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Proceeding Paper

A Parametric Study Investigating the Effect of Bead Morphologies of SS316L Through the GMAW Process †

by
Vatsal Vaghasia
1,
Jay Vora
1,
Manoj Jagdale
2,
Yogita Shinde
3 and
Rakesh Chaudhari
1,*
1
Department of Mechanical Engineering, Pandit Deendayal Energy University, Gandhinagar 382007, India
2
Department of Mechanical Engineering, Vishwakarma Institute of Information Technology (An Autonomous Institute Affiliated to SPPU), Pune 411048, India
3
School of Pharmacy, Vishwakarma University, Pune 411048, India
*
Author to whom correspondence should be addressed.
†
Presented at the 4th International Conference on Advanced Manufacturing and Materials Processing, Bali, Indonesia, 26–27 July 2025.
Eng. Proc. 2025, 114(1), 13; https://doi.org/10.3390/engproc2025114013
Published: 6 November 2025
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Abstract

In the present study, the gas metal arc welding (GMAW) process was used to investigate the effect of bead morphologies of SS316L. Single-layer deposition forms the base for manufacturing metal additive structures using a GMAW-based wire-arc additive manufacturing (WAAM) process. Thus, the current work focused on the analysis of bead morphologies of SS316L through single-layer depositions. Experimental trials were conducted using Taguchi’s L9 approach with travel speed (TS), gas mixture ratio (GMR), and voltage as input WAAM variables, and bead width (BW) and bead height (BH) as output responses. The effect of WAAM variables on output measures was studied using main effect plots. The relevance and reliability of the derived regressions were verified using ANOVA analysis. For the BH response, TS was found to be the most significant factor, followed by voltage, while GMR did not have any contributing impact on the output response variable. For the BW response, voltage was found to be the largest contributing factor, followed by GMR and TS. The R2 values were measured at 0.9276 and 0.9962 for BH and BW, respectively. The outcomes proved that the model fits the data well and can successfully predict new observations, as the R2 values for all responses were near one. Lastly, the best input set of WAAM variables was determined for individual output variables.

1. Introduction

The additive manufacturing (AM) process is one of the most popular developing technologies and is widely preferred for various applications [1,2]. AM is used to manufacture multiple components with complex shape geometries which cannot easily be produced by conventional techniques [3,4]. Recent advancements in AM have ensured that it can be utilized for high-performance components from critical engineering materials such as aluminum, titanium, and steel [5,6,7]. AM offers several advantages, including easier design, reduction in wastage of material, on-demand production, and a capability to generate highly customized and complex objects [8,9,10]. Among AM techniques, wire-arc additive manufacturing (WAAM) has shown promise in the production of metallic components [11,12]. It operates by using a wire feeding mechanism, a welding torch/power source, and a computer control system [13,14,15]. Among several arc and energy sources, the gas metal arc welding (GMAW) process exhibits the largest deposition rate, which is 2–3 times greater than those of other energy sources [16,17]. Also, the GMAWAM process displayed suitable mechanical properties, enabled the fabrication of large-scale components, and had a lower cost of equipment [18,19]. Readily available programming systems, feeder wire systems, and welding torches make WAAM a cost-effective technology [20,21]. The WAAM process is highly capable of producing products with better quality and precision at a higher rate of deposition [22,23]. In order to produce favorable characteristics, it is essential to select suitable parameters, such as travel speed (TS), voltage (V), wire feed speed (WFS), gas mixture ratio (GMR), and path design.
Superior mechanical characteristics and resistance to corrosion are the key features of stainless steels (SSs) owing to the presence of Ni, Mo, and Cr elements [24]. Owing to its exceptional weldability, SS316L has a higher welding temperature, which is ideal for the WAAM process to achieve better fusion in successive layers [25]. Therefore, key characteristics of SS316L, like high strength, resistance to corrosion, superior weldability, good ductility, and biocompatibility, make it favorable for various sectors such as the oil and gas industries, automotive, biomedical equipment, marine, and aerospace components [26,27,28].
Using an optimization technique, the optimal parameter settings for WAAM variables for the production of neat and clean single-layered structures of SS316L were determined by Wahsh et al. [29]. Optimized parameter settings enable the generation of single-layered structures with identical bead-on-plate deposition and no significant boundary effects. The reviewed literature indicates that adequate control of design parameters is necessary for the fabrication of single-layered structures with few defects and advantageous mechanical properties. Then, it is necessary to use single-bead depositions to optimize the WAAM parameters for bead geometry. A study conducted by Chaudhari et al. [30] examined the effects of input variables for the bead geometries of GMAW-based WAAM of SS316L. They performed single-layer deposition using response surface methodology. The optimal processing parameters for achieving the desired bead geometry were found to be a travel speed of 5 mm/s, a voltage of 20 V, and a current of 100 A. Their investigation revealed that the voltage and wire feed speed were the most significant contributors to the bead geometries. The analysis highlighted the importance of selecting appropriate input variables to attain the desired bead geometries. Belotti et al. [31] provided a comprehensive investigation on the microstructural analysis of stainless steel. Their study included a detailed analysis of the microstructural features of WAAM stainless steel, such as the grain size, morphology, and phase distribution. They highlighted the importance of microstructural analysis for understanding the properties and performance of WAAM materials. This research demonstrated that changes in process variables can have a considerable impact on the grain size, morphology, and phase distribution of the material, which in turn influences its mechanical properties. A study carried out by Kumar et al. [32] characterized stainless steel structures produced from WAAM. The obtained results showed that the quality of WAAM components depends on several factors such as the choice of welding parameters, the type of filler material, and the results of the bead morphologies. Mai et al. [33] preferred the GMAWAM process over the PAWAM and GTAWAM processes owing to their larger rate of deposition. A thin-walled structure of SS308L was built using optimum variables of the GMAWAM process.
As per the studied literature, single-layer deposition forms the base for manufacturing metal additive structures using the WAAM process. Thus, the present study concentrates on experimental investigations of bead morphologies of SS316L through single-layer depositions by employing a GMAW-based WAAM technique. Experiments were performed using Taguchi’s L9 approach with voltage, gas mixture ratio, and travel speed as input WAAM variables, and BW and BH as output responses. The relevance and reliability of the derived regressions were verified using analysis of variance (ANOVA). The effectiveness of the regression in predicting values is indicated by an R2 value closer to one. The R2 values were used to test the proposed model’s capability. Main effect plots were used to understand the effect of input factors on individual output responses. Lastly, the best input set of WAAM variables was determined for individual output variables.

