Download file (9.78MB)
Download as zip (9.18MB)

コレクション

引用

説明:

We present an automated high-throughput method capable of gathering 2400 data points relating processing conditions, microstructure geometry and yield strength in just 13 days. An estimated 200 times faster than conventional methods using tensile testing specimens, a complete Process-Structure-Property (P-S-P) dataset is created from a single sample. The method is demonstrated by example of the aging heat treatment process of a γ/γ′ superalloy. By aging the sample in a temperature gradient, a wide range of aging process temperatures is mapped over the sample length. Structure analysis consists of fully automated, nanometer-resolution FE-SEM scanning, with precipitate fraction, size and shape distributions determined by automatic image analysis using the Python programming language. Mechanical properties are evaluated by nanoindentation inverse analysis, an approach combining instrumented indentation data with pile-up analysis to calculate stress/strain curves. While the neces sary topographic data is typically acquired using atomic force microscopy, a significant speedup was achieved by automatic indent detection and scanning using Angular selective Backscatter FE-SEM analysis. As a method to rapidly assemble comprehensive and consistent P-S-P datasets, we expect it to facilitate efficient alloy design, given a vast majority of modeling approaches still heavily rely on empirical data.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: high-throughput method, Nanoindentation, Image analysis, γ/γ' superalloy, Aging heat treatment

刊行年月日: 2025-06-20

出版者: Elsevier BV

掲載誌:

• Materials & Design (ISSN: 02641275) vol. 256 114279

研究助成金:

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1016/j.matdes.2025.114279

関連資料:

その他の識別子:

連絡先:

更新時刻: 2025-07-01 12:30:19 +0900

MDRでの公開時刻: 2025-07-01 12:26:49 +0900