锻压技术

神经网络小冲杆试验法确定材料的蠕变性能

英文标题：Identification of creep property by small punch creep test with neural networks

作者：许颖恒关凯书冯相赛

单位：华东理工大学

分类号：TB31

出版年,卷(期):页码：2016,41(3):116-122

摘要：

针对小冲杆试验提出了一种结合有限元模拟和神经网络来确定P91材料蠕变性质的方法，用小冲杆测试确定载荷条件下的材料状态。这是神经网络在小冲杆蠕变试验领域的拓展，其有限元模型采用基于Chakrabarty薄膜伸张理论的Norton蠕变准则模型。根据Norton蠕变法则，通过有限元得出一个由不同的蠕变性能参数组模拟获得的时间-位移曲线数据库。然后将小冲杆试验得到的一个时间位移曲线转化到训练好的人工神经网络，将模拟曲线数据库中的曲线与实验曲线进行对比，确定出与实际试验曲线最为吻合的模拟曲线，该条模拟曲线所对应的蠕变性能参数即为材料的蠕变性能参数。最后通过试验对反向方法得出的结果进行了验证。

For small punch creep test, the creep performance of material P91 was determined by combining the finite element simulations with neural network. The material state was confirmed by the small punch creep test under a certain load. Neural networks were applied and expanded in the field of small punch creep test. The Norton creep model was used in finite element simulation based on Chakrabarty membrane stretch theory. According to Norton creep law, the database of time-displacement curves was obtained by numerical simulation under the different creep performance parameters. Then, the time-displacement curves were transferred to a trained neural network, and compared by the experiment curves. Therefore, simulation curves corresponding to experimental curve were given and regarded as the creep performance parameters of material. At last, the results obtained by the reverse method were validated through the small punch creep test.

基金项目：

质检公益性行业科研专项经费资助项目（201510070）；高等学校博士学科点专项科研基金资助项目（20120074110013）

许颖恒（1989-），女，硕士研究生通讯作者：关凯书（1962-），男，博士，教授，博士生导师

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