Experimental Validation of Injection Molding Simulations of 3D Microparts and Microstructured Components Using Virtual Design of Experiments and Multi-Scale Modeling
Abstract
:1. Introduction
2. Optimization of 3D Micropart Production
2.1. Case 1—Micro-Ring
2.2. Case 2—Micro-Cap
2.3. Multi-Scale Modeling and Meshing of Single Micro-Components
2.4. Process Optimization Based on Calibrated Simulation Results—Case 1
2.5. Optimization of the Flash Formation Using Process Simulation—Case 2
3. Multi-Scale Filling Simulation of Low Aspect Ratio Structures in Parts Embedding Microfeatures
3.1. Case 3—Micro-Optical Reflector
3.2. Case 4—Fresnel Lens
3.3. Multi-Scale Meshing for Microstructured Parts
3.4. Multi-Scale Filling Simulation Validation at Mesoscale—Cases 3 and 4
3.5. Multi-Scale Simulation for Filling Time Prediction—Case 3
3.6. Multi-Scale Simulations for Microfeature Replication Prediction—Case 4
4. Conclusions
- Process simulation requires geometrical calibration of the domain by measuring the effective feature size on the mold insert and feeding this value to the simulation boundary condition.
- µIM process simulation can be used for the optimization of single-part production with a 1 mm feature dimension at a 10 µm accuracy level.
- µIM process simulation can be used in the prediction of the factors most affecting flash formation in single micropart production. The punctual estimation of the flash area requires further calibration of the model geometry and a venting flow volume has to be included in the part design.
- Virtual design of experiments using simulations is an effective digital optimization tool that has the capability to indicate the effect of µIM process parameters on micro-molded part characteristics.
- For parts featuring microfeatures, two methods were proposed for the modeling of complex microstructure arrays: (1) feature restriction and (2) feature refinement.
- The feature restriction approach allowed us to model the filling time delay from the flat polished and microstructured side of the cavity, allowing us to predict the influence of feature orientation on the melt front propagation.
- Feature refinement allowed us to punctually investigate the replication development of a single microfeature. Through combined meso- and micro-dimensional scale comparison, a multi-scale validation approach was proposed.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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µIM Product | Average Part Size | Part Mass | Dimensional Tolerance Range | Equipment |
---|---|---|---|---|
Single Microparts | <10 mm | 0.0001–0.1 g | 10 µm | µIM metering and dosing system |
Parts featuring micro- or nanostructures | >10 mm | > 0.1 g | 0.01–1 µm (on features) | Conventional IM injection system |
Micro precision IM Parts | >10 mm | > 0.1 g | 10–100 µm | µIM and IM systems |
Macro/Meso | Micro (µ) | Nano (n) |
---|---|---|
Conservation of Mass | Wall-Slip Effect | Molecular Dynamics |
Conservation of Momentum | Surface Tension | |
Conservation of Energy | Local HTC | |
Polymer constitutive equation (pvT) | Unvented air | |
Viscosity model | Surface Roughness |
Mesh Parameters | Case 1 Micro-Ring | Case 2 Micro-Cap |
---|---|---|
Element type | 3D Tetrahedral | 3D Tetrahedral |
Meshing algorithm | Advancing Front | Advancing Front |
Modeling of sprue | Yes | Yes |
Number of cavities | 4 | 1 |
Minimum element size | 50 µm | 20 µm |
Maximum element size | 500 µm | 300 µm |
Growth rate | 1.2 | 1.2 |
Total elements | 1.4 × 106 | 1.0 × 106 |
Mesh Parameters | Case 3 Micro-Optical Reflector | Case 4 Fresnel Lens |
---|---|---|
Element type | 3D Tetrahedral | 3D Tetrahedral |
Meshing algorithm | Advancing Layer | Advancing Front |
Meshing approach | Feature restriction | Partition refinement |
Minimum element size | 10 µm | 10 µm |
Maximum element size | 1.000 mm | 1.000 mm |
Growth rate | 1.2 | 1.5 |
Total elements | 3 047 407 | 3 248 186 |
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Loaldi, D.; Regi, F.; Baruffi, F.; Calaon, M.; Quagliotti, D.; Zhang, Y.; Tosello, G. Experimental Validation of Injection Molding Simulations of 3D Microparts and Microstructured Components Using Virtual Design of Experiments and Multi-Scale Modeling. Micromachines 2020, 11, 614. https://doi.org/10.3390/mi11060614
Loaldi D, Regi F, Baruffi F, Calaon M, Quagliotti D, Zhang Y, Tosello G. Experimental Validation of Injection Molding Simulations of 3D Microparts and Microstructured Components Using Virtual Design of Experiments and Multi-Scale Modeling. Micromachines. 2020; 11(6):614. https://doi.org/10.3390/mi11060614
Chicago/Turabian StyleLoaldi, Dario, Francesco Regi, Federico Baruffi, Matteo Calaon, Danilo Quagliotti, Yang Zhang, and Guido Tosello. 2020. "Experimental Validation of Injection Molding Simulations of 3D Microparts and Microstructured Components Using Virtual Design of Experiments and Multi-Scale Modeling" Micromachines 11, no. 6: 614. https://doi.org/10.3390/mi11060614
APA StyleLoaldi, D., Regi, F., Baruffi, F., Calaon, M., Quagliotti, D., Zhang, Y., & Tosello, G. (2020). Experimental Validation of Injection Molding Simulations of 3D Microparts and Microstructured Components Using Virtual Design of Experiments and Multi-Scale Modeling. Micromachines, 11(6), 614. https://doi.org/10.3390/mi11060614