Effect of GMAW Process Parameters and Heat Input on Weld Overlay of Austenitic Stainless Steel 316L-Si

Abstract

Abstract

Overlay welding with stainless steels has gained attention in several sectors of industry by increasing the mechanical and electrochemical properties of surfaces, in order to obtain them using lower cost substrates. However, the control of welding parameters can become complex due to the multiple control variables, being of interest to obtain deposits with greater productivity. In this context, the objective of this paper is to analyze the effect of the heat input on weld overlays of austenitic stainless steel 316L-Si obtained by the GMAW process, to determine the best welding conditions. For this, a Design of Experiments was implemented to analyze the response variables, which include heat input, reinforcement, width, dilution and hardness of the weld metal. The models were considered significant, presenting R²>0.9 for all responses, based on a 95% confidence limit. Surface graphs indicated influence of welding speed or/and voltage for all responses, in addition to the results demonstrating optimal parameters for overlays such as high ratio between reinforcement and width (0.631), and low dilution values (8.81%). The microstructural analysis indicated the presence of residual γ-austenite and δ-ferrite in all samples, and also the formation of peninsulas and islands, added to defects such as voids, for some weld deposits.

Key-words:
GMAW process; Heat input; Stainless steel 316LSi; DoE

1. Introduction

Several welding processes, or their variations, are used for the deposition of material on a surface, aiming at the recovery of worn parts or for the formation of a coating with special characteristics [¹]. The combination of carbon steel adopted as substrate for the coated stainless steel is suitable for various applications that require high mechanical strength combined with good corrosion resistance, especially in the petroleum, petrochemical, shipbuilding and pressure vessel industries [²].

The broad field of applications for overlays obtained by welding, using mainly stainless steel alloys as filler metal, has been promoting the growth of studies related to the influence of heat input on the microstructure and the optimization of mechanical properties in coated products [³]. High alloy stainless steels, such as 316L, with higher chromium, molybdenum and nickel contents, are one of the most studied in the series, as they are characterized by high corrosion resistance, good ductility, high strength and good weldability at relatively low costs [⁴]. 316LSi stainless steel is a special grade of austenitic stainless steels (ASS) with the addition of molybdenum, which increases an indicator of high resistance to pitting corrosion, and the addition of silicon, which stabilizes ferrite in the austenite matrix in a level that provides greater resistance to solidification cracks [⁵].

Among the main overlay welding processes employed in the industry, as described by AWS (2004), the Gas Metal Arc Welding (GMAW) process is a popular and effective method, which can be semi-automated, easy to operate and with high productivity [⁶]. The performance of this process can be improved with the adoption of appropriate process parameters depending on the alloys and variables used [⁷]. Fusion welding requires heat to melt the contact surface (substrate) and the filler material (filler metal) so that coalescence can be effected and a welded joint with good mechanical and metallurgical characteristics is obtained after solidification. Thus, the heat input plays a significant role in welding, especially in the characteristics of the weld bead geometry, and can be quantified as a function of the welding current, welding voltage and torch displacement speed, depending on the foreseen metallic transfer mode [⁷,⁸].

It is known that coating processes, especially those involving heat input such as welding, require that their products have specified characteristics for validation of the procedure, such as low dilution and penetration values, in addition to optimal ratios of reinforcement and deposit width. In this context, it is extremely important to understand how the multiple input variables of the process influence the characteristics of the deposits, through experimental techniques [⁹,¹⁰], ANOVA [⁷,¹¹], and neural network application [¹²,¹³]. In this context, prospective studies on the analysis of the effect of heat input and selection of welding parameters to obtain weld overlays in austenitic stainless steel 316L-Si by the GMAW process are of great relevance for the understanding and optimization of the physical and metallurgical properties in coatings for industrial application, as well as the growth of the technical bibliography specialized in welding.

2. Methodology

2.1. Model for design of experiments

The experiments were based on two factors, directly related to heat input, with two levels through first-order full factorial design. The independent input variables of the input process were welding speed (200 to 300 mm·min^-1) and electric arc voltage (22 to 26 V). The range of values for both factors was selected based on the literature concerning the GMAW process with the same class of deposited material [¹⁴-¹⁷]. The experimental data are analyzed by the response surface methodology using the software Statistica. The F-test and analysis of variance (ANOVA) serve to evaluate the adequacy of the developed mathematical models. The experimental data were fitted with the first order polynomial equation by the regression method to identify related terms in the mathematical model [¹⁸] in Equation 1

$y\left(x_1,x_2\right)={\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+{\beta }_{12}{x}_{12}+\epsilon \left(x_1,x_2\right)$ (1)

where ${\beta }_0$ is the average value of all factorial design responses; ${\beta }_1$, ${\beta }_2$ e ${\beta }_{12}$ are the population values of the two main effects and the interaction effect for each $x_1$ e $x_2$; and $\epsilon \left(x_1,x_2\right)$ is the random error associated with the response $y\left(x_1,x_2\right)$.

2.2. Experimental execution

In this work, an AISI 1015 steel was used as substrate and a stainless steel ER316L-Si wire of a diameter 1.2 mm was used as weld overlay. Both nominal chemical compositions are presented in Tables 1 and 2. The dimensions of each substrate to be overlayed were 100 mm long x 50 mm wide by 6.35 mm thickness. All welds were performed in the horizontal position and without preheating.

Table 1
Nominal chemical composition (weight %) of the AISI 1015 steel.

%C	%Mn	%Cr	%Zn	%Fe
0.148	0.419	0.043	0.033	balance

Table 2
Nominal chemical composition (weight %) of filler metal ER316L-Si.

%C	%Cr	%Ni	%Mo	%Mn	%Si	%P	%S	%Cu
0.03	18.0-20.0	11.0-14.0	2.0-3.0	1.0-2.5	0.65-1.0	0.03	0.03	0.75

During all depositions, commercial argon gas (99.8% Ar) was used as a shielding gas, maintaining it at a flow rate of 16 L·min^-1. The contact tip-to-work distance (CTTW) was maintained at 20 mm and the wire at a rate of 6 m·min^-1 continuously fed the torch. The variation of the parameters in the different depositions was carried out according to the range of values observed in Table 3, based on specialized literature [¹⁹-²¹].

Table 3
Input parameters and experimental limits.

Parameters	Level -1	Level 0	Level +1
Welding speed, WS (mm·min-1)	200.0	250.0	300.0
Welding voltage, WV (V)	22.0	24.0	26.0

All samples were transversely sectioned in the middle region of the weld beads, embedded, sanded to 2400 grain size, polished in silica solution and chemically etched with acidic glycerin (3 parts HCl + 2 parts glycerol + 1 part nitric acid). For the metallographic analysis, an optical microscopy (Olympus, SC30) was used. For the macrograph analysis of the weld deposit, a stereoscopic equipment (Kontrol, KET-300) was used with a 10x magnification, and post-processing of the images was carried out in the software Image J in order to estimate the areas of reinforcement of the weld metal and penetration into the base metal of each welded bead for the calculation of dilution.

The voltage and the welding speed are independent variables when calculating the heat input of the welding, being estimated from Equation 2:

$H_l=\frac{60\mathrm{*}WV\mathrm{*}WC}{WS}\mathrm{*}\eta$ (2)

where H_l corresponds to the heat input, in kJ·m^-1; WV corresponds to the welding voltage, in V; WC corresponds to the welding current, in A; WS corresponds to the welding speed, in mm·min^-1; and η corresponds to the efficiency of the welding process, taken as 0.75 for the GMAW process [¹, 22]. Concomitantly with the execution of the welds, the data referring to the welding signals were stored by means of a data acquisition system (IMC Soldagem, SAT) with a rate of 5000 Hz, from which the effective average value of WC and WV were extracted to estimate H_l for each sample.

