Robust Multiobjective Optimization of Setback Feature in MEMS Safety and Arming Device Considering Parameters Uncertainty
- Resource Type
- Periodical
- Authors
- Lei, S.; Cao, Y.; Ma, W.; Zhu, H.; Lu, H.; Yao, J.; Nie, W.; Xi, Z.
- Source
- IEEE Sensors Journal IEEE Sensors J. Sensors Journal, IEEE. 24(8):12197-12206 Apr, 2024
- Subject
- Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Robotics and Control Systems
Optimization
Micromechanical devices
Fabrication
Uncertainty
Sensors
Robustness
Sensitivity analysis
Algorithm
inertial setback feature (ISF)
micro-electromechanical systems (MEMS)
robust multiobjective optimization (RMO)
robustness
safety
and arming device (SAD)
- Language
- ISSN
- 1530-437X
1558-1748
2379-9153
In this study, we present a multistage robust multiobjective optimization (MRMO) method that takes into consideration uncertainties stemming from fabrication errors. This efficient method has been employed to optimize an inertial setback feature (ISF) within the micro-electromechanical system (MEMS) safety and arming device (SAD). Sensitivity analysis and meta-model techniques are utilized to reduce the computational dimension and cost of optimizing the objectives. The accuracy of the approximation between the global meta-model (GMM) in the method and finite element analysis (FEA) results is guaranteed through appropriate calculation samples. Two optimization algorithms, the fuzzy multiobjective particle swarm optimization (f-MOPSO) algorithm and the adaptive accelerated gravitational search algorithm (AAGSA), are performed to global optimization and local search for the fitness values sequentially to enhance the optimization performance. Robust optimal solutions are shortlisted and selected from among candidate solutions exhibiting superior performance. The effectiveness of the method is demonstrated through a comparison of the results obtained from FEA and the GMM with experimental data collected from some fabricated ISF prototypes. This method achieves the objective of reducing the burden of repetitive FEA calculations and better performance enhancement in robustness optimization.