High-Dimensional Data-Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

To solve the contradiction between consumer's personal demand and mass production of steel enterprise, the optimal design for hot strip rolling of steel attracts researcher's attention. The core of the ensemble system is the artificial neural network (ANN) model, which is based on the industrial data. However, industrial data are difficult to be used for modeling because of their high dimension, low quality, and unbalanced. In current work, the method for industrial data processing is proposed for microalloyed steel. Based on the treated data, the mechanical property prediction models are established with the relative error of +/- 8% for yield strength (YS), +/- 6% for tensile strength (TS), and the absolute error of +/- 4% for elongation (EL), respectively. Combined with the multi-objective optimization algorithm, the ANN models are implemented for designing the hot rolling process for S360 grade steel to control the stability of product quality.