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Manufacturing Systems Data Integration (MSDI) Conference; Chicago, Illinois; 2025

$48,210FY2024ENGNSF

Ui Labs, Chicago

Investigators

Abstract

This award will support the Manufacturing Systems Data Integration (MSDI) conference, establishing a foundation for digital equity in the manufacturing industry. The conference will address the urgent need for interoperable data frameworks, facilitating the seamless movement of information from manufacturing systems to support critical business decisions. By bringing together key stakeholders, the conference will define the minimum viable data sources, structure, format, and corresponding frameworks, providing crucial insights into manufacturing processes and equipment. With the abundance of diverse digital solutions, equipment, frameworks, and methodologies, there is a need for an agreed-upon series of data requirements and frameworks. This will ensure that manufacturers operate on a level digital playing field. Developing a series of interoperable data frameworks for digital manufacturing has the potential to significantly advance the state of knowledge and technology in the field. This effort will contribute to understanding how disparate data systems can be integrated to create a seamless flow of information that will drive operational efficiencies required for the next generation of manufacturing processes and equipment. This conference will lay the groundwork for more efficient and effective manufacturing systems by addressing data interoperability challenges, thereby pushing the boundaries of current technological capabilities. Participation from the DoD, industry, and academia will give government participants a pulse on the state of the art and allow the industry to define better the gaps remaining to help scale and guide academia in understanding future research challenges. The specific goal is to bring together leading experts to define the fundamental elements required for establishing a manufacturing data stream that enables easy access to information throughout the process. The conference will converge manufacturing requirements with key critical social indicators that can foster digital adoption and will focus on three crucial aspects: (1) technologies required to connect the data stream, (2) determining required data architecture(s) to facilitate the movement of the data stream, and (3) use cases that demonstrate outcomes (i.e., NIST Digital Thread for Manufacturing) and define future needs. The first workgroup will focus on practical technologies that interact with the data stream throughout the manufacturing process. MxD has established a technology roadmap to identify current gaps, and the output will generate an updated roadmap and research needs. This output will be available on their member exchange for public consumption. The second workgroup aims to define the minimal viable data sources and corresponding frameworks needed for successful data transmission. They will focus on different functional data architecture types and required data outputs, as well as discuss integrating AI into these frameworks for capturing and delivering insights efficiently. The result will be a set of recommended frameworks and data formats for essential business information, available on our MSDI workshop resources page. The third workgroup will review current use cases such as NIST Digital Thread for Manufacturing and Purdue’s Model-Based Feature Information Network (MFIN) program, which was initially funded by MxD in 2015. These examples have produced outcomes and impacted organizations. The workgroup will identify remaining gaps and requirements to expand these efforts. The final output of this conference will lay the foundations for delivering digital equity to manufacturers and advancing adoption with an agreed-upon series of frameworks to move data seamlessly through the manufacturing process. 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.

View original record on NSF Award Search →