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Article

Optimization of Joining Parameters in Pulsed Tungsten Inert Gas Weld Brazing of Aluminum and Stainless Steel Based on Response Surface Methodology

by
Huan He
1,
Xu Tian
1,
Xiaoyang Yi
1,
Pu Wang
1,
Zhiwen Guo
1,
Ao Fu
2,* and
Wenzhen Zhao
1,*
1
Yantai Key Laboratory of Advanced Nuclear Energy Materials and Irradiation Technology, College of Nuclear Equipment and Nuclear Engineering, Yantai University, Yantai 264005, China
2
State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China
*
Authors to whom correspondence should be addressed.
Coatings 2024, 14(10), 1262; https://doi.org/10.3390/coatings14101262
Submission received: 29 August 2024 / Revised: 21 September 2024 / Accepted: 24 September 2024 / Published: 1 October 2024
Browse Figures
Versions Notes

Abstract

:
Combining aluminum and steel offers a promising solution for reducing structural weight and fuel consumption across various industries. Pulse in tungsten inert gas (TIG) weld brazing effectively suppresses interfacial brittle intermetallics and enhances joint strength by influencing pool stirring and heat input during aluminum-to-steel joining. However, optimizing the pulsed TIG weld brazing process is challenging due to its numerous welding parameters. This study established statistical models for Al/steel joint strength without reinforcement using response surface methodology (RSM) based on central composite design (CCD). The models’ adequacy and significance were verified through analysis of variance (ANOVA). The four welding parameters influence weld strength in the following descending order: pulse on time > base current > pulse current > pulse frequency. Additionally, interactions between pulse current and pulse frequency, and between pulse on time and base current, were observed. Numerical optimization using RSM determined the optimal pulsed GTA weld brazing parameters for aluminum and stainless steel. With these optimized parameters, the joint strength reached 155.73 MPa, and the intermetallic compound (IMC) thickness was reduced to 3.4 μm.

