Measurement Science for Additive Manufacturing
The Measurement Science for Additive Manufacturing is a program in the Engineering Laboratory featuring several projects primarily focused on metals-based additive manufacturing applications and technologies. The program aims to develop and deploy advances in measurement science that will enable rapid design-to-product transformation through: material characterization; in-process sensing, monitoring, and model-based optimal control; performance qualification of materials, machines, processes and parts; and end-to-end digital implementation and analysis of Additive Manufacturing (AM) processes and systems. Read more.
Project Leaders: Brandon Lane & Paul Witherell
Fundamental Measurements for Metal AM
To instill confidence and aid adoption of AM as a viable technology for production in critical applications, a strong understanding of how to measure, characterize and qualify AM parts, processes, and feedstock materials is required. Experts in powder testing and characterization, surface topography, defect detection, x-ray computed tomography, dimensional characterization, instrumented indentation, destructive melt pool characterization, and laser material interactions are working together to produce a broad range of products. Read more.
Project Leader: Jason Fox
Advanced Machines, Monitoring, and Control for AM
Despite its potential, widespread adoption of AM technology faces two major obstacles - inconsistent part quality and low production efficiency. This project will address these challenges by developing and implementing advanced AM control and monitoring methods and demonstrate their positive impact on enhancing part quality and efficiency by integrating these innovations into AM machines/testbeds. Read more.
Project Leader: Ho Yeung
Metrology for AM Model Validation
Multi-physics and data-driven models are necessary to simulate, study, and optimize metal additive manufacturing (AM) processes, such as powder bed fusion (PBF) and directed energy deposition (DED). This project, along with a large number of collaborators across NIST and outside research organizations, aims to provide trusted measurement data for the purpose of AM model validation, primarily disseminated through the Additive Manufacturing Benchmark Test Series (AM-Bench). Read more.
Project Leader: Brandon Lane
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 (AM) into an industrialization (wide-scale production) technology requires an expanded notion of integration of both AM systems and AM data. AM data integration and analytics need to scale up as well to automate workflows and improve decision-making across the AM supply chain. Read more.
Project Leader: Yan Lu
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