A Mechanics‐Informed Modeling and Optimization Framework for Hot Rolling of Thick Plate Using Convolutional Neural Networks and Multiobjective Particle Swarm Optimization Algorithm

The optimization of the hot rolling process for thick plates critically depends on establishing an appropriate rolling schedule. This study presents a multiobjective optimization framework targeting energy consumption and power difference within the rolling schedule. Initially, industrial big data across various steel grades are balanced to enhance model generalizability. Subsequently, convolutional neural network (CNN) models of varying depths and a backpropagation (BP) network model are developed to predict rolling power, with the influence of convolutional layer width on CNN performance also investigated. The rolling power model is then optimized using the multiobjective particle swarm optimization algorithm, yielding a Pareto front. Comparative analyses demonstrate that models trained on the balanced dataset achieve superior predictive performance over those using the original data. Furthermore, the CNN models exhibit greater accuracy and stability than the BP model, while a single‐layer CNN with an optimized width outperforms a three‐layer LeNet‐5 architecture. The solutions on the Pareto front can be selectively implemented based on specific production requirements, offering flexible guidance for optimizing the thick plate hot rolling process.