For the Vickers hardness test, carried out in a digital microdurometer (Future-Tech, FM-700), a load of 500 g was used for an indentation time of 15 s. In each specimen, the hardness measurements were made in the region of the weld metal, in a 4 x 2 matrix pattern of indentations spaced 0.5 mm apart. The output parameters measured by the macrograph, as well as the diagrammatic scheme of the indentations made during the hardness test, are shown in Figure 1.

Figure 1
Schematic diagram of output geometric parameters, where “R” indicates height, “L” indicates width, and “P” indicates penetration, for the overlay welds.

As shown in Figure 1, the height (R), width (L) and penetration (P) of the welds were determined, together with a form factor (R·L^-1), dilution (D) of the weld metal in relation to the substrate, and the average value of the indentations hardness (H). The mentioned output parameters were used to evaluate the relationship among these and the process parameters, as well as the heat input through regression models and analysis of the response surfaces.

3. Results and discussions

Figure 2 shows the cross sections of the welded samples for different welding parameters levels (welding speed and voltage variation). After preliminary visual inspection, very distinct conditions are verified in the geometric aspects of the deposited weld beads. Especially, it was possible to observe notable differences for the responses of penetration, dilution and width of the deposits, which were properly evaluated by the post-processing of the macrographs, whose evaluated output parameters were categorically measured.

Figure 2
Macrograph of the embedded welded samples.

The experiments were conducted with a view to the specified factorial design and the samples were cross-sectioned for the analysis of the responses mentioned in Table 4.

Table 4
Matrix of factorial design 2² + 3 for the overlay welds.

Exp. No.	WC (A)	WV (V)	Hl (kJ·m-1)	R (mm)	L (mm)	R·L-1	D (%)	H (HV)
1	161.860	22.461	817.996	5.148	10.370	0.496	14.85	175.988
2	169.395	26.244	1000.263	5.251	13.197	0.398	18.19	174.525
3	171.549	22.348	575.061	4.874	7.723	0.631	8.81	178.388
4	184.936	26.222	727.400	3.751	9.739	0.385	28.66	187.125
5	174.736	24.349	765.840	4.600	10.200	0.451	18.64	180.025
6	180.988	24.251	790.050	4.750	10.000	0.475	19.00	180.500
7	169.300	24.257	739.214	4.690	9.316	0.503	15.00	179.000

3.1. ANOVA - Analysis of Variance

Analysis of variance helped to test the adequacy of the models. For this test, the calculated F-value must be within the confidence limit (95%) for p < 0.05 [⁷], since statistical models with a reliability of 95% have high reliability and predictive capacity. The fit values indicate that the mathematical models are significant for all output responses. The final reduced models for the output responses are derived from Tables 5 to 8 of the ANOVA results, presenting the formats described by Equations 3 to 6. P values less than 0.05 are highlighted in bold.

Table 5
ANOVA results for heat input H_l.

Factors	Sum of squares	Degrees of freedom	Mean square	F-value	P-value
WS	66511.8900	1	66511.8900	118.3230	0.0017
WV	27990.2211	1	27990.2211	49.7939	0.0058
WS·WV	223.9365	1	223.93650	0.3984	0.5727
Error	1686.3637	3	562.1212	-	-
Sum squared total	96412.4113	6	-	-	-

• R² = 98.251; R²_adjusted = 96.502.

Table 8
ANOVA results for hardness $\mathit{H}$.

Factors	Sum of squares	Degrees of freedom	Mean square	F-value	P-value
WS	56.2500	1	56.2500	71.1684	0.0035
WV	13.2278	1	13.2278	16.7360	0.0264
WS*WV	26.0100	1	26.0100	32.9083	0.0105
Error	2.3711	3	0.7904	-	-
Sum squared total	97.8589	6	-	-	-

$H_l=773.689-128.949·WS+83.651·WV$ (3)

Table 6
ANOVA results for the R·L^-1 ratio.

Factors	Sum of squares	Degrees of freedom	Mean square	F-value	P-value
WS	0.0037	1	0.0037	8.0718	0.0656
WV	0.0297	1	0.0297	64.4326	0.0040
WS·WV	0.0054	1	0.0054	11.7982	0.0414
Error	0.0014	3	0.0005	-	-
Sum squared total	0.0402	6	-	-	-

• R² = 96.564; R²_adjusted = 93.127.

