Abstract
The layered structure of the green parts formed by additive manufacturing considerably influences the densification and sintering process. This work focused on the interlayer microstructure evolution during the sintering of alumina ceramics formed by vat photopolymerization as well as its influencing factors and patterns. A cellular automata (CA) model combined with a back propagation neural network (BPNN) was developed to simulate the sintering process. The BPNN+CA model successfully predicted the porosity with an error of less than 12 % for specific particle size gradations (3 µm/0.5 µm and above) and sintering temperature. The simulated interlayer microstructure evolution was in agreement with the experimental results. The validated model showed that smaller particle size and higher sintering temperature induce stronger sensitivity to the interlayer gap. This simulated system is of great assistance in predicting interlayer microstructure evolution and change of porosity during sintering for ceramics manufactured by vat photopolymerization.
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