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Description:

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.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: high-throughput method, Nanoindentation, Image analysis, γ/γ' superalloy, Aging heat treatment

Date published: 2025-06-20

Publisher: Elsevier BV

Journal:

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

Funding:

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1016/j.matdes.2025.114279

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Updated at: 2025-07-01 12:30:19 +0900

Published on MDR: 2025-07-01 12:26:49 +0900