Multi-Objective Ensemble Learning with Multi-Scale Data for Product Quality Prediction in Iron and Steel Industry

High quality product quality prediction is very important for iron and steel enterprises to ensure stable production. However, most existing prediction methods are manually designed learning models. These methods consider only macroscopic data while ignoring mesoscopic data that also have a significant impact on product quality. Thus, they are often poor at accuracy and generalization performance in practice. To address this issue, a multi-objective convolutional neural networks ensemble learning method with multi-scale data fusion (MOCNNEL-MSDF) is developed. Using data fusion of macro/meso data derived from kinetic models, MOCNNEL-MSDF first evolves a swarm of convolutional neural networks (CNNs) by knowledge-transferring based reproduction and adaptive weights initialization adjustment to improve learning performance, and then a sparse ensemble approach based on differential evolution is applied to achieve the final prediction model from the evolved CNNs. Experimental results on both benchmark data and practical data of continuous annealing show that MOCNNEL-MSDF achieves competitive or better accuracy and robustness compared with other powerful learning methods, and outperforms the existing strip quality prediction models. The proposed method can be used in the product quality modeling of each process in the iron and steel industry, where it is desirable to combine mechanism models with production process data to construct a product quality prediction model with higher accuracy and generalization.