NIST Advanced Manufacturing Series 100-5

Measurement Science
Roadmap for Polymer-Based
Additive Manufacturing
Prepared forMaterial Measurement Laboratory
National Institute of Standards and Technology
Gaithersburg MD 20899
Division of Civil Mechanical and Manufacturing Innovation
National Science Foundation
Arlington, VA 22230
Prepared by
Joan Pellegrino
Tommi Makila
Shawna McQueen
Emmanuel Taylor
Energetics Incorporated
Columbia MD 21046
This publication is available free of charge from:https://doi.org/10.6028/NIST.AMS.100-5

NIST Advanced Manufacturing Series 100-5

Measurement Science
Roadmap for Polymer-Based
Additive Manufacturing
Prepared forMaterial Measurement Laboratory
National Institute of Standards and Technology
Gaithersburg MD 20899
Division of Civil Mechanical and Manufacturing Innovation
National Science Foundation
Arlington, VA 22230
Prepared by
Joan Pellegrino
Tommi Makila
Shawna McQueen
Emmanuel Taylor
Energetics Incorporated
Columbia MD 21046
This publication is available free of charge from:https://doi.org/10.6028/NIST.AMS.100-5
December 2016

U.S. Department of Commerce
Penny Pritzker, Secretary
National Institute of Standards and Technology
Willie May, Under Secretary of Commerce for Standards and Technology and Director

TABLE OF CONTENTS
Preface ................................................................................................................................................................................................ iii
Acknowledgments ............................................................................................................................................................................ iv
Executive Summary........................................................................................................................................................................... v
Background .................................................................................................................................................................................... v
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Summary of Results...................................................................................................................................................................... v
Chapter 1: Overview........................................................................................................................................................................1
Background ....................................................................................................................................................................................1
Scope ...............................................................................................................................................................................................1
Process ............................................................................................................................................................................................1
Speaker Contributions ................................................................................................................................................................3
Plenary ........................................................................................................................................................................................3
Panels ..........................................................................................................................................................................................3
Chapter 2: Characterization of Materials Throughout Their Lifecycle ................................................................................7
Overview of the Topic Area ......................................................................................................................................................7
Future/Desired Capabilities........................................................................................................................................................7
Technological & Measurement/Standards Challenges..........................................................................................................9
Roadmap for Priority R&D...................................................................................................................................................... 11
Chapter 3: Process Modeling ...................................................................................................................................................... 17
Overview of the Topic Area ................................................................................................................................................... 17
Future/Desired Capabilities..................................................................................................................................................... 17
Technological & Measurement/Standards Challenges....................................................................................................... 18
Roadmap for Priority R&D...................................................................................................................................................... 20
Chapter 4: In situ Processing Measurements ........................................................................................................................... 24
Overview of the Topic Area ................................................................................................................................................... 24
Future/Desired Capabilities..................................................................................................................................................... 24
Technological & Measurement/Standards Challenges....................................................................................................... 26
Roadmap for Priority R&D...................................................................................................................................................... 27
Chapter 5: Performance ............................................................................................................................................................... 32
Overview of the Topic Area ................................................................................................................................................... 32
Future/Desired Capabilities..................................................................................................................................................... 32
Technological & Measurement/Standards Challenges....................................................................................................... 34
Roadmap for Priority R&D...................................................................................................................................................... 35
Chapter 6: Cross-cutting Challenges ........................................................................................................................................ 41
Measurement Science Roadmap for Polymer-Based Additive ManufacturingiAppendix A: Agenda ...................................................................................................................................................................... 43
Appendix B: Participants............................................................................................................................................................... 45
Appendix C: Acronyms/Abbreviations ..................................................................................................................................... 49

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Cover Photos• Top: Maxillary part of the skull used for treatment planning for dental implant, printed withstereolithography (Credit: Gerald Grant, University of Louisville)• Upper Middle: Functional prototype of motorbike component, printed with continuous liquid interface
production (Credit: Carbon)• Lower Middle: Numerical simulation of polymer conformations during polymers extrusion based
additive manufacturing (Credit: Claire McIlroy, Georgetown University)• Bottom: IR thermography measure of polymer extrusion additive manufacturing (Credit: Jon Seppala,
NIST)

ii

Table of Contents

PREFACE

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

The Roadmap Workshop on Measurement Science for Polymer-Based Additive Manufacturing was hosted by the
National Institute of Standards and Technology (NIST). Sponsorship for the workshop was provided by
National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation and by NIST, Material
Measurement Laboratory (MML). Workshop planning, execution, and preparation of this report was conductedunder the direction of Kalman Migler and Richard Ricker, NIST, Steven Schmid, University of Notre Dame, and
ZJ Pei, National Science Foundation (NSF). The information contained herein is based on the results of theworkshop, which was attended by a diversity of stakeholders working in the field of additive manufacturing(AM). It represents the expert perspectives of participants, but is not intended to be all-inclusive of the views of
the AM community.

National Institute of Standards and Technology Advanced Manufacturing Series 100-5
Natl. Inst. Stand. Technol. Advanced Manufacturing Series 100-5 53 pages (December 2016)
This publication is available free of charge from:https://doi.org/10.6028/NIST.AMS.100-5

Disclaimer
This report was prepared as an account of work cosponsored by National institute of Standards and Technology (NIST).
The views and opinions expressed herein do not necessarily state or reflect those of NIST. Certain commercial entities,
equipment, or materials may be identified in this document in order to illustrate a point or concept. Such identification is
not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the entities, materials, or
equipment are necessarily the best available for the purpose.

Measurement Science Roadmap for Polymer-Based Additive Manufacturingiii

ACKNOWLEDGMENTS
Many thanks to all those who participated in the Roadmap Workshop on Measurement Science for Polymer-Based
Additive Manufacturing held June 9-10, 2016 at the NIST campus in Gaithersburg, Maryland. The presentations
and discussions that took place at the workshop provided the foundation for this report. Special thanks areextended to the plenary speakers, moderators and to the many expert participants (listed in Appendix A).
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Speakers and Moderators
Mark Dadmun, University of Tennessee-KnoxvilleCarl Dekker, Met-L-Flo
Matthew Di Prima, Food and Drug Administration
Scott Fish, University of Texas
Courtney Fox, Carbon
Slade Gardner, Slade Gardner Advanced Manufacturing and Materials, LLC
Rob Gorham, America Makes
Gerald Grant, University of Louisville
Abraham Joy, University of Akron
Greg Kittlesen, FDA
Lyle Levine, NIST
Robert Maxwell, Lawrence Livermore National Laboratory
Kalman Migler, NIST
Mike Molnar, Advanced Manufacturing Program Office, NISTPeter Olmsted, Georgetown University
ZJ Pei, NSF
Miriam Rafailovich, State University of New York at Stony Brook
David Roberson, University of Texas-El Paso
Jon Seppala, NIST
Praveen Tummala, 3DSystems
Bryan Vogt, University of Akron
Chris Williams, Virginia Tech
Angel Yanguas-Gil, Northwestern Argonne National Laboratory

Steering Committee
Kevin Jurrens (NIST)
Lonnie Love (ORNL)
Kalman Migler (NIST)
Zhijian Pei (NSF)
Ralph Resnick (NCDMM)
Richard Ricker (NIST)
Steven Schmid (ND)
Katherine Vorvolakos (FDA)

Special thanks are due to the Energetics Incorporated team who provided support for workshop planning,facilitation, and preparation of the summary report. This report, the oral presentations, and supporting
documents can be found on the workshop website at: https://www.nist.gov/newsevents/events/2016/06/measurement-science-roadmap-polymer-based-additive-manufacturing-3d. Further
information about this report can be obtained by contacting Kalman Migler at kalman.migler@nist.gov
iv

