Thermal History-Dependent Deformation of Polycarbonate: Experimental and Modeling Insights
Abstract
1. Introduction
2. Materials and Methods
2.1. Materials and Specimen Preparation
2.2. Annealing Experiment
2.3. Uniaxial Tension Test
2.4. Constitutive Modeling
2.5. Finite Element Modeling
3. Results and Discussion
3.1. Experimental Results
3.2. Model Predictions
3.2.1. Solution for Model Parameters
3.2.2. Prediction Results
4. Conclusions
- (1)
- During the injection molding process, a slower cooling rate or higher mold temperature results in higher yield stress, with the core layer exhibiting greater yield stress than the surface layer. During the annealing process, longer annealing times and higher annealing temperatures contribute to higher yield stress. In the service process, higher strain rates and lower temperatures lead to increased yield stress.
- (2)
- A formation thermal history-based constitutive model was developed to study the mechanical behavior of PC. Simulation of the formation process showed that the predicted yield stress of PC specimens at various mold temperatures closely matched experimental values. The absolute value of the error between the simulated value and the experimental value is less than 5%, indicating that the model can accurately simulate the yield stress in the thermal history during formation. In the service process, the new phenomenological model accurately predicted large deformation behavior at different strain rates and temperatures. The formation thermal history constitutive model can precisely capture the large deformation behavior of PC specimens prepared at various mold temperatures, including initial elasticity, strain hardening, yield deformation, and strain softening.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Injection Temperature (K) | Volume Flow Rate (cm3/s) | Injection Pressure (MPa) | Hold Pressure (MPa) | Hold Time (s) | Cooling Time (s) | Mold Temperature (K) |
---|---|---|---|---|---|---|
593 | 36.75 | 147 | 117.6 | 8 | 60 | 293, 303, 313, 323, 333 |
Mold Temperature (K) | Annealing Temperature (K) | Annealing Time (h) |
---|---|---|
293 | 353 | 0.2, 0.6, 2, 4, 6, 8, 23 |
373 | 0.2, 0.6, 2, 4, 6, 8, 23 | |
393 | 0.2, 0.6, 2, 4, 6, 8, 23 | |
403 | 0.2, 0.6, 2, 4, 6, 8, 23 |
Mold Temperature (K) | Annealing | Strain Rate (s−1) | Temperature (K) |
---|---|---|---|
293 | Yes | 10−2 | 293 |
293 | No | 10−3, 10−2, 10−1 | 293, 323, 353 |
303, 313, 323, 333 | No | 10−2 | 293 |
Time (h) | Yield Stress at 353 K (MPa) | Yield Stress at 393 K (MPa) | Yield Stress at 393 K (MPa) | Yield Stress at 403 K (MPa) |
---|---|---|---|---|
0.2 | 62.48 | 63.63 | 65.14 | 66.75 |
0.6 | 62.82 | 63.69 | 65.65 | 68.02 |
2 | 62.74 | 64.33 | 67.52 | 70.01 |
4 | 63.17 | 64.86 | 68.31 | 70.20 |
6 | 63.09 | 65.78 | 68.41 | 72.25 |
8 | 63.45 | 66.60 | 68.69 | 72.31 |
23 | 63.45 | 67.02 | 71.19 | 73.86 |
Material Coefficient | Value | |
---|---|---|
Constitutive model | ||
11.91 | ||
0.459 | ||
19.29 | ||
0.209 | ||
0.0149 | ||
705.8 K | ||
Yield stress | A | 58.23 MPa |
B | 3.54 MPa | |
m | 0.777 | |
Processing history | 205,000 J/mol | |
R | 8.314 J/(mol·K) | |
Tr | 293 K | |
Tm | 553 K | |
34.8 MPa | ||
c | 2.6 MPa |
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Li, M.; Wang, H.; Shen, G.; Huang, T.; Zhang, Y. Thermal History-Dependent Deformation of Polycarbonate: Experimental and Modeling Insights. Polymers 2025, 17, 2096. https://doi.org/10.3390/polym17152096
Li M, Wang H, Shen G, Huang T, Zhang Y. Thermal History-Dependent Deformation of Polycarbonate: Experimental and Modeling Insights. Polymers. 2025; 17(15):2096. https://doi.org/10.3390/polym17152096
Chicago/Turabian StyleLi, Maoyuan, Haitao Wang, Guancheng Shen, Tianlun Huang, and Yun Zhang. 2025. "Thermal History-Dependent Deformation of Polycarbonate: Experimental and Modeling Insights" Polymers 17, no. 15: 2096. https://doi.org/10.3390/polym17152096
APA StyleLi, M., Wang, H., Shen, G., Huang, T., & Zhang, Y. (2025). Thermal History-Dependent Deformation of Polycarbonate: Experimental and Modeling Insights. Polymers, 17(15), 2096. https://doi.org/10.3390/polym17152096