Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Procedure
2.2. Models and Methods
2.2.1. Wavelet Packet Decomposition
2.2.2. GALSSVM Model
3. Results and Discussion
3.1. Experimental Results
3.2. Predictive Model Results
4. Conclusions
- Tunneling defects are formed when sudden perturbations are experienced during welding. In the feed direction, plastic flow and heat generation of the material behind the tool is insufficient. The welding temperature signal shows the amplitude variation of the signal component near the tool rotation frequency.
- When inappropriate process parameters are used, welding defects, such as large flints and uneven surface texture, will be caused, and changes in low-frequency components (0~10 Hz) and statistical mean characteristics will be shown in the welding temperature signal.
- The low-frequency component of the original temperature signal and the frequency component containing the rotation frequency of the tool were extracted using the three-layer wavelet packet method, and the energy value of the component signal was obtained. The characteristics of these temperature signals played an important role in improving the efficiency of the weld quality identification. Using the extracted temperature component signal energy, statistical mean temperature, rotational speed, and welding speed as input variables, the prediction accuracy of the weld quality classification prediction model established by the GA-LSSVM algorithm can reach 90.6%, and the AUC value is 0.939.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter | Rotation Speed (r/min) | Traverse Speed (mm/min) | Shoulder Plunge (mm) | Tilt Angle (°) |
---|---|---|---|---|
Value | 850; 1000; 1150; 1300 | 100; 200; 300; 400 | 0.1 | 1.5 |
Type | Type 1 | Type 2 | Type 3 |
---|---|---|---|
Coefficient of strength | σ ≥ 75% | 65% < σ < 75% | σ ≤ 65% |
Rotational Speed (r/min) | Traverse Speed (mm/min) | Mean Temperature (°C) | Energy 7 | Energy 8 | Tensile Strength (MPa) | Type |
---|---|---|---|---|---|---|
850 | 100 | 479 | 0.24 | 0.76 | 264.3 | 3 |
1000 | 100 | 512 | 0.36 | 0.13 | 318.2 | 1 |
1150 | 100 | 518 | 0.11 | 0.62 | 296.3 | 2 |
1300 | 100 | 529 | 0.15 | 0.89 | 259.6 | 3 |
850 | 200 | 485 | 0.23 | 0.24 | 205.5 | 3 |
1000 | 200 | 510 | 0.86 | 0.16 | 327.3 | 1 |
1150 | 200 | 512 | 0.92 | 0.14 | 328.3 | 1 |
1300 | 200 | 513 | 0.35 | 0.24 | 317.2 | 1 |
850 | 300 | 528 | 0.44 | 0.68 | 205.6 | 3 |
1000 | 300 | 489 | 0.14 | 0.84 | 297.3 | 2 |
1150 | 300 | 493 | 0.21 | 0.67 | 336.2 | 2 |
1300 | 300 | 511 | 0.38 | 0.14 | 316.3 | 1 |
850 | 400 | 493 | 0.29 | 0.45 | 302.6 | 2 |
1000 | 400 | 479 | 0.76 | 0.24 | 264.3 | 3 |
1150 | 400 | 485 | 0.83 | 0.16 | 234.9 | 3 |
1300 | 400 | 506 | 0.08 | 0.24 | 319.7 | 1 |
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Wang, H.; He, D.; Liao, M.; Liu, P.; Lai, R. Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal. Materials 2021, 14, 3496. https://doi.org/10.3390/ma14133496
Wang H, He D, Liao M, Liu P, Lai R. Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal. Materials. 2021; 14(13):3496. https://doi.org/10.3390/ma14133496
Chicago/Turabian StyleWang, Haijun, Diqiu He, Mingjian Liao, Peng Liu, and Ruilin Lai. 2021. "Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal" Materials 14, no. 13: 3496. https://doi.org/10.3390/ma14133496