Machine Learning-Driven Creep Law Discovery Across Alloy Compositional Space

2026-01-13

AI Summary

This study addresses the inefficiency of conventional high-temperature creep testing, which hinders high-throughput discovery across broad alloy composition spaces. By integrating a dimple array bulge inflation (DABI) apparatus with 3D digital image correlation, the work enables parallel creep deformation measurements for multiple alloys. A phenomenological creep law incorporating a time-dependent stress exponent is proposed to capture the sigmoidal characteristics of primary creep, and a unified constitutive model synthesizing multiple classical forms is developed. Leveraging a recurrent neural network surrogate model combined with particle swarm optimization and sparse regularization inversion, the framework automatically identifies the dominant constitutive form and creep parameters for each alloy. The approach is successfully demonstrated on INCONEL 625 and 47 Fe/Ni/Co-based alloys, establishing a high-throughput creep characterization platform that synergistically combines data-driven learning with mechanistic constraints.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Search and Optimization: Algorithm Configuration
• Machine Learning: Feature Construction/Reformulation

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Hihg-temperature creep characterization of structural alloys traditionally relies on serial uniaxial tests, which are highly inefficient for exploring the large search space of alloy compositions and for material discovery. Here, we introduce a machine-learning-assisted, high-throughput framework for creep law identification based on a dimple array bulge instrument (DABI) configuration, which enables parallel creep testing of 25 dimples, each fabricated from a different alloy, in a single experiment. Full-field surface displacements of dimples undergoing time-dependent creep-induced bulging under inert gas pressure are measured by 3D digital image correlation. We train a recurrent neural network (RNN) as a surrogate model, mapping creep parameters and loading conditions to the time-dependent deformation response of DABI. Coupling this surrogate with a particle swarm optimization scheme enables rapid and global inverse identification with sparsity regularization of creep parameters from experiment displacement-time histories. In addition, we propose a phenomenological creep law with a time-dependent stress exponent that captures the sigmoidal primary creep observed in wrought INCONEL 625 and extracts its temperature dependence from DABI test at multiple temperatures. Furthermore, we employ a general creep law combining several conventional forms together with regularized inversion to identify the creep laws for 47 additional Fe-, Ni-, and Co-rich alloys and to automatically select the dominant functional form for each alloy. This workflow combined with DABI experiment provides a quantitative, high-throughput creep characterization platform that is compatible with data mining, composition-property modeling, and nonlinear structural optimization with creep behavior across a large alloy design space.

Problem

• creep characterization
• alloy design space
• high-throughput testing
• material discovery
• compositional space

Innovation

• machine learning
• high-throughput creep testing
• recurrent neural network
• inverse parameter identification
• time-dependent creep law