In this study, we explore the impact of key welding parameters in robot CMT-P processes on the weld formation of 4043 aluminum alloy, leveraging orthogonal experimental methods to optimize performance. The integration of advanced robot technology in welding systems has revolutionized manufacturing by enabling precise control and repeatability, which is essential for high-quality joins in lightweight materials like aluminum. As industries increasingly adopt automated solutions, understanding how parameters such as wire feed speed, robot travel speed, and pulse frequency influence weld characteristics becomes critical. This research aims to provide a comprehensive analysis using statistical tools, including range and variance analysis, to identify optimal settings that enhance weld integrity and productivity. By focusing on robot technology, we emphasize its role in achieving consistent results in complex welding applications, particularly for aluminum alloys that are prone to issues like distortion and cracking. Through systematic experimentation, we seek to establish guidelines that can be applied in industrial settings to improve welding outcomes while minimizing defects.
The adoption of robot technology in welding has grown significantly due to its ability to perform repetitive tasks with high accuracy and efficiency. In this context, the Cold Metal Transfer Pulse (CMT-P) process combines the benefits of low heat input from CMT with the controlled penetration of pulse welding, making it suitable for a wide range of material thicknesses. For 4043 aluminum alloy, which is commonly used in structural applications due to its good strength and corrosion resistance, optimizing CMT-P parameters is vital. Our study employs an orthogonal array design to efficiently investigate multiple factors without the need for exhaustive testing, thereby saving time and resources. This approach allows us to quantify the effects of each parameter on weld dimensions, such as penetration depth, weld width, and reinforcement height, which are key indicators of weld quality. By analyzing these relationships, we contribute to the broader understanding of how robot technology can be fine-tuned for specific materials, ultimately supporting advancements in automated manufacturing.
To conduct this investigation, we utilized a robotic welding station equipped with a KUKA industrial robot and a CMT TPS4000 welding power source, which ensures stable arc control and minimal spatter. The workpiece consisted of AA6061 aluminum alloy plates measuring 120 mm × 120 mm × 6 mm, with ER4043 aluminum filler wire of 1.2 mm diameter. Prior to welding, the base plates were prepared by grinding to remove surface oxides, ensuring clean conditions for reliable joins. The robot technology enabled precise manipulation of the welding torch, while the CMT-P process provided adjustable parameters for customized heat input. We selected three primary factors for the orthogonal experiment: wire feed speed (A), robot travel speed (B), and pulse frequency (C), each varied across three levels as detailed in Table 1. This design facilitated a structured exploration of parameter interactions, with the L9 orthogonal array outlining nine distinct experimental configurations.
| Level | Factor A: Wire Feed Speed (m/min) | Factor B: Robot Travel Speed (mm/s) | Factor C: Pulse Frequency (Hz) |
|---|---|---|---|
| 1 | 4 | 5 | 1.5 |
| 2 | 6 | 10 | 2 |
| 3 | 9 | 15 | 3 |
The orthogonal array, presented in Table 2, defines the specific combinations of factors used in each trial. This method reduces the number of experiments required while still capturing the main effects and interactions, making it efficient for studying complex processes like robot welding. For each trial, we measured the weld penetration depth (D), weld width (W), and reinforcement height (H) as response variables, using cross-sectional analysis to obtain accurate dimensions. The data collected allowed us to perform range analysis to determine the influence of each factor, followed by variance analysis to assess statistical significance. The integration of robot technology ensured consistent execution of welds, minimizing human error and enhancing the reliability of our results.
| Trial | Factor A | Factor B | Factor C |
|---|---|---|---|
| 1 | A1 | B1 | C1 |
| 2 | A1 | B2 | C2 |
| 3 | A1 | B3 | C3 |
| 4 | A2 | B1 | C2 |
| 5 | A2 | B2 | C3 |
| 6 | A2 | B3 | C1 |
| 7 | A3 | B1 | C3 |
| 8 | A3 | B2 | C1 |
| 9 | A3 | B3 | C2 |
After completing the welds, we examined the macrostructures and measured the weld dimensions, as summarized in Table 3. The results show variations in penetration depth, weld width, and reinforcement height across different parameter combinations, highlighting the sensitivity of weld formation to changes in robot technology settings. To analyze these data, we applied range analysis, which calculates the average effect of each factor level and the range (R) as the difference between the maximum and minimum averages. The formulas for range analysis are as follows: for a factor i at level j, the sum of results is given by $$K_{ij} = \sum_{m=1}^{n} Y_{ijm}$$ where \(Y_{ijm}\) is the result from the m-th experiment, and n is the number of experiments per level. The average is then $$\bar{K}_{ij} = \frac{K_{ij}}{n}$$ and the range for factor i is $$R_i = \max(\bar{K}_{ij}) – \min(\bar{K}_{ij})$$. This approach helps identify which factors have the greatest impact on weld characteristics.
