Camber Prediction Based on Fusion Method with Mechanism Model and Machine Learning in Plate Rolling

Abstract

In order to realize the high-accuracy prediction of steel plate camber and the accurate control of roll gap tilt for straightness, a fusion method with mechanism model and machine learning of the strip at the exit side is investigated. Based on the basic equation of transverse asymmetric rolling, a mathematical model of plate camber curvature radius of the exit side and entry side is derived. Meanwhile, a machine vision method for camber measuring is adopted in which the subpixel coordinates of the rolled piece edges can be obtained, and the size of the plan view of rolled piece can also be settled indirectly to carry out feedback control on the camber defect. Tilt value of the roll gap can be controlled in advance to avoid the occurrence of camber which predicted with high accuracy. Prediction model of camber synthesis leveling based on PSO-LSSVM algorithm is used, the relative error is within ±5% of both the training set and the testing set. Combine the mathematical model of roll gap tilt adjustment and PSO-LSSVM camber prediction, the roll tilt adjustment for different processes and product specification is calculated by predicting plate camber accurately to obtain good straightness for the final product, the relative error range of curvature value is within ±6% after being compensated by PSO-LSSVM algorithm. The research result reveals that this method is suitable for camber prediction and model optimization in plate rolling process.