Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network
Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network
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DOI：
10.1016/j.commatsci.2023.112110
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发表时间：
2023-04
影响因子：
3.3
通讯作者：
Amir Abbas Kazemzadeh Farizhandi;M. Mamivand
中科院分区：
文献类型：
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作者：
Amir Abbas Kazemzadeh Farizhandi;M. Mamivand
预测材料加工过程中的微观组织演变对控制材料性能至关重要。基于物理概念的微观组织演化预测的模拟工具计算昂贵且耗时。因此，当工艺过程中迫切需要微观结构形态或需要生成大的微观结构数据集时，它们是不实用的。本质上，组织演化预测是一个时空序列预测问题，其中由于不同的工艺历史和化学性质，材料组织的预测是困难的。我们提出了一个预测递归神经网络（PredRNN）模型的微观结构预测，它扩展了LSTM中的记忆状态的内层转换功能的时空记忆流。作为一个案例研究，我们使用的数据集从相场方法创建的FeCrCo合金的亚稳分解模拟训练和预测未来的微观结构由以前的观察。结果表明，训练后的网络预测定量准确的微观组织形态，同时它是几个数量级的速度比相场法。
Prediction of microstructure evolution during material processing is essential to control the material properties. Simulation tools for microstructure evolution prediction based on physical concepts are computationally expensive and time-consuming. Therefore, they are not practical when either there is an urgent need for microstructure morphology during the process or there is a need to generate big microstructure datasets. Essentially, microstructure evolution prediction is a spatiotemporal sequence prediction problem, where the prediction of material microstructure is difficult due to different process histories and chemistry. We propose a Predictive Recurrent Neural Network (PredRNN) model for the microstructure prediction, which extends the inner-layer transition function of memory states in LSTMs to spatiotemporal memory flow. As a case study, we used a dataset from spinodal decomposition simulation of FeCrCo alloy created by the phase-field method for training and predicting future microstructures by previous observations. The results show that the trained network predicts quantitatively accurate microstructure morphologies while it is several orders of magnitude faster than the phase field method.