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

最终实现由客户确定制造能力和向生产者提供制造服务的全国范围的网络化基础设施，取决于是否有成本效益高的工具对制造工作进行分类。这类工具有可能找到已经在生产与所需部件类似的部件的制造商，并识别来自个别制造商的生产数据，这些数据可以被汇总起来，以培训基于人工智能的过程控制器，这些控制器的功能比个别制造商自己开发的更强大。一种描述工作特征的潜在方法是将所需部件与已经制造的部件的3D几何模型进行匹配，这反过来可能指向各自几何模型的特定“所有者”。大多数机械零件都可以使用3D模型，但这些模型是以各种不兼容的格式定义的。这个早期概念探索性研究资助项目将研究一种适用于搜索并兼容所有现有几何建模格式的分类方法。该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，并扩展到可分类。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的距离概念，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Ball分解(MDBD)，该分解将作为代理几何表示。重要的是，MDBD：(1)提供了几何图形的通用和分层描述，其细节级别可以根据需要进行调整；(2)引入了几何图形的分层参数化，该几何图形具有唯一性、旋转不变性和表示不可知性；(3)完全支持可加密的形状签名，可以为任何有效的几何表示计算形状签名；(4)与任何有效的几何表示相接口，而不需要进行表示转换。通过提供相对较少数量的形状的内在参数，相同的参数化可以以一种适合于预测最优设计方案的现代数据驱动的机器学习方法的方式来重组拓扑和形状优化问题。该方法的能力将在使用基于商业NURBS的边界表示、网格和点云的不同几何模型的数据库上进行评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.