The learning of the precipitates morphological parameters from the composition of Nickel-based superalloys
发布时间：2021-04-16
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影响因子：6.289
发表刊物：材料设计（Materials Design）
关键字：Machine learning, precipitated-phase characteristics, Ni-based Superalloy
摘要：It becomes a common practice to adopt high-throughput experiments on superalloys, which can generate a large amount of data. To address this large amount of data, we designed a machine learning (ML) based model to automate the experimental analysis process. More specifically, we adopted the Unet algorithm to segment the precipitated phases from superalloy images and subsequently used a regression algorithm to predict the morphological parameters of the microstructure of the segmented precipitated phases according to their composition. The method proposed in this work may provide guidance for the future design of the superalloy composition.
合写作者：刘锦, Zi Wang, 鲁鸣鸣
第一作者：王运强
论文类型：期刊论文
通讯作者：王建新
是否译文：否
发表时间：2021-04-15
收录刊物：SCI