基于大数据的C-Mn钢数据预处理及神经网络模型

在神经网络建模时,如果原始数据不加处理或经过简单剔除异常值后用于建模,则可能建立出错误的模型,即其规律并不符合物理冶金原理.因此建模前需要对原始数据进行处理,使其呈现出显著的规律性.针对钢铁生产采集的大量C-Mn钢数据进行了钢种归并,提出了数据预处理的一套方法,并采用LMBP神经网络建立了满足一定精度(94.21%)的多牌号C-Mn钢屈服强度预测模型.通过平均影响值(mean impact value,MIV)分析了成分及工艺参数对屈服强度的影响规律.结果表明,随着碳含量的增加,屈服强度增大;随着终轧厚度和卷取温度的降低,屈服强度增大.

Data Preprocessing and Neural Network Model of C-Mn Steel Based on Big Data

In neural network modeling， it may build a wrong model using original data without any treatment or only eliminating the abnormal value， for it could contain the law not to follow the physical metallurgy principle. To make the regularity significant， the original data need to be processed before modeling. In this work， based on the data of the C-Mn steel derived from a large number of data collected from different steel grades， a set of method for data preprocessing was proposed and a model for predicting yield strength of the C-Mn steel was established using LM-BP neural network， which could make the prediction accuracy meet the requirement (94.21%). The effects of the elements content and processing parameters on the yield strength were analyzed by the mean impact value (MIV). The results showed that the yield strength increased with the increase of carbon content and increased with the decrease of final rolling thickness and coiling temperature.