Optimization of Resistance Spot Welding Parameters and Shielding Atmosphere Effects on the Mechanical Performance of AISI 201 Stainless Steel

Abstract

Attaining uniform weld quality in the resistance spot welding (RSW) of AISI 201 stainless steel remains challenging due to the complex interdependence of process parameters and the limited understanding of shielding atmosphere effects on this lean austenitic grade. This study integrates Taguchi optimization, analysis of variance (ANOVA), and complementary trend surface visualization to evaluate the effects of welding time, current, electrode pressure, and shielding atmosphere. An L27 orthogonal array was employed, with welding current identified as the dominant parameter for both tensile strength and hardness while nitrogen shielding exhibited a significantly greater influence on hardness than on tensile force, attributable to interstitial solid solution strengthening. The optimal conditions yielded a maximum tensile force of 12.2 kN and a hardness of 353 HV, with prediction errors below 1.5% for tensile force and below 0.5% for hardness. Trend surface visualization further revealed significant current–pressure interactions governing weld quality. These findings provide a validated optimization framework for the industrial RSW of AISI 201, with direct implications for automotive and structural manufacturing.

1. Introduction

RSW is a joining process that is most commonly used in the automotive, aerospace, and manufacturing sectors because of its simplicity, high productivity, and suitability for sheet metal fabrication [1,2]. Austenitic stainless steels, especially AISI 201, have received growing research interest among other engineering materials due to their desirable balance of mechanical strength, corrosion resistance, and reduced nickel content relative to AISI 304, rendering them more economical for industrial applications [3,4]. However, achieving consistent and optimal weld quality in RSW remains a complex challenge, as the mechanical performance of welded joints is highly sensitive to process parameters such as welding current, time, electrode pressure, and shielding environment.

The quality of a spot weld is primarily governed by heat generation and nugget formation, which depend on the electrical and thermal interactions at the electrode–workpiece interface [5,6]. Improper parameter selection can lead to defects such as expulsion, insufficient fusion, and reduced mechanical strength. Achieving a minimum fusion zone size is particularly critical: insufficient nugget growth leads to interfacial failure modes under tensile loading, whereas optimal nugget formation promotes full plug-out failure and superior load-bearing capacity [5]. The systematic optimization of process parameters is therefore essential for structural integrity and manufacturing reliability.

Conventional trial and error methods are time-consuming and too costly in the industrial context. To overcome these limitations, the statistical design of experiments (DOE) methods, especially the Taguchi method, have been extensively used [7,8]. The Taguchi method enables the effective testing of a large number of variables using fewer experiments and incorporates process robustness through signal-to-noise (S/N) ratio analysis. Combined with ANOVA, this methodology provides quantitative information on the statistical significance and percentage contribution of each parameter [9,10].

Previous studies have consistently confirmed the dominance of welding current on joint strength and microstructural characteristics. Khaleel et al. [2] demonstrated the effectiveness of the Taguchi–ANOVA framework for high-strength low-alloy steel RSW, while Elitas [6] established a clear relationship between welding current, nugget diameter, and failure modes in DC01 steel. For stainless steels, Muthu [11] applied L27 arrays to optimize RSW of AISI 316L sheets, and Dahake et al. [12] extended this to response surface methodology for the same grade. However, while most studies report current as the dominant parameter, the relative contribution varies substantially across material systems, ranging from approximately 40% to over 80%, indicating material-dependent thermal sensitivity that is not yet fully understood for leaner austenitic grades. Furthermore, existing Taguchi-based RSW studies have predominantly addressed AISI 304 and 316L, with minimal systematic investigation of AISI 201 despite its growing industrial adoption. Critically, the role of shielding atmosphere as an experimentally controlled variable remains largely unexplored within statistical optimization frameworks for RSW, and no prior study has quantified the interaction between nitrogen shielding and process parameters for this grade using ANOVA-based contribution analysis.

Unlike previous studies, which focused on conventional shielding conditions or the single-response optimization of well-characterized grades, no prior work has combined Taguchi optimization, ANOVA, and trend surface visualization with shielding atmosphere as a controlled variable for AISI 201 stainless steel. The novelty of this study lies in three aspects: (i) quantifying the contribution of shielding atmosphere to both tensile strength and hardness through ANOVA, revealing its differential influence on each response; (ii) applying a combined statistical–graphical framework to a lean austenitic grade that has received limited attention in the RSW optimization literature; and (iii) employing trend surface visualization as a complementary analytical tool to interpret two-factor interaction effects that cannot be statistically isolated within the L27 orthogonal array due to partial confounding.

