Paper Info
Title
A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems
Abstract
Width deviation is an important metric for evaluating the quality of a hot-rolled strip in steel production systems. This paper considers a width deviation prediction problem and proposes a Machine-learning and Genetic-algorithm-based Hybrid method named MGH to obtain a prediction model. Existing work mainly focuses on high prediction accuracy, while ignoring interpretability. This work aims to build a prediction model that can make a good trade-off between two industry-required criteria, i.e., prediction accuracy and interpretability. It first collects some process variables in a hot rolling process and includes them as well as some constructed variables in a feature pool. Then we propose MGH to find representative variables from it and build a prediction model. MGH results from the integration of hierarchical clustering, genetic algorithm, and generalized linear regression. In detail, hierarchical clustering is applied to divide variables into clusters. Genetic algorithm and generalized linear regression are innovatively combined to select a representative variable from each cluster and develop a prediction model. The computational experiments conducted on both industrial and public datasets show that the proposed method can effectively balance prediction accuracy and interpretability of its resulting model. It has better overall performance than the compared state-of-the-art models.
Year
DOI
Venue
2022
10.1016/j.ins.2021.12.063
Information Sciences
Keywords
DocType
Volume
Feature construction,Generalized linear regression,Genetic algorithm,Hierarchical clustering,Hot-rolled strip,Width deviation prediction
Journal
589
ISSN
Citations 
PageRank 
0020-0255
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yingjun Ji100.34
Shixin Liu200.34
Mengchu Zhou300.34
ZiYan Zhao423.15
Xiwang Guo544.53
Liang Qi615627.14