Acknowledgments

EXECUTIVE SUMMARYBACKGROUNDThis publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Additive manufacturing (AM), a process for fabricating parts directly from 3-D digital models, has been shown tohave tremendous potential for producing high-value, complex, individually customized parts. Companies acrossthe globe are using AM to reduce time-to-market, improve product quality, and reduce the cost to manufacture
products. Polymers are attractive materials in this regard because they are economical, they provide for a large
range of properties and they are amenable to many low energy fabrication technologies. In the industrial sector,
polymers are being used in a wide range of part applications including aerospace, defense, automotive, sports,
telecommunications, and medical devices. Polymers are also the most common feedstock used in 3D printers
for consumer level printers.
While the use of AM has been growing, numerous challenges impede its more widespread adoption andcommercialization. In many cases, new measurement methods, standards, data, and models are needed toovercome these challenges. These challenges, and the tools that will enable industry to overcome them, were
explored in the Roadmap Workshop on Measurement Science for Polymer-Based Additive Manufacturing cosponsored by the National Institute for Standards and Technology (NIST) and the National Science Foundation(NSF), held on June 9-10, 2016 in Gaithersburg, Maryland. The objectives of the workshop were to gain expert
insights on:••••

Measurement science barriers, challenges, and gaps that prevent the broad use of polymer-based AM
Research and development needed to address the priority measurement and standards challenges
Future measurement- and standards-related targets and goals for AM
Pathways and approaches to address identified barriers and gaps
Figure E-1. Workshop
Breakout Topics• Characterization of Materials
Throughout Their Lifecycle• Process Modeling• In situ Measurements• Performance

The workshop opened with plenary presentations that provided context
on AM challenges and use cases. These were followed by expert panel
discussions on the major topics shown in Figure E-1. Over 100 expert
participants then addressed these technical topics during breakout
sessions.

This report is based on the workshop results. It provides useful
information to public and private decision-makers interested in furthering
the capabilities of polymer-based additive manufacturing (PB AM) andaccelerating its more widespread adoption. By focusing on polymers, theworkshop was able to target the unique aspects of these materials and their processing challenges. It is hopedthat the national research agenda for polymer-based AM will incorporate the consensus-based needs andpriorities established during this workshop and presented in this report.

SUMMARY OF RESULTS
Each breakout group identified future desired capabilities, challenges and barriers to achieving targets and goals,
and priority R&D topics for the measurement roadmap. Several of the higher priority challenges were examined
more closely to create a roadmap for R&D, standards development, and other future efforts for AM. The
roadmap addresses the priority challenges for each topic area as illustrated in Figure E-2. A number of thechallenges shown in Figure E-2 have elements that cut across all aspects of additive manufacturing, frommaterials and modeling to design and manufacturing processes. Examples of some of these cross-cutting
challenges include:
Measurement Science Roadmap for Polymer-Based Additive Manufacturing

v

●
●

●
●

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5●●

Multi-disciplinary collaboration – Establishing a better structure to support multi-disciplinary
collaboration was identified as a high priority.
Business models – A good business case and economic models are lacking for PB AM in general and thiswas identified as a priority to address. Models are needed that can identify applications where the mostvalue is added by AM in terms of customization, flexibility and speed to market.
Infrastructure issues – Challenges exist in balancing infrastructure requirements, materials, and use.
Predictable IT infrastructure requirements need to be established to support growth.
Life cycle and sustainability – Not enough is known about the life cycle of PB AM parts, particularly theeffects of aging, and the ability to recycle/reuse parts. Ensuring sustainability via materials and processes is anobjective.
Printer and equipment variability – A universal challenge is the variability between printers andprocessing equipment. This issue is exacerbated by the ‘black box’ approach taken by equipment suppliers,
i.e., there are many unknowns about equipment parameters, variability, and inner workings.
Surpassing conventional manufacturing capabilities – The ability to surpass conventional parts
processing techniques in 10 years is a challenge that should be met to ensure more widespread acceptanceof polymer-based additive manufacturing.
Figure E-2. Key Priority Topics and Challenges for Polymer-Based Additive Manufacturingvi

Executive Summary

CHAPTER 1: OVERVIEWBACKGROUNDThis publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Additive manufacturing (AM) is a high-priority technology growtharea for U.S. manufacturers. Innovative AM processes thatfabricate parts layer-by-layer directly from a 3-D digital model havegreat potential for producing high-value, complex, individuallycustomized parts. Companies are beginning to use AM as a tool
for reducing time to market, improving product quality, andreducing the cost to manufacture products. Polymer-based AM
parts have emerged for use in a number of applications, such as
bio-medical, light-weight aerospace components, custom fit
protective gear, composite tooling, and functional prototypes.

SCOPE
The National Institute of Standards and Technology (NIST) hosted
the Roadmap Workshop on Measurement Science for Polymer-Based
Additive Manufacturing at their Gaithersburg, MD campus on June 910, 2016. The workshop brought together over 100 AM experts
from industry, government, national laboratories, and academia to
identify measurement science challenges and associated R&D
needs for polymer-based AM systems. This workshop aimed togain insights from experts on the measurement science barriers,
challenges, and gaps that prevent the broad use of polymer-based
AM. The workshop included stage-setting plenary sessions, panel
discussions, and extended breakout sessions as described below.
The full workshop agenda is provided in Appendix A.

Research Highlight: In situ
Measurements of Temperature

The ability to make in situ non-contact
measurements of temperature during 3D
printing would enable feedback loops to beestablished between process models, material
parameters and real-time printing conditions.
Recently, researchers at NIST have used IR
thermography during the printing of a wall-likestructure to make such measurements of theweld as it forms.
“The thermography demonstrates the crucial
importance of the first tenth-of-a-second informing a weld, because that is when interdiffusion occurs,” says NIST researcher John
Seppala. See Additive Manufacturing 12 (2016)
71-76 .

Infrared image captures the hot metal nozzle
and the hot printed polymer layer in real time.
Credit: NISTThe workshop was a follow-up to one held in 2012 entitled
Measurement Science Roadmap for Metal-Based Additive
Manufacturing, with the goal of understanding and addressing the hurdles faced by the metals community fromthe perspective of measurement science. The resulting roadmap played an important role in setting research
priorities and funding by entities involved in Additive Manufacturing, such as America Makes. The 2016
workshop and this report are expected to play a similar role for the polymer community.

PROCESSThe lectures and panel discussions were designed to provide context and content to the four parallel breakout
groups. Each group focused on a different technical aspect of polymer-based AM, as shown in Figure 1-1.
Advances in these areas are needed to address the challenges and uncertainties that currently exist around input
materials and processing technologies. Uncertainties in the properties of input materials and equipment andprocessing lead to uncertainties in how final parts will perform as well as quality and characteristics. These
relationships are described in Figure 1-2.
Within each breakout group, several key questions were posed to gain insights on the important challenges andpathways to address them. These included:
Measurement Science Roadmap for Polymer-Based Additive Manufacturing

1This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5•

What are the desired future capabilities (e.g., technology,
processing methods, measurement techniques,
performance, etc.) that we want to achieve for PB AM?

•

What are the technology, measurement, and standards
barriers that keep us from achieving the desired future
end state, and why?

•

What R&D, standards development, or other activities
should be undertaken to address the barriers and enablewidespread use of polymer-based additive manufacturing?
What are the priorities for development of ameasurement science roadmap for PB AM?

Figure 1-1. Workshop Breakout
Topics••••Characterization of Materials Throughout
Their Lifecycle – methods forcharacterization of life cycle properties andmaterial behavior; efficient, modern
processing methods
Process Modeling – simulation and predictive
tools for optimizing processing and quality
In situ Processing Measurements –monitoring of process and part behavior andproperties during manufacturing
Performance – predicting, monitoring, andassessing the performance and quality of PB
AM parts

The results of these discussions are summarized in the following
report chapters, which are organized by breakout topic. Eachbreakout group used a voting scheme to indicate which challenges would potentially have the most impact, if
addressed, and those most urgent to address to ensure progress. After prioritizing the challenges as high,medium, or low, several of the higher priority challenges were examined more closely to create a roadmap for
research and development (R&D), standards development, and other future efforts for PB AM. The results ofthese in-depth examinations can be found in the Roadmap for Priority R&D section in each chapter.
The ideas captured here reflect the expert opinions of the workshop participants and do not necessarily
represent the entire polymer-based AM industry. As such, they should be viewed as a snapshot of what theseexperts consider to be the most important issues and not an all-inclusive summary.

