Processing time, temperature, and initial chemical composition prediction from materials microstructure by deep network for multiple inputs and fused data
Processing time, temperature, and initial chemical composition prediction from materials microstructure by deep network for multiple inputs and fused data
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DOI：
10.1016/j.matdes.2022.110799
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发表时间：
2022-06-08
影响因子：
8.4
通讯作者：
Mamivand, Mahmood
中科院分区：
文献类型：
--
作者：
Farizhandi, Amir Abbas Kazemzadeh;Mamivand, Mahmood
从微观形貌上预测材料的化学成分和加工历史，有助于材料的反设计。在这项工作中，我们提出了一个融合数据深度学习框架，可以预测微观结构的处理历史。我们用铁铬钴合金作为模型材料。所开发的框架能够通过读取铁分布及其浓度的形态来预测热处理时间、温度和初始化学成分。结果表明，所训练的深度神经网络对化学的预测精度最高，其次是时间和温度。我们确定了两种不准确预测的情况；1)相同的微观组织存在多条路径；2)长时间时效后，微观组织达到稳态形态。误差分析表明，大多数错误的预测确实不是错误的，而是其他正确的答案。我们用Fe-Cr-Co透射电子显微图成功地验证了模型。(c) 2022作者。Elsevier Ltd.出版。这是一篇基于CC by-nc- nd许可证（http://creativecommons.org/licenses/by-nc-nd/4.0/）的开放获取文章。
Prediction of the chemical composition and processing history from microstructure morphology can help in material inverse design. In this work, we propose a fused-data deep learning framework that can predict the processing history of a microstructure. We used the Fe-Cr-Co alloys as a model material. The developed framework is able to predict the heat treatment time, temperature, and initial chemical compositions by reading the morphology of Fe distribution and its concentration. The results show that the trained deep neural network has the highest accuracy for chemistry and then time and temperature. We identified two scenarios for inaccurate predictions; 1) There are several paths for an identical microstructure, 2) Microstructures reach steady-state morphologies after a long time of aging. The error analysis shows that the majority of the wrong predictions are indeed not wrong, but the other right answers. We validated the model successfully with an experimental Fe-Cr-Co transmission electron microscopy micrograph. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).