Exploring Resistance Spot Welding for Grade 2 Titanium Alloy: Experimental Investigation and Artificial Neural Network Modeling
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials and Specimens’ Preparation
2.2. Resistance Spot Welding Process
- Welding time: the duration applied to weld the samples.
- Welding current: the amount of current provided by the source.
- Pressure: the pressure exerted to hold the materials together during welding.
- Squeeze time: the duration between the application of pressure and the welding process.
- Hold time: the duration for which the pressure is maintained after the welding is completed.
- Pulse welding: the alternating modulation of amperage between high and low points.
2.3. Tensile Shear Test
2.4. Micro-Hardness Measurements
3. Artificial Neural Network (ANN)
- y: the outputs,
- : the activation function,
- x: the input,
- wi: the weights,
- b: the bias.
4. Results and Discussions
4.1. Tensile Shear Force
4.2. Failure Mechanism
4.3. Micro-Hardness Distribution
4.4. Artificial Neural Network Prediction
5. Conclusions
- Case A presented the highest tensile shear observations, while case C gave the second maximum tensile shear force. The lowest tensile shear force observations were noticed in case B.
- The maximum hardness was observed with a minimum tensile shear force sample, while the lowest hardness was recorded with the highest tensile shear force sample. Furthermore, the nugget zone presented the maximum hardness, followed by the HAZ, while the lowest hardness was noted at the base metal zone.
- The maximum micro-hardness was attained in case B. The second highest hardness measurements were determined to be with case C, and case A showed the minimum hardness.
- Pull-out nugget failure type was observed with all samples of case A, except one sample that failed with an interfacial mode. The failure mechanism of the case B samples was a pull-out nugget failure varying the failure location (around the nugget zone or the HAZ). Most of the case C samples failed with pull-out nugget type except one sample that failed with full interfacial mode. Some samples of case C showed partial failure between the pull-out and interfacial failure.
- Artificial neural network models presented a significant finding in predicting the actual data and minimizing the error in the RSW process.
- The most interesting observation about the neural network model is that the Trainlm training function gives the best performance when using the Logsig as a transfer function. The Trainlm training function with Logsig transfer function gives the best performance in predicting tensile shear force where the MSE and R2 were 0.01821 and 0.98433, respectively. The Purelin transfer function revealed a lower MSE and R2 of 0.10719 and 0.90377, respectively, when trained with Trainlm function.
- The lowest prediction of tensile shear force was achieved with Traincgb training function by adopting the Purelin transfer function with an MSE and R2 of 0.12269 and 0.89377, respectively. The Logsig transfer function gave a higher MSE and R2 of 0.0481 and 0.95941 when trained with the Traincgb function, but this was still lower than the validation metrics compared to the other training functions.
6. Recommendation for Future Work
- To study the effect of the RSW parameters on the joint quality of other thicknesses.
