Advanced Manufacturing Data Infrastructure and Analytics Program

Summary

The Advanced Manufacturing Data Infrastructure and Analytics (AMDIA) program will lay the groundwork for advanced data infrastructure to improve the productivity, resiliency, and sustainability of manufacturing operations and enterprises across the supply chain. As the manufacturing industry produces increasing volumes of diverse data, stakeholders need robust data infrastructure and trusted analytics to prepare, model, understand, and utilize their data effectively for improved control and better decision-making. Emerging technologies allow manufacturers to collect, structure, link, and analyze data in new ways. However, technologies are seldom one-size-fits-all solutions. Research into manufacturing data and the role of humans in the process is needed to effectively adopt and integrate solutions into existing operations.

The AMDIA program will develop methods, standards, tools, models, and datasets, to advance manufacturing data infrastructure, with a focus on data collection, transformation, traceability, and interoperability. AMDIA outputs will lower the barriers to incorporating new technologies and analysis methods into existing and emerging operations. These outcomes will enable trusted, understandable, and reproducible information workflows across engineered products, manufacturing enterprises, and supply chains to improve decision-making.

Description

Objective
The Advanced Manufacturing Data Infrastructure and Analytics (AMDIA) Program will develop and deploy measurement science to advance a data infrastructure that will improve the productivity, resiliency, and sustainability of manufacturing operations and supply chains to enhance U.S. innovation and industrial competitiveness.

Technical Idea
Manufacturers continually gain more capability to collect and monitor data throughout all levels of their operations and across their supply chains. As manufacturing operations become more advanced, so, too, does the amount, variability, and uncertainty of the corresponding data. Manufacturing processes and constituent enabling technologies are rapidly evolving including advanced sensing capabilities, technical language processing tools, augmented reality systems, and cybersecurity measures. One commonality among all of these elements and output from nearly every manufacturing process is data. Manufacturers and their industry partners are becoming greater generators and consumers of data output from their operations, particularly as automation increases. Turning this data into meaningful intelligence is difficult. 

A robust data infrastructure creates a vital foundation for manufacturers to build upon with advanced and emerging technologies to improve their operations. The AMDIA program will focus on data infrastructure to support data sharing across the supply chain. Infrastructure promotes data sharing throughout the entire community, including public datasets, open-source tools, and publicly available standard guidelines. A multi-disciplinary team will bring together data architecture and analysis expertise from manufacturing domain researchers, to ensure that the resulting data infrastructure advancements address both industry and research needs. This will improve manufacturing data infrastructure by understanding the nuances of the data and the specific needs of the manufacturing community for improved productivity, sustainability, resiliency, and security while also investigating new technologies that can enable improved analysis, decision-making, and control. 

Research Plan
The research plan consists of a portfolio of interrelated projects that focus on key standards, methods, and measurement science needed to achieve successful development and implementation of advanced manufacturing data infrastructure and analytics. Collectively the projects provide a comprehensive approach that will lead to new industry standards and practices. The program will take a multi-faceted approach by not only focusing on different parts of the manufacturing supply chain, but also different stages of the data lifecycle and critical emerging technologies. The AMDIA program will

•  Develop data infrastructure to enable a circular economy.
• Improve data collection through advanced sensing.
• Develop test methods and standards for digital twins.
• Enable improved interoperability between supply chain systems
• Build trustworthy, traceable throughout the supply chain

The AMDIA program is developing new measurement science and standards for systems integration in critical emerging technology areas. Current projects include

• Circular Economy
• Supply Chain Traceability
• Biomanufacturing
• Ontology Research and Development
• Digital Twin
• Human/AI Teaming
• Advanced Sensor technologies

Highlights

Circular Economy
NIST-ASTM Report on Standards Needs for Circular Economy
Circular Economy Workshop at IDETC 2023
Established ASTM Committee on Principles for Circular Product Design
ISO TC 323 59000 standards on Circular Economy
ASTM E60 and E60.13 Sustainable Manufacturing

Supply Chain Traceability
Traceability tools and Synthetic Traceability Datasets published
Released IOF Supply Chain ontology
NIST data models used by AgGateway Traceability API working group 
Pedigree and Provenance Model and Notation delivered to OMG

Biomanufacturing
NIIMBL Core ontology including Quantity and Unit ontology covering E2E biopharma processes
OAGIS Metadata and Dataset Metadata standard to facilitate AI at the plant and enterprise level
Developed new mapping specification language and engine to enable sharing of data mappings

Cross-Program Ontology Research and Development
Established IOF ontology development and publishing infrastructure 
Developed and released the first version of IOF Core ontology 
Integration IOF Core with OMG Common ontology used by Pharma 
IOF Core ontology adopted by NIIMBL
Developed Machine Learning Lifecycle (MLL) ontology

Digital Twin
ISO 23247 - Digital Twin Framework for Manufacturing Digital thread for digital twins Composition of digital twins
OMG IIC technical report on Digital Twin Core
ASME V&V 50 - VVUQ interactions with model lifecycle
ISO/IEC 30173 - Digital twin concepts and terminologies
ISO/IEC 30172 - Digital twin use cases

Augmented Intelligence for Manufacturing Systems
Position Sensor deployed to FuzeHub (NY MEP) and Buffalo Manufacturing Works for testing
Advanced spindle metrology system
New vision-based method for monitoring thermal drift of machine tools
ASME B5.64 - Led development of new linear axis performance standard 
Led CIRP Collaborative Working Group on Semiconductor and Microelectronic Manufacturing
Patent: Inertial measurement unit and diagnostic system U.S. Patent No. 11,085,793

Agencies 
OSTP working group on CE:  EPA, Dept of State, Dept of Energy, International Trade Administration.
Contributing to Dept. of State interagency groups preparing for UN negotiations around plastic pollution and circular economy.

Consortia/SDOs 
NIIMBL, BioMade, Open Applications Group, MTConnect, AgGateway, Industrial Ontology Foundry, PHM Society, ASTM International, ISO TC 323.

Industry
Boeing, Lockheed Martin, Land-O-Lakes, TechSolve, RedShred, Earth Shift Global, ADS Drainage Systems, Rheaply, Vinyl Institute, Amazon, Armstrong World Industries.

Academia
UC Davis, UW Australia, Texas State University, Ohio University, Kansas State University, Purdue University, University of Kentucky.