This report provides useful information to both public and private decision-makers interested in furthering thecapabilities of PB AM and accelerating its more widespread use in the industrial sector. It is hoped that thenational research agenda for polymer-based AM will incorporate the needs and priorities established during thisworkshop and presented in this report.
Figure 1-2. Current Status and Future Vision for Polymer-Based Additive Manufacturing

2

Overview

SPEAKER CONTRIBUTIONS
Presentations from leading experts were given to set the stage for the subsequent workshop discussions. Paneldiscussions provided additional context in specific areas of interest important to the roadmap. The fullpresentations can be downloaded from the workshop website, http://www.nist.gov/mml/measurement-scienceroadmap-for-polymer-based-additive-manufacturing.cfm.

PLENARY
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5•

Welcome ~ Mike Molnar, Director, Advanced Manufacturing Program Office, NIST
This presentation discussed the workshop purpose of developing a common understanding of thecrosscutting issues and bringing together the materials scientists and manufacturers with the hope thatparticipants leave the workshop with a common understanding of how they can and will proceed.

•Workshop Scope and Objectives ~ Kalman Migler, Polymers and Complex Fluids Group, NIST
This presentation included a review of the workshop objectives, the anticipated roadmap results andimpacts, and a description of the panels and breakouts.

•

Current Status of Polymers Roadmapping ~ Rob Gorham, Director of Operations, America Makes
Provided information on the America Makes roadmapping approach and the five “swim lanes:” design,material, process, value chain, and AM genome. This presentation also provided an overview of theAmerica Makes polymers projects.

PANELS
Moderated panel sessions were held to discuss challenges in each of the major breakout topics, as well as
technology integration and standards issues. The moderators and panelists are listed in Figures 1-3 to 1-7. The
key points that emerged are summarized below, and reflect the unique opinions of the panelists and theirrespective fields of endeavor. Many of the panel themes were echoed and expanded upon during the breakout
sessions.

Characterization of Materials throughout their Lifecycle Panel•

FDA’s Perspective on 3D Printing of Medical Devices, Matthew Di Prima. This presentation
provided an overview of FDA’s AM experience in the context of medical devices (not drugs or
biologics), and associated AM materials andFigure 1-3. Characterization of Materials
technologies. In May 2016, the FDA Center forthroughout their Lifecycle Panelists
Devices and Radiological Health (CDRH)
released its draft guidance, “TechnicalModerator, Mark Dadmun, Paul and Wilma
Zeigler Professor, Chemistry Department,
Considerations for Additive Manufactured
University of Tennessee-KnoxvilleDevices”, which addresses both manufacturing
and design considerations, as well as device• Matthew Di Prima, Materials Scientist at thetesting considerations. These considerations
Center for Devices and Radiological Health, Food
include device design, software workflow,
and Drug Administrationmaterials control, post-processing, process• Angel Yanguas-Gil, Principal Materials Scientist
validation and acceptance, quality data, device& Institute Fellow, Argonne National Laboratory
description, mechanical testing, material• Abraham Joy, Assistant Professor of Polymer
characterization, cleaning and sterilization,
Science, University of Akron
biocompatibility, and additional labelingconsiderations.

•

Building to Last: Challenges in Additive Manufacturing Going from Prototype to Functional
Component, Angel Yanguas-Gil. Two different perspectives were presented for the
Measurement Science Roadmap for Polymer-Based Additive Manufacturing

3

characterization of materials throughout their lifecycle: the advanced manufacturing perspective (beyond
prototype and design) and the characterization perspective (knowledge and cultural gaps). The firstchallenge is application dependence: performance metrics and degradation mechanisms of the materials
are strongly application-dependent. The second challenge is materials diversity: information available on
the impact of fabrication on the reliability of 3D printed materials is insufficient. Suggestions foraddressing challenges include increasing focus on reliability and performance and consolidating existing
published research.
•
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

3D Printed Polymers for Biomedical Applications, Abraham Joy: This presentation provided
advantages and disadvantages for methods for 3D printing scaffolds for biological applications such as
Polymers Extrusion (also known as FDM), SLA, extrusion, and ink-jet. The challenges to processoptimization include: the trade-off between printing speed and print quality and the mechanical failure ofa printed structure because of adhesion failure in between layers. The needs of process optimization
include: correlation between material bulk properties and print parameters, methods to quantify
filament adhesion to substrate and previous layers, and quantitative methods to determine localized
stress.

Process Models Panel•

Elements of Generative Design Driving the
Future of Process Modeling, Slade Gardner:
The key objectives presented for processmodeling include 1) quality manufacturing, toachieve design intent through processing; 2)
quality control, or production according toestablished metrics, with uniformity andconsistency throughout part; 3) quality assurance
for defect prevention and confidence that
requirements will be fulfilled, as well as
management of raw materials and equipment; and4) to support standards and measurement.
Process models are needed for feed-forward
predictions that simulate and guide the process
and also feed-back correlations of process
variables to guide control schemes.

Figure 1-4. Process Models Panelists
Moderator, Kalman Migler, Staff Scientist in the
Polymers and Complex Fluids Group, NIST
• Slade Gardner, President, Slade Gardner
Advanced Manufacturing and Materials, LLC• Peter Olmsted, Director of the Institute forSoft Matter Synthesis and Metrology (ISM2),
Georgetown University• David Roberson, Assistant Professor in the
Department of Metallurgical, Materials andBiomedical Engineering, University of Texas-El
Paso•

Measurement Science for Polymer- Based Additive Manufacturing, Peter Olmsted: This
presentation addressed the challenges in AM processing: limited combinations of materials, processing
conditions versus materials properties, temperature monitor and control (non-isothermal), nonequilibrium phenomena, urgent need for standards, etc. If there is going to be an understanding of thephysics behind modeling materials, there first must be an understanding of the melting or glasstransition. Most fundamental areas of polymer science can be linked to AM.

•

Materials and Process Development, David Roberson: Presented current and future perspectives
on materials and process development for thermoplastic extrusion. Current efforts include developing
new material systems and tuning the mechanical properties for a given application based on materials.
Future efforts include work towards AM specific material testing – in the past printing was a better
representative sample than cutting.

4

Overview

In situ Processing Measurements Panel•

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3-D Constructs, Molded vs. Printed –Differences from a Cell-Based Perspective,
Miriam Rafailovich: This presentation
discussed how cells respond to nanoscale surface
structure. AM surfaces have roughness on
multiple scales due to multiple factors. To move
AM forward, there must be a better
understanding of the underlying science. In situ
characterization of the polymer being printed
makes this possible.

Figure 1-5. In situ Processing
Measurements Panelists
Moderator, Robert Maxwell, Division Leader for
the Materials Science Division in the Physical andLife Sciences Directorate, Lawrence Livermore
National Laboratory• Miriam Rafailovich, Distinguished Professor in
the Department of Materials Science andEngineering, State University of New York at
Stony Brook• Jon Seppala, Chemical Engineer in the Materials•

Thermal and Fracture Characterization of
Science and Engineering Division, NIST
Welding Zones Produced by Polymer• Chris Williams, Associate Professor and theExtrusion 3D printing, Jon SeppalaElectro-Mechanical Corporation Senior Faculty(presented by Kalman Migler): The nuances
Fellow in the Department of Mechanicalof how thermography, rheology, and fracture
Engineering, Virginia Techstrength relate to printing were presented. Weld
formation in material extrusion occurs over approximately one second; most of the inter-diffusion
occurs over a time scale much less that one second. Temperature kinetics, rheology, and weld theory
provide the foundation for understanding fracture strength as a function of temperature and feed rate. In
situ monitoring is achievable – linking back to real time control is a challenge.

•

Molecules to Manufacturing: Expanding the Polymeric Materials Toolbox, Chris Williams:
This presentation discussed the idea of incorporating pre-process measurements that allow a computer
to predict whether a product is printable. Measurements must be monitored in situ and post process as
well. Stereolithography was the first printing modality to be considered to have all three processing
steps. The model should predict how deep the cure will be based on exposure. The role of opticalintensity is not yet fully understood. Additionally, AM is susceptible to cyber-attacks that are virtuallyunrecognizable. Side channel measurements can help protect against these cyber threats.