$\frac{R}{L}=0.47714-0.08612·WV-0.03685·WS·WV$ (4)

Table 7
ANOVA results for dilution $\mathit{D}$.

Factors	Sum of squares	Degrees of freedom	Mean square	F-value	P-value
WS	4.9062	1	4.9062	3.7222	0.1493
WV	134.4440	1	134.4440	101.9992	0.0021
WS·WV	68.1450	1	68.1450	51.6999	0.0055
Error	3.9543	3	1.3181	-	-
Sum squared total	211.4495	6	-	-	-

• R² = 98.130; R²_adjusted = 96.260.

$D=17.8071+5.7975·WV+4.1275·WS·WV$ (5)

R² = 97.577; R²_adjusted = 95.1540

$H=179.3644+3.7500·WS+1.8185·WV+2.5500·WS·WV$ (6)

The adjustment of the models was expressed by the coefficient of determination (R²_adjusted), which was higher than 90% for all ANOVAs. Since an R² value approaching 100% indicates a better fit between the model and observed responses, the R² > 90% suggests that the calculated models can satisfactorily predict the experimental responses [¹⁸]. However, to confirm whether the proposed model is statistically significant and predictive, an analysis of variance (ANOVA) was also performed.

The data presented in Tables 5 to 8 demonstrate that the statistical models of all output variables are significant and predictive, for effects with p < 0.05, according to the values of R² e R²_adjusted observed in the ANOVA.

3.2. Influence of process parameters on heat input

The heat input (H_l) can, in this case, be treated as a response variable, given that one of the independent variables for its calculation - the welding current (WC) - is treated as a response variable. This is justified by the selected transfer mode (conventional) and its respective control parameters. In this, the welding current is equivalent to the amperage output of the source during the deposition of the beads, being related to the wire feed speed and environmental conditions [²³].

As previously observed by the ANOVA table, and depending on the control parameters, there are results of high statistical significance for the heat input which validate the consolidated mathematical relationships used to calculate the input. Moreover, it is possible to establish that there was no noticeable influence of statistical randomness or experimental instability for this response variable. The response surface graphs for the influence of the speed and voltage welding parameters on the heat input obtained from the ANOVA are presented in Figure 3.

Figure 3
Heat input response surface graph in deposited weld beads.

By analyzing the response graph in Figure 3, it can be seen that lower values of heat input are the result of low values of welding voltage combined with high values of welding speed [¹¹, ¹⁴, ²¹]. Normally, high heat input values are not reflected in good results in overlay applications because it culminates in a high level of residual tensile stresses and distortion, which compromises the physical and mechanical integrity of the coated products [²⁴]. On the other hand, very low heat input values can cause defects such as lack of penetration, or lack of fusion and voids [¹]. In this sense, a selection of parameters based on adequate H_l values observed in the response surface can be taken as a viable solution for the prevention of welding defects.

3.3. Influence of process parameters on geometric characteristics

The response surface graphs for the geometric characteristics of the depositions, obtained from the results of the ANOVA table, can be seen in Figure 4. The variables of width/reinforcement and dilution, respectively, were analyzed as a function of the variation of the WS and WV.

Figure 4
Response surface plots of a) width/reinforcement ratio, and b) dilution on deposited weld beads.

With regard to reinforcement (R), Figure 4a, this response increases significantly when the welding speed is increased and welding voltage decreased [²⁵], which suggests that higher heat input values tend to result in weld beads with higher reinforcement values [²¹] for depositing stainless steel 316LSi. A similar analysis refers to Figure 4a, demonstrating that the reduction in the welding speed together with the increase in voltage causes a considerable increase in the width of the bead [²⁵], which can be inferred that higher values of heat input tend to result in greater bead widths [²¹] for stainless steel 316LSi.

