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<p>Nickel-based superalloys are crucial for high-temperature applications in power generation, aerospace, and chemical industries due to their superior mechanical properties and corrosion resistance. However, welding these alloys presents significant challenges, including complex microstructures, susceptibility to hot cracking, and intricate joint geometries, requiring precise control of welding parameters to ensure joint integrity. Traditional experimental methods are costly and time-consuming, making numerical simulations a vital alternative. The accuracy of these simulations depends on selecting appropriate heat source models, as incorrect models lead to flawed predictions of temperature distribution, thermal gradients, and residual stresses, ultimately affecting the performance and lifespan of welded components. This study reviews advancements in welding nickel-based superalloys, focusing on heat source models and finite element analysis (FEA), including Goldak’s double ellipsoidal, rotary Gaussian, and hybrid conical-cylindrical models. The transformative role of artificial intelligence (AI) in optimizing welding processes is also explored, highlighting improvements in productivity, material efficiency, and quality control. A patent landscape analysis provides insights into the evolving contributions of heat source models and AI to welding technologies. This research emphasizes enhancing weld reliability, reducing costs, and supporting sustainable manufacturing while laying the foundation for AI-driven adaptive welding systems in safety–critical applications.</p> Graphical abstract <p></p>

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Welding of nickel alloys interface by finite element modeling and artificial intelligence and envisage by patent landscape

  • 期刊论文

  • 发表日期：

    2025年3月15日

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  • S. Ajithkumar,
  • B. Arulmurugan,
  • R. Meiyazhagan

摘要

Nickel-based superalloys are crucial for high-temperature applications in power generation, aerospace, and chemical industries due to their superior mechanical properties and corrosion resistance. However, welding these alloys presents significant challenges, including complex microstructures, susceptibility to hot cracking, and intricate joint geometries, requiring precise control of welding parameters to ensure joint integrity. Traditional experimental methods are costly and time-consuming, making numerical simulations a vital alternative. The accuracy of these simulations depends on selecting appropriate heat source models, as incorrect models lead to flawed predictions of temperature distribution, thermal gradients, and residual stresses, ultimately affecting the performance and lifespan of welded components. This study reviews advancements in welding nickel-based superalloys, focusing on heat source models and finite element analysis (FEA), including Goldak’s double ellipsoidal, rotary Gaussian, and hybrid conical-cylindrical models. The transformative role of artificial intelligence (AI) in optimizing welding processes is also explored, highlighting improvements in productivity, material efficiency, and quality control. A patent landscape analysis provides insights into the evolving contributions of heat source models and AI to welding technologies. This research emphasizes enhancing weld reliability, reducing costs, and supporting sustainable manufacturing while laying the foundation for AI-driven adaptive welding systems in safety–critical applications.

Graphical abstract