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<p>Thermal barrier coatings (TBCs) are critical to enhancing the durability and thermal efficiency of components operating in extreme environments such as aerospace engines and gas turbines. Traditional deposition methods often fall short in enabling real-time control or microstructural optimization, leading to variability in coating performance and limited adaptability. Additive manufacturing (AM) techniques such as laser-directed energy deposition (L-DED), cold spray, wire arc spray, and flame spray have emerged as promising solutions for site-specific TBC fabrication and repair with high geometric fidelity. Concurrently, artificial intelligence (AI) is redefining the TBC development landscape by enabling data-driven process optimization, predictive defect analytics, and closed-loop control of deposition parameters. This review critically evaluates the synergistic integration of AI and AM for TBC production, outlining recent advances in physics-informed machine learning (PIML), digital twin architectures, and in-situ process monitoring. Emphasis is placed on AI’s role in optimizing porosity, mitigating thermally grown oxide (TGO) growth, enhancing service life, and reducing vulnerability to CMAS infiltration. The review concludes by identifying key research gaps and outlining future directions toward autonomous, adaptive, and sustainable TBC systems enabled by intelligent manufacturing platforms.</p>

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Advanced digitally-controlled and AI-enhanced coating techniques for smart thermal barrier coatings

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • Uday M. Basheer Al-Naib

摘要

Thermal barrier coatings (TBCs) are critical to enhancing the durability and thermal efficiency of components operating in extreme environments such as aerospace engines and gas turbines. Traditional deposition methods often fall short in enabling real-time control or microstructural optimization, leading to variability in coating performance and limited adaptability. Additive manufacturing (AM) techniques such as laser-directed energy deposition (L-DED), cold spray, wire arc spray, and flame spray have emerged as promising solutions for site-specific TBC fabrication and repair with high geometric fidelity. Concurrently, artificial intelligence (AI) is redefining the TBC development landscape by enabling data-driven process optimization, predictive defect analytics, and closed-loop control of deposition parameters. This review critically evaluates the synergistic integration of AI and AM for TBC production, outlining recent advances in physics-informed machine learning (PIML), digital twin architectures, and in-situ process monitoring. Emphasis is placed on AI’s role in optimizing porosity, mitigating thermally grown oxide (TGO) growth, enhancing service life, and reducing vulnerability to CMAS infiltration. The review concludes by identifying key research gaps and outlining future directions toward autonomous, adaptive, and sustainable TBC systems enabled by intelligent manufacturing platforms.