Accordingly, this study addresses the identified research gap through a comprehensive experimental investigation using an L27 orthogonal array, integrating Taguchi optimization, ANOVA, and trend surface visualization to evaluate welding time, current, electrode pressure, and shielding atmosphere, including nitrogen as a metallurgically beneficial option [4,13]. The objective is to identify optimal welding conditions that maximize tensile shear strength and hardness while ensuring process stability and reproducibility, and to provide mechanistic insight into the role of nitrogen shielding in governing weld nugget properties.

2. Experimental Setup

2.1. Materials and Specimen Preparation

The base material used in this study was commercial AISI 201 stainless steel sheets with a nominal thickness of 2.5 mm. AISI 201 is an austenitic stainless steel that offers a cost-effective alternative to AISI 304, with comparable corrosion resistance and mechanical strength [4]. The chemical composition of the AISI 201 stainless steel sheets used in this study is listed in

Table 1. Chemical composition of AISI201 stainless steel.

Element	Cr	Mn	Ni	C	Cu	Si	V	Co
wt.%	14.3	9.7	1.5	0.082	1.03	0.58	0.1	0.1

, with an ultimate tensile strength (UTS) of 700 MPa, a yield strength (YS) of 300 MPa, and an elongation to failure (El) of 62%.

Specimens were prepared with dimensions of 60 mm × 20 mm in accordance with lap joint configuration. Prior to welding, all surfaces were prepared using standard metallographic procedures: mechanical grinding with SiC abrasive papers up to 2200-grit, followed by polishing with a 1 µm alumina suspension to ensure reproducible surface contact conditions.

2.2. Welding Equipment and Process

RSW was performed on a DJE spot-welding machine (DJE Resistance Welding Co., Ltd., Shanghai, China) equipped with 8 mm in diameter Cu–C water-cooled electrodes. The process was conducted under a constant shielding gas velocity of 1.3 m/s at a supply pressure of 7 atm. The four process parameters and their experimental ranges are as follows: welding current (9, 11, 13 kA), welding time (20, 23, 26 cycles), electrode pressure (60, 70, 80 psi), and shielding atmosphere (air, argon, nitrogen). The welding machine operates on a 50 Hz AC power supply, with each welding cycle lasting 1/50 = 0.02 s. The selection of nitrogen as a shielding medium was motivated by its established role as an austenite stabilizer and solid solution strengthening agent in stainless steel welding [13], as well as its documented benefits in reducing porosity and improving microstructural uniformity in austenitic steel welds [14,15].

2.3. Mechanical Characterization

2.3.1. Vickers Microhardness Testing

Weld zone hardness was evaluated using a Vickers microhardness tester under a 100 gf load. Ten indentations were performed within each weld nugget region to ensure statistical reliability, and the mean value was recorded as the representative hardness for each specimen.

2.3.2. Tensile-Shear Testing

Tensile-shear tests were conducted on a WDW-300 universal testing machine at a constant crosshead speed of 10 mm/min, following standard lap-shear test procedures. Three specimens were tested per welding condition, and the mean tensile shear force was recorded. This triplicate testing protocol is consistent with practices reported in the recent literature for RSW characterization [2,6,12,16].

2.3.3. Microstructural Analysis

To prepare the specimens for microstructural characterization, the spot-welded joints were sectioned precisely along the weld centerline to expose the internal morphology and were subsequently subjected to a standard metallographic sequence, starting with mechanical grinding using silicon carbide (SiC) abrasive papers to a 2200-grit finish. Following grinding, the samples were polished with a 1 µm alumina suspension, then chemically etched using a specialized reagent composed of 6 mL HCl, 4 mL acetic acid, 4 mL HNO₃, and two droplets of glycerin to clearly differentiate the nugget zone (NZ), the heat-affected zone (HAZ), and the base metal (BM). Finally, the resulting weld morphology and grain structures were evaluated using an Olympus GX51 optical microscope (Olympus, Tokyo, Japan).

3. Taguchi Orthogonal Array Design

The experimental plan was designed with the Taguchi method based on an L27 orthogonal array that supports four factors with three levels each. This design, which offers 27 trials versus the 81 needed in a full factorial design, is a proven method for efficient multi-parameter optimization and includes both primary effects and partial interaction data [7,9,11]. The four control factors and their levels are summarized in

Table 2. Process parameters and their experimental levels.

Parameter	Levels	Values	Units
Welding Time (P1)	3	20, 23, 26	cycles
Welding Current (P2)	3	9, 11, 13	kA
Electrode Pressure (P3)	3	60 (413.7), 70 (482.6), 80 (551.6)	Psi, (KPa)
Shielding Atmosphere (P4)	3	Air, Argon, Nitrogen	—

; the complete assignment of factor levels to experimental runs is presented in

Table 3. L27 orthogonal array of Taguchi.

