EAGER: Exploration of Data-Driven Methods for Cyber Manufacturing

这个探索性研究（EAGER）项目将研究连接设计和制造所需的知识、数据驱动和网络工具，从而建立一个网络制造市场，确定快速实现产品设计所需的按需制造能力。通过在区域和全球供应链的广泛平台上将设计师与制造商无缝连接起来，并在不损害制造商的“专有技术”和商业秘密的情况下，使获得制造能力的民主化，这样的市场可以使大规模获取“专有技术”和生产资料的民主化。为了实现这种影响，有必要(1)创建数据驱动的方法来获取制造能力的知识，这些知识可以将计算机辅助设计（CAD）转化为所需的物理产品，以及(2)创建通过网络基础设施有效搜索这些知识的方法。该项目假设：(i)计算机器可以从可用的设计和制造数据中学习将CAD模型转换为所需物理部件所需的制造能力，以及（ii）通过基于统计相似度的搜索方法，可以实现零件设计所需制造能力的有效网络搜索。该假设将通过利用计算机辅助设计、制造科学和技术以及机器学习方面的专业知识的创新和融合数据驱动方法进行测试。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。

This EArly-concept Grant for Exploratory Research (EAGER) project will investigate the knowledge and data-driven and cyber-enabled tools needed to link design and manufacturing, thereby enabling a cyber manufacturing marketplace that identifies the on-demand manufacturing capabilities necessary for rapid realization of product designs. Such a marketplace can democratize access to “know-how” and means of production on a massive scale, by seamlessly connecting designers with manufacturers in a broad platform of regional and global supply chains and democratizing access to manufacturing capabilities without compromising manufacturers’ “know-how” and trade secrets.To realize this impact, it is necessary to (1) create data-driven methods to harvest knowledge of manufacturing capabilities that can transform a computer-aided design (CAD) into the desired physical product, and (2) create methods to efficiently search for this knowledge through the cyber infrastructure. The project hypothesizes that: (i) computing machines can learn the manufacturing capabilities required to transform a CAD model into the desired physical part from available design and manufacturing data, and (ii) efficient cyber-enabled search of the required manufacturing capabilities for a part design can be realized through statistical similarity-based search methods. The hypothesis will be tested through innovative and convergent data-driven approaches that utilize expertise in computer-aided design, manufacturing science and technology, and machine learning.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.