Numerical simulation and optimization for the vibration of the seed metering device based on a novel ML-IGA method

• Main Authors: Yan Lei Xu, Qi Wang, Long Tu Zhu, Dong Yan Huang
• Format: Article
• Language: English
• Published: JVE International 2017-08-01
• Series: Journal of Vibroengineering
• Subjects: seed-metering devices; vibration; optimization; novel ML-IGA model; GA model; PSO-GA model
• Online Access: https://www.jvejournals.com/article/17171

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.