Trial No.	P1 (Time)	P2 (Current)	P3 (Pressure)	P4 (Atmosphere)
1	1	1	1	1
2	1	1	2	2
3	1	1	3	3
4	1	2	1	2
5	1	2	2	3
6	1	2	3	1
7	1	3	1	3
8	1	3	2	1
9	1	3	3	2
10	2	1	1	2
11	2	1	2	3
12	2	1	3	1
13	2	2	1	3
14	2	2	2	1
15	2	2	3	2
16	2	3	1	1
17	2	3	2	2
18	2	3	3	3
19	3	1	1	3
20	3	1	2	1
21	3	1	3	2
22	3	2	1	1
23	3	2	2	2
24	3	2	3	3
25	3	3	1	2
26	3	3	2	3
27	3	3	3	1

.

S/N ratios were computed for each trial using Minitab 21.4.2 software, enabling the identification of parameter settings that yield high performance with minimum variability. The ‘larger-is-better’ criterion was applied for both tensile force and hardness, in accordance with established Taguchi optimization protocols for mechanical response maximization [8,10].

4. Results and Discussion

4.1. Experimental Results Overview

Table 4. L27 orthogonal array experimental results for tensile force and hardness.

Trial	Time (cyc)	Current (kA)	Pressure (psi)	Pressure (kPa)	Atmosphere	F (kN)	HV
1	20	9	60	413.7	Air	7.2 ± 0.30	301 ± 8
2	20	9	70	482.6	Argon	7.5 ± 0.35	306 ± 9
3	20	9	80	551.6	Nitrogen	7.4 ± 0.31	309 ± 7
4	20	11	60	413.7	Argon	8.0 ± 0.3	305 ± 7
5	20	11	70	482.6	Nitrogen	8.53 ± 0.24	322 ± 10
6	20	11	80	551.6	Air	7.79 ± 0.41	310 ± 11
7	20	13	60	413.7	Nitrogen	10.1 ± 0.42	323 ± 7
8	20	13	70	482.6	Air	9.92 ± 0.34	312 ± 9
9	20	13	80	551.6	Argon	9.89 ± 0.42	323 ± 12
10	23	9	60	413.7	Argon	8.0 ± 0.36	305 ± 7
11	23	9	70	482.6	Nitrogen	8.35 ± 0.42	320 ± 9
12	23	9	80	551.6	Air	7.8 ± 0.32	308 ± 11
13	23	11	60	413.7	Nitrogen	9.13 ± 0.42	320 ± 8
14	23	11	70	482.6	Air	9.0 ± 0.25	309 ± 11
15	23	11	80	551.6	Argon	9.0 ± 0.31	316 ± 10
16	23	13	60	413.7	Air	10.0 ± 0.36	317 ± 7
17	23	13	70	482.6	Argon	10.65 ± 0.41	330 ± 8
18	23	13	80	551.6	Nitrogen	10.65 ± 0.32	343 ± 10
19	26	9	60	413.7	Nitrogen	8.65 ± 0.38	320 ± 7
20	26	9	70	482.6	Air	8.6 ± 0.24	308 ± 11
21	26	9	80	551.6	Argon	8.59 ± 0.45	318 ± 7
22	26	11	60	413.7	Air	9.0 ± 0.39	309 ± 9
23	26	11	70	482.6	Argon	9.4 ± 0.42	320 ± 10
24	26	11	80	551.6	Nitrogen	9.49 ± 0.29	335 ± 9
25	26	13	60	413.7	Argon	11.5 ± 0.43	337 ± 12
26	26	13	70	482.6	Nitrogen	12.2 ± 0.45	347 ± 11
27	26	13	80	551.6	Air	10.35 ± 0.35	336 ± 11

presents the complete experimental matrix of tensile force and hardness responses measured. The findings indicate that both outputs are strongly dependent on the welding current, consistent with findings from similar RSW studies [2,3]. The lowest tensile force of 7.2 kN was measured in Trial 1 (20 cycles, 9 kA, 60 psi, air) and the highest tensile force of 12.2 kN was measured in Trial 26 (26 cycles, 13 kA, 70 psi, nitrogen), representing a performance improvement of approximately 69% between the weakest and strongest conditions, underscoring the substantial effect of current and shielding atmosphere selection.