Performance Panel••

Functional Prototyping with Polymeric
Materials, Courtney Fox: This presentation
discussed the definition of functional prototyping
and also introduced the concept of a dead zone,
where chemical reactions are inhibited. If one
can understand how the parts print, one canunderstand how they fail. Carbon has been able
to increase the stress strain curve to reach
injection molding standards. Field testing so far
has shown no issues with printed materials.

Figure 1-6. Performance Panelists
Moderator, Gregg Kittlesen, Materials Engineer,
FDA• Courtney Fox, Research Scientist on the Carbon
Materials Team, Carbon• Bryan Vogt, Professor in the Department of
Polymer Engineering, University of Akron• Gerald Grant, Professor and Chair of Oral
Health and Reconstruction, University of Louisville

Polymers Extrusion (FDM) from a
Polymer Processing Perspective:
Challenges and Opportunities, Bryan Vogt: This presentation discussed the challenges ofPolymers Extrusion (FDM) 3D printing: incomplete infill results in voids inside printed parts and fastsolidification leads to limited chain diffusion between fibers and layers. Despite unmet challenges, there
is a wealth of new possibilities to explore with FDM, such as new materials (filament extrusion anduniformity). Improved process knowledge is the key to future success.
Measurement Science Roadmap for Polymer-Based Additive Manufacturing5•This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Use of Additive Manufacturing in Reconstruction and Rehabilitation, Gerald Grant: This
presentation covered AM relating to medicine and dentistry. Generally, there are two approaches forfabrication using AM – medical models from medical scans, or design of a device (implant, cutting guide,
reduction bars, etc.…) to fit elements of a
medical/dental scan following a Scan/Plan/andFigure 1-7 Integration & Standards Panelists
Manufacture workflow. There are decisions atModerator, Carl Dekker, President, Met-L-Flo
each step and AM has provided flexibility and• Scott Fish, Sr. Research Scientist, University of
speed in the process of the fabrication of aTexascustom part. In the fields of medicine anddentistry, a Plan/Scan/ and Manufacture workflow• Praveen Tummala, Manager of R&D Materials
provides the ability to deliver devices without
and Process, 3DSystems
multiple direct interactions in a more non• Lyle Levine, Physicist at the Materials
invasive manner with the patient via an AM
Measurement Laboratory, NIST
fabricated mold, pattern, or a directly produced
device with remarkable accuracy, contrary to theconventional fabrication methods.

Integration and Standards Panel•

Polymer AM Integration and Standards, Scott Fish: This presentation covered technical andnon-technical barriers to growth and how measurement science and standards help. Technical barriers
include lack of tools for product design and lack of tools for fabrication planning/optimization. Nontechnical barriers include lack of understanding of application opportunities and associated economic
benefits, lack of widespread design experience with AM, and stigma of rapid prototyping (not for “real”
manufacturing). Measurement methods must become more prolific and tailored with the growth inmaterials and process understanding to surmount the technical barriers.

•

Factors Affecting the Adoption of 3D Printing Technologies (SLS) as Manufacturing
Platforms – Role of Standards for Adoption, Praveen Tummala: This presentation addressed
the factors affecting the adoption of 3D printing technologies. SLS uses thermoplastic semi-crystalline
polymers as print materials and is can produce robust, durable, and functional parts. Factors affecting the
adoption of AM include print speed, part cost, part performance, quality, and accuracy, thermal process
limitations, and intelligent machine controls.

•

AM-Bench: A Proposed Benchmarking Series for Additive Manufacturing, Lyle Levine(presented by R.E. Ricker): This presentation detailed the AM Benchmark Test Series (AM-Bench), a
continuing series of highly controlled benchmark tests for additive manufacturing, in conjunction with aconference series. A fourth (and final) draft of foundational documents are being written with input froman Exploratory Committee (40 participating organizations and 59 members). The goals of AM-Bench are1) to allow modelers to test their simulations against rigorous, highly controlled additive manufacturing
benchmark test data and 2) to encourage additive manufacturing practitioners to develop novel
mitigation strategies for challenging build scenarios.

6

Overview

CHAPTER 2: CHARACTERIZATION OF MATERIALSTHROUGHOUT THEIR LIFECYCLEOVERVIEW OF THE TOPIC AREA
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Material characterization plays a key role in additive manufacturing. Producing AM parts that have consistent,
predictable, repeatable properties requires that the characteristics of the feedstock materials are well known.
Characterization can also provide insights to the dependence of AM part mechanical properties on the input
material properties. Characterization of materials is also important to developing appropriate measurements andstandards. A number of physical characterization techniques have been employed, such as laser diffraction
particle size analysis, X-ray computed tomography for size and shape analysis, and optical and scanning electron
microscopy. Techniques for structure, chemistry and mechanical strength include but are not limited to X-ray
diffraction and vibrational spectroscopy and fracture tests, respectively. While many techniques are available
and being applied, significant challenges remain in life cycle characterization of polymer-based AM materials aswell as understanding materials behavior during processing and in the final product.

FUTURE/DESIRED CAPABILITIESIn the future, a wide range of characterization tools will be needed to effectively measure and correlatematerials properties and behavior. Further details on the desired capabilities and technologies for PB AM
materials are provided in Table 2-1 and described briefly below.
Predictive Capabilities for Parts/Materials
Predictive models are needed to help correlate material properties with AM process parameters, as well as toenable production of high quality parts. The main areas of interest include correlation of material properties tothe behavior of the finished part, including mechanical properties, impact of processing aids, and relationships
between input material properties and how well layers forming the part are joined. Developing predictive
capabilities will require wider availability and collection of data (both existing and new) on materials properties.
Areas of importance that were identified include interfacial data, phase distribution behavior, and temperaturedependent chemical properties.
Processing Parameters and Characterization
There is a need to identify and establish open source process parameters as well as baseline data for materials
used in selective laser sintering (SLS) and polymers extrusion (FDM). This will enable process optimization as
well as improved material selection. Characterization technologies such as size exclusion chromatography, mass
spectrometry, and thermal analysis are also needed for both pre- and post-printing measurement of polymerbased AM materials. Improved, robust measurement tools adapted for PB AM would also enable betterinterfacial analysis, characterization of multi-deposition orientation, and collection of needed materials
properties.
Life Cycle Sustainability and Safety
Life cycle characterization is an important element in encouraging the future growth of AM, as it provides criticalinformation on longevity, durability, recyclability, safety, and other sustainability factors. Some of the key
capabilities for life cycle include predicting the re-usability of polymers that have been printed, and more fully
understanding the cradle-to-grave aspects of materials. Safety considerations include the capability for producing
feedstock materials (safety in the workplace) as well as products that are safe and non-hazardous to people andthe environment (part safety throughout its use and disposal).
Measurement Science Roadmap for Polymer-Based Additive Manufacturing

7

PB Materials Characterization and Design
Characterization of starting materials is needed to enable more effective design and manufacturing processes.
Fully characterizing material classes and their properties, and being able to predict their behavior will enableproduction of more reliable, higher quality parts. In addition, technologies (and new materials) are needed toenable the combination of materials (or hybrids) with complementary functionalities (e.g., structural strength andmagnetic).
Materials Processing
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An overall objective is to be able to achieve reliable printing of parts in a cost-effective way that is competitive
with older manufacturing techniques, and to reduce the need for machining. This would require that equipment
have the capability for printing complex and reliable composites, using off-the-shelf materials and equipment.
Capabilities are needed to surpass conventional manufacturing techniques such as injection molding or
extrusion, and to quickly create parts with the same or better physical properties and performance. A key
aspect is the reduction of post-processing.
Advanced Printing Technology
While polymer 3D printing has been on the market for many years, there are still many advances that can be
made. Today’s printing technologies will require advances to achieve ‘true’ 3D printing. There are also a number
of approaches needed for printing of composites and/or multi-modal materials with defined gradients, such astransitioning from stiff to flexible materials. Another approach is to explore arbitrary path conformal printing in
3D, rather by today’s layer-by-layer approach.

