Numerical simulation and optimization for the vibration of the seed metering device based on a novel ML-IGA method
| Autor: | Dong Yan Huang, Qi Wang, Long Tu Zhu, Yan Lei Xu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Engineering
Correctness GA model lcsh:Mechanical engineering and machinery 020209 energy PSO-GA model 02 engineering and technology Set (abstract data type) Control theory Position (vector) Inclination angle 0202 electrical engineering electronic engineering information engineering lcsh:TJ1-1570 General Materials Science Metering mode Simulation novel ML-IGA model Computer simulation business.industry Mechanical Engineering seed-metering devices Vibration Amplitude vibration business optimization |
| Zdroj: | Journal of Vibroengineering, Vol 19, Iss 5, Pp 3151-3168 (2017) |
| ISSN: | 2538-8460 1392-8716 |
| DOI: | 10.21595/jve.2017.17171 |
| Popis: | The reported researches only analyze vibration parameters of the seed-metering device. However, they do not adopt any algorithm to conduct a multi-objective optimization for the vibration performance of the seed-metering device. In this paper, the vibration of the seed-metering device is numerically computed firstly, and the correctness of the computational model is validated by experimental test. Then, some parameters including the plate thickness, inclination angle, excitation source position and supporting leg thickness of the seed-metering device are studied. In this way, those parameters which have serious impacts on the vibration are obtained. Therefore, the vibration performance is tried to optimize using these parameters as the design variables. This paper proposes a novel multi-layer immune algorithm based on genetic algorithms (ML-IGA). In order to further verify validity of the novel ML-IGA model for optimizing the vibration of the seed-metering device, it is compared with the traditional GA model and PSO-GA model. When the iteration of ML-IGA model was conducted to the 156th generation, the predicted error is smaller than the set critical error. Compared with other two kinds of algorithms, the optimized time is reduced. Regarding optimization of the GA model, the vibration amplitudes are increased by 2.49 % and 4.22 % respectively. Regarding optimization of the PSO-GA model, the vibration amplitudes are increased by 9.74 % and 6.024 % respectively. Regarding optimization of the ML-IGA model, the vibration amplitudes are increased by 17.34 % and 6.78 % respectively. Obviously, structures with better performance can be obtained through using the novel ML-IGA model. |
| Databáze: | OpenAIRE |
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