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arXiv 2601.08970cond-mat.mtrl-scics.LG
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基于机器学习的合金成分空间中的蠕变定律发现

Machine Learning-Driven Creep Law Discovery Across Alloy Compositional Space

Northwestern University(西北大学)

Hongshun Chen, Ryan Zhou, Rujing Zha, Zihan Chen, Wenpan Li, Rowan Rolark, John Patrick Reidy, Jian Cao, Ping Guo, David C. Dunand, Horacio D. Espinosa

AI总结:

本文提出了一种基于机器学习的高通量框架,用于在合金成分空间中发现蠕变定律,结合DABI实验实现高效、高精度的蠕变特性表征。

摘要:

高温下的结构合金蠕变特性传统上依赖于串行单轴试验,这在探索合金成分的广阔搜索空间和材料发现方面效率极低。本文介绍了一种基于凹坑数组鼓胀仪器(DABI)配置的机器学习辅助、高通量框架,用于蠕变定律的识别,该框架能够在单次实验中并行测试25个由不同合金制成的凹坑。通过3D数字图像相关技术测量凹坑在惰性气体压力下随时间变化的蠕变鼓胀的全场表面位移。我们训练了一个循环神经网络(RNN)作为替代模型,将蠕变参数和加载条件映射到DABI的时间依赖变形响应。将此替代模型与粒子群优化方案结合,能够快速且全局地逆向识别蠕变参数,通过稀疏正则化从实验位移-时间历史中提取。此外,我们提出了一种现象学蠕变定律,具有时间依赖的应力指数,能够捕捉在锻造INCONEL 625中观察到的S型初级蠕变,并从DABI测试在多个温度下的数据中提取其温度依赖性。此外,我们采用一种通用的蠕变定律,结合了几种传统形式,并利用正则化反演来确定47种额外的铁、镍和钴丰富的合金的蠕变定律,并自动选择每种合金的主导功能形式。这种工作流程结合DABI实验提供了一个定量、高通量的蠕变特性表征平台,该平台兼容数据挖掘、成分-性质建模和非线性结构优化,适用于在广阔合金设计空间中的蠕变行为。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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