Ensemble Machine Learning Systems for the Estimation of Steel Quality Control

Authors

Fucun Li, University of Wollongong, Jiangsu Jinheng Information Technology Co., LtdFollow
Jianqing Wu, University of WollongongFollow
Fang Dong, Southeast UniversityFollow
Jiayin Lin, University of WollongongFollow
Geng Sun, University of WollongongFollow
Huaming Chen, University of WollongongFollow
Jun Shen, University of WollongongFollow

RIS ID

129593

Publication Details

Li, F., Wu, J., Dong, F., Lin, J., Sun, G., Chen, H. & Shen, J. (2018). Ensemble Machine Learning Systems for the Estimation of Steel Quality Control. 2018 IEEE International Conference on Big Data (Big Data) (pp. 2245-2252). United States: IEEE.

Abstract

Recent advances in the steel industry have encountered challenges in soliciting decision making solutions for quality control of products based on data mining techniques. In this paper, we present a steel quality control prediction system encompassing with real-world data as well as comprehensive data analysis results. The core process is cautiously designed as a regression problem, which is then best handled by grouping various learning algorithms with their massive resource of historical production datasets. The characteristics of the currently most popular learning models used in regression problem analysis are as well investigated and compared. The performance indicates our steel quality control prediction system based on ensemble machine learning model can offer promising result whilst delivering high usability for local manufacturers to address the production problem by aid of development of machine learning techniques. Furthermore, real-world deployment of this system is demonstrated and discussed. Finally, future directions and the performance expectation are pointed out.