Machine learning in Al ₂ TiO ₅ flexible ceramics with microcrcaks strengthened for damage mode automatic identification

Abstract The damage modes of Al 2 TiO 5 flexible ceramics (AT) with microcracks strengthened were studied by machine leaning for damage mode identification. Results show that the optimal number of clusters increases from 3 to 4 with the increase of ZrO 2 content. Meanwhile, the concentrated region of cluster with the same label gradually shifts to the direction of low amplitude with increase of ZrO 2 . In addition, the quantity of cluster is easy saturated since the AT grain boundary microcracks are strengthened by the formation of ZrTiO 4 . For achieving the automatic identification of different damage modes, support vector machine based on Kennard‐stone training set selection was used to distinguish the damage modes of other samples in the same sample set. Almost the same evolution trend of corresponding clusters in new samples and training samples proves high efficiencies of this method.