Response Surface Methodology Optimization of Resistance Welding Process for Unidirectional Carbon Fiber/PPS Composites
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Preparation of the Resistance Element
2.3. Experimental Methods
2.3.1. Resistance Welding Experiment
- (1)
- The single-lap resistance welding experiment
- (2)
- The operation procedure for resistance welding
2.3.2. Single-Factor Experimental Design
2.3.3. Optimization of the Resistance Welding Process Using Response Surface Methodology
2.4. Characterization
3. Results and Discussion
3.1. Effect of Resistance Elements on Welded Joints
3.1.1. Mesh Size
3.1.2. RE Process Method
3.2. Effect of the Welding Process on the Welded Joint
3.2.1. Pressure Holding Time
3.2.2. Welding Temperature
3.2.3. Welding Pressure
3.2.4. Power Density
3.3. Optimization of the Resistance Welding Process Using RSM
3.3.1. Quadratic Regression Model
3.3.2. Effect of Process Parameters on Welding Strength
3.4. Optimization and Verification of the Model
4. Conclusions
- (1)
- When the brass mesh size is 100 mesh, it has the best resin infiltration effect and the highest heating efficiency on the welding surface. After the 100-mesh brass mesh was oxidized, the surface roughness of the brass mesh was improved, and the interface bonding strength of the PPS resin was improved. This proves that a simple surface treatment can significantly improve welding strength. Finally, using Ox-RE/PPS for welding, the defects within the welding layer were reduced, and the welding strength reached 13.18 MPa. The failure mode changed from resin failure and implant tear to plate interlayer failure and carbon fiber fracture.
- (2)
- The LSS of the joint first increased and then decreased with the extension of the holding time, and then tended to be stable, reaching a maximum value at 60 s. The single-factor experiment determined that the maximum welding strength was obtained at 310 °C, 1.15 MPa, and 120 kW/m2 for welding temperature, pressure, and power density, respectively. These three process factors were the main process factors in resistance welding.
- (3)
- The parameters of the three main process factors were substituted into the BBD-RSM to construct a quadratic regression model with high fit and prediction ability. From the 3D surface diagram analysis, the influence of power density is the largest, and the interaction between welding temperature and power density is the most significant. Combined with the analysis of Design Expert 13 software, the optimal process parameters were obtained as follows: welding temperature: 313–314 °C, welding pressure: 1.04–1.2 MPa, power density: 124–128 kW/m2. After multiple factor optimization, the LSS of the welded joint prepared in the optimal parameter range reached 13.58 MPa, and the welding strength increased by 26.56% compared with the UT-RE without process optimization.
- (4)
- Combined with the above conclusions, it can be concluded that BBD-RSM can effectively analyze and optimize the process parameters of resistance welding. Moreover, an efficient and systematic resistance welding process can obtain high-quality welded joints. However, due to the limited properties of the material itself, if we want to improve the resistance welding performance, we need to take other measures, such as resin layer reinforcement and interface bonding strength. For example, on the basis of resistance welding combined with other connection methods to achieve higher strength, surface modifiers (silane coupling agents, surfactants, etc.) are used to further enhance the interface properties between RE and resin, and fillers (short fiber materials, nanomaterials, etc.) are added to enhance the mechanical properties of the resin and then improve the joint properties.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Level | Factor | ||
---|---|---|---|
A: T (°C) | B: F (MPa) | C: P (kW/m2) | |
−1 | 300 | 0.85 | 100 |
0 | 310 | 1.15 | 120 |
1 | 320 | 1.45 | 140 |
No. | A: T (°C) | B: F (MPa) | C: P (kW/m2) | LSS (MPa) |
---|---|---|---|---|
1 | 310 | 1.45 | 100 | 9.85 |
2 | 300 | 1.15 | 140 | 10.04 |
3 | 320 | 1.15 | 100 | 9.6 |
4 | 310 | 1.15 | 120 | 13.44 |
5 | 310 | 1.15 | 120 | 13.56 |
6 | 310 | 0.85 | 100 | 9.88 |
7 | 310 | 1.15 | 120 | 13.61 |
8 | 300 | 1.45 | 120 | 10.92 |
9 | 310 | 1.15 | 120 | 13.31 |
10 | 300 | 0.85 | 120 | 11.91 |
11 | 320 | 0.85 | 120 | 12.85 |
12 | 310 | 1.45 | 140 | 11.91 |
13 | 320 | 1.45 | 120 | 12.46 |
14 | 320 | 1.15 | 140 | 12.22 |
15 | 300 | 1.15 | 100 | 9.57 |
16 | 310 | 1.15 | 120 | 13.7 |
17 | 310 | 0.85 | 140 | 12.24 |
Source | Sum of Squares | Degrees of freedom | Mean Square | F-Value | p-Value | Significance |
---|---|---|---|---|---|---|
Model | 37.59 | 9 | 4.18 | 64.58 | <0.0001 | ** |
A | 2.75 | 1 | 2.75 | 42.51 | 0.0003 | ** |
B | 0.3785 | 1 | 0.3785 | 5.85 | 0.0462 | ** |
C | 7.05 | 1 | 7.05 | 108.99 | <0.0001 | ** |
AB | 0.09 | 1 | 0.09 | 1.39 | 0.2767 | |
AC | 1.16 | 1 | 1.16 | 17.87 | 0.0039 | ** |
BC | 0.0225 | 1 | 0.0225 | 0.3478 | 0.5739 | |
A2 | 4.65 | 1 | 4.65 | 71.87 | <0.0001 | ** |
B2 | 0.8087 | 1 | 0.8087 | 12.5 | 0.0095 | ** |
C2 | 18.85 | 1 | 18.85 | 291.38 | <0.0001 | ** |
Residual | 0.4528 | 7 | 0.0647 | |||
Lack of fit | 0.3603 | 3 | 0.1201 | 5.19 | 0.0727 | |
Pure error | 0.0925 | 4 | 0.0231 | |||
R2 | 0.9881 | Adjusted R2 | 0.9728 | |||
Mean | 11.83 | Predicted R2 | 0.8447 | |||
C.V. % a | 2.15 | Adeq Precision | 21.2956 |
No. | A: T (°C) | B: F (MPa) | C: P (kW/m2) | LSS (MPa) |
---|---|---|---|---|
1 | 313.272 | 1.199 | 124.981 | 13.707 |
2 | 314.205 | 1.133 | 127.969 | 13.722 |
3 | 313.125 | 1.059 | 122.983 | 13.737 |
4 | 314.107 | 1.089 | 125.004 | 13.763 |
5 | 314.064 | 1.038 | 124.350 | 13.744 |
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Yu, D.-W.; Qing, X.-T.; Lin, H.-Y.; Yang, J.; Yang, J.-C.; Wang, X.-J. Response Surface Methodology Optimization of Resistance Welding Process for Unidirectional Carbon Fiber/PPS Composites. Materials 2024, 17, 2176. https://doi.org/10.3390/ma17102176
Yu D-W, Qing X-T, Lin H-Y, Yang J, Yang J-C, Wang X-J. Response Surface Methodology Optimization of Resistance Welding Process for Unidirectional Carbon Fiber/PPS Composites. Materials. 2024; 17(10):2176. https://doi.org/10.3390/ma17102176
Chicago/Turabian StyleYu, Da-Wei, Xiao-Ting Qing, Hong-Yu Lin, Jie Yang, Jia-Cao Yang, and Xiao-Jun Wang. 2024. "Response Surface Methodology Optimization of Resistance Welding Process for Unidirectional Carbon Fiber/PPS Composites" Materials 17, no. 10: 2176. https://doi.org/10.3390/ma17102176