本文提出可利用粘塑性理论解决超塑性变形的力学问题。
The viscoplasticity theory is suggested to be available to analyze the mechanical behavior of superplastic deformation.
通过压缩试验，研究了供应态h 62黄铜棒材所表现出的超塑性变形的力学特性。
The mechanical behavior of commercial brass H62 bar under superplastic deformation was investigated by compression testing.
结果表明，BP神经网络用于材料超塑性变形后的力学性能及晶粒尺寸预测是可行的，其预测误差小于7%。
The results show that BP artificial neural network can be used in predicting mechanical properties and grain size of materials after superplastic forming and its predicting error is less than 7%.
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