The dominant role of welding current can be understood through the fundamental Joule heating relationship, $Q = I^{2}Rt$, where $Q$ is the heat generated, $I$ is the welding current, $R$ is the total resistance at the faying interface, and $t$ is the welding time. Because heat generation scales with the square of current, even modest increases in current produce disproportionately large increases in thermal energy input, directly accelerating nugget growth kinetics. At 9 kA, the heat input is insufficient to develop a fully consolidated nugget, resulting in small fusion zones and interfacial failure under shear loading. With a current of 13 kA, the thermal energy doubles (proportional to $I^2$) to facilitate rapid melting, expansion of the molten pool, and the development of a columnar-to-equiaxed solidification structure that increases load transfer across the joint interface [5,6]. However, at excessively high current levels, the internal pressure of the molten nugget may exceed the electrode clamping force, triggering expulsion—that is, the ejection of molten metal from the faying interface—which reduces the effective nugget volume and degrades joint strength [6]. This expulsion threshold is governed by the balance between the hydrostatic pressure of the molten pool and the mechanical restraint provided by electrode pressure, explaining the observed interaction between current and pressure in the response surface plots. The role of contact resistance is also critical: at the onset of welding, the asperity-based contact resistance at the faying interface concentrates Joule heating at the sheet–sheet interface, initiating nugget formation. As the interface heats and collapses, the bulk resistance of the workpiece becomes the primary heat source, and the nugget grows predominantly in the thickness direction. Electrode pressure modulates this transition by controlling the real contact area. Higher pressures flatten asperities earlier, reducing initial contact resistance and potentially delaying nugget initiation, which accounts for the moderate and sometimes non-monotonic effect of pressure observed in the experimental data.

4.2. Microstructural Evolution

The macrostructural features of the AISI 201 resistance spot weld, produced at a 23-cycle welding time, 70 psi pressure, and 9 kA current, are illustrated in Figure 1. The cross-section clearly delineates three characteristic regions: the NZ, the HAZ, and the BM. A significant change in grain morphology marks the transition from the BM to the NZ; while the BM maintains its original fine-grained wrought structure, the NZ exhibits a coarse columnar dendritic structure. These dendrites originate at the fusion line and propagate toward the nugget center, following the direction of the steepest thermal gradient during solidification. This variation in grain size and orientation across the three zones is a direct consequence of the localized thermal cycle and the high peak temperatures reached during welding.

The formation and integrity of these regions are heavily influenced by the high heat input resulting from the 23-cycle welding time. This prolonged duration allows for significant thermal accumulation, leading to substantial grain coarsening in the HAZ and a large molten volume in the NZ. The presence of a solidification crack in the center of the NZ, as observed in the macrograph, is attributed to the high thermal expansion coefficient and low thermal conductivity of this lean austenitic grade. These physical properties, combined with the intensive heat input, generate severe localized shrinkage stresses during cooling. Furthermore, while the electrode pressure of 70 psi was sufficient to contain the molten pool, it appears to have been inadequate to counteract the internal tensile stresses, leading to the initiation of the observed defect. The resulting microstructure and internal defects have a direct impact on the mechanical performance of the welded joint. The coarsening of grains in the HAZ and NZ typically reduces local yield strength and hardness compared to the BM, despite the interstitial strengthening provided by the nitrogen medium. The solidification crack acts as a critical stress concentrator, compromising the overall structural integrity of the joint. Under tensile-shear loading, such defects often shift the failure mechanism, favoring a less desirable interfacial fracture mode over a full button pull-out. Consequently, while optimizing current and time is necessary for nugget growth, managing the thermal gradient and forging pressure are equally vital to ensuring the mechanical reliability of AISI 201 spot welds.

Figure 2 shows the microstructure of the welded joints at different currents, times, and pressures in a nitrogen medium. The microstructural evolution of AISI 201 spot-welded joints in a nitrogen environment is significantly influenced by variations in welding parameters across the three sets. At 20 cycles and 9 kA, the nugget remains relatively small, with a more symmetrical, rounded profile, due to the moderate heat input. However, as the welding current and time increase from 11 kA/23 cycles to 13 kA/26 cycles, the total thermal energy (Q = I²Rt) increases substantially. This increased heat input results in deeper penetration and a wider fusion zone, visibly enlarging the nugget. The nitrogen atmosphere facilitates interstitial strengthening during this phase, but the intense thermal cycles also promote more pronounced grain coarsening in the HAZ and a more dominant columnar dendritic structure within the NZ. While increasing the current and time successfully expands the nugget size, the high temperatures reduce the yield strength of the material, making it more susceptible to deformation under the applied electrode pressure. This leads to a shift from a round nugget shape to a more irregular, flattened, or elongated geometry, particularly evident at 13 kA and 26 cycles. At these higher parameters, the combination of a large molten volume and the forging force of the electrodes (60–70 psi) causes the nugget to expand laterally, often resulting in increased indentation and a non-spherical fusion zone. This irregular shape, while indicative of a larger bonded area, must be carefully managed to prevent excessive metal expulsion or internal void formation due to the high shrinkage stresses inherent in this stainless-steel grade.

