Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method

Abstract

Industrial big data technology has become one of the important driving forces to intelligent manufacturing in the steel industry. In this study, the characteristics of data in steel production are analyzed and an industrial big data platform for steeling process is developed to extract the quality-related parameters. A data-driven approach to construct prediction intervals (PIs) of mechanical performances for hot-rolling strips is proposed to represent the uncertainty and reliability of the prediction results. The proposed method employs a new manifold visualization method, SLISEMAP, to reduce the feature dimensions with interpretability, utilizes lower upper bound estimation (LUBE) method to obtain the PIs, in which the broad learning system (BLS) is used as the basic training network model and the artificial bee colony (ABC) algorithm is applied to optimize the weighting parameters of BLS under the LUBE framework. A hot-rolling steel coil dataset consisting of 39 variables and 1335 coil samples is used to validate the proposed method. Two Delta-based approaches, namely back propagation neural network (BPNN) and extreme learning machine (ELM); and three LUBE-based approaches, namely ABC-BPNN, ABC-ELM, and ABC-support vector regression (SVR) are compared with the proposed method. Results show that the proposed ABC-BLS in LUBE is effective and efficient in constructing the PIs with a higher coverage probability and a narrower interval width.