Comprehensive Review of Hard Ceramic Coatings for Aerospace Alloys: Fabrication, Characterization, and Future Perspectives

Hard ceramic coatings are essential for extending the operational limits of metal components in the extreme thermal and mechanical conditions of the aerospace and defense sectors. While considerable research exists on individual synthesis and characterization methods, a critical knowledge gap persists in bridging experimental fabrication with predictive computational modeling, a gap that limits the rational design of next-generation coating systems. This review addresses this gap by critically synthesizing the lifecycle of aerospace coatings from atomic-scale design to industrial deployment. Unlike prior reviews that focus on either fabrication or individual coating chemistries in isolation, this work uniquely integrates Integrated Computational Materials Engineering (ICME) with emerging machine learning (ML) strategies to provide a unified design-to-deployment framework. The principal ceramic material systems, Tungsten Carbide (WC), Boron Nitride (BN), Boron Carbide (B₄C), Silicon Carbide (SiC), Alumina (Al₂O₃), and Zirconia (ZrO₂) are discussed within the context of their specific roles in protecting aerospace-grade alloys. A central contribution is the multiscale computational framework, spanning Density Functional Theory (DFT), Molecular Dynamics (MD), mesoscale modelling, Finite Element Analysis (FEA), and ML-driven inverse design, which collectively accelerate the prediction of thermal breakdown, multi-axial stress responses, and coating lifetime. By relating these advances to gas turbine engines, airframes, and supersonic and hypersonic aviation systems, this review offers a clear research roadmap. Future research should prioritize the development of ultra-high-temperature ceramics (UHTCs), multifunctional self-healing coatings, and data-driven approaches to surface engineering. The goal is to move the field beyond traditional trial-and-error methods toward a more predictive framework based on fundamental physics and accelerated by machine learning techniques.