Skip to main content
placeholder image

Ensemble Machine Learning Systems for the Estimation of Steel Quality Control

Conference Paper


Download full-text (Open Access)

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.

Authors


  •   Li, Fucun (external author)
  •   Wu, Jianqing (external author)
  •   Dong, Fang (external author)
  •   Lin, Jiayin (external author)
  •   Sun, Geng
  •   Chen, Huaming (external author)
  •   Shen, Jun

Publication Date


  • 2018

Citation


  • 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. IEEE Conference on Big Data (pp. 2234-2241). United States: IEEE.

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3159&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2153

Start Page


  • 2234

End Page


  • 2241

Place Of Publication


  • United States

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.

Authors


  •   Li, Fucun (external author)
  •   Wu, Jianqing (external author)
  •   Dong, Fang (external author)
  •   Lin, Jiayin (external author)
  •   Sun, Geng
  •   Chen, Huaming (external author)
  •   Shen, Jun

Publication Date


  • 2018

Citation


  • 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. IEEE Conference on Big Data (pp. 2234-2241). United States: IEEE.

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3159&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2153

Start Page


  • 2234

End Page


  • 2241

Place Of Publication


  • United States