1. Introduction

With the rapid advancement of modern manufacturing technologies, the demand for bimetallic structures is increasing due to their potential to reduce energy consumption, lower production costs, and enhance structural performance. Among the most commonly used engineering alloys, steel and aluminum offer unique and complementary properties. Aluminum is lightweight and known for its excellent thermal conductivity and corrosion resistance, whereas steel is prized for its high strength and hardness, making both materials essential in many engineering applications. Hybrid aluminum–steel structures take advantage of the best properties of both metals and are widely used in aerospace, shipbuilding, and the automotive industry [1,2,3,4].
However, joining aluminum and steel presents significant challenges. The considerable differences in their thermal and physical properties, combined with the limited solubility between aluminum and steel, often lead to the formation of hard and brittle intermetallic compounds (IMCs) at the interface, such as Fe2Al5 and Fe4Al13. These IMCs can severely weaken the joint, posing a major challenge for the widespread application of aluminum–steel hybrid structures [5,6,7].
Various welding methods have been employed to join aluminum and steel, including solid-state welding (resistance spot welding [8], friction stir welding [9,10,11,12], diffusion welding [13], explosion welding [14]), brazing [15], and weld brazing (laser welding [7], electron beam welding [16], arc welding [17]). Solid-state welding effectively suppresses the formation of IMCs due to its low heat input but is limited by the shape and size of the weld. Brazing for aluminum and steel often requires a specially designed weld structure, sometimes with transition layers, resulting in low joining efficiency. In comparison, although weld brazing for dissimilar metals has greater adaptability, it is difficult to control IMCs due to their high heat input. For weld brazing of aluminum to stainless steel, lap joints of 1 mm-thick aluminum alloy and stainless steel were successfully produced using MIG weld brazing with either an aluminized or galvanized zinc coating [18]. The IMC layer formed during this process varied in thickness, ranging from approximately 5 μm to 15 μm. Song et al. developed a TIG weld brazing method with pre-coating, achieving a butt joint between aluminum and stainless steel [19]. In this case, the IMC thickness ranged from 5 μm to 35 μm. He et al. demonstrated that butt joining of aluminum and steel is more challenging than lap joining. Moreover, the thicker the base material, the more difficult the welding becomes, and the harder it is to control the formation of IMCs [20].
The formation of brittle IMCs at the aluminum/steel dissimilar joining interface is highly probable due to the incompatibility of the metals and the large reaction driving force. Compounds such as β-FeAl, ε-Fe5Al8, ζ-FeAl2, η-Fe2Al5, and θ-Fe4Al13/FeAl3 have been widely investigated [21,22]. For the aluminum/steel liquid–solid interface, the IMCs are primarily composed of η-Fe2Al5 and θ-Fe4Al13/FeAl3 [23]. The type of parent steel significantly affects the IMCs formed between aluminum and steel. Furthermore, the morphology of the interface and the dominant IMC layer also vary. Specifically, the IMCs at the hot-dip aluminizing or weld brazing interface of high-purity iron, mild steel, and Fe-Cr alloys with liquid aluminum are predominantly Fe2Al5 on the steel side, accompanied by small amounts of FeAl3, Fe4Al13, or Fe2Al7 on the aluminum side [24,25,26,27,28,29,30]. Conversely, the interfaces of Fe–Ni and Fe–Cr–Ni alloys with aluminum liquids are dominated by FeAl3/Fe4Al13, exhibiting a significantly reduced total thickness compared to the aforementioned alloys [31,32,33]. The growth mechanism of Fe2Al5 has been identified as a diffusion-controlled process exhibiting parabolic growth behavior. It has been demonstrated that its thickness can be controlled by reducing the welding heat input [34,35] for the Fe4Al13, which has a more complex evolutionary mechanism involving interfacial reactions, diffusion, and dissolution [31]. Increasing the welding heat input within a reasonable range can effectively control its thickness.
To suppress the IMCs, pulse welding [36], cold metal transfer (CMT) [37], double electrode arc welding [38,39], hot wire welding [40], and axial magnetic field welding [41] have been employed in the weld brazing process of aluminum and steel to adjust the welding heat input. Among these methods, pulsed tungsten inert gas (TIG) welding is an economical and effective choice for joining aluminum and steel. However, optimizing pulse welding is challenging due to numerous parameters such as peak current, base current, pulse on time, and frequency. To address this challenge, experimental design methods are commonly used.
Experimental design methods for dissimilar welding of aluminum and steel include Artificial Neural Networks (ANN) [42,43], Taguchi Design [44,45,46], and Response Surface Methodology (RSM) [47,48]. RSM combines design, modeling, testing, optimization, and other statistical techniques to build regression-fitting equations and generate response surfaces that conveniently and intuitively show the response values corresponding to each factor level. Moreover, RSM requires a relatively limited number of trials, encompassing a substantial quantity of data, thereby enhancing efficiency and reducing expenditure. RSM is capable of geometrically displaying the location of the factor intervals corresponding to the optimization results in a more intuitive manner through the provision of three-dimensional (3D) response surface plots and contour plots, facilitating the stability design and optimization process. RSM provides a visual representation of the impact of each factor on the outcome, employing a mathematical model developed through analysis of variance (ANOVA) and interactions between factors with significant effects. Consequently, the experiment was conducted using a regression design in conjunction with the RSM of surface analysis.
After welding of aluminum and steel, nondestructive testing (NDT) is essential. Fasfous et al. employed the penetrant method to perform NDT on all specimens, detecting surface defects or cracks on the weld [49]. Feng et al. developed an infrared NDT method to inspect aluminum and steel joints with complex surfaces in a highly automated setup, providing effective and reliable inspection [50]. For internal defects, Patra et al. applied ultrasonic NDT, revealing that 80% of the joint’s integrity could be assessed using this technique [51]. Tallafuss et al. employed multiple NDT techniques, including active thermography, shearography, ultrasound, and guided wave EMATs, to evaluate both the exterior and interior aspects of the welds. This combination of methods ensured comprehensive detection of surface quality, such as smoothness and flatness, and internal defects [52].
This study applies an experimental design to the aluminum/steel pulse TIG weld brazing process. Regression design and RSM are used to predict the welding parameters. The least squares method determines the regression model coefficients for second-order model verification. A mathematical model is developed to predict the tensile strength of the joints, and the predicted values are compared with the actual results to evaluate the model’s applicability.

