table 9. Response table for the signal-to-noise ratios of the flexural strength value.

Level	I (A)	U (V)	Stick-Out (mm)	Speed (mm/min)
1	53.12	59.06	58	60.9
2	53.59	56.79	62.15	59.02
3	59.17	58.83	53.76	57.64
4	63.55	54.74	55.5	51.87
Delta	10.43	4.32	8.4	9.03
Rank	1	4	3	2

shows the response Table for the signal-to-noise ratios of the flexural strength value. Remarkably, the welding current was the most critical factor that impacts the flexural strength, similar to the tensile strength results. However, there was a replacement between the welding voltage and speed. The welding speed ranked second place, followed by the stick-out parameter. Finally, the welding current was the least important factor among these parameters. In other words, flexural strength is more sensitive to the speed than the tensile strength.

Figure 9 indicates that the optimal parameters for the UTS value with the “larger is better” option were a welding current of 100 A, a welding voltage of 15 V, a stick-out of 12 mm, and a welding speed of 450 mm/min. The predicted flexural strength was 2187.1 MPa, with a deviation of 83.5. This value is tested in the next section.

Table 10. Analysis of variance for flexural strength.

Source	DF	Adj SS	Adj MS	F-Value	p-Value
I (A)	3	2,317,297	772,432	89.96	0.002
U (V)	3	136,330	45,443	5.29	0.102
Stick-out (mm)	3	1,081,798	360,599	42	0.006
Speed (mm/min)	3	662,307	220,769	25.71	0.012
Error	3	25,759	8586
Total	15	4,223,490
S = 92.66 R-sq = 99.39% R-sq (adj) = 96.95%

displays the analysis of variance for flexural strength value of the dissimilar weld joint between SUS 304 and S20C. The R-squared value was 99.39%, which is significantly higher than the standard 50%, which is statistically significant. From the ANOVA table above, the p-value values of welding current, stick-out, and speed were all smaller than 0.05. Therefore, these parameters significantly affect the flexural strength with 95% confidence. From these results, the percentage of the influence of parameters on the quality of the weld, in a general way, is calculated in the following figure.

Figure 10 depicts the effects of factors on flexural strength values. The results show that welding current was the most influential parameter, accounting for 55.2%. Stick out and welding speed accounted for 25.77% and 15.78%, respectively. Welding voltage had the lowest impact, accounting for only 3.25%. Furthermore, the linear regression function equation of the flexural strength and the input parameters is

$${\mathsf{\sigma }}_{\mathrm{Flexural}} =988+31.9 \mathrm{I}-24.6 \mathrm{U}-62.3 \mathrm{d}-3.57 \mathrm{v} \left(\mathrm{MPa}\right)$$

(4)

In this equation, the welding current factor (I) has the highest ratio of 31.9 with a positive value, indicating its strong and positive impact on the flexural strength. Increasing the welding current in the surveyed range leads to an improvement in the flexural strength. This result is consistent with the report of Linger et al. [31], who studied the optimization of the TIG process on 304L stainless steel and indicated the most critical role of welding current on the weld joint strength. Ahmad et al. [32] also revealed that welding current is the most important factor impacting the weld joint strength of S30430 stainless steel. On the other hand, the different parameters, such as welding voltage, stick-out, and welding speed, had a lower ratio with negative values, indicating the lower rate and negative side of influences.

As previously mentioned, the optimal flexural strength value was 2187.1 ± 83.5 MPa with the conditions of a welding current of 100 A, a welding voltage of 15 V, a welding speed of 450 mm/min, and a stick-out of 12 mm.

Similar to the previous sample number, the confirmation test was conducted with three samples to verify the results. The flexural strength values of the optimal samples are shown in

Table 11. The flexural strength results of the verification experiment.

No.	Flexural Strength (MPa)
1	1825.65
2	1980.23
3	2006.46
Average	1937.45

. The average result was 1937.45 ± 79.8 MPa, which is close to the predicted value. This result is significantly higher than the results in Table 8, indicating the effectiveness of the Taguchi method.

4. Conclusions

This study examined the effects of welding current, speed, voltage, and stick-out on the structural and mechanical properties of MIG welding, focusing on the dissimilar welding connection between SUS 304 stainless steel and S20C steel. The Taguchi method was used to maximize the experimental outcomes. Some remarkable notes that can be concluded include the following:

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S20C and SUS304 steel’s base metals contained ferrite, pearlite, and austenite phases. Furthermore, martensite and bainite microstructures appeared in the weld bead zone. Fine columnar dendrites formed at the fusion zone, and δ-ferrite phases with dark lines and shapes accumulated between the fusion line and the austenite phases of the weld joints;

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Tensile strength was best achieved with the following settings: welding voltage of 15 V, welding speed of 500 mm/min, stick-out of 12 mm, and welding current of 110 A. The UTS value confirmation was 469.4 MPa, which agrees with the estimated value computed using the Taguchi method. The welding current had the most effect in the tensile test and was followed by the welding voltage, stick-out, and speed;

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The optimal welding parameters for flexural strength were 110 A, 15 V welding voltage, 450 mm/min welding speed, and 12 mm stick-out. The optimal flexural strength was confirmed to be 1937.45 MPa, which is higher than the other samples, demonstrating the effectiveness of the Taguchi method. The welding current had the most significant influence on flexural strength. Furthermore, compared to tensile strength, the flexural strength was more sensitive to welding speed;

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The study’s findings could provide helpful insight into the dissimilar welding field. Additional research could explore how heat annealing affects the mechanical properties of dissimilar weld junctions.