A Universal Framework for Geometric Information in Product Development

产品开发中几何信息的通用框架

• 批准号: 2312175
• 负责人: Horea Ilies
• 金额: $ 49.95万
• 依托单位: University of Connecticut
• 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 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312175&HistoricalAwards=false
• 关键词: Universal; Framework; Geometric; Information; Product

This award supports research which aims to establish a universal theoretical and computational framework aimed at breaking down key barriers that negatively affect the flow of 3D geometric information from engineering design into analysis and manufacturing, while supporting modern machine learning algorithms. The expected results will pave the way for long-term societal impacts by eliminating specific inefficiencies in 3D shape modeling and processing throughout the engineering product development cycle of complex engineered systems. The research developed through this project will also directly impact diversity initiatives for incoming underrepresented and minority engineering undergraduate and graduate students at the University of Connecticut. Through its integrated educational plan, this project will lead to the creation of new educational content for engineering curricula, and its targeted outreach will focus on K-12 students, teachers, and the local school district, serving groups that have traditionally been underrepresented in the engineering disciplines. The project framework uses the observation that any and all valid geometric models must be based on a valid notion of distance and must therefore fully support distance computations and queries. The new framework is based on a novel spherical decomposition of the 3D domain of interest in terms of maximal and mutually tangent spheres. The uniquely defined decomposition, denoted as the Maximal Disjoint Ball Decomposition (MDBD), has several key theoretical and computational attributes, including: (1) the ability to describe the geometry in a hierarchical manner and with a level of detail that can be adjusted on demand; and (2) its intrinsic support for modern geometric machine learning algorithms. Moreover, the framework naturally interfaces with all existing valid geometric representations without requiring representation conversions and integrates with recent powerful advances in CAD that represent shapes as implicitly and hierarchically defined 3D signals. Consequently, the framework has the potential to have a broad impact throughout the computational design and manufacturing process and is an ideal candidate for wide industry adoption.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.

该奖项支持旨在建立通用理论和计算框架的研究，旨在打破对工程设计到分析和制造的3D几何信息流产生负面影响的关键障碍，同时支持现代机器学习算法。 预期的结果将通过消除复杂工程系统的整个工程产品开发周期中3D形状建模和处理中的特定低效率，为长期的社会影响铺平道路。通过该项目开发的研究也将直接影响康涅狄格大学入学代表性不足和少数民族工程本科生和研究生的多样性举措。通过其综合教育计划，该项目将为工程课程创建新的教育内容，其有针对性的推广将集中在K-12学生，教师和当地学区，为传统上在工程学科中代表性不足的群体提供服务。项目框架使用的观察，任何和所有有效的几何模型必须基于一个有效的距离概念，因此必须完全支持距离计算和查询。新的框架是基于一种新的球形分解的3D域的最大和相互相切的领域。唯一定义的分解，表示为最大不相交球分解（MDBD），具有几个关键的理论和计算属性，包括：（1）以分层方式描述几何形状的能力，以及可以根据需要调整的细节水平;以及（2）其对现代几何机器学习算法的内在支持。此外，该框架自然地与所有现有的有效几何表示接口，而不需要表示转换，并与CAD中最近的强大进步相结合，将形状表示为隐式和分层定义的3D信号。因此，该框架有可能在整个计算设计和制造过程中产生广泛的影响，是广泛行业采用的理想候选者。该奖项反映了NSF的法定使命，并被认为值得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。