Study on cross-material property prediction of mineralizer fiber ceramic core materials based on deep learning
Source abstract
Abstract
In this study, silicon-based ceramic cores were prepared using aluminum silicate fiber and mullite fiber as mineralizing agents through injection molding. A comprehensive properties evaluation model was proposed, optimizing the weight coefficient. The back-propagation (BP) neural networks were employed to predict the effects of different mineralizers on ceramic core properties, establishing a cross-material property mapping and prediction model. The results showed an abnormal increase in mean square error with the addition of 1 wt.% aluminum silicate fiber, indicating complex material behavior. Blending model predictions demonstrated the neural network’s strong capability in cross-material property prediction. The predictions closely matched experimental results, confirming the model’s accuracy.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced ceramic materials synthesis · Recycling and utilization of industrial and municipal waste in materials production · Aerogels and thermal insulation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Advanced Special Steel, Shanghai University
Shanghai Key Laboratory of Advanced Ferrometallurgy, Shanghai University
School of Materials Science and Engineering, Shanghai University
References (39)
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