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This paper explores the real-time modeling and simulation of steelmaking processes using Unreal Engine 5. The study focuses on the development of a 3D model of a Continuous Casting Machine (CCM), its integration into a virtual environment, and the use of digital twins to optimize industrial workflows. Key development stages of an interactive graphical application are outlined, including technology stack selection, physical simulation implementation, and VR/AR support. The research highlights the advantages and limitations of utilizing Unreal Engine 5 for metallurgical process simulation and discusses the potential of digital twins for process monitoring and optimization. The results confirm that 3D visualization, real-time physics simulations, and interactive training environments significantly enhance steel manufacturing process control, predictive maintenance, and operator training. The study identifies challenges related to data integration, computational performance, and interoperability with industrial control systems. Future research should focus on AI-driven process optimization, cloud-based digital twins, and real-time industrial data integration to further advance the efficiency and sustainability of steel manufacturing within the Industry 4.0 framework.

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Digital Simulation of Metallurgical Production: 3D Scene Development of Steel-Making Furnaces in Unreal Engine 5

  • 会议论文

  • 发表日期：

    2026年

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  • Tetiana Selivorstova,
  • Danylo Myrhorodsky,
  • Kateryna Ostrovska,
  • Vadim Selivorstov

摘要

This paper explores the real-time modeling and simulation of steelmaking processes using Unreal Engine 5. The study focuses on the development of a 3D model of a Continuous Casting Machine (CCM), its integration into a virtual environment, and the use of digital twins to optimize industrial workflows. Key development stages of an interactive graphical application are outlined, including technology stack selection, physical simulation implementation, and VR/AR support. The research highlights the advantages and limitations of utilizing Unreal Engine 5 for metallurgical process simulation and discusses the potential of digital twins for process monitoring and optimization. The results confirm that 3D visualization, real-time physics simulations, and interactive training environments significantly enhance steel manufacturing process control, predictive maintenance, and operator training. The study identifies challenges related to data integration, computational performance, and interoperability with industrial control systems. Future research should focus on AI-driven process optimization, cloud-based digital twins, and real-time industrial data integration to further advance the efficiency and sustainability of steel manufacturing within the Industry 4.0 framework.