The microstructural characteristics of AISI 201 spot welds are profoundly influenced by the shielding medium, as evidenced by the comparative analysis of joints produced in air (Figure 3), argon (Figure 4), and nitrogen. In an air atmosphere, the fusion zone is highly susceptible to oxidation and the loss of alloying elements, which can lead to a standard austenitic–ferritic solidification structure with potential surface scaling. Argon, serving as an inert shield, effectively prevents these chemical reactions, resulting in a cleaner fusion boundary and a more stable dendritic growth pattern. In contrast, the nitrogen medium acts as an active metallurgical participant; as a strong austenite stabilizer and interstitial element, nitrogen diffuses into the molten pool, significantly refining the grain structure and increasing the hardness of both the NZ and HAZ compared to the relatively softer and coarser structures found in air or argon environments.

The relationship between welding parameters and nugget morphology across these media shows that increasing current and time drives higher heat input, which consistently expands the nugget size while altering its geometric symmetry. At lower heat inputs, such as 9 kA and 20 cycles, the nuggets maintain a stable, rounded profile. However, as parameters escalate to 13 kA and 26 cycles, the substantial thermal accumulation lowers the yield strength of the material at temperature, allowing the electrode pressure to deform the molten volume into a wider, more irregular shape. This transition is marked by deep indentation and a shift from spherical to elongated nugget geometries. While the expanded nugget diameter increases the overall bonded area, the concurrent grain coarsening in the HAZ creates a localized region of lower strength that must be balanced against the benefits of increased nugget volume. Correlating these microstructural features with mechanical properties reveals a definitive microstructure–property relationship where the peak tensile-shear force is primarily dictated by the nugget diameter, while joint hardness is significantly enhanced by the nitrogen atmosphere. The interstitial strengthening from nitrogen helps sustain high hardness levels (reaching approximately 353 HV) despite the grain coarsening typically associated with high heat inputs. The mechanical reliability of the joint depends on the transition from a brittle interfacial failure to a robust pull-out failure mode, which is achieved when the nitrogen-enriched matrix provides sufficient strength to shift the fracture path away from the fusion line. Consequently, the optimal mechanical performance is realized by utilizing a nitrogen medium to maximize matrix hardness while tuning the current and time to produce a large, defect-free nugget.

4.3. Taguchi Signal-to-Noise Analysis

4.3.1. Optimization of Tensile Force

The ‘larger-the-better’ S/N ratio criterion was applied to identify parameter levels that maximize tensile force while minimizing process variability. The S/N response table for tensile force is shown in

Table 5. S/N ratio response table—tensile force (F, kN).

Level	Time (Cycles)	Current (kA)	Pressure (psi)	Atmosphere
1	18.50	18.05	19.07	18.88
2	19.20	18.89	19.33	19.17
3	19.72	20.47	19.01	19.37
Delta (Δ)	1.22	2.42	0.32	0.49
Rank	2	1	4	3

. Welding current (kA) emerged as the most significant factor (rank 1, Δ = 2.42 dB), followed by welding time (rank 2, Δ = 1.22 dB), shielding atmosphere (rank 3, Δ = 0.49 dB), and electrode pressure (rank 4, Δ = 0.32 dB). This ranking corroborates the findings of Khaleel et al. [2], who identified current as the dominant parameter in RSW of HSLA steel, and aligns with the results of Nguyen et al. [3] for SUS201 stainless steel. The dominance of current is attributable to the quadratic dependence of Joule heat generation on current magnitude ($Q = I^{2}Rt$), which produces a steeper thermal gradient response compared to the linear contributions of time and pressure.

The optimal combination for tensile force maximization was found to be time = 26 cycles, current = 13 kA, pressure = 70 psi, and atmosphere = nitrogen. The sensitivity of the S/N ratio to each parameter is shown in the main effects plot (Figure 5). Increasing the welding current to 13 kA enlarged the nugget size and heat input, which facilitated adequate fusion and metallurgical bonding. The positive impact of nitrogen shielding is attributable to the role of nitrogen as a strong austenite stabilizer that inhibits the formation of δ-ferrite during rapid solidification; at the same time, it is an interstitial solid solution strengthener that increases the load-bearing capacity of the weld nugget [4,13]. In contrast to argon, which serves only as an inert protective atmosphere, nitrogen actively participates in the weld metallurgy of AISI 201, a grade already designed to exploit nitrogen as a partial substitute for nickel in stabilizing the austenitic phase.

