Motor load balancing with roll force prediction for a cold-rolling setup with neural networks

Sangho Lee, Youngdoo Son

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The use of machine learning algorithms to improve productivity and quality and to maximize efficiency in the steel industry has recently become a major trend. In this paper, we propose an algorithm that automates the setup in the cold-rolling process and maximizes productivity by predicting the roll forces and motor loads with multi-layer perceptron networks in addition to balancing the motor loads to increase production speed. The proposed method first constructs multilayer perceptron models with all available information from the components, the hot-rolling process, and the cold-rolling process. Then, the cold-rolling variables related to the normal part set-up are adjusted to balance the motor loads among the rolling stands. To validate the proposed method, we used a data set with 70,533 instances of 128 types of steels with 78 variables, extracted from the actual manufacturing process. The proposed method was found to be superior to the physical prediction model currently used for setups with regard to the prediction accuracy, motor load balancing, and production speed.

Original languageEnglish
Article number1367
JournalMathematics
Volume9
Issue number12
DOIs
StatePublished - 2 Jun 2021

Keywords

  • Artificial intelligence
  • Intelligent manufacturing systems
  • Neural networks
  • Smart factory
  • Steel manufacturing

Fingerprint

Dive into the research topics of 'Motor load balancing with roll force prediction for a cold-rolling setup with neural networks'. Together they form a unique fingerprint.

Cite this