Digital-Twin-Based Monitoring System for Slab Production Process

Tianjie Fu, Peiyu Li (), Chenke Shi and Youzhu Liu
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Tianjie Fu: School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
Peiyu Li: School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
Chenke Shi: School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
Youzhu Liu: School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China

Future Internet, 2024, vol. 16, issue 2, 1-16

Abstract: The growing demand for high-quality steel across various industries has led to an increasing need for superior-grade steel. The quality of slab ingots is a pivotal factor influencing the final quality of steel production. However, the current level of intelligence in the steelmaking industry’s processes is relatively insufficient. Consequently, slab ingot quality inspection is characterized by high-temperature risks and imprecision. The positional accuracy of quality detection is inadequate, and the precise quantification of slab ingot production and quality remains challenging. This paper proposes a digital twin (DT)-based monitoring system for the slab ingot production process that integrates DT technology with slab ingot process detection. A neural network is introduced for defect identification to ensure precise defect localization and efficient recognition. Concurrently, environmental production factors are considered, leading to the introduction of a defect prediction module. The effectiveness of this system is validated through experimental verification.

Keywords: digital twin; defect recognition; process monitoring (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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