The intrinsic brittleness and high defect sensitivity of ceramics result in significant strength variability, presenting substantial challenges for structural reliability assessments. Experimental characterization of strength scatter in ceramic components is both time-consuming and costly. Conventional physics-based (forward) analyses can model strength scatter based on microstructural data; these methods are computationally intensive. This study introduces a deep learning – based surrogate model that directly predicts the Weibull distribution parameters of ceramic bending strength from equivalent crack length distributions, achieving substantial reductions in computational cost without compromising predictive accuracy. Additionally, an inverse analysis framework is developed by integrating the surrogate model with intelligent swarm optimization, enabling the estimation of defect distributions from reference strength measurements. The proposed approach demonstrates high accuracy and efficiency, achieving over 200-fold speedup in forward analysis and 18,000-fold in inverse analysis. It facilitates a rapid and reliable evaluation of the relationship between defect distributions and strength scatter as characterized by Weibull parameters. This methodology provides a robust tool for accelerating the design and development of high-performance ceramic materials.
Maeda et al. (Thu,) studied this question.