Modelling of Deformation Resistance with Big Data and Its Application in the Prediction of Rolling Force of Thick Plate

Abstract

The precision of traditional deformation resistance model is limited, which leads to the inaccuracy of the existing rolling force model. In this paper, the back propagation (BP) neural network model was established according to the industrial big data to accurately predict the deformation resistance. Then, a new rolling force model was established by using the BP neural network model. During the establishment of the neural network model, the data set of deformation resistance was established, which was calculated back from the actual rolling force data. Based on the data set after normalization, the BP neural network model of deformation resistance was established through the optimization of algorithm and network structure. It is shown that both the prediction accuracy of the neural network model on the training set and the test set are high, indicating that the generalization ability of the model is strong. The neural network model of the deformation resistance is compared with the theoretical one, and the maximum error is only 3.96%. Furthermore, by comparison with the traditional rolling force model, it is found that the prediction accuracy of the rolling force model imbedding with the present neural network model is improved obviously. The maximum error of the present rolling force model is just 3.86%. The research in this paper provides a new way to improve the prediction accuracy of rolling force model.

Reduce Product Surface Quality Risks by Adjusting Processing Sequence: A Hot Rolling Scheduling Method

“…Simultaneously, the rolls experience wear due to the deformation resistance from the slab. Deformation resistance is the force required in per unit area for plastic deformation of a metal material under stress conditions [10], for which the study holds significant guidance for subjects such as developing rolling processes, formulating process systems, and developing new steel grades [11]. Its formula is the fundamental stone of rolling force models whose magnitude is influenced by internal factors such as chemical composition, microstructure, and grain size of product, as well as external factors like rolling temperature, rolling speed, and property jump amount of adjacent rolling piece arranged in the same rolling unit [12], given by Equation (7).…”

“…In Equation (11), 𝑀𝑛, 𝐶𝑟, 𝑀𝑜, 𝑉, 𝑁𝑖, 𝐶𝑢, and 𝑆𝑖, respectively, represent the compositional proportions of alloying elements manganese, chromium, molybdenum, vanadium, nickel, copper, and silicon, respectively.…”