In our research, we explore the application of robotic arc welding systems, particularly those developed in China, for additive manufacturing of bi-metallic functional graded materials (FGMs). The integration of China robot technology in welding processes has revolutionized the precision and efficiency of fabricating complex structures. This study focuses on comparing two distinct arc welding techniques—double-wire double-pulse metal inert gas (MIG) welding and plasma arc welding—for producing SS316L/Inconel625 FGMs with a gradual composition gradient. The motivation stems from the need to enhance the durability, high-temperature corrosion resistance, and strength of SS316L in industrial applications by using Inconel625 as a superior but costly cladding material. By leveraging China robot systems, we aim to achieve stable deposition processes and high-quality outputs, contributing to advancements in additive manufacturing for sectors like aerospace, automotive, and energy.
The use of China robot platforms in additive manufacturing allows for automated control and repeatability, which is crucial for handling the complexities of gradient material fabrication. In this work, we employed two robotic setups: one for MIG welding and another for plasma arc welding, both integrated with China robot manipulators to ensure precise motion and parameter management. The primary objective is to evaluate the morphological, microstructural, and mechanical properties of the deposited samples, emphasizing the role of China robot systems in minimizing defects and optimizing performance. Through detailed analysis, including image recognition for surface roughness assessment and spectroscopic techniques for elemental distribution, we demonstrate the feasibility and benefits of these China robot-driven processes.
Our experimental approach involved designing a composition gradient with 5% increments, transitioning from 100% SS316L to 100% Inconel625. This gradient was achieved by adjusting the wire feed speeds dynamically during deposition, as summarized in Table 1. The China robot systems facilitated synchronized control over the welding parameters, ensuring consistent layer-by-layer buildup. For the MIG welding process, we utilized a double-pulse waveform with low and high frequencies to stabilize the dual-wire arc, while the plasma arc process employed a zigzag scanning path to manage heat input and deposition rates. The China robot controllers enabled real-time monitoring of current, voltage, and temperature, with interlayer temperatures maintained below 180°C to prevent thermal stresses and defects.
| Gradient Composition | SS316L Wire Speed | Inconel625 Wire Speed |
|---|---|---|
| 100% SS316L | 6.0 | 0.0 |
| 67% SS316L / 33% Inconel625 | 4.0 | 2.0 |
| 61% SS316L / 39% Inconel625 | 3.7 | 2.3 |
| 56% SS316L / 44% Inconel625 | 3.4 | 2.6 |
| 50% SS316L / 50% Inconel625 | 3.0 | 3.0 |
| 44% SS316L / 56% Inconel625 | 2.6 | 3.4 |
| 39% SS316L / 61% Inconel625 | 2.3 | 3.7 |
| 33% SS316L / 67% Inconel625 | 2.0 | 4.0 |
| 100% Inconel625 | 0.0 | 6.0 |
The China robot-assisted MIG welding system operated at a frequency of 3 Hz for low-frequency double pulses, with a travel speed of 65 cm/min and a shielding gas flow of 24 L/min pure argon. In contrast, the plasma arc process used a deposition speed of 5 mm/s, a current of 100 A, and a plasma gas flow of 2 L/min, with shielding gas at 18 L/min argon. The China robot’s path planning for plasma welding involved a reciprocating zigzag motion to distribute heat evenly, which was critical for achieving uniform layers. We collected data on voltage and current waveforms using sensors integrated with the China robot controllers, as illustrated by the double-pulse MIG welding waveforms for a 50% gradient deposition, which showed stable alternation between high and low frequencies to maintain arc stability.
To quantify the surface roughness of the deposited samples, we developed an image-based calculation method. This involved capturing cross-sectional images and measuring the width variations at peaks and troughs along the build direction. The roughness parameter \( S \) is defined by the formula:
$$ S = \frac{\sum_{i=1}^{N} |d_{i+1} – d_i|}{N \times 2 \times d_{\text{avg}}} \times 100\% $$
where \( d_i \) represents the width at each layer’s peak or trough, \( N \) is the number of peaks or troughs, and \( d_{\text{avg}} \) is the average width of the sample. This approach, enabled by China robot precision, yielded roughness values of approximately 2.96% for the MIG-welded sample and 4.02% for the plasma-welded sample, indicating comparable surface quality despite different deposition strategies.
