A Machine Learning Framework for Bridging the Mechanical Responses of a Material at Multiple Structure Length Scales

用于桥接材料在多个结构长度尺度上的机械响应的机器学习框架

• 批准号: 2027105
• 负责人: Surya Kalidindi
• 金额: $ 50万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027105&HistoricalAwards=false
• 关键词: Machine; Learning; Framework; Bridging; Mechanical

The safety of structures bearing load depends upon the ability of the engineers to predict the behavior under operating conditions during the design phase. This means that the engineers must know the behavior of materials that are used in these structures accurately. Damage and failure in materials start at the atomic scale and propagate through the structural length scales to manifest itself. Therefore, it is important to be able to predict material response through multiple scales based on experimental observations and computational modeling. Much effort has been put towards achieving this capability but the immense amount of information that is obtained from advanced experimental techniques and physics-based numerical simulations has proven intractable. This award aims to transform the current practices in the field of mechanics of materials by introducing machine learning to optimize the extraction and fusion of information and knowledge from the disparate and expansive experimental and numerical datasets. The goal is to dramatically improve the efficiency and output compared to the current protocols of material behavior prediction, which will also accelerate the materials discovery process. It is expected that theses outcomes will provide significant competitive advantages to the US industry in a broad range of advanced materials technology areas, including those related to healthcare, energy, and national security. The award will also be a vehicle to train graduate and undergraduate students at the intersection of materials science, mechanics of materials, and data and information sciences. The research outcomes and developed tools will be transferred into commercial practice through multiple industrial collaborations.It is proposed to accomplish the research objective described above by developing and deploying a novel Bayesian machine learning framework that is centered on systematically uncovering the physics controlling the multiscale materials phenomena of interest. The overall strategy involves establishing suitable high-fidelity reduced-order (i.e., surrogate) models to capture the localization tensors for elastic and plastic deformations in multiphase polycrystalline microstructures. In turn, these models will be used to formulate a computationally efficient strategy for Bayesian sequential design of experiments that identifies the most optimal experiments offering the highest potential for information (or knowledge) gain. As a result, several high-throughput experimental assays will be designed and evaluated to critically examine their value for reliably calibrating the unknown material parameters in sophisticated plasticity theories. Based on the results of these investigations, novel high-throughput protocols will be designed and implemented to demonstrate the significant cost and time savings achieved in the multiscale characterization of the mechanical behavior of heterogeneous structural materials. Specifically, the new protocols will be validated using samples of polycrystalline dual-phase steels.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

承重结构的安全性取决于工程师在设计阶段对运行条件下的行为进行预测的能力。这意味着工程师必须准确地了解这些结构中使用的材料的性能。材料中的损伤和失效始于原子尺度，并通过结构长度尺度传播，从而显现出来。因此，能够在实验观测和计算模型的基础上通过多个尺度来预测材料响应是很重要的。人们为实现这一能力付出了大量努力，但事实证明，从先进的实验技术和基于物理的数值模拟中获得的大量信息是难以处理的。该奖项旨在通过引入机器学习来优化从完全不同和庞大的实验和数值数据集中提取和融合信息和知识，从而改变材料力学领域的当前做法。其目标是与当前的材料行为预测协议相比，显著提高效率和产量，这也将加速材料发现过程。预计这些成果将在广泛的先进材料技术领域为美国行业提供显著的竞争优势，包括与医疗保健、能源和国家安全相关的领域。该奖项还将成为培养材料科学、材料力学以及数据和信息科学交叉领域的研究生和本科生的工具。研究成果和开发的工具将通过多个行业合作转化为商业实践。建议通过开发和部署一种新型的贝叶斯机器学习框架来实现上述研究目标，该框架的核心是系统地揭示控制感兴趣的多尺度材料现象的物理。总体策略包括建立合适的高保真降阶(即替代)模型来捕捉多相多晶微结构中弹性和塑性变形的局域化张量。反过来，这些模型将被用于制定贝叶斯序贯试验设计的计算效率策略，以确定提供最大潜在信息(或知识)收益的最优试验。因此，将设计和评估几种高通量实验分析，以严格检验它们对于可靠地校准复杂塑性理论中未知材料参数的价值。基于这些研究的结果，将设计和实施新的高通量协议，以证明在多尺度表征非均质结构材料的力学行为方面所实现的显著成本和时间节约。具体地说，新协议将使用多晶双相钢的样本进行验证。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Development of a Robust CNN Model for Capturing Microstructure-Property Linkages and Building Property Closures Supporting Material Design

• DOI: 10.3389/fmats.2022.851085
• 发表时间: 2022-03-11
• 期刊: FRONTIERS IN MATERIALS
• 影响因子: 3.2
• 作者: Mann, Andrew;Kalidindi, Surya R.
• 通讯作者: Kalidindi, Surya R.

Recurrent localization networks applied to the Lippmann-Schwinger equation

应用于 Lippmann-Schwinger 方程的循环定位网络

• DOI: 10.1016/j.commatsci.2021.110356
• 发表时间: 2021
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Kelly, Conlain;Kalidindi, Surya R.
• 通讯作者: Kalidindi, Surya R.

Bayesian calibration of continuum damage model parameters for an oxide-oxide ceramic matrix composite using inhomogeneous experimental data

• DOI: 10.1016/j.mechmat.2022.104487
• 发表时间: 2022-10
• 期刊: Mechanics of Materials
• 影响因子: 3.9
• 作者: Adam P. Generale;R. Hall;R. Brockman;V. R. Joseph;G. Jefferson;L. Zawada;J. Pierce;S. Kalidindi

Gaussian process autoregression models for the evolution of polycrystalline microstructures subjected to arbitrary stretching tensors

• DOI: 10.1016/j.ijplas.2023.103532
• 发表时间: 2023-01
• 期刊: International Journal of Plasticity
• 影响因子: 9.8
• 作者: S. Hashemi;S. Kalidindi

Multiresolution investigations of thermally aged steels using spherical indentation stress-strain protocols and image analysis

• DOI: 10.1016/j.mechmat.2022.104265
• 发表时间: 2022-02
• 期刊: Mechanics of Materials
• 影响因子: 3.9
• 作者: Almambet Iskakov;S. Kalidindi