Machine learning guided design and ablation behavior of ZrC-TaC-SiC ternary coatings

Ultra-high-temperature ceramic coatings are critical for thermal protection systems in hypersonic vehicles. However, their compositional optimization is constrained by the inefficiency of traditional trial-and-error method. In this study, a machine learning-driven design strategy is proposed for the ZrC-TaC-SiC ternary system (denoted ZnTmSt, n, m, t = 0–100) to enhance ablation resistance. A random forest regression model was constructed using 170 literature data to predict surface ablation temperature, mass ablation rate, and linear ablation rate across the full compositional space of the ZrC-TaC-SiC ternary system. Four optimized formulations (Z60T30S10, Z60T10S30, Z80T15S5, Z80T5S15), each exhibiting stable mass and linear ablation rates in the order of 10⁻⁴ g/s and 10⁻⁴ mm/s, respectively, were selected through the model and fabricated via supersonic atmospheric plasma spraying. Oxyacetylene ablation test results confirmed the reliability of the model, with prediction errors for linear ablation rate within the order of 10⁻⁵ mm/s. Microstructural characterization revealed that high-TaC content (15–30 wt%) led to excessive formation of Zr₆Ta₂O₁₇ during ablation, resulting in reduced thermal stability of the oxide scale and discontinuities in structure. Moreover, non-uniform sintering caused an increase in porosity. Interestingly, the low-TaC (5 wt%) Z80T5S15 sample achieved a dense and continuous oxide layer by a proper Zr/Ta content ratio in oxide phase, exhibiting excellent ablation resistance. This study establishes a machine-learning–assisted strategy combined with experimental validation, which can provide a new paradigm for intelligent design and performance prediction of ultra-high-temperature ceramic coatings.