Materials Genome Engineering Advances (Sep 2023)

Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

  • Xiaoxiao Geng,
  • Feiyang Wang,
  • Hong‐Hui Wu,
  • Shuize Wang,
  • Guilin Wu,
  • Junheng Gao,
  • Haitao Zhao,
  • Chaolei Zhang,
  • Xinping Mao

DOI
https://doi.org/10.1002/mgea.10
Journal volume & issue
Vol. 1, no. 1
pp. n/a – n/a

Abstract

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Abstract With the development of new information technology, big data technology and artificial intelligence (AI) have accelerated material research and development and industrial manufacturing, which have become the key technology driving a new wave of global technological revolution and industrial transformation. This review introduces the data resources and databases related to steel materials. It then examines the fundamental strategies and applications of machine learning (ML) in the design and discovery of steel materials, including ML models based on experimental data, industrial manufacturing data, and simulation data, respectively. Given the advancements in big data, AI/ML, and new communication technologies, an intelligent manufacturing mode featuring digital twins is deemed critical in guiding the next industrial revolution. Consequently, the application of intelligence manufacturing with digital twins in the iron and steel industry is reviewed and discussed. Furthermore, the applications of ML in service performance prediction of steel products are addressed. Finally, the future development trends for data‐driven and AI approaches throughout the entire life cycle of steel materials are prospected. Overall, this work presents an in‐depth examination of the integration of data‐driven and AI technologies in the steel industry, highlighting their potential and future directions.

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