Unlocking the strength: the predictions of silicon carbide fracture toughness revealed through data-driven approach

Abstract

Abstract Silicon carbide ceramics are widely used within various applications, including mechanical, chemical, aerospace and military; where the fracture toughness plays a crucial role. From the processing perspectives, the fracture toughness is controlled by the combination of starting phases and sintering conditions (including additives, atmosphere, temperature and pressure). However, the interplay of these factors makes the forward predictions of fracture toughness untreatable neither through experimentation nor physical modeling; not mention to the reverse estimations of optimal processing parameters. In this work, a data-driven strategy was proposed that firstly to predict the fracture toughness from processing parameters; and then to explore certain parameters that have large impacts on the fracture toughness. From running four different machine learning (ML) algorithms on a well-established dataset of SiC sintering recipe, it was found that the eXtreme Gradient Boosting (XGBoost) model possess the best performance with accuracy up to 88%. Further, the feature importance scores revealed that the sintering temperature and the types of sintering additives show their significant influence on fracture toughness. It was found that the sintering temperature is the most critical factor affecting the obtained fracture toughness of SiC, where the optimum temperature range is of 1800 °C–2000 °C; and also, the sintering additives of Al and Al2O3 have great influences on the obtained fracture toughness, where the optimum range of their mass fraction within the whole additives is 3–8 wt%. Finally, the developed model shows its capability to propose sintering strategy for the preparation of SiC ceramics with target fracture toughness.