Measurement Science for Additive Manufacturing Program

Summary

The Measurement Science for Additive Manufacturing 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. Common challenges often faced when working towards the successful implementation of AM include: high levels of process variability; low part accuracy and surface quality; inconsistent material properties; and lack of process and part qualification and certification methods. To address these challenges, and reduce perceived risks in order to facilitate widespread AM adoption, the program will develop: methods for part and material characterization; exemplar data, datasets, and databases to accelerate the design, fabrication, and acceptance of AM parts; process metrology, sensing, and control methods to maximize part quality and production throughput in AM; test methods, protocols, and reference data to reduce the cost and time to qualify AM materials, processes, and parts; and an information systems architecture, including metrics, models, and validation methods to shorten the design-to-product cycle times in AM. It is anticipated that this programmatic effort will result in: accelerated proliferation of AM parts in high-performance applications benefiting from AM's unique capabilities; improved quality and throughput for AM; rapid qualification of AM materials and processes leading to increased confidence in AM products used in industry; and streamlined design-to-product transformations leading towards more accessible AM technologies for small and medium-sized companies, increasing industrial competitiveness. 

Description

Objective
To develop and deploy measurement science that will enable rapid design-to-product transformation through advances in: 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 integration of additive manufacturing processes, parts, and systems. 
 

What is the Problem?
A number of major trends are shaping the future of global manufacturing. Among them are the increase in the variety of products and shorter product cycles required to meet customer needs; greater intelligence in product design and manufacturing; and growing importance of innovative products and services.

Additive manufacturing (AM) refers to a class of technologies used for producing highly complex, customized components by building up materials to make objects based on a three-dimensional (3D) computer model, typically built layer upon layer. Parts are fabricated directly from an electronic file representing the 3D part design that is virtually sliced into many thin layers and sent to an AM system where the layers are built up in sequence into a complete part.

AM processes and have matured over the past few decades– ranging from rapid prototyping to facilitate product design through physical concept models, to creating of one-of-a-kind patterns used to improve metal casting processes, and more recently to directly fabricating functional end-use parts. While early AM systems were primarily limited to producing parts in polymer (plastic) materials, systems that produce metal parts are now being widely used in a variety of applications. Metal-based additive processes form parts by melting or sintering material in powder, wire or other feedable forms until all layers are completed.

AM provides the agility needed to rapidly make innovative customized complex products and replacement parts that are not economically or physically realizable by more traditional manufacturing technologies. Common advantages associated with AM adoption include reduced time-to-market, just-in-time production, reduced material waste, and lower energy intensity.

Although metal AM technology has been continuously improving over the last few decades, several technical barriers still exist that prevent AM processes from reaching their full potential. Recent reports and roadmapping activities for AM (Refs 1-6) outline research gaps and recommendations in several areas to advance the industry. These reports emphasize that the ability to achieve predictable and repeatable operations is critical. The issues with surface quality, part accuracy, material properties, and computational requirements are significant barriers to and/or limitations for widespread implementation of AM processes throughout U.S. manufacturers. Furthermore, the Standardization Roadmap for Additive Manufacturing published by the Additive Manufacturing Standardization Collaborative (AMSC) (Ref 7) in July 2023 identifies more than 90 standards and technology gaps in various degrees of research needs. The following are listed among the highest priority gaps:

• Machine calibration and preventive maintenance – Standard AM machine system health and performance characterization methods and metrics to inform preventative maintenance;
• Measurement methods and metrics to characterize complex 3D part shapes, including surface finish and texture, and geometric accuracy;
• Reference radiographic images and standards for additive manufacturing anomalies;
• Benchmark reference measurement data for validation and verification of data-driven or multiphysics computational models;
• Best practices and/or specifications for registering and fusing data sets generated during AM manufacturing and inspection process.

To mitigate these challenges, this program focuses on the problems associated with AM process metrology, material, machine, and part qualification, AM process planning and control, as well as AM data management, integration and analytics. 
 

What is the Technical Idea?
The program will develop measurement science solutions for pre-process, in-process, and post-process metrology, characterization, and inspection needs in metal-based AM. Through robust measurement methods and tools, as well as unambiguous measurement data representation, data analytics, and machine learning tools, the program aims to improve the understanding of AM process physics and implementation of process control to establish guidelines for new AM design rules and to enable rapid qualification of AM machines, processes and resulting parts.

• In the area of pre-process metrology, the program will focus on characterizing the precursor materials and machine performance. Understanding performance of precursor materials in both virgin and recycled states will enable optimum use of materials characterization techniques. Precursor materials testing and characterization will inform best practices for quality assurance. Test methods to characterize various aspects of machine performance, such as laser power distribution, will improve understanding of how sub-systems impact process and part variability.
• In the area of in-process metrology, the program will focus on real-time measurements of process signatures such as melt-pool temperatures and associated emissivity variation, powder layer characteristics, such as layer uniformity and density, material phase evolution, as well as in-situ non-destructive evaluation for detecting process-induced defects in real time. Reference and exemplar data sets will be generated and provided, through publicly accessible data base, to AM modeling community to validate and improve AM models.
• In the area of post-process metrology, the program will focus on developing and deploying test methods and protocols, standard test artifacts, exemplar data, data processing tools, and automation tools that create robust post-process measurements and non-destructive testing to enable qualification of AM parts by manufacturers.