2. Experimental Section

The current study utilized a GMAW-based WAAM setup to deposit a single-bead deposition on SS316L through a special-purpose machine with a GMAW power source and wire feeder setup. Figure 1 depicts the experimental setup utilized in the current study. A metal wire of SS316L with a 1.2 mm diameter and a substrate plate of SS316L with a 20 mm thickness was used for the depositions. Table 1 depicts the composition by wt. % for SS316L. The deposition was constructed using a shielding gas range of 95 to 99% Ar and 1 to 5% CO2. The substrate plate of SS316L was secured to the worktable, and a torch was utilized to deposit the material onto the plate. The torch in this application uses an active GMAW arc to stabilize the welding process which occurs on a base plate clamped to a supporting base.
The input WAAM parameters of voltage, TS, and GMR were selected from the studied literature. GMR stands for the percentage of CO2 gas and the remainder is argon gas. BH and BW were treated as response parameters. Table 2 depicts the WAAM process variables along with their levels. The input variables with their range were selected on the basis of preliminary experimental runs, the studied literature, and machine limits. Taguchi’s DOE was employed to perform the experimental trials. Taguchi’s 33 design with an L9 array was selected. Taguchi’s method involves designing an experimental matrix to optimize a component or a process using a limited number of trials, which in turn saves time and cost [35,36,37]. The L9 method is based on the concept of orthogonal arrays, which are mathematical matrices helping to identify the best vital parameters which modify the quality of a product or process [38].
Figure 2 displays single-layered deposition as per Taguchi’s L9 OA. The relevance and reliability of the derived regression equations was verified using ANOVA analysis. With a 95% confidence level, significant and insignificant model terms were evaluated using Minitab v17.
BH and BW response values were analyzed using optical microscopy as shown in Figure 3. For each single-layer deposition, three readings were taken at different locations across the cross-section. The mean value of those readings was taken into consideration during investigation for better accuracy.

3. Results and Discussion

Table 3 represents Taguchi’s L9 array design with WAAM variables along with the obtained results of selected responses. The BH and BW response values were analyzed by using optical microscopy. The bead morphology results for the nine conducted trials showed the largest value of BH and smallest value of BW as 5.81 mm and 6.23 mm, respectively. Figure 3 shows the single-layer depositions for all nine trials performed as per the DOE matrix. Regression equations were generated, which primarily explain the effect of WAAM variables on output responses. Equations (1) and (2) depict the regression models for BH and BW, respectively.
B H = 19.23 − 0.4308 × T S − 0.1692 × V + 0.0542 × G M R
B H = − 3.338 − 0.0633 × T S + 0.6142 × V − 0.1808 × G M R

