Collaborative Research: AI-Driven Multi-Scale Design of Materials under Processing Constraints

协作研究：人工智能驱动的加工约束下材料的多尺度设计

• 批准号: 2053840
• 负责人: Pinar Acar
• 金额: $ 27.24万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053840&HistoricalAwards=false
• 关键词: Collaborative; Research; AI; Driven; Multi

The objective of this project is to improve the knowledge of materials design by developing a multi-scale methodology that combines physics-based models of thermo-mechanical processing and materials with artificial intelligence (AI) and machine learning (ML). The underlying hypothesis is that the metallic components can be designed to achieve targeted macro-scale properties and performance by optimizing the underlying microstructural features and processing parameters. The project will build design methodology that enables: (i) investigation of the effects of microstructures and processing parameters on macro-scale properties; and (ii) identification of multiple optimum material designs that provide desired macro-scale performance. The ability to optimize macro-scale properties by designing microstructures and processes will improve the performance of current and future engineering systems. Additionally, with the consideration of manufacturing constraints, this multi-scale design framework will not merely identify mathematical solutions, but the designs that will be manufacturable. The researched methods and results will be tested against the experimental data of a Titanium-Aluminum alloy. The societal impacts of the project will be on the economy, with performance improvement in metallic components and minimization of the time and costs associated with manufacturing. The gained knowledge will be disseminated to academia and industry with technical activities and open-access software tools. Additional deliverables of the project include curriculum development at both undergraduate and graduate levels, research and education experiences for students, and other outreach activities involving students and educators with a special focus on individuals from underrepresented groups.The overarching goal of this project is to advance knowledge in the design of metallic materials by developing a multi-scale optimization strategy that will be driven by the physics-based models of thermo-mechanical processing and microstructures, and AI/ML-based predictive modeling and knowledge discovery approaches. The research will address the inverse design problem that aims to optimize the thermo-mechanical processing parameters (i.e., strain rate, temperature, duration) to achieve desired microstructural features (i.e., crystallographic texture, grain morphology) and macro-scale properties by investigating the coupled, multi-scale, and high-dimensional interactions within the processing-(micro)structure-property chain. To achieve this goal, the project will develop physics-based models that enable explicit quantification of microstructural orientations and morphology (grain sizes and shapes), and an ML-guided feedback-aware identification strategy for key processing/(micro)-structure parameters, which will be subsequently explored by targeted sampling. The research will improve the understanding of inverse materials design by also integrating manufacturing constraints into the design framework and exploring multiple optimum material solutions that provide desired macro-scale properties. The physics-based and AI/ML-driven models, as well as the optimum results obtained by the multi-scale design framework, will be validated using the experimental processing, microstructure, and property data of a Titanium-Aluminum alloy. The education and outreach objectives of the project focus on training students and the future workforce to create new knowledge on computational and ML-driven design of materials, which will be supported with curriculum development and an extensive dissemination and outreach plan.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.

