Data-driven Surplus Material Prediction in Steel Coil Production

Ziyan Zhao, Xiaoyue Yong, Shixin Liu, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A steel enterprise is currently trying to avoid the presence of surplus materials since they can greatly increase its operational cost. The complicated production process of steel products makes it difficult to find the causes of surplus materials. In this work, we propose a surplus material prediction problem and solve it based on statistical analysis and machine learning methods. In the concerned problem, we predict whether there are surplus materials under a given group of production parameters. The dataset used in this work is from a real-world three-month steel coil production process. First, data cleaning is conducted to standardize the industrial dataset. Then, the production parameters highly correlated with surplus material prediction results are selected by a series of feature selection methods. Finally, two prediction models based on extreme gradient boosting and logistic regression are presented according to the selected features. The experimental results reveal that the proposed prediction models have similar effectiveness. A visible regression function makes the logistic regression method more suitable for practical application.

Original languageEnglish (US)
Title of host publication2020 29th Wireless and Optical Communications Conference, WOCC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728161242
DOIs
StatePublished - May 2020
Externally publishedYes
Event29th Wireless and Optical Communications Conference, WOCC 2020 - Newark, United States
Duration: May 1 2020May 2 2020

Publication series

Name2020 29th Wireless and Optical Communications Conference, WOCC 2020

Conference

Conference29th Wireless and Optical Communications Conference, WOCC 2020
Country/TerritoryUnited States
CityNewark
Period5/1/205/2/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

Keywords

  • Surplus material prediction
  • extreme gradient boosting
  • industrial data
  • logistic regression
  • machine learning

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