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

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

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.