A hot rolling full process rolling force prediction method based on transfer learning and Inception-LSTM neural network

Abstract

A rolling force prediction method based on transfer learning and Inception-LSTM neural network is proposed to address the problem of low efficiency in predicting rolling forces for individual rolling mill stands due to the complex rolling conditions and distribution differences in the collected process data. The Inception-LSTM neural network combines the spatial feature extraction of the Inception model and the time sequence modeling capability of the LSTM network to comprehensively capture the features in the rolling process, thus establishing a baseline prediction model. Then, the transfer learning method is employed to transfer part of the parameters and structure of the baseline prediction model to the new prediction model. Simultaneously, the model is fine-tuned to establish a new transferred model for rolling force prediction, which is compared and analyzed against the neural network prediction model without using transfer learning. Experimental results show that the model built with transfer learning is higher fitting accuracy than the model built directly for rolling force prediction, and the training time of the model is significantly reduced. It can be used for steel shape and thickness control and digital twin simulation of rolling process.