Composition-Processing-Property Correlation Mining of Nb–Ti Microalloyed Steel Based on Industrial Data
Siwei Wu, Jian Yang, Zhenyu Liu
Abstract
Modeling strength of hot rolled strip based on industrial data may cause misleading predictions because of the high dimension, low quality and unbalanced original data. Industrial data processing is essential to building a successful composition-process-property corresponding relationship model. In current work, the Pauta criterion was implemented to eliminate abnormal data, and uniform grid technique was applied to select out the represented data for obtaining the balanced training data set. Prior to modeling by Bayesian regularization neural network, principal component analysis was applied to alleviate the effect of correlation variables on the modeling. The yield strength prediction model of Nb–Ti microalloyed steel was established with the relative error of ±8%, indicating a good agreement between predicted value and measured value. Finally, the relationship of chemical composition, process parameters and yield strength of hot rolled strip under specific process parameters was analyzed for a further investigation.