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

基本信息

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

项目摘要

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.

承载结构的安全性取决于工程师在设计阶段预测操作条件下行为的能力。这意味着工程师必须准确了解这些结构中使用的材料的行为。材料的损坏和失效从原子尺度开始，并通过结构长度尺度传播以显现出来。因此，能够基于实验观察和计算模型通过多个尺度预测材料响应非常重要。为了实现这种能力已经付出了很多努力，但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已被证明是棘手的。该奖项旨在通过引入机器学习来优化从不同且广泛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实践。与当前的材料行为预测协议相比，目标是显着提高效率和输出，这也将加速材料发现过程。预计这些成果将为美国工业在广泛的先进材料技术领域（包括与医疗保健、能源和国家安全相关的领域）提供显着的竞争优势。该奖项还将成为在材料科学、材料力学以及数据和信息科学交叉领域培养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工业合作转化为商业实践。建议通过开发和部署一种新颖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多尺度材料现象的物理原理的兴趣。总体策略包括建立合适的高保真降阶（即代理）模型来捕获多相多晶微结构中弹性和塑性变形的局部张量。反过来，这些模型将用于为实验的贝叶斯顺序设计制定计算有效的策略，以识别提供信息（或知识）增益的最高潜力的最佳实验。因此，将设计和评估几种高通量实验测定法，以严格检验它们在复杂塑性理论中可靠校准未知材料参数的价值。基于这些研究的结果，将设计和实施新颖的高通量协议，以证明在异质结构材料的机械行为的多尺度表征中实现了显着的成本和时间节省。具体来说，新协议将使用多晶双相钢样品进行验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

任意拉伸张量下多晶微结构演化的高斯过程自回归模型

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

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

使用非均匀实验数据对氧化物-氧化物陶瓷基复合材料连续损伤模型参数进行贝叶斯校准

DOI：
10.1016/j.mechmat.2022.104487
发表时间：
2022-10-01
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
通讯作者：
S. Kalidindi

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

使用球形压痕应力应变协议和图像分析对热老化钢进行多分辨率研究