The Taguchi model predicted a tensile force of 12.02 kN (S/N = 21.61 dB) at the optimal conditions. Confirmation trials yielded 12.2 kN, a prediction error of less than 1.5%, validating the accuracy of the model. The interaction plot for S/N ratios is shown in Figure 6.

4.3.2. Optimization of Hardness

A separate Taguchi analysis was performed for weld zone hardness using the same L27 design and ‘larger-the-better’ criterion. The S/N response table for hardness is shown in

Table 6. S/N ratio response table—hardness (HV).

Level	Time (Cycles)	Current (kA)	Pressure (psi)	Atmosphere
1	49.89	49.84	49.97	49.89
2	50.06	50.00	50.08	50.04
3	50.25	50.36	50.15	50.27
Delta (Δ)	0.36	0.52	0.18	0.39
Rank	3	1	4	2

. Welding current retained its dominant ranking (rank 1, Δ = 0.52 dB), followed by shielding atmosphere (rank 2, Δ = 0.39 dB), welding time (rank 3, Δ = 0.36 dB), and electrode pressure (rank 4, Δ = 0.18 dB). The elevated influence of atmosphere on hardness, compared to its lower rank for tensile force, is noteworthy and corroborates findings on the solid solution strengthening effect of nitrogen in the weld nugget [4,13].

The optimal parameter combination for hardness was identified as 26 cycles, 13 kA, 80 psi, and nitrogen shielding. The main effects plot in Figure 7 illustrates the sensitivity of the S/N ratio to each parameter, confirming a progressive improvement in hardness with increasing current and a clear superiority of nitrogen shielding over both air and argon atmospheres. The interaction plot in Figure 8 further confirms that welding current produces the steepest S/N gradient across all factor combinations, with nitrogen consistently yielding the highest S/N values, while electrode pressure exhibits the least variation across the investigated range, consistent with its lowest parameter ranking. The predicted hardness was 352.56 HV, while experimental measurements yielded 353 HV (error < 0.5%). This close agreement, below 2% for both responses, falls within the confirmation accuracy range reported by Khaleel et al. [2] and Rajasekar and Gurusami [17], validating the predictive reliability of the L27-based model.

4.3.3. Comparative Discussion of Taguchi Results

The comparison of the two Taguchi analyses (

Table 7. Summary: predicted vs. experimental results at optimal conditions.

Response	Optimal Conditions	Predicted	Experimental	Error (%)
Tensile Force (kN)	26 cyc, 13 kA, 70 psi, N2	12.02 kN	12.2 kN	<1.5%
Hardness (HV)	26 cyc, 13 kA, 80 psi, N2	352.56 HV	353 HV	<0.5%

) confirms that welding current is the dominant factor in both responses, a well-established finding in the RSW literature of stainless and high-strength steels [2,3,6,11]. Electrode pressure was found to have a higher relative effect on tensile force than on hardness, and welding time had a moderate and consistent effect on both. The nitrogen shielding atmosphere, in particular, exerted a greater influence on hardness than on tensile force, and this effect is important to note in terms of its metallurgical impact on nugget microstructure. Prediction errors below 1.5% for both responses confirm the adequacy of the L27 orthogonal array design.

4.4. Analysis of Variance (ANOVA)

Minitab 21.4.2 was used to perform ANOVA at a 95% confidence level (α = 0.05) to measure the statistical significance and percentage contribution of each parameter. Statistical significance was determined using a p-value threshold of 0.05 [7,9]. It is important to note that the ANOVA used here only assesses the main effects; the interaction terms (such as current × pressure) were not incorporated in the ANOVA model. Although the L27 orthogonal array is capable of capturing two-factor interactions partially because of its partial confounding structure, the number of degrees of freedom is small compared to the number of potential interaction pairs, limiting the statistical power of a complete interaction ANOVA [7,8]. Therefore, the interaction effects in this study were interpreted through complementary trend surface visualization (Section 4.5), which provides graphical insight into combined parameter effects and curvature trends that the main effect ANOVA tables alone cannot convey. This approach is consistent with the hybrid visualization strategy adopted in recent studies [8,9,12].

4.4.1. ANOVA Results for Tensile Force

The ANOVA results of tensile force are shown in

Table 8. ANOVA results—tensile force (F, kN).