Table 2-1. Desired Capabilities for PB AM Materials Characterization
Predictive Capabilities for Parts/Materials••

Predictive models to correlate material properties with AM process parameters to generate parts with acceptable properties
o Prediction of material/mechanical properties of final part from starting material characteristicso Predictive relationships between filament scale properties and final part/weld/interface scale properties
o Impact of processing aids and additives on the part and/or process
Data for input into predictive models and simulations
o Searchable database of material properties to accurately simulate process prior to manufacturingo High temperature polymer chemical and performance information to integrate into design and predictive models
o Interfacial data – characterization of structure and strength of interfaces from nm to mm; understanding polymer phase
distribution/behavior at interfaces for three (3) structureso Uniform material characterization property list for all materials (in situ and realized)o Data on compatibility with conventional parts (e.g., surface conditions)o Performance metrics through lifetime of printed device (ex-vivo and in-vivo)

Processing Parameters and Characterization
•
•
•
•
•

Open source process parameters/baseline data for materials in selective laser sintering (SLS), polymers extrusion (FDM)andstereolithography (SLA)
SEC (size exclusion chromatography), mass spectrometry (MS), thermal analysis (TA) to provide fast, accurate information on
characterizing materials – both pre and post printing
How to create variable stiffness parts by altering toolpath or modifying material during print
Multi-deposition orientation capabilities and characterization
Capabilities to evaluate interfaces between parts/layers – robust measurement tools

Life Cycle Sustainability and Safety
8Characterization of Materials

Table 2-1. Desired Capabilities for PB AM Materials Characterization
•
•
•
•
•
•

Ability to predict the re-usability of polymers and powders
Understand cradle to grave life cycle aspects of materials (i.e., re-usability)
Ability to make “safe” starting materials, non-hazardous, and safe products
Assurance of workplace safety (i.e., after repeated exposure)
Traceability for parts, including recycle history
Data on shelf life of filaments and sustainability

PB Materials Characterization and Design
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

•
•
•
•
•
•
•

Classes of materials that can reliably replace injection molded parts over long periods of time
Ability to purchase filament material produced to a standard and with more consistency
End-user filament that prints to a highly specified technical data sheet and comes with key processing history and material
properties
Understanding of how to tailor material properties voxel-by-voxel
Ability to enable complementary functionalities; in addition to structural strength – electronic, magnetic, bio interface, etc.
Technology to combine a variety of functional materials
Hybrid material systems and blends, alloys, composites – that can be printed in situ

Materials Processing/Printing••

Successful, reliable printing of parts competitive with older manufacturing techniqueso Able to use commercial off-the-shelf (COTS) materials on COTS printerso Capability to print complex and reliable structural compositeso Printing processes and materials that require less/no post processingo Printing high performance AM parts with well-defined and predictable performance and lifetimes
o 3D printing as fast, effective, and inexpensive as injection molding or extrusion – but with more capability and producing
same or better physical properties
o Understanding of part reliability with minimal verification testing
Advanced Printing Capabilities Advanced printing capabilitieso “True” 3D printing as opposed to layer-by-layero Print multimodal materials with defined gradients (e.g., transition from stiff to flexible material or low to high dielectric
constant)
o Optimized toolpaths to minimize and/or take advantage of anisotropies in AM partso Arbitrary path, conformal printing in 3D rather than layer-by-layer

TECHNOLOGICAL & MEASUREMENT/STANDARDS CHALLENGES
The barriers and challenges currently impeding characterization of PB AM materials are presented in Table 2-2,
categorized by common themes. In each category, specific barriers or challenges are prioritized based on expert
and stakeholder input.
One of the most important challenges identified is navigating the enormous amount of interacting and complex
variables of materials and processes. A major issue is identifying the most important parameters and determining
which are common to many materials, and which are unique to specific materials. Adding to the complexity islarge number of machine variables and types of machines, plus additives and other external factors. Closely
linked to this is the challenge presented by the black box approach used by suppliers of materials as well as
printers (e.g., closed loop systems), leading to a lack of transparency in processing parameters and ability to
optimize.
Another important barrier is the lack of research grade, standardized feedstocks. In particular, standards arelacking for filaments, plastics additives, and basic material formulation requirements (e.g., powders for specificprocesses). This makes it difficult to design a part or experiment to take advantage of specific material
Measurement Science Roadmap for Polymer-Based Additive Manufacturing9

properties. Other key barriers include lack of data on the life cycle of PB AM parts and materials, and a poor
understanding of how feedstock properties affect the finished part.

Table 2-2. Barriers and Challenges for PB AM Materials Characterization
Materials

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5High Priority••

Lack of techniques to understand non-equilibrium path of materials during the deposition process
Lack of materials, suitable for AM having comparable functional performance as conventional materials

Medium Priority•

Highly fragmented market requiring niche resins/polymers as feedstocks

Low Priority•

High cost of analytical tools that enhance collection of information and characterization of polymers

Standardization of Materials•

Lack of research grade materials (feedstocks) or standards; unpredictability between batches making it difficult to design to thatmaterial; lack of minimum baseline capabilities for materials; standards lacking for:o Filamentso Wetness of particles
o Plastic additive materials – composition, etc.
o Material formulation requirements (e.g., powder for SLS)

Medium Priority•

Closed source companies that use English vs. metrics

Low Priority•

Poor understanding of what should be included in standards and what should be standardized (e.g., composition vs. mechanicalproperties, material parameters vs. end-user applications, etc.)

High Priority

Properties/Process Variables and Data•

High Priority

•
•
•

•
•

Medium Priority

•
•
•
•

Low Priority

10

•
•

Enormous number of variables, all interacting, leading to nearly infinite parameter space; highly multifactorial, but not always
clear what factors matter; some factors are polymer and application-specific; makes correlations with property performancedifficult; hard to establish properties that can provide suitable guidance to repeatable, robust processes
Number of printers
Number of print settings on each printerNumber of materials (matrix)
Number of additives (fiber, flow agents, minerals)
Lack of experimental AM data on part lifetime/aging performance with respect to conventional parts; insufficient understanding oflayers and effect of machine-material interface
Ability to benchmark at multiple scales (nm to mesoscopic)/interfaces against known ”conventional” materials and processes andfinal product
Poor understanding (lack of data) regarding impact of process parameters and material properties on finished part performance;
knowing what the key best printing parameters are
Data and understanding of one polymer does not translate to another, even in the same application
Lack of understanding what occurs at the interfaceso Studies (properties data) applying polymer physics to interfaces in 3D print parts to predict strengtho Understanding what affects properties at interfaces
Lack of studies into key “rate limiting” steps of AM (chemistry, heat transfer, mass transfer, etc.)
Lack of transparency in available technical data (How were samples made?)Lack of awareness of analytical technologies that effectively characterize and ensure the safety of polymers used in additive
manufacturing
Polymer microstructure not widely characterized in AM literature
Lack of consistency between printers, slicing routines, print paths, same materials, and ambient conditions
Polymer properties depend on molecular weight, polydispersity index (PDI), degradation, orientation/stretch, and other factors(unlike metals/ceramics)

Characterization of Materials

Table 2-2. Barriers and Challenges for PB AM Materials Characterization
Machine/Process Issues•

Black box approach in 3D printers and materials suppliers (closed loop suppliers); high variability of machines and processing
conditions and associated documentation; material patents are owned by machine manufacturer and not material manufacturer;
multiple machine manufacturers are not supported; inability to catalog all the properties due to the black box of machine; closed
loop supply chains for commercial AM materials

Medium Priority

•
•
•
•
•
•
•
•
•

Slow speed, high cost, poor mechanical properties
Understanding the properties of finished parts compared to test specimens
Lack of certification qualification standards and protocols
Understanding how to quantify variable stiffness parts (e.g., models, multiple bend/tensile tests)
Designing of printers around narrow process window of materials (narrowly designed now)
No standard characterization of relevant material properties and process parameters
Insufficient fidelity of material/process history and reporting data
Cost and schedule for generating statistical data
Lack of understanding of what needs to be controlled

Low Priority•••

Additive, layer-wise part build-up makes use of existing monitoring equipment obsolete
Machines/technology continue to evolve while feedstocks are being developed/optimized (moving targets)
Lack of modeling or commercial in situ process for monitoring inter-layer adhesion in the hands of the user

High Priority

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ROADMAP FOR PRIORITY R&D
A roadmap action plan was developed to enumerate the activities and approaches for overcoming the mostcritical barriers identified in Table 2-2. Roadmaps were articulated for the individual R&D priorities listed below
and are outlined in more detail in Figures 2-1 through 2-4.

