FMRG: Manufacturing USA: Cyber: Data-Driven Methods for Future Cyber Manufacturing as a Service

网络制造期望为工程零件和产品的按需制造提供网络化服务。这样的服务有可能使离散零件制造的手段和专业知识以更短的时间、更小的规模和更低的成本普及。数字化设计、低成本传感和网络化制造机器的进步有可能使人们能够随时访问生产资料沿着设计和生产数据。然而，需要使未来的网络制造服务能够使用可用的生产手段将数字设计自动转换为制造产品的计算方法和工具。该奖项支持基础研究，以自动选择，规划和采购生产离散零件和按需产品所需的制造流程和资源。 这种能力适用于广泛的制造业，包括航空航天，汽车，能源和生物医学。因此，这项研究将有利于美国制造业和社会的竞争力。该研究融合了设计、制造科学与技术、人机交互、人工智能和机器学习等多个学科。通过与西班牙裔服务机构，行业，美国制造业研究所的合作伙伴关系，以及与其他代表性不足的社区的联系，该项目将教育和培训多元化的未来网络制造劳动力。本项目将研究一个数据驱动的计算框架，使计算机能够从以前制造的零件的工业积累的设计和生产经验中推断新零件的设计元素和加工细节。 这种能力依赖于从过去和当前的设计和制造数据中提取核心制造智能，其形式为可扩展和生成的制造过程能力知识和制造设计知识。具体而言，该研究将产生制造过程的生成能力的基础知识，以改变零件形状，材料性能和质量，以满足产品设计要求，以及生成功能和可制造性的再设计。工艺规划和零件重新设计方法将结合新的深度学习、形状描述符和制造推理技术来开发。由此产生的数据驱动的计算框架将提供基本的软件工具，以实现未来的网络制造服务，提供一个可扩展的和有效的解决方案，自动转换数字设计到物理产品的需求。这个未来制造奖是由助理主任办公室（OAD）和民政司，工程理事会（ENG）的机械和制造创新（CMMI），教育和人力资源局（EHR）本科教育司（DUE）和计算机和网络系统司（CNS）该奖项反映了NSF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。

Cyber manufacturing anticipates a networked service for on-demand manufacturing of engineered parts and products. Such a service has the potential to democratize access to the means and know-how of discrete parts manufacturing at the reduced time, scale, and cost. Advances in digital design, low-cost sensing, and networked manufacturing machines have potential to enable ready access to the means of production, along with design and production data. However, computational methods and tools that enable a future cyber manufacturing service to automatically translate a digital design into a manufactured product using the available means of production are needed. This award supports fundamental research to automate the selection, planning and acquisition of the manufacturing processes and resources required to produce discrete parts and products on-demand. This capability is applicable to a wide range of manufacturing industries including aerospace, automotive, energy, and biomedical. Therefore, this research will benefit the competitiveness of the U.S. manufacturing sector and society. The research integrates several disciplines including design, manufacturing science and technology, human-computer interaction, artificial intelligence, and machine learning. Through partnership with a Hispanic Serving Institution, industry, a Manufacturing USA institute, and outreach to other underrepresented communities, the project will educate and train a diverse future cyber manufacturing workforce. This project will research a data-driven computational framework that enables computers to infer design elements and processing details for new parts from the accumulated design and production experience of industry with parts that have been manufactured previously. This capability rests on extracting core manufacturing intelligence in the form of scalable and generative manufacturing process capability knowledge and design-for-manufacturing knowledge from past and current design and manufacturing data. Specifically, the research will yield fundamental knowledge of the generative capability of manufacturing processes to transform part shape, material properties, and quality to meet product design requirements, and generative redesign-for-functionality and manufacturability. Process planning and part redesign methods will be developed from a combination of new deep learning, shape descriptor, and manufacturing reasoning technologies. The resulting data-driven computational framework will provide the fundamental software tools to implement future cyber manufacturing services that provide a scalable and efficient solution for automatically translating digital designs into physical products on-demand.This Future Manufacturing award was supported by the Office of the Assistant Director (OAD) and the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) of the Directorate for Engineering (ENG), the Division of Undergraduate Education (DUE) of the Directorate for Education and Human Resources (EHR) and the Division of Computer and Network Systems (CNS) of the Directorate for Computer and Information Science and Engineering (CISE).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.