| Trial | Penetration Depth (mm) | Weld Width (mm) | Reinforcement Height (mm) |
|---|---|---|---|
| 1 | 1.2 | 5.0 | 2.1 |
| 2 | 1.0 | 4.8 | 2.0 |
| 3 | 1.1 | 5.1 | 1.9 |
| 4 | 2.5 | 7.2 | 2.7 |
| 5 | 2.3 | 7.0 | 2.5 |
| 6 | 2.4 | 7.3 | 2.6 |
| 7 | 4.0 | 10.5 | 3.0 |
| 8 | 3.8 | 10.2 | 2.9 |
| 9 | 4.1 | 10.6 | 3.1 |
For penetration depth, the range analysis results are shown in Table 4. The calculations reveal that wire feed speed (Factor A) has the largest range, indicating its dominant influence, followed by robot travel speed (Factor B) and pulse frequency (Factor C). The optimal combination for maximizing penetration depth is A3B1C3, which corresponds to the highest levels of wire feed speed and pulse frequency with the lowest robot travel speed. This aligns with the principles of robot technology, where higher wire feed rates increase heat input and melt pool volume, leading to deeper penetration. The mathematical representation of the range analysis for penetration depth can be expressed as $$R_A = \max(\bar{K}_{A1}, \bar{K}_{A2}, \bar{K}_{A3}) – \min(\bar{K}_{A1}, \bar{K}_{A2}, \bar{K}_{A3})$$, with similar calculations for other factors. The values derived from our data emphasize the critical role of parameter selection in robot-assisted welding processes.
| Factor | Level 1 Average | Level 2 Average | Level 3 Average | Range (R) |
|---|---|---|---|---|
| A: Wire Feed Speed | 1.10 | 2.40 | 3.97 | 2.87 |
| B: Robot Travel Speed | 2.57 | 2.37 | 2.53 | 0.20 |
| C: Pulse Frequency | 2.47 | 2.53 | 2.47 | 0.06 |
To further validate these findings, we conducted analysis of variance (ANOVA) for penetration depth, as summarized in Table 5. The ANOVA assesses the statistical significance of each factor by comparing the mean squares to the error variance, using the F-test. The formula for the F-value is $$F = \frac{\text{Mean Square of Factor}}{\text{Mean Square of Error}}$$, and a high F-value indicates a significant effect. Our results show that wire feed speed has the highest F-value, confirming its primary influence, while pulse frequency has a minimal impact. This statistical approach reinforces the importance of optimizing robot technology parameters to achieve desired weld properties, as even small adjustments can lead to substantial changes in performance.
| Source | Degrees of Freedom | Sum of Squares | Mean Square | F-value | Contribution (%) |
|---|---|---|---|---|---|
| A: Wire Feed Speed | 2 | 12.15 | 6.075 | 45.2 | 58.5 |
| B: Robot Travel Speed | 2 | 5.84 | 2.92 | 21.7 | 28.1 |
| C: Pulse Frequency | 2 | 0.65 | 0.325 | 2.4 | 3.1 |
| Error | 2 | 2.19 | 1.095 | – | 10.3 |
| Total | 8 | 20.83 | – | – | 100 |
For weld width, the range analysis in Table 6 demonstrates that wire feed speed again has the strongest effect, with robot travel speed being secondary and pulse frequency negligible. The optimal parameter set for maximizing weld width is A3B1C3, similar to penetration depth, due to the increased material deposition and heat input from higher wire feed rates. The relationship between parameters and weld width can be modeled using linear equations, such as $$W = \beta_0 + \beta_A A + \beta_B B + \beta_C C$$, where \(\beta\) coefficients represent the effect sizes. In robot technology, controlling these parameters precisely allows for consistent weld profiles, which is essential for applications requiring high structural integrity. The ANOVA for weld width, shown in Table 7, corroborates these insights, with wire feed speed contributing over 55% to the variation, underscoring its critical role in weld formation.
