Deep learning revealed statistics of the MgO particles dissolution rate in a CaO-Al₂O₃-SiO₂-MgO slag.

ABSTRACT: Accelerated material development for refractory ceramics triggers possibilities in context to enhanced energy efficiency for industrial processes. Here, the gathering of comprehensive material data is essential. High temperature-confocal laser scanning microscopy (HT-CLSM) displays a highly suitable in-situ method to study the underlying dissolution kinetics in the slag over time. A major drawback concerns the efficient and accurate processing of the collected image data. Here, we introduce an attention encoder-decoder convolutional neural network enabling the fully automated evaluation of the particle dissolution rate with a precision of 99.1%. The presented approach provides accurate and efficient analysis capabilities with high statistical gain and is highly resilient to image quality c

SUBMITTER: Chamasemani FF 

PROVIDER: S-EPMC11390962 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature