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Measurement Science for Additive Manufacturing Program


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.


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.

Why is it Hard to Solve?
Key to overcoming the barriers to widespread use of AM is to address the lack of measurement science for obtaining material and process data, converting that data into actionable knowledge, incorporating knowledge into operating models, and using those operating models to optimize AM processes and equipment to produce high quality mission critical parts.  Developing this measurement and standards infrastructure for AM is challenging because:

  1. Process physics for AM technologies, especially metal-based AM, are complex, dynamic, and multi-scale, and the knowledge base for understanding or predicting these physics is still emerging. Advancements require the integration of diverse knowledge and information on expensive fabrication platforms. Interdisciplinary collaboration across a wide variety of areas of expertise is required to optimize the application of technologies needed for reliable and robust AM capability. 
  2. Integrated, complex systems are inherently difficult to build and to evaluate. An optimized AM system will require tightly integrated hardware and software components. The interactions and dependencies among the components must be well-understood and characterized, but it is challenging to define and measure the contributions of each component and subsystem to the overall AM performance because of the large number of process variables in metals-based AM. 
  3. Most commercial AM machines are closed "black boxes." It is impossible for end users and process developers to access the internal hardware and software components to integrate new sensors, models, or control algorithms without extensive collaboration with AM vendors.

How is it Solved Today and by Whom?
Some large U.S. companies in aerospace, defense and biomedical fields have made strategic decisions to adopt AM technologies for their high-value complex part manufacturing.  These early adopters spend significant resources and efforts to learn how to use these technologies, how to optimize them to meet their own production and legal requirements, and how to test the products made by these technologies to integrate them into their overall production.  However, due to the lack of adequate measurement science, all these usually are accomplished by empirical trial-and-error procedures.  Therefore, any simple modification in materials, design, or end use requires them to go through costly efforts for finding optimal solutions.  As a result, all the knowledge and experience gained within a company are highly protected, preventing effective collaborations among interested parties to advance the technology.  Such protective environment also causes a significant knowledge and capability gap between these large companies and the second and third tier suppliers.  This gap is also filled with more trial-and-error type costly learning process.

To address these challenges, there are several programs in the U.S. and elsewhere to stimulate collaboration among AM vendors and users to advance these technologies.  America Makes in the U.S. is an example of such effort in collaborative R&D aiming to advance AM in the U.S.  ASTM International has supports an AM Center of Excellence to expedite R&D necessary to fill standards gaps identified in the AMSC roadmap.  Similarly, the Fraunhofer Competence Field AM Alliance integrates nineteen institutes across Germany.

NIST’s mission is to promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology in ways that enhance economic security and improve our quality of life.  NIST’s role is to provide measurement science solutions to problems that hinder the progress of advanced manufacturing.  The focus of this program is on projects that support enabling infrastructural metrology and technology that are not attractive to commercial investment yet offer significant leverage in a broad range of AM applications across a wide range of manufacturing sectors. Further, NIST is able to act as a trusted third-party to provide methods and data needed to characterize models and systems currently in research and development.

Several workshops and roadmapping activities (Ref 2-4, 8) involving a wide range of industry, academia, and government agency participants have called for NIST leadership in infrastructural R&D to enable additive manufacturing, including standards and measurements to support and accelerate 1) consistent and enhanced systems integration, 2) new capabilities for increased flexibility and automation in manufacturing processes to improve production systems, 3) new approaches to reduce manufacturing costs while improving product quality and reliability, and 4) advanced modeling, simulation, and digital twins to reduce production cycle times and allow U.S. manufacturers to rapidly and efficiently respond to changes in customer demand, foreign competition, or raw material availability. 

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.

Why Can We Succeed Now?
There is a tide of nationwide (as well as global) interest in AM technologies as evidenced by significant investments by governments and industry, as well as unprecedented levels of participation in technical conferences and industrial events with AM focus.  Collaboration among small and large companies as well as academia is increasing, promising innovative solutions to many of the AM challenges.  The AM industry realizes the necessity of standards and performance metrics for continued growth, with multiple standards development organizations (SDOs) addressing the needs for additive manufacturing technologies. NIST is participating in many of these committees with significant technical contributions.  NIST is also functioning as liaison among the various standards committees to ensure harmonized development of multiple AM standards by these SDOs.

In parallel, academia has been producing young scientists and engineers with in-depth experience in AM to contribute further developments.  This program plans to leverage the expertise in academia to the fullest extent by funding cooperative research agreements, creating postdoctoral research opportunities, and hiring new graduates with AM expertise.  It also seeks more collaborative opportunities with America Makes, the ASTM AM Center of Excellence, and other consortia and key organizations influencing AM research and development.

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.

How Can Teamwork be Ensured?
The program involves staff from two EL divisions as well as collaborations across all NIST Operating Unit boundaries.  Collaboration has been designed into the projects and programs and staff have demonstrated effective teamwork during the execution of previous research activities in the area of AM. They have also established strong interactions with industrial partners, academia as well as other government agencies.  These interactions will be strengthened and leveraged to deliver maximum impact to our stakeholders.  Interactions and information sharing among the project teams will be facilitated through periodic formal and informal meetings, topical seminars, as well as utilization of IT infrastructure such as shared file servers, OneDrive and OneNote.  In addition to periodic project and program meetings, regular project leaders' meetings are scheduled to share important developments among projects.

What is the Standards Strategy
There is a growing list of SDOs, standards committees, and active work items relevant to AM.  Furthermore, the scopes of the AM-focused standards committees continue to evolve.  These present the risk for duplication and overlapping efforts, which could lead to conflicting standards.  Additionally, stakeholders find it difficult to know where to invest their time, since participation in all relevant standards activity is highly unlikely.  The program looks to address these challenges by identifying the industry needs and priorities for AM standards and conducting measurement science research to develop the technical basis for standards.  Program staff serve on a variety of standards committees, especially in leadership, allowing an additional role to coordinate, facilitate, and communicate among various SDOs and standards committees, regulatory agencies, and users. The end goal is the development of high-quality, technically accurate, usable standards the meet stakeholders’ needs. Furthermore, we seek an integrated and cohesive set of standards across SDOs that are consistent, non-contradictory, and non-overlapping.  Good coordination should lead to little duplication of efforts. 


  1. America Makes Additive Manufacturing Technology Roadmap (
  2.  NASA / NIST / FAA Technical Interchange Meeting on Computational Materials Approaches for Qualification by Analysis for Aerospace Applications (
  3. Vision 2040: A Roadmap for Integrated, Multiscale Modeling and Simulation of Materials and Systems, NASA (
  4. Strategic Guide: Additive Manufacturing In-Situ Monitoring Technology Readiness, ASTM International Additive Manufacturing Center of Excellence (
  5. Joint FAA-EASA Additive Manufacturing Workshop (
  6. The Roadmap for Automotive Additive Manufacturing, USCAR (
  7. Standardization Roadmap for Additive Manufacturing, Version 3.0; Prepared by the America Makes and ANSI Additive Manufacturing Standardization Collaborative (AMSC), July, 2023 (
  8. Challenges in innovation in additive manufacturing: Industry Drivers and R&D Needs, NIST Workshop, November 2009 (
Created December 18, 2018, Updated May 22, 2024