DBO-DELM Method for Predicting Rolling Forces in Cold Rolling

Aiming at the problems of many assumptions, large computational errors and poor generalisation performance of the traditional rolling force prediction model, a cold rolling force prediction model (DBO-DELM) using the dung beetle optimizer algorithm (DBO) to optimise the deep extreme learning machine (DELM) is proposed. Based on the Bland-Ford-Hill rolling force model, the characteristic parameters of the DELM rolling force prediction model are selected for each frame of cold continuous rolling. Using the actual production data of a four- frame cold continuous rolling unit in China, the DBO-DELM rolling force prediction model for each frame is generated based on the optimisation of the bias and weight parameters of DELM by the dung beetle optimizer algorithm. The test results show that the relative error of the DBO-DELM rolling force prediction model can reach more than 77% within ± 5% , which is more than 6% higher than that of the existing MA-SVM, DBN, ELM-AE models in the second and third frame, and more than 10% higher than that of the first and fourth frame. In summary, the DBO-DELM rolling force prediction model has obvious improvement in prediction accuracy compared with the existing neural network model in multiple frames and shows good generalisation ability, which provides an effective method for the rolling force prediction in cold continuous rolling.