Table 2-3. Key Barriers and Corresponding Priority Roadmap Topics for
Characterization of Materials Throughout Their Lifecycle
High-Priority Challenge/Barrier

Priority Roadmap Topic

Companies have proprietary process control, material
limits, and a wide breadth of materials and processoptimization within the black box of equipment, making
parameters non-transparent.

System Manufacturing Collaboration – Material-Machine Standardization(Figure 2-1)
An internal database of materials properties and variability is needed, including
data sharing, to enable material-to-machine standardization.

Materials standards are lacking for polymer AM
feedstock or polymer AM finished parts.

Standardization of Materials (Figure 2-2)
Guidelines are needed to which polymer AM material standards can be written;
this will require material feedstock reporting requirements and understanding ofindustry-specific needs.

There is a lack of techniques and information to define
the process history of the material during the print
process.

Materials Characterization – Process-Induced Transformations (Figure 23)
Characterization and modeling techniques should be developed to monitor
structure and interface development during printing, including in situ techniques
to define reactions, heat transfer, and stress relaxation.

Capability is lacking to compare results and separate
process variables, material variables, and process
conditions to obtain reproducible results across all
printer platforms.

Large Variable Problem: Identify/Prioritize Key Variables (Figure 2-4)
Methods and data are needed to enable a standard material printed on astandard tolerance printer to yield reproducible results; requires an open source
database of materials in use.

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

11

FIGURE 2-1. ROADMAP ACTION PLAN:
SYSTEM MANUFACTURING COLLABORATION – MATERIAL-MACHINE STANDARDIZATION
MAJOR BARRIER: Companies have proprietary process control, material limits, and a wide breadth of materials and processoptimization approaches, and related patents. This black box around the system precludes access to material history, composition, and otherparameters; many are unknown and non-standard. The build sheet is proprietary. Machine design is narrowed to the material properties; thiscreates a limited process space with no known properties at the quality control (QC) stage.

APPROACH: The objective is to create a database of materials properties and variability, including data sharing on feedstocks. Conflict of

ROADMAP ACTION PLAN••

NEAR (1-2
YRS)

TIME

•

MID (3-5 YRS)

•

•
•
•

LONG (>5
YRS)

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5interest, policies, and other issues may need to be resolved. The approach is to catalog and disseminate material data relevant to AM, andapply to the supply chain (and use policy pressure to enact changes). The end result would be a material database, better QC tools, a certified
supply chain and standardized polymer grades.

•

MILESTONES

Measure feedstock variability
Identify most relevant variables for
qualification
Establish dissemination method for data
(Who, how, etc.)
Consider packaging and storage and premolding••

Material database
Parameter space narrowed down

Develop the material standards
Work with regulatory agencies andcustomers to adopt standards
Develop production for feedstock QC – watercontent, etc.

•••

Certified supply chain
Grading of material identified
New production QC tools

Uniform machine standards to takeadvantage of material standards••

Processes that can be certified
Machines that can be graded

STAKEHOLDERS & POTENTIAL ROLES
•
•
•
•
•

Industry/AM Users: Push machine supplies towards newmodel.
Industry/AM Providers: Push back? Better process control(feedback, process) populate database.
Academia: Make measurements. Develop/perform
improvements to process.
Standards Committees: Write and publish material
standards.
Government: Government researchers similar to academia;
supporting role; regulatory role.

12

PERFORMANCE TARGETS

Characterization of Materials•••

No more single supplier –redundant, flexible supply chain
Good material leads to good
fundamental researchMore robust processing

RELATIVE IMPACTS
High

Improves Product Quality: Better control of material
and equipment
Medium Reduces Costs: Graded options may be more
expensive for more complex parts, less expensive due tocompetition and existing offset
High
Accelerates Innovation: More material choices, lessboundaries, better understanding of capabilities
High
Enhances Competitiveness: More players
High
Reduces Time to Develop Products: Understanding of
the machines and polymers
Other: N/AFIGURE 2-2. ROADMAP ACTION PLAN:
STANDARDIZATION OF MATERIALSMAJOR BARRIER: No materials standards exist for polymer AM feedstock or polymer-based AM finished parts. The current scope andof materials available for use is too small.

APPROACH: The objective is to establish guidelines to which polymer AM material standards can be written. The approach will be toestablish reporting requirements, which can then be used to establish guidelines for standards. This will enable better prediction of materials
performance and upfront design, and will facilitate development of new polymer materials for AM.

NEAR (1-2
YRS)

•
•
•

Establish reporting requirements for AM
material feedstock
Establish guidelines for polymer AM material
standards
Encourage standards committees to begin
work on polymer AM material standards

MILESTONES••PERFORMANCE TARGETS

Consistent quality in polymer AM
feedstock
Establishment of horizontal material
standards for polymer AM
feedstock/incoming materials

MID (3-5 YRS)•••LONG (>5
YRS)

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

ROADMAP ACTION PLAN

TIME

•
•

Expand feedstock requirements tocapture/minimize lot to lot variation – Bothcomposition and geometry
Conduct market research to define the needs
for polymer AM materials

Identify use-specific and/or industry-specificpolymer AM material needs
Public database of polymer AM material
properties – Feedstock and Finished-part••

Established and acceptable variability
in feedstock – leads to increased
finished part property confidence

•

•

•
•

•

End-user ability to select appropriatematerial and process for specific partend-useSTAKEHOLDERS & POTENTIAL ROLES••

Consistent and predictablematerial performance – both
feedstock and finished part
Database of polymer AM materials– develop library of materials fordifferent applications
Facilitate development of polymer
materials to be used in AM
processes

Industry/AM Users: Needs and requirements
establishment. Participate on standards committees. Publish
data for materials database.
Industry/AM Providers: Provide guidance tochemical/material industries on material needs. Participate
on standards committees.
Academia: Develop new materials, processes, analysis
tools, measurement tools. Lead creation of polymer AM
database.
Standards Committees: Develop horizontal and verticalstandards (see above).
Government: Participate on standards committees.
Develop test methods. Anticipate impact in regulatory realm.

RELATIVE IMPACTS

High

Improves Product Quality: Improved quality control offinished parts
Medium Reduces Costs: Reduces need/frequency of testingHigh
Accelerates Innovation: Incentivizes material
development
Medium Enhances competitiveness: See above
High
Reduces Time to Develop Products: confidence in
feedstock leads to confidence in finished parts
High
Other: Expanded capability of materials. Lower barrier
to entry.

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

13

FIGURE 2-3. ROADMAP ACTION PLAN:
MATERIALS CHARACTERIZATION – PROCESS-INDUCED TRANSFORMATIONSMAJOR BARRIER: There is a lack of techniques and information to define the process history of the material during the print process.
Multi-scale models to describe reaction, flow, heat transfer, and stress relaxation for a given print process are also lacking.

APPROACH: The objective is to improve reproducibility and performance through better monitoring and understanding of structural and

ROADMAP ACTION PLAN

TIME
NEAR (1-2 YRS)•••

MID (3-5 YRS)

•
•
•
•

LONG (>5
YRS)

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

thermal parameters that impact part properties. The approach is to develop characterization and modeling techniques to monitor structure andinterface development during printing. Implement in situ characterization techniques are needed to define reaction, flow, heat transfer, andstress relaxation during printing; these can provide input to process models and controls.

••

Define all parameters that define process
history and that are needed for multi-scale
model
Begin to identify important parameters that
impact structure and interface formation withprinting
Aggregate known data on correlation
between process history and properties
Development of in situ techniques to monitor
important parameters that impact structure
and interface during processing
Separation of impact of material parametersand processing parameters
Develop multi-scale models
Prioritize importance of processing
parameters
Develop methods to control important
processing parameters during printing
process
Establish round robin to evaluate universality

MILESTONES

•

•
•

•
•

Repository of current information on
importance of processing space

Tailored properties of final structurefrom processing protocol
Validation of models against tested
parts

•
•
•

•

Industry/AM Users: Provide input on needs, problems, andcurrent state of characterization and modeling techniques.
Conduct demo/pilot/round robin.
Industry/AM Providers: Provide tools, software andmaterial information (non-IP or IP with NDA).
Academia: Develop data, experiments, and analyses.
Standards Committees: Develop standards document onterminology and certification processes. Documentation ofdefinitions and experimental methodologies.
Government: Maintain repository, oversize round robin.
Develop/test algorithms.

