CAREER: Design of Cellular Mechanical Metamaterials under Uncertainty with Physics-Informed and Data-Driven Machine Learning

职业：利用物理信息和数据驱动的机器学习在不确定性下设计细胞机械超材料

• 批准号: 2236947
• 负责人: Pinar Acar
• 金额: $ 54.94万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236947&HistoricalAwards=false
• 关键词: CAREER; Design; Cellular; Mechanical; Metamaterials

The objective of this CAREER project is to engage and educate graduate and undergraduate students in materials design, with a particular focus on designing cellular mechanical metamaterials (CMMs) under the effects of fabrication-related material uncertainty. The plan includes physics-based computations, machine learning (ML), and design under uncertainty strategies, as well as the development of outreach activities. The underlying hypothesis is that the CMMs can be designed to achieve targeted mechanical properties and performance by developing a multi-scale computational framework that investigates the relationship between component-scale properties and underlying micro-scale architectures. The societal impacts of the project will be on the economy, with the promise of designing sustainable, lightweight, and high-performance materials. The gained knowledge will be disseminated to academia and industry through technical activities and open-access graphical software tools. Additional deliverables of the project include curriculum development at undergraduate and graduate levels, research 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 improve the current knowledge of CMM design and enhance the performance of 3-D printed products using a multi-scale framework that will explore complex and non-linear relationships between the microstructure and component by allowing non-periodically repeating microstructure designs and accounting for the fabrication-related uncertainty. This goal will be accomplished by developing a multi-scale design strategy driven by physics-based material models, data-driven and physics-informed ML, design optimization, and uncertainty quantification approaches. The ability to model non-periodical microstructure arrangements of CMMs will be essential to explore their true component-level mechanical performance, thereby substantially increasing their potential for use in new-generation engineering systems for hypersonics, structural applications, energy absorption, sensors, and soft robots. The findings of the project will also identify designs that improve mechanical performance and reliability by considering the effects of material uncertainty. In addition, the design methodology for CMMs will be extended to nature-inspired cellular materials, such as artificial bone structures, for designing such systems to achieve target mechanical performance under uncertainty. The activity will also promote teaching, training, and learning through the development of outreach activities, such as camps, programs, and workshops targeting both youths and teachers. The participation of underrepresented groups is guaranteed by specifically addressing outreach programs for female students, first-generation college students, students from underserved communities in Southwest Virginia, and other minorities. The project data and findings will be made publicly available at Virginia Tech’s open-access repository, VTechData.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.

该职业项目的目的是参与材料设计的毕业生和本科生，特别着重于在与织物相关的材料不确定性的影响下设计细胞机械超材料（CMM）。该计划包括基于物理学的计算，机器学习（ML）和不确定性策略的设计以及外展活动的发展。潜在的假设是，可以通过开发一个多规模的计算框架来设计CMMS来实现目标的机械性能和性能，该计算框架研究组件尺度属性与基础微型体系结构之间的关系。该项目的社会影响将对经济产生，并有望设计可持续，轻巧和高性能的材料。获得的知识将通过技术活动和开放式图形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当前发展，学生的研究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人群的个人。该项目的超整齐目标是改善CMM设计的当前知识，并通过允许使用3-D印刷的产品来探索多个框架复杂的绩效，以促进效果非隔离重复的微观结构设计和与织物相关的不确定性的核算。该目标将通过制定由基于物理的材料模型，数据驱动和物理信息的ML，设计优化和不确定性量化方法驱动的多尺度设计策略来实现。对CMM的非周期性微观结构排列进行建模的能力对于探索其真正的组件级机械性能至关重要，从而实质上增加了它们在新产生的工程系统中用于高超为性，结构应用，能量吸收，传感器和软机器人的潜力。该项目的发现还将通过考虑材料不确定性的影响来确定可以提高机械性能和可靠性的设计。此外，CMM的设计方法将扩展到自然风格的细胞材料，例如人造骨结构，以设计此类系统以在不确定性下实现目标机械性能。这项活动还将通过开展推出活动，例如针对年轻人和教师的营地，计划和研讨会来促进教学，培训和学习。通过专门针对女学生，第一代大学生，来自弗吉尼亚州西南部服务不足的社区的学生以及其他少数群体的学生，可以保证代表性不足的团体的参与。该项目数据和调查结果将在弗吉尼亚理工大学的开放访问存储库VTECHDATA上公开提供。该奖项反映了NSF的法定任务，并使用基金会的知识分子和更广泛的影响评估标准，通过评估来诚实地获得支持。