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Measurement Science for Additive Manufacturing Program - 2018


This program addresses measurements and standards necessary to develop and deploy advances in measurement science that will enable rapid design-to-product transformation; material characterization; in-process process sensing, monitoring, and model-based optimal control; performance qualification of materials, machines, processes and parts; and end-to-end digital implementation of Additive Manufacturing (AM) processes and systems.  Reducing the barriers to widespread implementation and use of AM, such as high level of process variability, low part accuracy and surface quality, and inconsistent material properties, lack of process and part qualification and certification methods, and lack of adequate, well-controlled and traceable measurement data and analysis tools to validate and improve AM process models for better understanding of the processes and their outcomes, as well as resulting methods for decision support (i.e. predictive analytics and design guidelines) will be a main goal of the program. The program will develop: standardized material characterization methods, exemplar data and databases to accelerate the design, processing, and use of AM parts; process metrology, sensing and control methods and algorithms 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, information models, and validation methods to shorten the design-to-product cycle time in AM.  It is anticipated that this programmatic effort will facilitate: 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 better understanding of AM and more 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 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 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.

AM refers to a class of emerging technologies 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.

Over the past few decades, several AM processes and systems have been developed and their capabilities have grown significantly – ranging from rapid prototyping of simple physical concept models to help in product design, to creation of one-of-a-kind patterns necessary for metal casting processes, and more recently to direct fabrication of functional end-use parts. While early AM systems produced parts primarily in polymer (plastic) materials, systems that produce metal parts are now also available and are being 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.  It offers additional advantages, including 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  outline research 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)  in June 2018 identifies more than 80 standards and technology gaps in various degrees of research needs.  The following areas are listed among the highest priority gaps:

  • Powder characterization and qualification – sampling, storage, recycle, reuse
  • Machine calibration and preventive maintenance
  • Measuring complex 3D geometries, including surface finish and texture
  • Characterization of AM flaws, guide to Non-destructive evaluation of AM parts
  • Design guidelines and design limits
  • Unified qualification terminology and minimum set of qualification requirements
  • Technical data package standards

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 wide spread 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. AM technologies, especially metal-based AM, are 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.
  3. All AM machines on the market today 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 recently initiated several Centers of Excellence to expedite R&D necessary to fill standards gaps identified in the AMSC roadmap.  Similarly, Standardization in AM (SASAM) project in EU intends to drive the growth of AM and creating and supporting a standardization organization in the field of AM.   Fraunhofer AM Alliance in Europe provides another collaborative, interdisciplinary environment to develop new rapid strategies, concepts, technologies, and processes to enhance the performance and competitiveness of small and medium-sized AM enterprises in Europe.

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 smart 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.

Several workshops  and roadmapping activities 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 and simulation 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 in additive manufacturing in both virgin and recycled states to enable optimum use of materials characterization.
  • In the area of in-process metrology, the program will focus on real-time measurements of process parameters 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 physics-based 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 has recently realized the necessity of standards and performance metrics for continued growth, with new standards committees formed in 2009 with NIST participation to address the needs for additive manufacturing technologies. Since then multiple standard development organizations (SDOs) have established committees to address the standardization gaps identified in the AMSC Roadmap.  NIST is participating most 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 proper backgrounds 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 and ASTM Centers of Excellence in AM standardization.

What is the Research Plan?
The program focuses on four areas which are closely interrelated: (1) process metrology, (2) material, machine, and part qualification, (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 qualification, 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.  The effort to develop data structures and formats to represent predictive models for AM applications will focus on developing standards needed to achieve interoperability needed to enhance the use of machine learning algorithms in the AM product, process and material development lifecycle.  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 three Engineering Laboratory divisions as well as from Physical Measurement Laboratory (PML), Material Measurement Laboratory (MML), and NIST Center for Neutron Research (NCNR). They have demonstrated effective team work 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 are facilitated through periodic formal and informal meetings, topical seminars, as well as utilization of IT infrastructure such as shared file servers, Sharepoint and OneNote.  In addition to periodic project meetings, biweekly project leaders' meetings are scheduled to share important developments among projects.

Created December 18, 2018, Updated February 22, 2019