3.1. Main Effect Plots

The effect of WAAM variables (GMR, TS, and V) was studied on BW and BH responses. The desired values of BH and BW are maximum and minimum for the fabrication thin-walled structure. Figure 4 depicts the influence of input factors on the BH response. Intensification of voltage value shows a declining trend for the BH value. With an increase in voltage, the arc length increases, leading to higher deposition of molten material, which results in a decrease in BH [39,40]. GMR did not have a substantial effect on the BH value. TS had the largest impact on the BH response. As the value of TS increases, a marginal decline trend in the BH response was observed. The increased speed of the torch is the reason behind this trend. A lower amount of material is deposited as the torch speed increases, causing a reduction in BH [41,42]. Therefore, to attain higher BH values, voltage at a lower level of 20 V, GMR at a higher level of 5, and TS at a lower level of 24 mm/s are desirable.
Figure 5 presents the main effect plot which shows the influence of WAAM variables on BW. An increase in voltage causes arc length to increase, which results in more molten material deposition. This causes an obvious increase in BW due to the spreading of molten droplets [43,44]. An increase in GMR was observed to be favorable for the BW response as its value was decreased. TS had a minimal impact on the BH response. An increase in TS indicates an increase in torch speed, which results in reduced material deposition. This leads to a decrease in the BW value [45,46]. Thus, BW slightly reduces with increased TS values. Therefore, to attain lower BW values, voltage at a lower level of 20 V, GMR at a higher level of 5, and TS at a lower level of 28 mm/s are desirable.

3.2. Analysis of Variance for BH and BW

The relevance and reliability of the obtained results, as shown in Table 3, and the derived regression equations were analyzed and verified using ANOVA analysis. With a 95% confidence level, significant and insignificant model terms were evaluated using Minitab v17. The respective term’s effect is considered significant when the probability value is less than 0.05 [47,48]. Table 4 depicts the ANOVA table for the BH and BW responses. The regression model term signifies that both output responses (BH and BW) were found to be significant. In the case of the BH response, TS and voltage term had a significant impact, showing contributions of 81.48% and 15.91%, respectively, while GMR did not have any contributing impact on the output response variable with a minor involvement of 1.53%. The higher F-value of TS suggested that changes in the level of TS had a significant effect on BH values. For the BW response, all three WAAM variables were found to be significant, with voltage having the largest impact (90.78% contribution), followed by GMR (7.87% contribution) and TS (0.96% contribution). The developed model correctly predicted the outcomes with minimal errors, as the error term had a negligible impact in the case of both response measures, BH (1.08% involvement) and BW (0.39% involvement). The R2 values were found to be 92.76% and 99.62% for the BH and BW models, respectively, while the Adj. R2 values for the BH and BW models were 88.42% and 99.39%, respectively. The outcomes proved that the model fits the data well and can successfully predict new observations, as the R2 values for all responses were near one.
The residual plots were examined to confirm the reliability of the ANOVA results. When specific assumptions are met, ANOVA is considered a valid and appropriate method for evaluating the proposed model [49,50]. Residual analysis is a key step in this validation process. Figure 6 presents the residual plot for BH, which includes a normal probability plot, a fitted versus predicted plot, a histogram, and a residual versus observation order plot. The normal probability plot shows residuals aligning along a straight line, indicating the adequacy of the model [51]. The second plot reveals a random distribution of data points, supporting the absence of systematic errors. The bell-shaped curve in the histogram further confirms normality of the residuals. Additionally, the lack of any discernible pattern in the residual versus observation plot suggests that the ANOVA is statistically meaningful [52]. Together, these results demonstrate that the model satisfies all four residual diagnostics. Similar patterns observed in Figure 7 for BW confirm the robustness and suitability of the regression models and ANOVA results.

4. Conclusions

The current work used a GMAW-based WAAM method to deposit a single-bead deposition on an SS316L substrate plate using a metal wire of SS316L with a 1.2 mm diameter. Taguchi’s L9 design was used to perform the experimental trials. The bead morphology results for the nine conducted trials showed the largest value of BH and smallest value of BW as 5.81 mm and 6.23 mm, respectively. As per the ANOVA results, TS was found to be the most significant factor with an 81.48% contribution, followed by voltage with a 15.91% contribution, while GMR did not have any contributing impact on the BH response. For the BW response, all three WAAM variables were found to be significant, with voltage having the largest impact (90.78% contribution), followed by GMR (7.87% contribution) and TS (0.96% contribution). Both the models of BH and BW successfully validated the entire selected deign space. Thus, regression equations were generated which will be useful for users to predict the response values within the defined range of WAAM variables. Main effect plots were established to understand the impact of WAAM variables on output characteristics. To attain higher BH values, voltage at a lower level of 20 V, GMR at a higher level of 5, and TS at a lower level of 24 mm/s are desirable. To attain lower BW values, voltage at a lower level of 20 V, GMR at a higher level of 5, and TS at a lower level of 28 mm/s are desirable.

