Abstract:Targeting the manual adjustment-based, typical mineral processing shaking table surface adjustment operation, which has a high worker labor intensity, makes it simple to produce variations in concentrate grade and other problems. In this study, an automatic slope adjustment method for shaking tables based on machine vision is developed.The servo slope adjustment mechanism is designed to replace the manual slope mechanism adjustment, with a bed surface tilt angle adjustment error of ±0.03° and a speed of 0.48° per second; the shaking table surface automatic slope control model is constructed to replace the manual adjustment of the operating experience, and an improved YOLOv8 target detection algorithm is proposed for the feature extraction of shaking table ore belt images, with detection accuracy increased by 8% up to 95%, rather than adjusting the slope operation experience by hand. The automatic slope adjustment system of the surface created in this work can fully replace the manual to achieve the automatic adjustment of the slope of the shaking table bed surface, according to the experimental study, and it operates steadily.