Long Short-Term Memory to Predict the Quality Parameters in the Hot Rolling Process

Key engineering materials, vol. 1050, pp. 81–91

Abstract

Accurate prediction of quality-relevant material parameters, such as thickness and grain size, ensures product quality in hot forming processes. This task becomes especially complex in hot rolling, where the sequential and time-dependent nature of the process results in pass schedules with varying numbers of passes and grain size evolution that depends on the deformation history. To address this complexity, this study aims to develop and train deep learning models based on Long Short-Term Memory (LSTM) networks, which are well-suited for modelling sequential data. As input features for the model, real-time process parameters such as rolling force and rolling temperature are used, which can be captured by sensors during operation. Simulation data for both material and process parameters are acquired using Simulation as a Service (SaaS) through the Fast Rolling Model (FRM) called Rolling Calculation Tool (RoCaT), focusing on steel grade S355. The performance of the LSTM model is evaluated by analysing loss curves over training epochs and comparing predicted values to reference data. The maximum relative percentage error for thickness between the LSTM predicted value and the RoCaT value is 18.818% and 16.56259%, respectively, for pass schedules of 15 and 17 passes, with a starting thickness of 205mm. The percentage of relative error values for grain size is more pronounced during the initial passes as compared to later passes for both pass schedules. The statistical validation is performed on the denormalized data using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-Squared (R 2 ), which demonstrate the model’s ability to predict key material properties in the hot rolling process reliably. The RMSE, MAE, and R² values for thickness are obtained as 45.2175mm, 18.7555mm, and 0.8115, respectively. For grain size, the corresponding values are 25.8287µm, 13.4319µm, and 0.9192.

Authors 5

1. Tamoghna Majumder corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. Nilesh Thakare Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. Holger Brüggemann Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

4. Emad Scharifi Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

5. Junhe Lian Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University