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

Abstract

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.

Optimization of Dephosphorization Parameter in Consteel Electric Arc Furnace Using Rule Set Model

“…This section will merge the input and output data into the new data set. For the new data set, we use the principal component analysis (PCA) [33] to observe the distribution of whole data, as shown in Figure 5. In Figure 5, the first principal component and the second principal component are the new variable transformed from scrap ratio, lime weight, dolomite weight, oxygen consumption, and smelting temperature, and it is obvious that the sample data from high L p and low L p are overlapping each other, indicating that in the actual production process, the corresponding smelting parameter data for the high L p and low L p are crucial similar, which increase the burden of smelting parameter adjustment.…”

Prediction of Endpoint Sulfur Content in KR Desulfurization Based on the Hybrid Algorithm Combining Artificial Neural Network With SAPSO

“…In our work, Pauta criterion is implemented for data cleaning [38]. Assuming X is data of a certain variable, X ={X 1 , X 2 ,.…”

Physical Metallurgy Guided Industrial Big Data Analysis System with Data Classification and Property Prediction

“…Xie et al [18] developed a deep neural network (DNN) model to predict the mechanical properties of four different types of hot-rolled steel plates, and this model was adopted for an actual production line. However, in these works, [13][14][15][16][17][18][19][20][21][22][23][24][25][26] most of the computational analysis systems achieved accurate property prediction based on a simple database with a single or a few specific steel grades, and few studies paid attention to establishing the universal prediction framework regarding a complex industrial database containing various kinds of commercial steels. Thus, the extensibility of prediction models is limited and hinders the wide applications of ML-based DOI: 10.1002/srin.202100820 Various computational analysis systems based on machine learning (ML) methods have been established for the analysis of steel industrial data.…”

“…All the data in the database were extracted from the actual hot rolling production line of Benxi Iron and Steel Co., Ltd., China, to guarantee the adaptability of the model to real industrial data. Unlike previous works that performed a direct analysis on a database containing samples of multiple types of steels, [13,[15][16][17][18][19][20][21][22] in this work, the whole database was first classified into different categories using a K-nearest neighbor (KNN) model. Then, the accurate prediction models were established by selecting the appropriate ML algorithms for each category, finally contributing to improving the extensibility and universality of the modeling framework.…”