- To analyze in detail the microstructure of the welded joints using X-ray diffraction (XRD), scanning electron microscopy (SEM), or tunneling electron microscope (TEM) techniques.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Thickness (mm) | Length (mm) | Width (mm) | Overlap Region (mm) |
---|---|---|---|
0.5 | 76 | 16 | 16 |
1 | 100 | 25 | 25 |
Property | Tensile Strength (MPa) | Yield Strength (MPa) | Poisson’s Ratio | Young Modulus (GPa) | Elongation (%) |
---|---|---|---|---|---|
Standard | ≥400 | 275–450 | 0.37 | 105–120 | ≥25 |
Measured | 428 | 318 | 0.37 | 113 | 28 |
Element wt.% | Fe | O | N | C | H | Ti |
---|---|---|---|---|---|---|
Standard | ≤0.3 | ≤0.25 | ≤0.03 | ≤0.08 | ≤0.015 | Balance |
Measured | 0.062 | 0.11 | 0.0067 | 0.0055 | 0.001 | Balance |
No. | Welding Cases | Welding Current (A) | Pressure (bar) | Welding Time (s) | Squeeze Time (s) | Holding Time (s) | Pulse Welding (-) | ||
---|---|---|---|---|---|---|---|---|---|
1 | A1 | B1 | C1 | 5000 | 2.0 | 0.6 | 0.6 | 0.50 | 1 |
2 | A2 | B2 | C2 | 5000 | 3.5 | 0.8 | 0.8 | 0.75 | 2 |
3 | A3 | B3 | C3 | 5000 | 5.0 | 1.0 | 1.0 | 1.00 | 3 |
4 | A4 | B4 | C4 | 5000 | 6.5 | 1.2 | 1.2 | 1.25 | 4 |
5 | A5 | B5 | C5 | 5000 | 8.0 | 1.4 | 1.4 | 1.50 | 5 |
6 | A6 | B6 | C6 | 5500 | 2.0 | 0.8 | 1.0 | 1.25 | 5 |
7 | A7 | B7 | C7 | 5500 | 3.5 | 1.0 | 1.2 | 1.50 | 1 |
8 | A8 | B8 | C8 | 5500 | 5.0 | 1.2 | 1.4 | 0.50 | 2 |
9 | A9 | B9 | C9 | 5500 | 6.5 | 1.4 | 0.6 | 0.75 | 3 |
10 | A10 | B10 | C10 | 5500 | 8.0 | 0.6 | 0.8 | 1.00 | 4 |
11 | A11 | B11 | C11 | 6000 | 2.0 | 1.0 | 1.4 | 0.75 | 4 |
12 | A12 | B12 | C12 | 6000 | 3.5 | 1.2 | 0.6 | 1.00 | 5 |
13 | A13 | B13 | C13 | 6000 | 5.0 | 1.4 | 0.8 | 1.25 | 1 |
14 | A14 | B14 | C14 | 6000 | 6.5 | 0.6 | 1.0 | 1.50 | 2 |
15 | A15 | B15 | C15 | 6000 | 8.0 | 0.8 | 1.2 | 0.50 | 3 |
16 | A16 | B16 | C16 | 6500 | 2.0 | 1.2 | 0.8 | 1.50 | 3 |
17 | A17 | B17 | C17 | 6500 | 3.5 | 1.4 | 1.0 | 0.50 | 4 |
18 | A18 | B18 | C18 | 6500 | 5.0 | 0.6 | 1.2 | 0.75 | 5 |
19 | A19 | B19 | C19 | 6500 | 6.5 | 0.8 | 1.4 | 1.00 | 1 |
20 | A20 | B20 | C20 | 6500 | 8.0 | 1.0 | 0.6 | 1.25 | 2 |
21 | A21 | B21 | C21 | 7000 | 2.0 | 1.4 | 1.2 | 1.00 | 2 |
22 | A22 | B22 | C22 | 7000 | 3.5 | 0.6 | 1.4 | 1.25 | 3 |
23 | A23 | B23 | C23 | 7000 | 5.0 | 0.8 | 0.6 | 1.50 | 4 |
24 | A24 | B24 | C24 | 7000 | 6.5 | 1.0 | 0.8 | 0.50 | 5 |
25 | A25 | B25 | C25 | 7000 | 8.0 | 1.2 | 1.0 | 0.75 | 1 |
No. | Case A | Shear Force (kN) | Case B | Shear Force (kN) | Case C | Shear Force (kN) |
---|---|---|---|---|---|---|
1 | A1 | 4.423 | B1 | 2.422 | C1 | 3.665 |
2 | A2 | 4.052 | B2 | 2.317 | C2 | 3.916 |
3 | A3 | 4.918 | B3 | 3.352 | C3 | 3.412 |
4 | A4 | 4.737 | B4 | 3.687 | C4 | 3.960 |
5 | A5 | 4.315 | B5 | 4.234 | C5 | 4.116 |
6 | A6 | 4.713 | B6 | 4.086 | C6 | 4.421 |
7 | A7 | 4.555 | B7 | 3.215 | C7 | 4.121 |
8 | A8 | 3.958 | B8 | 2.895 | C8 | 2.916 |
9 | A9 | 4.455 | B9 | 3.125 | C9 | 3.058 |
10 | A10 | 4.618 | B10 | 2.552 | C10 | 3.896 |
11 | A11 | 4.600 | B11 | 2.430 | C11 | 3.492 |
12 | A12 | 4.239 | B12 | 2.591 | C12 | 3.969 |
13 | A13 | 4.198 | B13 | 2.965 | C13 | 3.207 |
14 | A14 | 3.861 | B14 | 2.360 | C14 | 3.918 |
15 | A15 | 4.171 | B15 | 2.191 | C15 | 3.161 |
16 | A16 | 4.271 | B16 | 3.760 | C16 | 3.343 |
17 | A17 | 4.671 | B17 | 3.511 | C17 | 3.404 |
18 | A18 | 4.558 | B18 | 2.789 | C18 | 3.570 |
19 | A19 | 4.406 | B19 | 2.504 | C19 | 3.785 |
20 | A20 | 4.141 | B20 | 2.721 | C20 | 3.402 |
21 | A21 | 5.028 | B21 | 3.017 | C21 | 4.149 |
22 | A22 | 5.009 | B22 | 3.031 | C22 | 4.165 |
23 | A23 | 4.536 | B23 | 2.852 | C23 | 4.040 |
24 | A24 | 5.106 | B24 | 3.261 | C24 | 3.741 |
25 | A25 | 4.715 | B25 | 3.149 | C25 | 2.155 |
