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超塑性材料在变形过程中往往空洞化。空洞的存在严重降低超塑成形零件的室温使用性能，因此必须建立超塑变形后材料室温机械性能变化的理论预测模型。本文以铝合金LY12CZ为例，以实验数据为基础，利用人工神经网络首次建立了预测经超塑变形后的材料室温机械性能变化的理论模型。所建模型不但可以预测铝合金LY12CZ超塑变形后的刚度．强度以及韧性等室温性能指标，而且亦能充分反映超塑变形工艺参数对其室温机械性能变化的影响规律。同时，由于本文建模方法具有通用性，因此，该模型的建立为超塑成形零件的使用性能提供了理论依据和一般方法。
Superplastic materials are often hollow in the deformation process. The presence of voids seriously degrades the room temperature performance of the superplastic formed parts. Therefore, a theoretical prediction model of the mechanical properties of the material after the superplastic deformation must be established. Taking aluminum alloy LY12CZ as an example, based on the experimental data, a theoretical model for predicting the change of mechanical properties at room temperature after superplastic deformation was established for the first time by using artificial neural network. The model can not only predict the stiffness of LY12CZ superplastic deformation of aluminum alloy. Strength and toughness room temperature performance indicators, but also can fully reflect the superplastic deformation process parameters of the mechanical properties at room temperature changes. At the same time, due to the versatility of the modeling method, the establishment of the model provides theoretical basis and general method for the performance of the superplastic forming parts.