Both reinforcement and bead width can be analyzed together with the R·L^-1 ratio, termed as the inverse of the reinforcement form factor (RFF). In this perspective, the analysis of Figure 3a presents an inverse relationship with the heat input, so that it is possible to obtain weld deposits with higher R·L^-1 values, which reflects in an overlay with less spreading on the substrate with lower values of heat input within the range of values calculated in the present study [⁷, ²¹, ²⁶]. Considering that a good overlay weld is one that manages to cover the largest possible area of the substrate [²⁵], the best conditions for the R·L^-1 ratio are those that obtain its maximum value (the closest to 1), which are the employed in sample 3 (22 V and 200 mm·min^-1).

In parallel analysis, for dilution, the response surface in Figure 4b indicates that low percentages of dilution are obtained when simultaneously employing low voltage values with high welding speeds [²⁵, ²⁷]. With regard to the effectiveness of overlay welds, dilution values greater than 20% are not recommended for a satisfactory overlay in terms of preserving the characteristics of the weld metal [²⁰]. In general, low levels of dilution were obtained, as shown in Table 4, with the exception of sample 4 (D = 28.66%), which was justified by the highest WC level identified among the samples (WC = 184.936 A) [²⁸].

In a similar analysis, sample 3 showed the lowest dilution of the factorial design (D = 8.81%), based mainly on the lowest heat input value among the samples (H_l = 575.061 kJ·m^-1) [²⁹], being a result of good relevance for further work on parameter optimization for overlay welding using the GMAW process with stainless steel 316L-Si. Thus, the lowest dilution presented among the samples configures the best condition of the welding parameters, in the range of values analyzed, which was presented by sample 3, when combining the welding speed at 200 mm·min^-1 and the welding voltage at 22 V.

3.4. Influence of process parameters on hardness

Analogously to what was presented in the previous section, the response surface for the hardness as a function of the welding speed and voltage, presented in Figure 5, was significant based on the ANOVA results, to the detriment of the adopted reliability.

Figure 5
Hardness response surface graph in deposited weld beads.

In terms of absolute values, the experiments demonstrated hardness values within the range of 174.525 HV and 187.125 HV for austenitic stainless steel 316L-Si, [³⁰] when applying a heat input of 520 kJ·m^-1 [²⁹]. The response surface graph observed in Figure 5 suggests that higher hardness values are obtained by combining high values of welding speed and voltage. As both factors are concurrently related to heat input, both value ranges can be used to investigate how hardness will be influenced by input. There was an increase in hardness measured between 500 and 800 kJ·m^-1 [¹⁹, ³¹]. It has already been described in the literature that, for lower values of heat input, in a comparative way, there is a tendency to obtain a greater number of indentations with high hardness values (Table 4) [³²], which was also observed in the present work.

3.5. Microstructural analysis

In the metallographic analysis, the weld deposit was observed according to the microstructural state of the Heat Affected Zone (HAZ). From this, the microstructures obtained from samples 2 and 4 are observed in Figures 6 and 7, with heat inputs equivalent to 1000.263 kJ·m^-1 and 727.400 kJ·m^-1, respectively.

Figure 6
Micrograph of sample 2. Etch: acidic glycerin.

Figure 7
Micrograph of sample 4. Etch: acidic glycerin.

In general, the weld metal microstructure of all samples showed pearlite (dark region) and ferrite (light region) in the refined grains. It is also possible to observe the presence of columnar dendrites of γ-austenite (in white) and some residues of δ-ferrite (in black) in the region of the deposit, observed in Figure 6 on the right, predicted for deposition of austenitic stainless steel with addition of silicon [³³], and when the deposition was performed using stainless steel 316LSi [⁵]. The residual δ-ferrite was mainly distributed in the austenite boundaries for the weld overlay of the same class of material with the addition of molybdenum and 316LSi [³⁴, ³⁵]. On the other hand, the base metal microstructure in the HAZ region for all samples, as exemplified on the right in Figure 6, is composed of bainite and pearlite [³⁶,³⁷].

