HT-DABI: High-Throughput Extraction of Creep and Static Material Properties of Metallic Alloys via Dimple Array Bulging

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Authors: Rujing Zha, Hongshun Chen, Zihan Chen, Malachi Landis, Wenpan Li, Garrett Mathesen, John Patrick Reidy, Rowan Rolark, Itzel Salgado, Deepak Sharma, Sanjana Subramaniam, Ryan Zhou
+9 more Rick Weber, Jared Rafferty, Stephen K. Wilke, Scott Oppenheimer, Horacio D. Espinosa, David C. Dunand, Ian McCue, Ping Guo, Jian Cao
Cited by: 1
References: 36

Abstract

To facilitate data-driven composition-structure-property models suitable for various mechanical and thermal loading conditions, high-throughput material testing methods are highly desirable. In this work, we develop the High-Temperature Dimple Array Bulge Instrument (HT-DABI), a novel materials testing instrument that uses a 5×5 array of induction-heated dimple cells which are inert-gas pressurized to achieve a heterogeneous yet readily resolvable stress condition. Deformation data of the 25 independent cells are measured simultaneously using high-temperature 3D digital image correlation; then, finite-element coupled optimization frameworks extract the elastoplastic and creep properties from the displacement and pressure data. HT-DABI can conduct high-temperature creep experiments parallelized across stress levels, temperatures, and material compositions, as well as elevated-temperature static tensile experiments parallelized across compositions and temperatures. We validate the approach using a wrought IN625 plate and show that this approach can accurately determine the creep exponent and activation energy within 10% of the ground-truth value, at 1/200th of the testing time needed for 25 materials, and the yield stress to within 15%. The DABI system paves the way for a unified component design-testing framework, in which experiments dynamically sample locations in composition space or test interfacial material behavior requested by the optimization framework for rapid co-design of part geometry and composition in a component fabricated from a functionally graded material.

Fields of Study & Subjects

High Temperature Alloys and Creep Additive Manufacturing Materials and Processes Machine Learning in Materials Science

Publication details

Journal / source
SSRN Electronic Journal
Publisher
Elsevier BV
Publication date
1 January 2026
Document type
Preprint