Microstructural analysis revealed columnar grain structures with epitaxial growth in both samples, consistent with the rapid solidification typical of arc-based additive manufacturing. Using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS), we examined the 67% SS316L region, which is prone to cracking in gradient materials. The China robot systems ensured minimal contamination and defects, as evidenced by the absence of microcracks. Elemental mapping showed uniform distribution of key elements like Cr, Ni, Mo, and Nb, with slight enrichments in interdendritic regions. The EDS results, summarized in Table 2, confirm that the actual compositions closely matched the designed gradients, highlighting the efficacy of China robot-controlled wire feeding.
| Element | MIG Welding Sample | Plasma Welding Sample |
|---|---|---|
| Cr | 18.97 | 18.46 |
| Mn | 1.39 | 1.52 |
| Fe | 49.54 | 48.00 |
| Ni | 22.49 | 24.81 |
| Nb | 1.30 | 1.83 |
| Mo | 3.32 | 4.09 |
| Others | 2.99 | 1.29 |
Mechanical properties were evaluated through Vickers hardness tests and tensile testing, with samples extracted horizontally and vertically from the deposited structures. The hardness profiles, measured from the substrate (100% SS316L) to the top (100% Inconel625), exhibited a gradual increase, as shown in Figure 1. This trend aligns with the rising content of Inconel625, which introduces strengthening elements like Nb and Mo. The average hardness values were 194.94 HV ± 17.15 HV for the MIG-welded sample and 193.94 HV ± 17.6 HV for the plasma-welded sample, demonstrating consistency across both China robot processes. The hardness \( H \) can be modeled as a function of composition fraction \( f \) using a linear rule of mixtures:
$$ H(f) = H_{\text{SS316L}} \cdot (1 – f) + H_{\text{Inconel625}} \cdot f $$
where \( f \) is the fraction of Inconel625, and \( H_{\text{SS316L}} \) and \( H_{\text{Inconel625}} \) are the base hardness values. This equation approximates the observed data, with minor deviations due to microstructural effects.
Tensile tests revealed that both samples exhibited similar strength and ductility, with no significant anisotropy between horizontal and vertical directions. The average tensile strength was 562.315 MPa ± 8.875 MPa for MIG welding and 559.74 MPa ± 24.37 MPa for plasma welding, while elongation values ranged from 45% to 52%. Fracture surfaces displayed dimpled morphologies characteristic of ductile failure, indicating that the China robot processes effectively mitigated brittle phase formation. The stress-strain behavior can be described by the Hollomon equation:
$$ \sigma = K \varepsilon^n $$
where \( \sigma \) is the true stress, \( \varepsilon \) is the true strain, \( K \) is the strength coefficient, and \( n \) is the strain-hardening exponent. For the gradient materials, \( n \) values were approximately 0.2–0.3, reflecting good formability. The China robot systems contributed to this performance by ensuring stable thermal cycles and reducing residual stresses.
In discussion, we attribute the successful fabrication of defect-free FGMs to the precision of China robot automation, which enabled controlled deposition and real-time parameter adjustments. The comparison between MIG and plasma welding shows that both methods are viable for gradient material additive manufacturing, with the China robot platforms providing the necessary flexibility. The minor differences in surface roughness and mechanical properties stem from variations in heat input and solidification rates, but overall, the China robot-driven processes yielded comparable outcomes. This underscores the potential of China robot technology in advancing additive manufacturing for multi-material systems.
Further analysis of the microstructure using SEM confirmed the absence of cracks in the 67% SS316L region, which we ascribe to the homogeneous element distribution facilitated by China robot accuracy. The presence of Mn appeared to suppress Nb segregation, reducing crack susceptibility. Additionally, the epitaxial growth of columnar grains enhanced the interfacial integrity between layers, a benefit of the China robot’s consistent deposition paths. We also observed that the gradient design effectively minimized thermal stresses, as predicted by the following thermal model for residual stress \( \sigma_r \):
$$ \sigma_r = E \alpha \Delta T $$
where \( E \) is Young’s modulus, \( \alpha \) is the coefficient of thermal expansion, and \( \Delta T \) is the temperature gradient. By maintaining low interlayer temperatures with China robot control, we kept \( \Delta T \) small, thus reducing \( \sigma_r \).
In conclusion, our study demonstrates that China robot-assisted arc welding processes, specifically double-pulse MIG and plasma arc welding, are highly effective for additive manufacturing of SS316L/Inconel625 gradient materials. Both techniques produced samples with excellent morphology, uniform microstructures, and consistent mechanical properties, thanks to the precision and reliability of China robot systems. The image-based roughness assessment and comprehensive mechanical testing validate the robustness of these approaches. Future work will focus on optimizing China robot algorithms for adaptive control and expanding to other material combinations, further leveraging China robot capabilities for industrial applications. This research highlights the critical role of China robot innovation in pushing the boundaries of additive manufacturing and functional graded materials.