Training Function | Transfer Function | MSE | R2 |
---|---|---|---|
Trainlm | Logsig | 0.01821 | 0.98433 |
Tansig | 0.04151 | 0.96390 | |
Purelin | 0.10719 | 0.90377 | |
Traincgp | Logsig | 0.03533 | 0.97006 |
Tansig | 0.05336 | 0.95587 | |
Purelin | 0.11669 | 0.89503 | |
Trainscg | Logsig | 0.04449 | 0.96146 |
Tansig | 0.08234 | 0.92700 | |
Purelin | 0.10950 | 0.90158 | |
Traincgf | Logsig | 0.04450 | 0.96200 |
Tansig | 0.06238 | 0.94526 | |
Purelin | 0.11491 | 0.90088 | |
Traincgb | Logsig | 0.04810 | 0.95941 |
Tansig | 0.06574 | 0.94382 | |
Purelin | 0.12269 | 0.89377 |
Sample No. | Welding Cases | ||||||||
---|---|---|---|---|---|---|---|---|---|
A | B | C | |||||||
Actual (kN) | Predicted (kN) | Error % | Actual (kN) | Predicted (kN) | Error % | Actual (kN) | Predicted (kN) | Error % | |
1 | 4.423 | 3.973 | 10.174 | 2.422 | 2.228 | 8.010 | 3.665 | 4.162 | 11.941 |
2 | 4.052 | 4.2017 | 3.677 | 2.317 | 2.339 | 0.950 | 3.916 | 4.125 | 5.067 |
3 | 4.918 | 4.836 | 1.647 | 3.352 | 3.249 | 3.073 | 3.412 | 3.601 | 5.221 |
4 | 4.737 | 4.806 | 1.436 | 3.687 | 3.750 | 1.709 | 3.960 | 3.989 | 0.702 |
5 | 4.315 | 4.322 | 0.162 | 4.234 | 4.187 | 1.110 | 4.116 | 4.126 | 0.218 |
6 | 4.713 | 4.599 | 2.419 | 4.086 | 3.857 | 5.580 | 4.421 | 4.292 | 3.006 |
7 | 4.555 | 4.698 | 3.117 | 3.215 | 3.404 | 5.879 | 4.121 | 4.064 | 1.403 |
8 | 3.958 | 3.918 | 0.985 | 2.895 | 2.465 | 14.853 | 2.916 | 2.779 | 4.894 |
9 | 4.455 | 4.322 | 2.963 | 3.125 | 3.002 | 3.936 | 3.058 | 3.071 | 0.391 |
10 | 4.618 | 4.577 | 0.866 | 2.552 | 2.550 | 0.039 | 3.896 | 3.969 | 1.839 |
11 | 4.600 | 4.644 | 0.935 | 2.430 | 2.547 | 4.774 | 3.492 | 3.591 | 2.757 |
12 | 4.239 | 4.307 | 1.604 | 2.591 | 2.638 | 1.775 | 3.969 | 3.945 | 0.583 |
13 | 4.198 | 4.118 | 1.882 | 2.965 | 2.868 | 3.238 | 3.207 | 3.117 | 2.855 |
14 | 3.861 | 3.988 | 3.289 | 2.360 | 2.444 | 3.559 | 3.918 | 3.901 | 0.436 |
15 | 4.171 | 4.139 | 0.743 | 2.191 | 2.228 | 1.643 | 3.161 | 3.167 | 0.158 |
16 | 4.271 | 4.370 | 2.318 | 3.760 | 3.764 | 0.106 | 3.343 | 3.432 | 2.593 |
17 | 4.671 | 4.658 | 0.257 | 3.511 | 3.512 | 0.028 | 3.404 | 3.375 | 0.830 |
18 | 4.558 | 4.609 | 1.119 | 2.789 | 2.785 | 0.108 | 3.570 | 3.546 | 0.649 |
19 | 4.406 | 4.451 | 0.999 | 2.504 | 2.466 | 1.478 | 3.785 | 3.756 | 0.772 |
20 | 4.141 | 4.129 | 0.266 | 2.721 | 2.902 | 6.652 | 3.402 | 3.547 | 4.060 |
21 | 5.028 | 4.999 | 0.557 | 3.017 | 3.117 | 3.281 | 4.149 | 4.123 | 0.606 |
22 | 5.009 | 4.938 | 1.397 | 3.031 | 3.023 | 0.231 | 4.165 | 4.169 | 0.072 |
23 | 4.536 | 4.524 | 0.243 | 2.852 | 2.888 | 1.262 | 4.040 | 3.989 | 1.253 |
24 | 5.106 | 5.083 | 0.450 | 3.261 | 3.277 | 0.491 | 3.741 | 3.739 | 0.053 |
25 | 4.715 | 4.703 | 0.233 | 3.149 | 3.095 | 1.683 | 2.155 | 2.571 | 16.180 |
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Mezher, M.T.; Carou, D.; Pereira, A. Exploring Resistance Spot Welding for Grade 2 Titanium Alloy: Experimental Investigation and Artificial Neural Network Modeling. Metals 2024, 14, 308. https://doi.org/10.3390/met14030308
Mezher MT, Carou D, Pereira A. Exploring Resistance Spot Welding for Grade 2 Titanium Alloy: Experimental Investigation and Artificial Neural Network Modeling. Metals. 2024; 14(3):308. https://doi.org/10.3390/met14030308
Chicago/Turabian StyleMezher, Marwan T., Diego Carou, and Alejandro Pereira. 2024. "Exploring Resistance Spot Welding for Grade 2 Titanium Alloy: Experimental Investigation and Artificial Neural Network Modeling" Metals 14, no. 3: 308. https://doi.org/10.3390/met14030308