The anti‐oxidation mechanism of SiC _f /SiC‐B ₄ C modified with Al ₂ O ₃ in wet atmosphere based on machine learning

To study the anti‐oxidation mechanism of SiC f /SiC–B 4 C modified with Al 2 O 3 in wet atmosphere, the damage evolution of composites after oxidation was explored by unsupervised machine learning technology ( k ‐means). Results display that the mean feature values of cluster‐1 (some small cracks in oxidation layer and matrix as well as fiber debonding) in composites modified with Al 2 O 3 are larger than that in virgin after oxidation. Meanwhile, as the oxidation time increases, the concentrated area of cluster‐2 (fiber breakage, cracks in axial and transverse yarns) in both composites gradually shifts toward the direction of high mean feature values. Because Al 2 O 3 can protect the BN from oxidation, the increase of peak frequency and risetime of composites modified with Al 2 O 3 is less than that of virgin. Moreover, the proportion of cluster‐3 (big transverse matrix cracks) in composites modified with Al 2 O 3 is always more than that of virgin. Based on the research results, the corresponding relationship between each damage behavior and each cluster is thoroughly built.