Abstract
Screening is an important step in the preparation of machine made sand, and the screening performance of the screening machine is the key to improving the production efficiency and quality of machine made sand. This article first explores the influence of airflow generated by sieve plate vibration on particle motion on the sieve through numerical simulation, and verifies the reliability of the simulation model through experimental prototypes. Then, based on the CFD-DEM coupling model, the relationship between screening parameters and screening performance was studied. Finally, based on the screening results and the BP neural network optimized by the sparrow search algorithm, an evaluation model for screening parameters and screening performance was established, and a multi-objective genetic algorithm was used to optimize the high-performance screening parameter combination scheme. The comprehensive optimal performance parameters were obtained: vibration frequency of 15.5885 Hz, amplitude of 2.3789 mm, vibration direction angle of 50.5227, sieve obliquity of 16.6676, and feed rate of 20921 pieces/s.
Acknowledgments
No part of this work has been published or submitted elsewhere. The authors declared that they have no conflicts of interest in this work. All authors have seen the manuscript and approved it to submit to your journal.
Disclosure statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.