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，旨在打破关键障碍，对3D几何信息从工程设计到分析和制造的3D几何信息流动，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的3D形状建模和处理中的特定效率来为长期的社会影响铺平道路。通过该项目开发的研究还将直接影响康涅狄格大学的代表性不足和少数族裔工程本科生和研究生的多样性倡议。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，其目标外展活动将重点关注K-12学生，老师和当地学区，这些组织在工程学科中传统上代表性不足。项目框架使用这样的观察结果，即任何和所有有效的几何模型都必须基于有效的距离概念，因此必须完全支持距离计算和查询。新框架是基于最大和相互切线球的3D域的新型球形分解。唯一定义的分解，表示为最大的分离球分解（MDBD），具有多种关键的理论和计算属性，包括：（1）能够以层次结构方式描述几何形状，并具有可根据需求调整的细节级别； （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形式互动，而无需表示表示转换并与CAD中最新的强大进步集成，该进步将形状表示为隐式和层次定义的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，并且是广泛采用行业的理想候选人。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的审查标准的评估来通过评估来获得支持的。