Strength prediction and optimization for microwave sintering of large-dimension lithium hydride ceramics: GA-BP-ANN modeling

Abstract

Failure typically occurs during sintering due to high thermal stress and poor strength of LiH ceramics. The short sintering time has shown to be beneficial in preventing excessive grain growth and improving ceramic performance. In this study, we built a genetic algorithm back propagation artificial neural network (GA-BP-ANN) model to predict the strength margins under different work conditions. Sensitivity analysis showed that the thickness and end control time were the most relevant parameters for strength margins, and the GA-BP-ANN model demonstrated the most efficient sintering work condition for a given thickness. Through statistical analysis of the strength margin predicted by the GA-BP-ANN model, we found that the bilinear temperature control method expanded the range of safe sintering conditions by 30% compared to the linear temperature control method. The research results of this study may serve as a reference for the safe and efficient sintering of LiH ceramics.