该项目的目的是通过开发一种多尺度方法来提高材料设计的知识，该方法将基于物理的热机械处理和材料与人工智能（AI）和机器学习（ML）结合起来。潜在的假设是，可以通过优化基础微观结构特征和处理参数来设计金属组件来实现目标宏观尺度属性和性能。该项目将构建设计方法，以：（i）研究微观结构和处理参数对宏观级属性的影响； （ii）识别提供所需宏观性能的多种最佳材料设计。通过设计微观结构和流程来优化宏观规模属性的能力将改善当前和未来工程系统的性能。此外，考虑到制造限制，这个多规模的设计框架不仅会识别数学解决方案，而且还可以确定可以制造的设计。研究的方法和结果将根据钛 - 铝合金的实验数据进行测试。该项目的社会影响将对经济产生影响，金属组件的绩效提高以及与制造业相关的时间和成本的最小化。获得的知识将通过技术活动和开放式软件工具传播到学术界和行业。该项目的其他可交付成果包括在本科和研究生水平的课程开发，为学生提供的研究和教育经验，以及其他涉及学生和教育工作者的外展活动，特别关注来自代表性不足的小组的个人，该项目的总体目标是通过开发由Metal-SCALE型号的MIROCH型号和TORMAIN-MAINGER DERMICTIANS驱动的Mrich-Mochan Modical demons-Mochan Mody-Mochan型号来促进的知识，以促进Meter-Mechan型号的设计，并了解TORMON MAINDOR的设计，并了解TORMOR-MACHIN的模型。基于AI/ML的预测建模和知识发现方法。这项研究将解决旨在优化热机械处理参数（即应变率，温度，持续时间）以实现所需的微观结构特征（即晶体学纹理，谷物形态）和宏观尺度特性来实现所需的微观结构特征（即，通过研究辅助的多数尺度和高度相互作用）的结构（MICCRATER）（MICCRAPTRATE）（MICCRAPTRATE）（MICCRAPTRATE）（MICCRAPTRAPTRAPTRAPTRAPTRAPTRAPTRAPTION），该研究将解决多个尺度和高度相互作用，以实现所需的微观结构特征（即应变率，温度，即，构造）（MICCRAPTRATER），该研究将解决所需的微结构特征（即应变率，温度，持续时间）。为了实现这一目标，该项目将开发基于物理的模型，以明确量化微观结构取向和形态（晶粒尺寸和形状），以及ML引导的反馈感知识别策略（密钥处理/（Micro）结构参数），该策略将通过有针对性的采样来探索。这项研究将通过将制造约束纳入设计框架并探索提供所需的宏观尺度属性的多种最佳材料解决方案，从而提高对材料设计的理解。基于物理学的和AI/ML驱动的模型以及由多尺度设计框架获得的最佳结果，将使用实验处理，微结构和钛合金合金的属性数据进行验证。该项目的教育和外向目标集中在培训学生和未来的劳动力上，以创建有关材料的计算和ML驱动设计的新知识，这将得到课程开发以及广泛的传播和外展计划的支持。该奖项反映了NSF的法规任务，并被认为是通过基金会的知识优点和广泛的Criter criter scritia criter criter criter criter criter criter criter criter criter criter criter criter criter criter criter criter criter criter criter criteria criter criter criteria criter criteria criteria criteria criter criteria均值得一提。

项目成果

New Methodologies for Grain Boundary Detection in EBSD Data of Microstructures

微观结构 EBSD 数据中晶界检测的新方法

• DOI: 10.2514/6.2022-1424
• 发表时间: 2021
• 期刊: New Methodologies for Grain Boundary Detection in EBSD Data of Microstructures
• 作者: Catania, Richard K.;Senthilnathan, Arulmurugan;Sions, John;Snyder, Kyle;Al-Ghaib, Huda;Zimmerman, Ben;Acar, Pinar
• 通讯作者: Acar, Pinar

Microstructure-Sensitive Material Design with Physics-Informed Neural Networks

• DOI: 10.2514/6.2023-0539
• 发表时间: 2023-01
• 期刊: AIAA SCITECH 2023 Forum
• 作者: Md Mahmudul Hasan;Zekeriya Ender Eğer;Arulmurugan Senthilnathan;P. Acar

Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures

• DOI: 10.1007/s40192-022-00258-3
• 发表时间: 2022-04-06
• 期刊: INTEGRATING MATERIALS AND MANUFACTURING INNOVATION
• 影响因子: 3.3
• 作者: Hasan, M.;Mao, Y.;Acar, P.
• 通讯作者: Acar, P.

Parameter Space Exploration of Cellular Mechanical Metamaterials Using Genetic Algorithms

• DOI: 10.2514/1.j062864
• 发表时间: 2023-06
• 期刊: AIAA Journal
• 影响因子: 2.5
• 作者: Sheng Liu;P. Acar