Evidence-Theory-Based Robust Optimization and Its Application in Micro-Electromechanical Systems
Abstract
:Featured Application
Abstract
1. Introduction
2. Robustness Analysis Using Evidence Theory
3. Formulation of the EBRO Model
4. The Proposed Algorithm
4.1. Decomposition into Sub-Problems
4.2. Iteration Framework
5. Application Discussion
5.1. A Micro-Force Sensor
5.2. An Ultra-Low-Noise Image Sensor
5.3. A Capacitive Accelerometer
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Results | ||||||
Proposed method | (mm) | 0.937, 0.086, 0.072 | 0.937, 0.086, 0.072 | 0.937, 0.086, 0.072 | 0.937, 0.086, 0.072 | 0.937, 0.086, 0.072 |
22.1 mV, 80.4% | 20.0 mV, 85.3% | 18.2 mV, 90.6% | 15.7 mV, 95.5% | 9.6 mV, 99.9% | ||
Reference solution | 22.4 mV, 80.0% | 20.0 mV, 85.3% | 18.5 mV, 90.0% | 16.1 mV, 95.0% | 9.6 mV, 99.9% |
Xi, i = 1, 2, 3 (mm) | P1 (W) | P2 (W) | |||
---|---|---|---|---|---|
Subinterval | BPA | Subinterval | BPA | Subinterval | BPA |
6.7% | 37.5% | 6.5% | |||
24.2% | 17.6% | 23.8% | |||
38.3% | 5.4% | 32.2% | |||
24.2% | 19.1% | 12.5% | |||
6.7% | 20.4% | 24.9% |
Results | Case 1:2 Dimensions | Case 2:3 Dimensions | Case 3:5 Dimensions | |
---|---|---|---|---|
Proposed method | 128 | 142 | 198 | |
Iterations | 3 | 4 | 4 | |
(mm) | (0.200, 0.185, 0.000) (0.200, 0.186, 0.000) (0.200, 0.188, 0.000) | (0.200, 0.185, 0.000) (0.200, 0.186, 0.000) (0.200, 0.187, 0.000) | (0.200, 0.187, 0.000) (0.200, 0.188, 0.000) (0.200, 0.190, 0.000) | |
(1.35 μm, 85.1%) (1.70 μm, 96.5%) (2.00 μm, 100.0%) | (1.41 μm, 85.8%) (1.55 μm, 96.4%) (1.65 μm, 100.0%) | (1.74 μm, 86.4%) (2.06 μm, 96.1%) (2.62 μm, 100.0%) | ||
Reference solution | (1.35 μm, 85.1%) (1.69 μm, 95.5%) (2.00 μm, 100.0%) | (1.41 μm, 85.8%) (1.55 μm, 96.4%) (1.65 μm, 100.0%) | (1.71 μm, 85.8%) (2.02 μm, 95.4%) (2.56 μm, 99.9%) |
Part | Fixed Electrode | Movable Electrode | Coil | Counter Weight | Block 1 | Block 2 |
---|---|---|---|---|---|---|
Material | Silicon | Silicon | Copper | Wolfram | Wolfram | Aluminium |
Results | Proposed Method | Reference Solution |
---|---|---|
(mm) | (9.0661, 8.9042) (9.0659, 8.9040) (9.0657, 8.9038) (9.0653, 8.9035) (9.0653, 8.9035) (9.0653, 8.9035) | (9.0661, 8.9042) (9.0660, 8.9040) (9.0657, 8.9039) (9.0654, 8.9036) (9.0648, 8.9031) (9.0644, 8.9028) |
(0.244 μm, 81.8%) (0.302 μm, 86.3%) (0.350 μm, 90.8%) (0.449 μm, 96.1%) (0.607 μm, 99.4%) (0.776 μm, 100.0%) | (0.244 μm, 81.8%) (0.279 μm, 85.2%) (0.338 μm, 90.1%) (0.424 μm, 95.5%) (0.594 μm, 99.2%) (0.733 μm, 99.9%) |
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Huang, Z.; Xu, J.; Yang, T.; Li, F.; Deng, S. Evidence-Theory-Based Robust Optimization and Its Application in Micro-Electromechanical Systems. Appl. Sci. 2019, 9, 1457. https://doi.org/10.3390/app9071457
Huang Z, Xu J, Yang T, Li F, Deng S. Evidence-Theory-Based Robust Optimization and Its Application in Micro-Electromechanical Systems. Applied Sciences. 2019; 9(7):1457. https://doi.org/10.3390/app9071457
Chicago/Turabian StyleHuang, Zhiliang, Jiaqi Xu, Tongguang Yang, Fangyi Li, and Shuguang Deng. 2019. "Evidence-Theory-Based Robust Optimization and Its Application in Micro-Electromechanical Systems" Applied Sciences 9, no. 7: 1457. https://doi.org/10.3390/app9071457
APA StyleHuang, Z., Xu, J., Yang, T., Li, F., & Deng, S. (2019). Evidence-Theory-Based Robust Optimization and Its Application in Micro-Electromechanical Systems. Applied Sciences, 9(7), 1457. https://doi.org/10.3390/app9071457