Open AccessPrediction of Mechanical Properties of Hot Rolled Strips With Generalized RBFNN and Composite Expectile Regression
Author(s) -
Xiaoxia He,
Xiaodan Zhou,
Tong Tian,
Weigang Li
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3212053
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The prediction of mechanical properties of hot rolled strips can be used for on-line dynamic control of product properties and optimal design of new steel grade. Tensile strength is an important index of the mechanical properties for hot rolled strips. The influencing factors of tensile strength include alloy elements, microstructure, and production process parameters. It is very crucial to establish a reliable prediction model of steel tensile strength based on these factors to improve the mechanical properties. This paper proposes a prediction model which combines a generalized radial basis function neural network and composite expectile regression to solve nonlinear problems and data heterogeneity problems in modeling. At the same time, CS algorithm (Cuckoo Search) was applied to develop an estimation procedure, which overcomes the shortcoming of traditional gradient descent algorithm by avoiding to fall into local optimum. Because expectile regression can describe the conditional distribution, and neural network has strong non-linear interpretation ability, the proposed model enables us to explore potential nonlinear relationships among variables. Based on the measured data collected from a hot rolling production process, the experimental results show that the model proposed in this paper has better prediction accuracy, the mean absolute percentage error (MAPE) and root mean square error (RMSE) are 2.49