2. Materials and Methods

A 3.0 mm 5A06 aluminum alloy and SUS321 stainless steel sheets, with a 1.6 mm ER1100 aluminum filler, were used in this study. The chemical compositions of the above materials are provided in Table 1.
The specimens measured 100 mm by 50 mm and featured a 45° single-V groove on both the parent alloys. Initially, the metal surfaces were meticulously cleaned, and then a flux layer of KAlF4 was applied to the grooves, as illustrated in Figure 1. Aluminum–steel butt TIG weld brazing experiments were carried out using Miller Dynasty 350 welding equipment (Miller Electric Mfg. Co., Appleton, WI, USA). Single-sided welding with double-sided forming technology was employed in the experiment, using a copper backing plate with a forming groove that is 6 mm wide and 0.8 mm deep. An AC square wave at 100 Hz with a 4:1 AC balance was used. The arc length was set to 3.0–4.0 mm, the welding speed to 150 mm/min, and the argon gas flow rate to 8–10 L/min.
The weld macrostructure and microstructure of the joint were analyzed using an optical microscope (OM) (Olympus-PMG3, Olympus Corporation, Japan), and a scanning electron microscope (SEM) (Quanta-200FEG, Quanta Computer Inc., Taiwan, China) equipped with an energy dispersive spectrometer (EDS). The microstructure was captured at the center of the joining interface. Tensile tests were performed using the INSTRON-5569 testing machine (INSTRON, Norwood, MA, USA), at a loading speed of 0.5 mm/min, with each test conducted three times.

3. Results and Discussion

3.1. Statistical Modeling Establishment

In response to surface analysis, selecting an appropriate experimental design method is critical for gathering sample points and developing regression equations. The Central Composite Design (CCD) is ideal for this purpose, as it improves orthogonal samples through the inclusion of central and axial points, ensuring benefits such as consistency, rotation, and representativeness. Furthermore, CCD minimizes the number of tests required while still providing extensive data, making it particularly well-suited for this experiment. The process includes the following steps:
Control Variable Identification: Before conducting pulse TIG weld brazing experiments, control variables are identified to establish the key parameters and their respective working limits.
Specimen Design: In accordance with CCD principles, test specimens are designed for the four pulse TIG welding parameters.
Specimen Preparation and Testing: Based on the experimental design, specimens are prepared through welding, and the joint strength, without reinforcement, is subsequently tested.
The principal parameters identified are pulse current, base current, pulse on time, and pulse frequency. The operational limits for these parameters are listed in Table 2. Based on preliminary test results, the remaining fixed parameters are identified in Table 3. During the actual process, each set of parameters was repeatedly welded to ensure repeatability. Standard tensile specimens were then cut from each welding test plate. The average tensile strength of the three specimens was taken as the corresponding response value for each set of parameters. The experimental results are presented in Table 4.
To analyze the relationship between welding parameters and joint strength without reinforcement, a second-order mathematical model of the specimen was developed. The tensile strength of the joint without reinforcement, denoted as σ, was taken as the response value. The second-order regression equation based on the sample data is as follows:
σ = −1028.70737 − 0.68590 × Ip + 18.75890 × tp + 28.5384 × Ib − 4.14917 × f + 0.027682 × Ip × f − 0.15627 × tp × Ib − 0.097896 × tp2 − 0.18250 × Ib2
In this equation, σ represents the tensile strength (MPa) of the joint without reinforcement, while Ip denotes the peak current (A), Ib signifies the base current (A), tp is the pulse on time (%), and f is the pulse frequency (Hz).
The model was subjected to an ANOVA test for statistical significance, and the results are presented in Table 5. The ANOVA results demonstrate that the second-order regression model is statistically significant, indicating that it is an appropriate fit for the test sample. The interactions between the factors, specifically pulse current versus pulse frequency and pulse on time versus base current, are also identified.
Furthermore, the construct validity of the model was evaluated through residual analysis. As illustrated in Figure 2, the normal probability plot of the residuals, ordered from smallest to largest, reveals that the residuals exhibit a normal distribution with a probability distribution close to linear. It can be observed that the residual value has no bearing on the order of the model’s predicted value. All points fall within the horizontal band centered on the residual value of 0, representing an irregular random distribution without a systematic positive or negative bias. Additionally, there are no outliers or anomalies, indicating that the regression model is appropriate.

