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Manufacturing

Additive manufacturing

Data Science for Additive Manufacturing

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Additive manufacturing (AM) is a truly digital manufacturing process, relying on data for decision making through development lifecycles. The National Institute of Standards and Technology (NIST) supports advancements in AM by developing methods, metrics, models, standards, tools, and testbeds to enable the use of AM data for industrialization. Feel free to contact us with questions or opportunities to collaborate.

Learn about our AM data science work by exploring the content below.
Projects | News | Publications

‘Time above melt’ values processed from high speed, in-situ thermographic imaging of each layer a laser powder bed fusion 3D build, and compiled into a digital twin of the part shape. Part of AM-Bench 2022 datasets.
We develop AM data, like these ‘Time above melt’ values from the AM-Bench 2022 datasets. The values were processed from high speed, in-situ thermographic imaging of each layer of a laser powder bed fusion 3D build, and compiled into a digital twin of the part shape.

Advancing AM Through Data Science

The extensive amounts of data generated during design-to-product transformation provide a valuable resource on which new insights can be gained into and about parts and processes. The application of data science to AM, ranging from information modeling to machine learning to process and simulation to digital twins, has become an integral aspect of advancing AM technologies and their adoption. Our team develops valuable data resources to support these advancements in the AM community, like the NIST Additive Manufacturing Material Database, pictured below.
 

Top Left: Process Control, Top Middle: Melt Pool Size, Top Right: Layerwise Optical Image, Bottom: X-ray computed tomography scan.
This screenshot from the NIST Additive Manufacturing Material Database shows registered multi-modal powder bed fusion process data and inspection data. 

 

Projects 

Click the plus icon (+) below to learn about our additive manufacturing data science projects. 

Advanced Informatics and Artificial Intelligence for Additive Manufacturing

Advancements in additive manufacturing are progressively driven by digital technologies, with advanced sensors and measurements informing increasingly complex modeling and simulation paradigms and playing an important role in part design, production, and qualification. Advanced informatics are providing new opportunities to harness trusted data and information to acquire knowledge and develop actionable assessments in complex AM systems and environments. Read more.

Project Leader: Paul Witherell

Data Management and Fusion for AM Industrialization

The maturation of additive manufacturing into an industrialization (wide-scale production) technology requires an expanded notion of integration of both heterogenous systems and data in a production environment. Data fusion combines integrated data from various sources and uses advanced data analytics to achieve inferences and decision making that cannot be obtained from a single source. Methods and standards for AM data integration and fusion need to scale up as well to streamline production workflows and improve decision-making across the AM supply chain. Read more.

Project Leader: Yan Lu

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News 

Click the plus icon (+) below to learn about our additive manufacturing data science news. 

Updated Standard Provides Fundamental 3D-Printing Design Guidance to Streamline

The American Society of Mechanical Engineers (ASME) published an updated standard, based in large part on research by NIST, that includes language specifically for 3D printing. Read more.

Four 3D models of 3D printing designs. The top two are spherical, bottom two are different shapes.
These 3D models exhibit many of the unique degrees of freedom afforded by additive manufacturing, also called 3D printing, such as producing parts with complex geometry and made of multiple materials. A new ASME standard, Y14.46, provides guidance for how to relay 3D-printing specific considerations in design documents.
Credit: Reprinted from ASME Y14.46-2022, by permission of The American Society of Mechanical Engineers. All rights reserved.

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NIST AM publishes research about data for additive manufacturing. View some of our publications here.

Manufacturing and Additive manufacturing

Contacts

Additive Manufacturing Program Coordinators

Created April 10, 2025, Updated April 23, 2025