Author Contributions

Conceptualization, V.V., R.C. and J.V.; methodology, V.V., R.C. and J.V.; software, M.J. and Y.S.; validation, V.V., R.C., M.J., Y.S. and J.V.; formal analysis, V.V.; investigation, V.V., R.C. and J.V.; resources, V.V., R.C., M.J., Y.S. and J.V.; data curation, V.V., R.C. and J.V.; writing—original draft preparation, V.V., R.C. and J.V.; writing—review and editing, R.C. and J.V.; visualization, V.V., R.C., M.J., Y.S. and J.V.; supervision, R.C. and J.V.; project administration, R.C. and J.V.; funding acquisition, R.C. and J.V.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in this article.

Conflicts of Interest

The authors declare no conflict of interest.

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Engproc 114 00013 g001
Figure 1. Experimental setup of WAAM [34].
Figure 1. Experimental setup of WAAM [34].
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Figure 2. Single-layer depositions as per Taguchi’s L9 array.
Figure 2. Single-layer depositions as per Taguchi’s L9 array.
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Figure 3. Evaluation of BH, and BW response measures.
Figure 3. Evaluation of BH, and BW response measures.
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Figure 4. Effect of GMAW parameters on BH.
Figure 4. Effect of GMAW parameters on BH.
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Figure 5. Effect of GMAW parameters on BW.
Figure 5. Effect of GMAW parameters on BW.
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Figure 6. Residual plots for BH.
Figure 6. Residual plots for BH.
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Figure 7. Residual plots for BW.
Figure 7. Residual plots for BW.
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Table 1. Chemical composition of SS316L.
Table 1. Chemical composition of SS316L.
Elem.CrNiMoMnSiCPSNFe
Wt. %17.0910.612.381.170.590.0130.0110.0110.09Bal.
Table 2. WAAM input parameters.
Table 2. WAAM input parameters.
WAAM Input ParametersUnitValues
Travel Speed (mm/s)mm/s20; 22; 24
Voltage (V)V24; 26; 28
Gas Mixture Ratio-1; 3; 5
Table 3. WAAM input variables with output responses.
Table 3. WAAM input variables with output responses.
Sr. No.Voltage
(V)		GMRTravel Speed
(mm/s)		BH
(mm)		BW
(mm)
1201245.637.33
2203264.526.75
3205283.946.23
4221264.328.31
5223283.917.84
6225245.817.72
7241283.219.46
8243244.799.24
9245264.068.98
Table 4. ANOVA for BH and BW.
Table 4. ANOVA for BH and BW.
SourceDFSSMSFPSignificance% Contr.
For BH
Regression35.21201.737321.370.003Significant
TS14.45484.454854.790.001Significant81.48%
Voltage10.68680.68688.450.034Significant15.91%
GMR10.07040.07040.870.395Insignificant1.53%
Error50.40660.0813 1.08%
Total85.6186
R2 = 92.76%; R2 (Adj.) = 88.42%.
For BW
Regression39.93393.3113436.590.000Significant
TS10.09620.096212.690.016Significant0.96%
Voltage19.05289.05281193.60.000Significant90.78%
GMR10.78480.7848103.480.000Significance7.87%
Error50.03790.0075 0.39%
Total89.9718
R2 = 99.62%; R2 (Adj.) = 99.39%.
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MDPI and ACS Style

Vaghasia, V.; Vora, J.; Jagdale, M.; Shinde, Y.; Chaudhari, R. A Parametric Study Investigating the Effect of Bead Morphologies of SS316L Through the GMAW Process. Eng. Proc. 2025, 114, 13. https://doi.org/10.3390/engproc2025114013

AMA Style

Vaghasia V, Vora J, Jagdale M, Shinde Y, Chaudhari R. A Parametric Study Investigating the Effect of Bead Morphologies of SS316L Through the GMAW Process. Engineering Proceedings. 2025; 114(1):13. https://doi.org/10.3390/engproc2025114013

Chicago/Turabian Style

Vaghasia, Vatsal, Jay Vora, Manoj Jagdale, Yogita Shinde, and Rakesh Chaudhari. 2025. "A Parametric Study Investigating the Effect of Bead Morphologies of SS316L Through the GMAW Process" Engineering Proceedings 114, no. 1: 13. https://doi.org/10.3390/engproc2025114013

APA Style

Vaghasia, V., Vora, J., Jagdale, M., Shinde, Y., & Chaudhari, R. (2025). A Parametric Study Investigating the Effect of Bead Morphologies of SS316L Through the GMAW Process. Engineering Proceedings, 114(1), 13. https://doi.org/10.3390/engproc2025114013

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