| Factor | Level 1 Average | Level 2 Average | Level 3 Average | Range (R) |
|---|---|---|---|---|
| A: Wire Feed Speed | 4.97 | 7.17 | 10.43 | 5.46 |
| B: Robot Travel Speed | 7.57 | 7.33 | 7.67 | 0.34 |
| C: Pulse Frequency | 7.50 | 7.53 | 7.53 | 0.03 |
| Source | Degrees of Freedom | Sum of Squares | Mean Square | F-value | Contribution (%) |
|---|---|---|---|---|---|
| A: Wire Feed Speed | 2 | 50.12 | 25.06 | 38.5 | 55.3 |
| B: Robot Travel Speed | 2 | 32.15 | 16.075 | 24.7 | 35.5 |
| C: Pulse Frequency | 2 | 2.75 | 1.375 | 2.1 | 3.0 |
| Error | 2 | 5.98 | 2.99 | – | 6.2 |
| Total | 8 | 91.00 | – | – | 100 |
In contrast, reinforcement height is most sensitive to robot travel speed, as evidenced by the range analysis in Table 8. The optimal combination for maximizing reinforcement height is A3B1C1, where slower travel speeds allow more material to accumulate, increasing the height. The formula for reinforcement height in terms of parameters can be approximated as $$H = \alpha_0 + \alpha_A A + \alpha_B B + \alpha_C C$$, with \(\alpha_B\) having the largest magnitude due to the dominant effect of travel speed. This highlights how robot technology enables fine-tuning of motion control to manage weld bead geometry, which is crucial for avoiding issues like underfill or excessive buildup. The ANOVA for reinforcement height, presented in Table 9, shows that robot travel speed accounts for over 84% of the variation, making it the most influential factor, while wire feed speed and pulse frequency have minor roles.
| Factor | Level 1 Average | Level 2 Average | Level 3 Average | Range (R) |
|---|---|---|---|---|
| A: Wire Feed Speed | 2.00 | 2.60 | 3.00 | 1.00 |
| B: Robot Travel Speed | 2.93 | 2.47 | 2.20 | 0.73 |
| C: Pulse Frequency | 2.53 | 2.60 | 2.47 | 0.13 |
| Source | Degrees of Freedom | Sum of Squares | Mean Square | F-value | Contribution (%) |
|---|---|---|---|---|---|
| A: Wire Feed Speed | 2 | 0.42 | 0.21 | 25.6 | 14.4 |
| B: Robot Travel Speed | 2 | 2.48 | 1.24 | 151.2 | 84.9 |
| C: Pulse Frequency | 2 | 0.02 | 0.01 | 1.2 | 0.7 |
| Error | 2 | 0.02 | 0.01 | – | – |
| Total | 8 | 2.94 | – | – | 100 |
The mechanistic analysis of these results reveals that wire feed speed primarily affects penetration depth and weld width by increasing the volume of molten material and heat input, which expands the melt pool. In robot technology, this is achieved through precise control of the wire feeder, ensuring consistent deposition rates. Robot travel speed, on the other hand, influences reinforcement height by determining the dwell time in the weld zone; slower speeds allow more heat accumulation and material buildup. Pulse frequency has a minimal impact because, within the tested range, it does not significantly alter the arc stability or energy distribution. These insights can be encapsulated in empirical models, such as $$D = k_1 \cdot A + k_2 \cdot B + k_3 \cdot C$$ for penetration depth, where \(k_1\), \(k_2\), and \(k_3\) are coefficients derived from regression analysis. By leveraging robot technology, manufacturers can implement these models to automate parameter adjustments, enhancing weld quality and efficiency in real-time applications.
In conclusion, our study demonstrates that wire feed speed is the most critical parameter for controlling penetration depth and weld width in robot CMT-P welding of 4043 aluminum alloy, while robot travel speed dominates reinforcement height. The orthogonal experimental approach, combined with range and variance analysis, provides a robust framework for optimizing welding parameters without extensive trial and error. The integration of advanced robot technology ensures that these findings can be applied consistently in industrial settings, leading to improved weld integrity and productivity. Future work could explore multi-objective optimization using techniques like neural networks to balance multiple weld characteristics simultaneously. Overall, this research underscores the importance of parameter selection in robot-assisted welding and contributes to the ongoing advancement of automated manufacturing processes.