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

客户识别制造能力和向生产商提供制造服务的网络化、全国性基础设施的最终实现，取决于对制造业工作进行分类的成本效益工具的可用性。这些工具有可能找到已经在生产与所需部件相似的部件的制造商，并识别来自单个制造商的生产数据，这些数据可以被汇总起来，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。描述作业特征的一种潜在方法是将所需部件与已经制造的部件的3D几何模型相匹配，这反过来又可以指向各自几何模型的特定“所有者”。3D模型可用于绝大多数机械部件，但模型以各种不兼容的格式定义。这个探索性研究早期概念资助（EAGER）项目将研究一种适合搜索并与所有现有几何建模格式兼容的分类方法。本研究的目的是探索一个通用的理论和计算框架，可以使已经成功生产的部件的宇宙可搜索，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。该框架基于一种新颖而独特的3D形状的最大不相交球分解（MDBD），它将作为代理几何表示。重要的是，MDBD:(1)提供了几何的通用和分层描述，其细节级别可以根据需要调整；(2)诱导几何的分层参数化，它是唯一的、旋转不变的和表示不可知的；(3)完全支持可以计算任何有效几何表示的加密形状签名；(4)与任何有效几何表示的接口，而不需要表示转换。通过提供相对少量的形状的内在参数，相同的参数化可以以适合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新构建拓扑和形状优化问题。该方法的能力将在异构几何模型数据库上进行评估，该数据库使用基于商业nurbs的边界表示、网格和点云。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.