链式神经网络在Mg2SixSn1−x Spinodal分解过程-结构映射学习中的应用

IF 2.4 3区材料科学 Q3 ENGINEERING, MANUFACTURING

Integrating Materials and Manufacturing Innovation Pub Date : 2022-09-01 DOI:10.1007/s40192-022-00274-3

Grayson H. Harrington, Conlain Kelly, V. Attari, R. Arróyave, S. Kalidindi

引用次数: 3

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Application of a Chained-ANN for Learning the Process–Structure Mapping in Mg2SixSn1−x Spinodal Decomposition

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Application of a Chained-ANN for Learning the Process–Structure Mapping in Mg2SixSn1−x Spinodal Decomposition

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来源期刊

Integrating Materials and Manufacturing Innovation Engineering-Industrial and Manufacturing Engineering

CiteScore

5.30

自引率

9.10%

发文量

审稿时长

39 days

期刊介绍： The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.