It is observed that sample 4, Figure 7, presented differentiated morphologies called “peninsula” and “islands” swirl patterns, at the interface between the WM (Weld Metal) and the BM (Base Metal) [³⁸,³⁹]. These grains are part of the so-called Partially Diluted Zone (PDZ) which is characterized by the penetration of WM into the BM [⁴⁰,⁴¹]. Such morphologies were also presented in previous studies [³², ³⁹, ⁴²]. The regions observed in Figures 6 and 7 are also present for samples 6 (H_l=790.050 kJ·m^-1) and 7 (H_l=739.214 kJ·m^-1).

The greater amount of unmelted particles may be related to whether the intensity of current used is sufficient or not to ensure complete fusion of the WM [²⁰]. For the welding conditions that produced samples 1, 4 and 5, they did not present any type of defect in their microstructure. In samples 2 and 3, the presence of voids was observed in the deposit region, close or not to the melting line, which can be explained by the low average level of WC [²⁰].

4. Conclusions

Through the factorial experimental design, mathematical response models were obtained for the variables related to the macrograph characterization, with the geometric parameters of reinforcement (R), width (L), and dilution (D), in addition to the mechanical characterization, with the response of hardness (H). All mathematical models proved to be statistically significant and predictive, resulting in values for coefficient of determination above 90% for all analyzed response variables. The presented results confirm the use of DoE as a useful tool for optimization and selection of welding parameters.

With regard to the geometric characteristics, the welding voltage parameter was the most significant for the R·L^-1 form factor and dilution, in view of its direct influence on the weld deposit spreading and heat input, which collaborates in the degree of penetration of the weld metal to the substrate. Lower welding voltage levels generated better results for the dilution (14.85% for sample 1, and 8.81% for sample 3), when using 22 V, and for the R·L^-1 ratio, whose best results were obtained for its highest values (0.631 for sample 3 and 0.503 for sample 7), when using 22 V and 24 V, respectively. For the geometric characteristics, the best results of the response factors observed and analyzed via DoE, indicating the best combination of input parameters, were obtained in sample 3 obtained by combining a welding speed of 200 mm·min^-1 and a welding voltage of 22 V.

For hardness, all factors, as well as their interaction, were significant for the results, demonstrating a subtle increase with the increase in heat input, in view of its relationship with the cooling rate of the deposit, which influences the formation of harder constituents. It is noteworthy that high levels of welding speed produced better hardness results (187.125 HV for sample 4), when using 300 mm·min^-1, while using higher levels of welding speed together with higher levels of welding voltage produced comparable hardness results (180.025 HV for sample 5, 180.5 HV for sample 6, and 179.0 HV for sample 7), when using 250 mm/min and 24 V. Therefore, for the output parameters of heat input and Vickers hardness (HV), it was considered that the best results were obtained by samples whose applied parameters were at their average level. Thus, the best conditions for these responses were shown by setting the welding speed at 250 mm·min^-1 and the welding voltage at 24 V.

For the microstructural analysis, the microconstituents foreseen in the deposit region, namely residual γ-austenite and δ-ferrite, were in accordance with the type of filler material employed. Furthermore, some discontinuities inherent to the welding process, such as voids, were observed in the weld beads. In this case, the use of low levels of welding current was attributed to the appearance of this type of defect in some samples (169.395 A for sample 2, and 171.549 A for sample 3), relating them to incomplete melting of the weld metal.

[22]KouS Welding metallurgy. 2nd ed. New Jersey: John Wiley & Sons; 2003 Vol. 4 https://doi.org/10.22486/iwj.v4i3.150243

Acknowledgements

The authors are grateful to the Laboratory of Welding (UFCG-Campina Grande) for the assistance with this research.

• How to cite: Souto JIV, Ferreira SD, Lima JS, Castro WB, Grassi END, Santos TFA. Effect of GMAW process parameters and heat input on weld overlay of austenitic stainless steel 316L-Si. Soldagem & Inspeção. 2023;28:e2809. https://doi.org/10.1590/0104-9224/SI28.09

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Publication Dates

• Publication in this collection
  18 Dec 2023
• Date of issue
  2023

History

• Received
  04 May 2023
• Accepted
  18 Oct 2023