Data-driven width spread prediction model improvement and parameters optimization in hot strip rolling process

The width spread is one of the key indices affecting hot rolling processes and product quality. The traditional Shibahara spread prediction model (SSM) does not take into account the wear and interference of production equipment. As a result, the prediction accuracy cannot satisfy the high precision and high reliability requirements. To improve the prediction accuracy, first, this paper analyzes the strip deformation mechanism and the source of prediction errors and proposes an improved Shibahara spread model (ISSM) based on wear and interference. Second, a Bayesian-optimized differential evolution and adaptive gradient descent (BDE-AGD) algorithm is proposed for ISSM parameter optimization. This algorithm adopts the Bayesian algorithm to optimize the differential evolution (BDE) algorithm in stage 1, which improves the global search ability. Furthermore, the adaptive strategy is used for gradient descent (AGD) to improve the local search capability in stage 2. Finally, the experimental results show that the BDE-AGD algorithm reduces the error by 21.9% and 3.1% compared with DE-AGD and BDE-GD, respectively. This shows that BDE-AGD has a better global optimization capability. In addition, the prediction precision of ISSM is improved by 9.77% compared with the traditional SSM. Moreover, the precision of ISSM is improved by 8.56% on average compared with seven machine learning algorithms. This shows that ISSM optimized by BDE-AGD can achieve high-precision prediction of strip spread.