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Vibrating flip-flow screens are widely employed in the deep screening processes of coal washing, solid waste treatment, metallurgy, and other fields, playing a crucial role in enhancing product quality and production efficiency. The screen surface and material movement of vibrating flip-flow screens are highly complex, and there is currently insufficient understanding of their screening mechanism, limiting further optimization and application. In this paper, the Discrete Element Method (DEM), Finite Element Method (FEM), and Multi-Body Dynamics (MBD) were integrated to establish a numerical coupling model for vibrating flip-flow screens, considering material loads, screen surface deformation, and screen machine dynamics. The Response Surface Method was utilized to analyze the significant impact of relative amplitude, tension amount, amplitude of driving screen frame, vibration frequency, and screen surface inclination on screening efficiency and material velocity. The results indicate that the most significant factor influencing the screening of flip-flow screens is the screen surface inclination. Based on a BP neural network, a five-degree-of-freedom inclination surrogate model for flip-flow screens was established. The whale algorithm was employed for multi-objective optimization of the surrogate model, resulting in a screen surface inclination distribution that meets the requirements of different operating conditions.
Xu et al. (Wed,) studied this question.