Simulation of Temperature Distribution during Ceramic Sintering Based on BP Neural Network Technology

Abstract

It is difficult to calculate with finite element method or actually measure the temperature distribution of the ceramic sintering at irregular sintering curve. Trained by the temperature distribution data of ceramic sintering analyzed with ANSYS under linear sintering curves including different slopes, the neural network can be used to simulate that under irregular sintering curve at certain precision, so the temperature evolution of the ceramic hot geometry centroidal point (HGCP) can be fast obtained by the result simulated with the trained neural network. This research sets up series-parallel BP neural network model with MATLAB. The ceramic sintering date analyzed with ANSYS under linear sintering curves with ten different slopes is used as training sample of the neural network which is tested by the sample under non-linear sintering curve. The result indicates that the BP neural network is feasible for simulating temperature distribution of the ceramic sintering.