3.2. Prediction of Joint Strength

A mathematical model developed using RSM was employed to predict the joint strength without reinforcement. As shown in Table 6, the predicted values closely match the experimental results, validating the model’s ability to optimize and predict joint strength. This demonstrates the model’s suitability. The microstructure and tensile strength of the joint interface are illustrated in Figure 3. Through parameter optimization, a maximum joint strength of 155.73 MPa was achieved, and the thickness of the IMC was effectively reduced to approximately 3.4 μm.

3.3. Connector Analysis

Perturbation plots at the center point of the factor region were employed to assess and quantify the influence of individual factors on joint strength, as shown in Figure 4. The ranking of the four process parameters by their impact on joint strength is as follows: pulse on time > base current > pulse current > pulse frequency. Both pulse current and pulse on time show a positive correlation with joint strength, indicating that increasing pulse current and extending pulse on time result in higher joint strength. The influence of base current is divided into two distinct phases: an initial positive correlation followed by a negative correlation. In contrast, pulse frequency was found to have a minimal impact on joint strength.
The ANOVA revealed the existence of two distinct interactions: the interaction between pulse current and pulse frequency and the interaction between pulse on time and base current. These interactions were analyzed as follows:
(1)
Figure 5 shows the interaction between pulse current and pulse frequency. At a constant pulse frequency, joint strength increases with a higher peak current. However, the effect of peak current on joint strength depends on the pulse frequency. As pulse frequency rises, the influence of peak current becomes stronger. When the peak current is constant, joint strength decreases with higher pulse frequency at lower peak currents but increases at higher peak currents. Similarly, the effect of pulse frequency on joint strength varies with peak current: at lower peak currents, its impact decreases, while at higher peak currents, it increases.
(2)
Figure 6 shows the relationship between pulse on time and base current. With a constant base current, joint strength increases as pulse on time increases. However, the effect of pulse on time decreases as the base current increases. Similarly, with constant pulse on time, joint strength increases with base current when pulse on time is low but increases and then decreases when pulse on time is high. Changes in pulse on time also affect the influence of base current: at low pulse on times, the impact of base current decreases, but at high pulse on times, it decreases first and then increases.