The program will also focus on developing algorithms, methods, and standard protocols for AM process control, and implement it with software and hardware tools for open control of AM systems to enable more flexible process optimization.

To facilitate rapid qualification, the program will investigate new test methods and protocols, provide exemplar data, and establish requirements to reduce the cost and time needed for manufacturers to qualify metal AM machines and processes.

In order to facilitate the effective and efficient curation, sharing, processing and use of measurement data and enable AM knowledge discovery for process improvement, the program will focus on developing and deploying models, methods and best practices for data management, data integration, and data fusion in additive manufacturing.

Finally, the advanced analytics and machine learning methods and tools will be applied to the curated measurement data to develop guidelines and provide decision-support (feed forward and feedback) for AM design and process planning to manage uncertainty and reduce the lead times in AM part fabrication. 
 

What is the Research Plan?
The program focuses on four areas which are closely interrelated: (1) process metrology, (2) material, machine, and part metrology, (3) process planning and control, and (4) AM Data management, integration, and analytics.

The main goal of the process metrology effort will be to explore the sensor signature-part quality relationships by generating numerous intercomparable and well-controlled process monitoring reference datasets utilizing industrially relevant monitoring systems, potentially combining new or experimental sensor systems. In addition to the reference data, metrology and analysis techniques, and the standard guidelines necessary to measure temperature, stress, and phase evolution for model validation of multi-physics models of PBF and DED processes will be developed.

In the area of material, machine and part metrology, characterization of metal powders used in powder bed fusion and directed energy deposition processes will be one element. After evaluating relevant conventional methods, new characterization techniques will be developed to complement those to improve prediction of powder behavior in AM applications. Performance metrics of AM machine functions and the methods to assess and communicate them among the stakeholders will be another element. For the part qualification element, X-ray computed tomography (XCT); optical and tactile surface, form, and coordinate metrology; and other non-destructive testing (NDT) systems will be used to develop more detailed and quantitative characterization of part dimensions, form, surface finish, defect morphology, and defect locations. Test artifacts based on the unique dimensional characteristics of AM parts and defects will be developed to better understand how part and surface complexity affect a metrological system’s performance and perform probability of detection studies in various NDT systems.

In the area of AM process planning and control, new algorithms, methods and standard protocols for process control will be developed and implemented with new software and hardware tools for open control of AM systems to enable more flexible process optimization. Unique capabilities at NIST, such as the Additive Manufacturing Metrology Testbed (AMMT), will be utilized to investigate the causal relationships between scan strategy and part quality metrics.

In the area of AM data management, integration, and analytics, one element will be the development of best practices for AM data creation, collection, sanitization, anonymizing, curation, validation and storing. In addition, best practices will be established for adopting emerging analytics and machine learning techniques to support knowledge discovery in AM, including: identify design, process, and material fundamentals; identify and establish patterns in materials, process, and part datasets; analyze data in support of AM design support (e.g. support structures); and optimize processing conditions in AM systems.

References

1. America Makes Additive Manufacturing Technology Roadmap (https://www.americamakes.us/technology-roadmap/)
2. NASA / NIST / FAA Technical Interchange Meeting on Computational Materials Approaches for Qualification by Analysis for Aerospace Applications (https://ntrs.nasa.gov/api/citations/20210015175/downloads/NASA-TM-20210015175%20Final.pdf)
3. Vision 2040: A Roadmap for Integrated, Multiscale Modeling and Simulation of Materials and Systems, NASA (https://ntrs.nasa.gov/api/citations/20180002010/downloads/20180002010.pdf)
4. Strategic Guide: Additive Manufacturing In-Situ Monitoring Technology Readiness, ASTM International Additive Manufacturing Center of Excellence (https://amcoe.org/in-situtechnologyreadiness/)
5. Joint FAA-EASA Additive Manufacturing Workshop (https://www.faa.gov/aircraft/air_cert/step/events/additive_mfg_workshop)
6. The Roadmap for Automotive Additive Manufacturing, USCAR (https://uscar.org/download/50/publications/13457/uscar-roadmap-for-automotive-am-final.pdf)
7. Standardization Roadmap for Additive Manufacturing, Version 3.0; Prepared by the America Makes and ANSI Additive Manufacturing Standardization Collaborative (AMSC), July, 2023 (https://www.ansi.org/standards-coordination/collaboratives-activities/additive-manufacturing-collaborative)
8. Challenges in innovation in additive manufacturing: Industry Drivers and R&D Needs, NIST Workshop, November 2009 (https://www.nist.gov/publications/national-workshop-challenges-innovation-advanced-manufacturing-industry-drivers-and-rd)

Created December 18, 2018, Updated July 30, 2026