Materials informatics approach to understand aluminum alloys
Materials informatics approach to understand aluminum alloys
复制标题
DOI：
10.1080/14686996.2020.1791676
复制
发表时间：
2020-01-31
影响因子：
5.5
通讯作者：
Shoji, Tetsuya
中科院分区：
文献类型：
--
作者：
Tamura, Ryo;Watanabe, Makoto;Shoji, Tetsuya
利用材料信息学技术提取了铝合金力学性能、热处理工艺与元素成分之间的关系。在我们的策略中，机器学习模型首先由准备好的数据库进行训练，以预测材料的特性。预测属性的解释变量，即热处理和元素组成的类型的依赖性，搜索使用马尔可夫链蒙特卡罗方法。从依赖关系，一个因素，以获得所需的性能进行了调查。使用5000、6000和7000系列铝合金的目标，我们提取了通过简单相关分析难以找到的关系。我们的方法也被用来设计一个实验方案，以优化材料的性能，同时促进目标材料的理解。
The relations between the mechanical properties, heat treatment, and compositions of elements in aluminum alloys are extracted by a materials informatics technique. In our strategy, a machine learning model is first trained by a prepared database to predict the properties of materials. The dependence of the predicted properties on explanatory variables, that is, the type of heat treatment and element composition, is searched using a Markov chain Monte Carlo method. From the dependencies, a factor to obtain the desired properties is investigated. Using targets of 5000, 6000, and 7000 series aluminum alloys, we extracted relations that are difficult to find via simple correlation analysis. Our method is also used to design an experimental plan to optimize the materials properties while promoting the understanding of target materials.