3.4. Relationship between Welding Parameters, IMCs, and Joint Strength

In this experiment, three typical joining interfaces with varying tensile strengths were selected for analysis. The interface formed a single layer of IMC. Based on the EDS results (Table 7) and related literature [31], the IMC was identified as Fe4Al13. As mentioned in the introduction, Increasing the welding heat input within a reasonable range can effectively suppress the thickness of Fe4Al13. The growth and dissolution of Fe4Al13 occur simultaneously in the aluminum solution, with the layer’s thickness determined by the relative rates of these processes. When the growth rate of Fe4Al13 surpasses its dissolution rate, the layer’s thickness increases. Conversely, when the dissolution rate exceeds the growth rate, the layer becomes thinner [53].
However, due to the interaction between the pulse welding parameters and the complexity of the IMC evolution, analyzing the effect of each pulse welding parameter on the IMC separately is challenging. It is evident that pulse current, pulse on time, and base current significantly affect the heat input, which in turn influences the interface temperature and the thickness of the Fe4Al13 compound. Specifically, the pulse current notably affects the peak temperature, while the pulse on time significantly influences the duration of the interfacial reaction, which in turn alters the IMC evolution mechanism [53]. Furthermore, the difference between the peak current and base current plays a crucial role in the flow dynamics of the molten pool, impacting the dissolution process of the IMC and consequently affecting its thickness.
The strength of the joint without reinforcement was observed to be inversely related to the thickness of IMC, as shown in Figure 7. The thickness of the IMC plays a crucial role in determining the mechanical properties, primarily due to the significant residual stresses generated by the substantial disparity in thermal expansion coefficients between the aluminum filler, IMC, and base steel. This mismatch in expansion rates leads to stresses that weaken the joint [38]. The optimized maximum strength without reinforcement achieved in this study is 155.73 MPa, surpassing the strength of joints produced by laser-MIG (154 MPa) [54] and TIG-MIG (148 MPa) [55] weld brazing methods. This result demonstrates an improvement in joint performance, indicating the effectiveness of the optimization process used in this research.

4. Conclusions

Statistical models for Al/steel joint strength were established using RSM and CCD. These models optimize pulsed welding parameters and predict joint strength without reinforcement. With optimized parameters, joint strength reached approximately 155.73 MPa, and IMC thickness was reduced to approximately 3.4 μm.
Among the various factors influencing joint strength, pulse current, pulse on time, and base current had significant impacts. The hierarchy of influence on tensile strength without reinforcement was observed in the following order: pulse on time > base current > pulse current > pulse frequency. Moreover, two key interactions were identified: the interaction between pulse current and pulse frequency and the interaction between pulse on time and base current, both contributing to the optimization of the welding process.