14

Characterization of Materials

•
•
•
•

Reproducibility improvement
Tuning part performance fromprinting process knobs
Identify inherent limitations of agiven printing process
Provide foundation to implement
feedback control during printing

Report on and complete round robin
Offer control of processing during print

STAKEHOLDERS & POTENTIAL ROLES•PERFORMANCE TARGETS

RELATIVE IMPACTS
High

Improves Product Quality: Provide foundation forreproducibility tailor-ability
Low
Reduces Costs: Foundational information will not impact
cost initiallyHigh
Accelerates Innovation: Foundation for models andreproducible tailor-ability
Medium Enhances competitiveness: Offers path to competewith other manufacturing techniques
High
Reduces Time to Develop Products: Provides
foundation
Other: N/AFIGURE 2-4. ROADMAP ACTION PLAN:
LARGE VARIABLE PROBLEM: IDENTIFICATION/PRIORITIZATION OF KEY VARIABLES
MAJOR BARRIER: Currently the capability is lacking to compare results and separate key process variables, material variables, andprocess conditions to aid in achieving reproducible results across all printer and material platforms.

APPROACH: The objective is to identify and prioritize the key variables for all parts of the process, from material to finished part. The

ROADMAP ACTION PLAN

MID (3-5 YRS)NEAR (1-2
YRS)

TIME

•
•
•

•
•

LONG (>5
YRS)

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approach will first develop standard process settings for standard materials, define parameter requirements, and provide data sets to createtechnical data sheets for research grade materials. This will ultimately help to enable standardized printing and tolerances with reproducible
results and part properties.

••

Obtain standard material set for each printing
technology
Well-defined machine tolerances defined for
R&D test bed (dimensional, temperature)
Define DOE parameter space for eachprinting technique

Revisit material test standards to ensure thekey identified parameters are included instandards
Execute DOE and generate shared data-sets

Extend paradigm to exotic and innovative
materials (composites, hybrid materials,
graded materials)
Input shared data-sets into informatics basedmaterial genome project to predict material
performance

MILESTONES•

Reporting standard process settings
used in a given print (i.e., metadata,
G-code)
•

•
•

•
•

“Research grade” materials that comewith technical data sheet
Plethora of data available for analysis
to uncover critically sensitive
variables

New measurement techniquesdeveloped specifically for additive
manufacturing processes
Input for modeling material
performance

STAKEHOLDERS & POTENTIAL ROLES
•
•
•
•

•

PERFORMANCE TARGETS

Industry/AM Users: Participate in characterization ofstandards.
Industry/AM Providers: Provide data and tolerances as set
forth by appropriate governing bodies (NIST, ASTM, ISO).
Academia: Participate in generation of DOE andcharacterization metrics.
Standards Committees: Demand compliance for current
published standards and/or generate new standards specificto polymer AM.
Government: Support research initiatives financially inorder to achieve stated manufacturing goals for USA•

A standard material printed on astandard tolerance printer with afixed programming code (G-code)yields reproducible results andproperties
Open source database ofmaterials used for R&D/industrial
3D printing process this ispreferably provided by the
manufacturer of the feedstock
material

RELATIVE IMPACTS
High
MediumMedium
High
High
High

Improves Product Quality: Improves repeatability andproduct confidence
Reduces Costs: Less redundant data collection/faster
optimization
Accelerates Innovation: Predictive analysis unlocks
new material potential
Enhances competitiveness: Make AM competitive withexisting manufacturing techniques
Reduces Time to Develop Products: More upfront
knowledge of materials and machines
Other: Facilitates scientific knowledge

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

15

CHAPTER 3: PROCESS MODELINGOVERVIEW OF THE TOPIC AREA

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Development of accurate process models is an important fundamental building block as additive manufacturing
technologies are being developed and deployed. Good validated models decrease the need for real-world testingof technologies and processes, and give product designers a predictive capability for optimization of part designs.
Models are the basis for developing the required control technologies and software for PB AM production
processes, and they will also provide support for standards development as well as qualification and certification
methods.
Accurate models cannot be developed without comprehensive and validated data on materials and processes.
These models also require an excellent understanding of the fundamental processes and physical phenomenathat underlay PB AM feedstock inputs, approaches, and technologies.
Note that the modeling needs outlined in this chapter are relevant to PB AM processes and production, but may
also broadly encompass other aspects of modeling.

FUTURE/DESIRED CAPABILITIESIn the future, enhanced AM process modeling capabilities will enable manufacturers to minimize productdevelopment time and cost and improve part quality. The desired future process modeling capabilities aredescribed briefly below and listed in Table 3-1.
Models to Inform Design
There is a need to better understand the process-structure-property links in PB AM. Better understanding ofthese relationships will enable designers to predict certain part characteristics based on used materials andmanufacturing processes. This knowledge and resulting capabilities will enable parts to be designed for their
desired function and performance, greatly streamlining product development and manufacturing processes.
Processing and Engineering
Enhanced process modeling capabilities will allow for significant advances in PB AM processes and systems.
Better understanding of the processes will enable the manufacture of nearly any conceivable product in needed
size and resolution. Once PB AM processes are more thoroughly understood, material properties of printed
parts can be predicted without regard to the print geometry of the part. Models that encompass everythingfrom molecular level to device scale enable connecting the part manufacturing process to the performance ofthe completed part. The development of high-quality models will require well-defined, and accepted, standards
along with common nomenclature for PB AM.
Build Variability and Performance
For future success of PB AM processes and products, it is important that build variability is minimized and part
performance maximized. To achieve this goal, process models need to account for the impact of feed material
properties on printed part quality. Because build variability is process-dependent, variability must be
characterized by process.
Multi-component Materials Models
For PB AM to gain further market share it will need to become a viable technology for parts containing multiple
materials. There is not only a need to have processing models for the different materials, but comprehensive
multi-material models that incorporate several single-material models also need to be developed.
Measurement Science Roadmap for Polymer-Based Additive Manufacturing

17

Table 3-1. Desired Capabilities for PB AM Process Modeling
Models to Inform Design: Process – Structure – Property (P-S-P)••

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

•
•
•
•

Prediction of strength and ductility (i.e., parameters a designer can actually use); predict properties from in situ
measurements of the structure (e.g., voids, orientation, temperature)
Full understanding of process-structure-property links to enable design for performanceo Predictive capabilities to drive design, material, and process parameters based on the desired part function
Fully characterized mechanical properties of printed products
Characterization of interfaces (structures, formation, dynamics, effects, properties, etc.)
Thermal history predictions for printed part that takes into account property changes with phase change
Models consistent with non-equilibrium thermodynamics

Processing and Engineering
•
•
•
•
•
•
•
•
•
•
•
•

Design of optimal materials and processing conditions to manufacture any conceivable product of any size and resolution
Molecule to device-scale model connecting processing to performance (trends then numbers)
Ability to relate strength of the part to the printing parameters and material properties
Ability to have fully user-defined processing parameters
Achieve near-isotropic (or simply predictable) material properties regardless of print geometry (thermal history,
microstructure, toolpath, etc.) on x, y scale at least
Model to relate final properties from shape to processing to individual filament to the microscale (at multi-length scale)In extrusion (fusion deposition modeling – FDM, Big Area Additive Manufacturing – BAAM), predict interlayer bondstrength as a function of temperature
Real-time modeling of P-S-P
Temperature measurement accuracy on a certain scale (scale TBD) across time and space
In situ molecular information in real-time and nanometer to millimeter scale (not via synchrotron) with or without coupons
Defined and accepted standards to model additive manufacturing processes (i.e., nomenclature)
Distributed data infrastructure (standardized/integrated) models

