EAGER: FINDFabs: Searching The Universe of Manufactured Parts Through Proxy Geometric Representations

能否最终实现一个联网的、全国规模的基础设施，以供客户确定制造能力并向生产者提供制造服务，取决于是否有成本效益高的工具对制造业工作进行分类。 这些工具有可能找到已经在生产与所需零件相似零件的制造商，并识别来自各个制造商的生产数据，这些数据可以聚合起来，以训练基于人工智能的过程控制器，这些过程控制器比单个制造商自己开发的过程控制器更强大。 一种表征作业的潜在方法是将所需零件与已经制造的零件的3D几何模型相匹配，这反过来又可以指向相应几何模型的特定“所有者”。3D模型可用于绝大多数机械零件，但模型以各种不兼容的格式定义。 EARLY概念探索性研究资助（EAGER）项目将研究一种适用于搜索并与所有现有几何建模格式兼容的分类方法。本研究的目标是探索一种通用的理论和计算框架，可以使已经成功生产的零件的宇宙可搜索，并通过扩展可分类。它依赖于这样一个事实，即所有有效的几何模型必须基于在适当的度量空间中定义的有效距离概念，因此必须完全支持距离计算和查询。该框架是基于一个新颖的和独特的最大不相交球分解（MDBD）的3D形状，将作为一个代理几何表示。重要的是，MDBD：（1）提供了一种通用的、层次化的几何描述，其细节级别可以根据需要进行调整，（2）引入了几何的层次化参数化，该参数化是唯一的、旋转不变的、以及表示不可知的，（3）完全支持可识别的形状签名，其可以针对任何有效的几何表示进行计算，以及（4）与任何有效的几何表示接口，而不需要表示转换。 通过提供形状的相对少量的内部参数，相同的参数化可以以适合于现代数据驱动的机器学习方法来预测最佳设计解决方案的方式重构拓扑和形状优化问题。该方法的能力将在异构几何模型的数据库上进行评估，使用商业的基于NURBS的边界表示，网格和点云。该奖项反映了NSF的法定使命，并被认为是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的支持。

The eventual realization of a networked, national-scale infrastructure for the identification of manufacturing capabilities by customers and provision of manufacturing services to producers depends on the availability of cost-efficient tools for categorizing manufacturing jobs. Such tools have potential to find manufacturers who are already producing similar parts to those that are needed and to identify production data from individual manufacturers that can be aggregated to train AI-based process controllers that are more powerful than individual manufacturers can develop on their own. One potential approach to characterizing jobs is to match the desired part to the 3D geometric models of parts that have already been manufactured, which could, in turn, point to the specific "owners" of the respective geometric models. 3D models are available for the vast majority of mechanical parts, but the models are defined in a wide variety of incompatible formats. This EArly-Concept Grant for Exploratory Research (EAGER) project will research a categorization method that is suitable for search and compatible with all existing geometric modeling formats.The objective of this research is to explore a universal theoretical and computational framework that can make the universe of parts that have already been successfully produced searchable and, by extension, categorizable. It relies on the fact that all valid geometric models must be based on a valid notion of distance defined in appropriate metric spaces and must therefore fully support distance computations and queries. The framework is based on a novel and unique Maximal Disjoint Ball Decomposition (MDBD) of a 3D shape that will serve as a proxy geometric representation. Importantly, MDBD: (1) provides a universal and hierarchical description of geometry whose level of detail can be adjusted on demand, (2) induces a hierarchical parametrization of the geometry that is unique, rotation-invariant, and representation agnostic, (3) fully supports encryptable shape signatures that can be computed for any valid geometric representation, and (4) interfaces with any valid geometric representation without requiring representation conversions. By providing a relatively small number of intrinsic parameters of a shape, the same parametrization can reframe the topology and shape optimization problem in a way that is suitable for modern data-driven machine learning approaches to predicting optimal design solutions. The capabilities of the method will be evaluated on databases of heterogeneous geometric models using a commercial NURBS-based boundary representation, meshes and point clouds.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.