Author Contributions

Validation, P.W.; Investigation, Z.G. and A.F.; Resources, X.Y.; Writing—original draft, H.H. and X.T.; Writing—review & editing, W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China [No. 51605263] and the Doctoral Scientific Research Foundation of Yantai University [grant number HD20B59].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Coatings 14 01262 g001
Figure 1. Schematic of the weld brazing process and cross-section of the joint. (a) Joining process, (b) cross-section, (c) sampling location, (d) tensile specimen.
Figure 1. Schematic of the weld brazing process and cross-section of the joint. (a) Joining process, (b) cross-section, (c) sampling location, (d) tensile specimen.
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Figure 2. Normal plot of residuals and plot of residuals versus predicted tensile strength. (a) Plot of residuals vs. predicted results, (b) normal plot of residuals.
Figure 2. Normal plot of residuals and plot of residuals versus predicted tensile strength. (a) Plot of residuals vs. predicted results, (b) normal plot of residuals.
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Figure 3. Interface microstructure and tensile strength of the joint. (a) Microstructure of the interface, (b) tensile strength of the joint.
Figure 3. Interface microstructure and tensile strength of the joint. (a) Microstructure of the interface, (b) tensile strength of the joint.
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Figure 4. Perturbation of the reference point.
Figure 4. Perturbation of the reference point.
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Figure 5. Interaction between pulse current and frequency. (a) 3D surface, (b) contour, (c,d) interaction.
Figure 5. Interaction between pulse current and frequency. (a) 3D surface, (b) contour, (c,d) interaction.
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Figure 6. Interaction between pulse on time and base current. (a) 3D surface, (b) contour, (c,d) interaction.
Figure 6. Interaction between pulse on time and base current. (a) 3D surface, (b) contour, (c,d) interaction.
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Figure 7. (a–c) IMC images, (d) relationship between IMC thickness and joint strength.
Figure 7. (a–c) IMC images, (d) relationship between IMC thickness and joint strength.
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Table 1. Chemical compositions of the alloys (wt.%).
Table 1. Chemical compositions of the alloys (wt.%).
ElementsCMnMgAlSiCuZnTiNiCrFe
SUS3210.122--1--0.28–1017–19Bal.
5A06-0.5–0.85.8–6.8Bal.0.40.10.20.1--0.4
ER1100-0.05-Bal.(1)0.05–0.20.1---(1)
(1) Si plus Fe 0.95.
Table 2. Key parameters and their respective operating limits.
Table 2. Key parameters and their respective operating limits.
RangePeak Current (A)Pulse on Time (%)Basic Current (A)Frequency (Hz)
−1145355528
+1155456576
Table 3. Fixed parameters.
Table 3. Fixed parameters.
ParametersWelding Speed (mm/min)Balance (%)Frequency of AC (Hz)
Magnitude15050100
Table 4. Sample of central composite design.
Table 4. Sample of central composite design.
OrderPeak Current (A)Pulse on Time (%)Basic Current (A)Frequency (Hz)Tensile Strength without Reinforcement (MPa)
1145355576108.83
2150406052155.69
3150406052131.33
4145355528125.68
5145356576128.24
6155455576153.84
7155456528137.86
8145356528138.05
915040604148.66
10145455576133.63
11140406052136.22
12150306052115.89
13150406052140.15
14150406052140.49
15150506052155.73
16160406052151.26
17155456576144.95
18150405052124.84
19145456528136.04
20155455528147.53
21155356528127.86
22150407052129.86
231504060100148.92
24155355576125.91
25155356576140.10
26145455528134.20
27150406052143.51
28145456576137.35
29155355528124.32
30150406052144.32
31150406052139.74
Table 5. Analysis of variance.
Table 5. Analysis of variance.
Source Sum of SquaresDegree of FreedomMean SquareFProbability
Model3025.798378.2210.23<0.0001
Ip340.731340.739.220.0061
tp1442.9011442.9039.04<0.0001
Ib90.29190.292.440.1323
f0.1410.140.0037750.9516
Ip × f176.561176.564.780.0398
tp × Ib244.221244.226.610.0174
tp2174.861174.864.730.0407
Ib2607.661607.6616.440.0005
Residual813.122236.96--------
Lack of fit493.601630.850.580.8211
Pure error319.52653.25--------
Table 6. Results of prediction by RSM.
Table 6. Results of prediction by RSM.
OrderPeak Current (A)Pulse on Time (%)Basic Current (A)Frequency (Hz)Prediction (MPa)Experimental Results (MPa)Relative Error (%)
114040568142.59142.840.18
214040564143.68130.699.94
314040562144.23135.546.41
4155555576156.17158.061.19
5155555528149.38153.222.51
Table 7. Analysis of EDS (at. %).
Table 7. Analysis of EDS (at. %).
ElementsAlFeCrNi
IMC layer75.1621.312.361.17
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He, H.; Tian, X.; Yi, X.; Wang, P.; Guo, Z.; Fu, A.; Zhao, W. Optimization of Joining Parameters in Pulsed Tungsten Inert Gas Weld Brazing of Aluminum and Stainless Steel Based on Response Surface Methodology. Coatings 2024, 14, 1262. https://doi.org/10.3390/coatings14101262

AMA Style

He H, Tian X, Yi X, Wang P, Guo Z, Fu A, Zhao W. Optimization of Joining Parameters in Pulsed Tungsten Inert Gas Weld Brazing of Aluminum and Stainless Steel Based on Response Surface Methodology. Coatings. 2024; 14(10):1262. https://doi.org/10.3390/coatings14101262

Chicago/Turabian Style

He, Huan, Xu Tian, Xiaoyang Yi, Pu Wang, Zhiwen Guo, Ao Fu, and Wenzhen Zhao. 2024. "Optimization of Joining Parameters in Pulsed Tungsten Inert Gas Weld Brazing of Aluminum and Stainless Steel Based on Response Surface Methodology" Coatings 14, no. 10: 1262. https://doi.org/10.3390/coatings14101262

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