Source	DF	Contribution	Adj SS	Adj MS	F-Value	p-Value
Time (cycles)	2	17.57%	7.3039	3.6519	60.10	0.0000
Current (kA)	2	75.10%	31.2170	15.6085	256.86	0.0000
Pressure (psi)	2	1.53%	0.6358	0.3179	5.23	0.0162
Atmosphere	2	3.17%	1.3164	0.6582	10.83	0.0008
Error	18	2.63%	1.0938	0.0608	—	—
Total	26	100.00%	—	—	—	—

. The contribution of welding current to total variance is 75.10% (F = 256.86, p < 0.001), which makes it the overwhelmingly dominant variable. Welding time ranks second with 17.57% contribution (F = 60.10, p < 0.001). Shielding atmosphere and electrode pressure add 3.17% and 1.53%, respectively, but are statistically significant (p < 0.05). The residual error (2.63%) is small, confirming that the chosen factors can sufficiently explain the observed variability. The model has a high predictive power: R² = 97.37% and adjusted R² = 96.20%. These values of R² are comparable to those reported by Dahake et al. [12] for the RSW of 316L steel and Khaleel et al. [2] for HSLA steel, which validates the strength of the Taguchi–ANOVA model in material systems.

4.4.2. ANOVA Results for Hardness

The ANOVA results for hardness are summarized in

Table 9. ANOVA results—hardness (HV).

Source	DF	Contribution	Adj SS	Adj MS	F-Value	p-Value
Time (cycles)	2	19.85%	787.2	393.59	26.03	0.0000
Current (kA)	2	44.28%	1756.1	878.04	58.06	0.0000
Pressure (psi)	2	5.29%	209.9	104.93	6.94	0.0058
Atmosphere	2	23.71%	940.1	470.04	31.08	0.0000
Error	18	6.86%	272.2	15.12	—	—
Total	26	100.00%	—	—	—	—

. Welding current again leads with 44.28% contribution (F = 58.06, p < 0.001), reaffirming its critical thermal influence on nugget microstructure. However, in contrast to the tensile force model, shielding atmosphere ranks second for hardness (23.71%, F = 31.08, p < 0.001), followed by welding time (19.85%, F = 26.03, p < 0.001) and electrode pressure (5.29%, F = 6.94, p = 0.0058). All four parameters are statistically significant. The markedly higher contribution of atmosphere to hardness, compared to its 3.17% contribution to tensile force, provides quantitative evidence that nitrogen’s influence operates primarily through a microstructural strengthening mechanism (interstitial solid solution hardening) rather than through macroscopic changes in nugget geometry. This differential response is physically meaningful: tensile shear strength depends predominantly on nugget diameter and fusion zone area, which are governed by bulk heat input, whereas hardness reflects the local metallurgical state of the solidified nugget, which is sensitive to compositional effects such as nitrogen dissolution [4,13]. The hardness model achieves R² = 93.14% and adjusted R² = 90.08% (

Table 10. ANOVA model performance summary.

Response	R2 (%)	Adjusted R2 (%)	Error (%)
Tensile Force (kN)	97.37	96.20	2.63
Hardness (HV)	93.14	90.08	6.86

).

4.4.3. Residual Analysis and Model Adequacy

Model adequacy was assessed through residual diagnostic plots (Figure 9 and Figure 10). For both responses, the normal probability plots indicate residuals closely follow the reference line, confirming normality. The histograms exhibit approximately symmetric, bell-shaped distributions centered at zero with no pronounced outliers. Residuals versus fitted values show random scatter with no discernible trends or heteroscedastic patterns, confirming constant variance. Residuals versus observation order show no systematic drift, confirming independence of errors. These diagnostic results confirm that ANOVA assumptions are satisfied for both models and that the regression estimates are statistically reliable.

4.5. Trend Surface Visualization

Three-dimensional trend surface plots were generated from the L27 experimental data to visualize the combined and interactive effects of process parameters on both responses. These plots represent interpolated visualizations of the experimental data and are not derived from a formally fitted second-order regression model; they are presented as graphical complements to the Taguchi and ANOVA findings, providing intuitive insight into curvature trends and parameter interactions that statistical tables alone cannot fully convey [12].

4.5.1. Response Surfaces for Tensile Force

Figure 11a–c presents the 3D surface plots for tensile force interactions. In Figure 11a, tensile force increases with both current and pressure up to approximately 13 kA and 70 psi; beyond this range, the onset of expulsion, driven by the molten nugget’s hydrostatic pressure exceeding the electrode clamping force, reduces the effective fusion zone volume and degrades joint strength, a phenomenon reported by Elitas [6] for DC01 steel. This current–pressure interaction reflects the competing mechanisms of enhanced Joule heating ($Q = I^{2}Rt$) at higher currents and reduced contact resistance at elevated pressures, which together govern the thermal balance at the faying interface. Figure 11b shows the current–time interaction: tensile force rises with both parameters up to 13 kA and 26 cycles, beyond which the nugget growth rate saturates as the thermal boundary approaches the electrode contact zone, limiting further improvement and potentially initiating electrode indentation. Figure 11c, depicting the pressure–time interaction, indicates that moderate pressure paired with longer welding time enhances bonding through sustained heat accumulation and improved consolidation of the molten pool during solidification. These visual patterns corroborate and extend the ANOVA findings by revealing the nonlinear curvature and interaction effects that main-effect analysis alone cannot capture.

