基于BP神经网络的金属陶瓷TiC-Ni触变成形本构关系模型

Model of Constitutive Relationship for TiC-Ni Based on BP Neural Network

利用Gleeble-2000动态材料热模拟机,对TiC-Ni自蔓延燃烧后的试样进行压缩试验,获得了不同变形温度、不同应变速率和不同真应变下的流动应力数据。结合实验数据和神经网络知识,建立了具有BP算法的人工神经网络,训练结束后的神经网络即成为TiC-Ni金属陶瓷的一个知识基的本构关系模型。误差分析表明,该神经网络本构关系模型具有较高的精度,可为TiC-Ni金属陶瓷反应热压工艺的制定及其热压过程的有限元模拟提供参考。

The compression tests of TiC-Ni alloy were carried out by Gleeble-2000 simulator, and the flow stress data were obtained at different temperature, strain rates and various true strain. Based on the experimental datas and network knowledge, an artificial neural network with back propagation algorithm was established and knowledge based on constitutive relations model was developed after training. Error analysis shows that the artificial neural network model for constitutive relationship has higher predicted precision, and it can be used for guiding the reactive hot press process and applying in finite element simulation of TiC-Ni.