It is important to note that no expulsion events were visually detected in any of the 27 experimental trials within the parameter ranges investigated (welding current: 9–13 kA, electrode pressure: 60–80 psi and welding time: 20–26 cycles). The electrode pressure levels applied were sufficient to maintain molten pool containment in all cases. The monotonically increasing tensile force trends observed up to the optimal conditions (Trial 26: 13 kA, 26 cycles, 70 psi, nitrogen; F = 12.2 kN) confirm the absence of expulsion-induced nugget volume reduction within the tested parameter domain. The discussion of expulsion in this section is therefore mechanistic and predictive in nature, identifying the threshold conditions beyond which expulsion would be expected based on the current–pressure interaction physics, consistent with observations reported by Elitas [6] for DC01 steel. The absence of expulsion across all 27 trials is what enables the clear, monotonic response trends observed in both the ANOVA and trend surface analyses and confirms that the tested parameter window was maintained below the expulsion threshold throughout the experimental campaign.

4.5.2. Response Surfaces for Hardness

Figure 12a–c illustrates the surface plots for hardness. Figure 12a reveals that moderate pressure near 70 psi combined with high current maximizes hardness, while very high pressures reduce localized resistance and weaken the nugget. Figure 12b shows that hardness increases steadily with current and time up to 13 kA and 26 cycles, reflecting improved fusion and grain refinement from sufficient heat input. A slight hardness decline beyond these values suggests grain coarsening under excessive thermal exposure, a microstructural effect documented for spot-welded austenitic stainless steels [4,11]. Figure 12c confirms the cooperative role of time and pressure in achieving favorable metallurgical bonding conditions.

4.5.3. Summary of Response Surface Findings

The trend surface plots support the Taguchi and ANOVA results, which indicate that the welding current is the most significant variable in tensile force and hardness. The optimal combination of high current, moderate-to-high pressure, and adequate welding time with nitrogen shielding yielded a maximum tensile force of 12.2 kN and a weld nugget hardness of 347 HV. This hardness value is significantly greater than BM hardness of approximately 169 HV (85 HRB) for annealed AISI 201 [4,18], indicating significant microstructural strengthening within the nugget through two complementary mechanisms: rapid solidification-induced grain refinement driven by the steep thermal gradients inherent to RSW, and nitrogen-assisted interstitial solid solution strengthening that elevates lattice resistance to dislocation motion in the austenitic matrix. The convergence of all three analytical methods, namely Taguchi S/N ranking, ANOVA contribution analysis, and trend surface visualization, on consistent optimal conditions strengthens confidence in these results and validates the integrated statistical framework adopted in this study.

5. Conclusions

This study investigated the optimization of RSW parameters and shielding atmosphere effects on the mechanical performance of AISI 201 stainless steel using an integrated Taguchi–ANOVA framework with trend surface visualization. Welding current emerged as the dominant parameter for both tensile shear strength (75.10% ANOVA contribution) and hardness (44.28%), consistent with the quadratic dependence of Joule heat generation on current magnitude. Taguchi optimization identified response-specific optima differing only in electrode pressure: 70 psi maximizes tensile force (12.2 kN) and is recommended as the unified setting for structural applications, while 80 psi is preferred where hardness (353 HV) governs. Nitrogen shielding contributes significantly more to hardness (23.71%) than to tensile force (3.17%), providing quantitative evidence that its primary mechanism is the interstitial solid solution strengthening of the weld nugget microstructure rather than the macroscopic modification of fusion zone geometry.

This study provides a systematic ANOVA-based quantification of nitrogen shielding effects in the RSW of AISI 201, an aspect that has received limited attention in the optimization literature for this lean austenitic grade. The complementary trend surface visualization addresses the partial confounding of two-factor interactions inherent in orthogonal array designs, enabling interaction effects between current and pressure to be visualized and interpreted beyond what main-effect analysis alone can convey. The high ANOVA model adequacy (R² > 93%) validates the integrated approach, confirming that the selected parameters adequately explain the observed variability in both responses. The 67% reduction in experimental trials relative to full factorial testing, combined with prediction accuracy below 2%, demonstrates the direct transferability of this framework to production-floor optimization in automotive and structural fabrication settings.

Future work should address microstructural characterization via SEM/EBSD to directly correlate statistical findings with nugget morphology and phase composition, corrosion performance evaluation under service-representative environments, and machine learning-assisted weld quality prediction for real-time process control [1,16,17,19].