Build Variability and Performance
•
•
•
•
•

Predictive models and understanding of build variability (process-dependent, so characterization of variability by process)Tighter control of process variation and impacts of feed material properties
Optimization of materials and process to minimize/eliminate voids in polymers extrusion (FDM)
Prediction of deformation and accuracy: “as printed” vs. “as modeled”
Nonlinear material models for failure mechanisms (e.g. temperature constrained properties)

Multi-component Materials Models••••

Optimal placement of reinforcing phase for lightweight structural component design
Multi-material and multi-scale modeling (length scale of carbon or other additive)
Controllable fiber orientation (short fiber) throughout bead/voxel processing volume for any volume fraction composite
Models for different materials (melts, semi-crystalline, containing particles)

TECHNOLOGICAL & MEASUREMENT/STANDARDS CHALLENGES
The barriers and challenges currently impeding large scale deployment of PB AM process modeling technologies
are presented in Table 3-2, categorized by common themes. In each category, specific barriers or challenges areprioritized based on expert and stakeholder input.
One of the most significant barriers to develop high-quality models for PB AM processes is the lack of acomprehensive understanding of the material and process sciences that are involved. The interfacial science18

In situ Process Measurementsbetween different voxels, layers, phases, and multi-materials is an area where more research is needed. There isalso a lack of non-equilibrium material and process measurements and models. Physical models are needed forfactors such as polymer relaxation, kinetics, diffusion kinetics and rheology.
Lack of standards to guide model development is another high priority challenge. There are no agreed-uponguidelines and standards for PB AM model development and validation. A widely accessible and standardizedmaterial and process metadata collection structure and a central data repository are also lacking.

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Non-technical infrastructural issues are also hindering modeling progress. Because development ofcomprehensive PB AM process models requires multi-disciplinary collaboration, there is need to implement
better programmatic structures at universities and other research entities to enable and encourage suchcollaborative efforts

Table 3-2. Barriers and Challenges for PB AM Process Modeling
ChemistryHigh Priority•

Fully understanding the interfacial science (between voxels, layers, phases, or multi-materials)

Medium Priority•

Ability to/support for design of new polymer chemistries optimized for additive manufacturing (including reinforcementcompatibility)

Low Priority••

Understanding of post-processing chemistryUnknown feedstock variability

Standardized Data and Protocols
High Priority••

Lack of agreed-upon model systems for development/validation (e.g., benchmarks)
Limited test protocols for testing additive manufacture parts (mechanical)

Medium Priority•••

Lack of standardized material and process metadata collection structure or a central repository that is widely accessible
Model repurpose and reuse is limited
Lack of cyber-secure and pedigreed datasets (with traceability, schema)

Non-Equilibrium Physics
Lack of non-equilibrium material and process measurements and models Physical models of polymer relaxation,
kinetics and diffusion modes with temperature, orientation, stress, etc.
o Reliable physics-based constitutive models far from equilibriumo Rheology measurements near glass transition temperature, 𝑇𝑇𝑔𝑔•

High Priority
Medium Priority
Low Priority••

Large range of length and time scales – physical descriptions
Bridging multiple time/length scales•

Lack of flexible in situ time (t) and space (𝑟𝑟 ) resolved molecular measurements (3D, nanometer scale, millisecond scale)•

Understanding the appropriate levels of description for reliable modeling of the important dynamic processing variables(different length scales, minimum number)~

Design and Engineering
Medium Priority
Low Priority•

Tools to tailor structural designs (better than 3DCAD) to take maximum advantage of unique capabilities of additive
manufacturing
Part quality and variability•

Lack of sensors for measuring fusion•

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

19

ROADMAP FOR PRIORITY R&D
A roadmap action plan was developed to enumerate the activities and approaches for overcoming the mostcritical barriers identified in Table 3-2. Roadmap action plans are outlined in more detail in Figures 3-1
through 3-3. The key barriers being addressed and the corresponding priority roadmap topics are summarizedin Table 3-3.

Table 3-3. Key Barriers and Corresponding Priority Roadmap Topics for PB AM
Process ModelingThis publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

High-Priority Challenge/Barrier

Priority Roadmap Topic

Lack of non-equilibrium material and process
measurements and models hinders development of
accurate PB AM process models.

Non-equilibrium Material and Process Measurements and Models(Figure 3-1)
Predictive models of variations in multiple properties of materials in the nonequilibrium state need to be developed.

The interfacial science between different voxels, layers,
phases, and multi-materials is not fully understood.

Understanding Interfacial Science of AM Polymers (Figure 3-2)
Data, characterization methods, and ultimately models for the interfacial
property prediction and control need to be developed.

There are no agreed-upon guidelines and standards for
PB AM model development and validation.

Standards for Validation, Certification, and Qualification of Models andParts (Figure 3-3)
The approach is to standardize and evaluate data to enable development ofstandards for validation, certification, and qualification of process models
and AM parts.

20

In situ Process Measurements

FIGURE 3.1. ROADMAP ACTION PLAN:
NON-EQUILIBRIUM MATERIAL AND PROCESS MEASUREMENTS AND MODELS
MAJOR BARRIER: There is currently a lack of physics-based constitutive modeling and measurements of chemical, thermal, mechanicaland diffusive properties in the non-equilibrium spectrum of materials that can be both liquid and solid.

APPROACH: The objective is to develop predictive models of variations in multiple properties of materials in the non-equilibrium state. The
approach is to apply existing tools, develop new tools (experiments and models) and integrate all into predictive physics-based multi-scalemodels.

NEAR (1-2 YRS)•••

MID (3-5 YRS)•••LONG (>5
YRS)

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

ROADMAP ACTION PLAN

TIME

•
•

Conduct combined temperature/flow (nonisothermal rheology) modeling andexperiments
Perform stress and structural relaxation andaging measurements of common feedstocks
for PB AM
Measure relevant thermodynamic properties(e.g., coefficient of thermal expansion)Develop diffusion measurement methods forpolymers in non-isothermal situations
Develop a rheology model valid both aboveand below glass transition or melting
temperature development
Investigate effects of fillers and processing
conditions on properties of the printed part
Incorporate physics-based rheologicalmodels into larger multiscale computationalframework
Validate property prediction of printed part

MILESTONES
•
•
•

•
•

•

Existing tool to design some aspectsof AM
Better reproducibility and known initialconditions
Materials database for feedstocks

Reliable measurement methods andmeasurements
Reliable validated rheological models
spanning solids and fluids

•
•
•
•

•

Predictive models of variation inresidual stresses, strength ofinterface, mechanical properties,
dimensional tolerances, void
volume fraction, and surface finish

Computational tools that can predict
final properties for given processing
conditions

STAKEHOLDERS & POTENTIAL ROLES•PERFORMANCE TARGETS

Industry/AM Users: Identify desirable properties to control;
provide problems.
Industry/AM Providers: Provide feedstocks and printing
parameters; provide printers that can be controlled.
Academia: Develop measurement methods, physics basedmodels computation models.
Standards Committees: Develop standardized testing
methods, standards for feedstocks.
Government: Support high performance computing enabled
infrastructure for collaborative and multidisciplinary
research.

RELATIVE IMPACTS
High

Improves Product Quality: Reduced variation in
product properties
Low
Reduces Costs: Reduced defects; small effect on costs
High
Accelerates Innovation: Facilitates design
Medium Enhances Industry Competitiveness: Eliminatesbarrier for quality control
High
Faster Product Development Time: Predictive models
as design tools reduce design time
Other: N/A

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

21

FIGURE 3-2. ROADMAP ACTION PLAN:
UNDERSTANDING INTERFACIAL SCIENCE OF AM POLYMERSMAJOR BARRIER: Interfaces are naturally formed during most polymer AM processes. These interfaces control many properties(mechanical, thermal, optical, electrical, etc.) because of the high interface/volume ratio with uncontrolled and often negative effects.

APPROACH: The objective is to better understand interfacial science of polymers in AM to improve ultimate part performance andproperties, as well as processing variations. The approach is to develop data, characterization methods, and ultimately models for interfacial
property prediction and control.

ROADMAP ACTION PLAN

NEAR (1-2 YRS)•

MID (3-5 YRS)

•

•
•

•

LONG (>5 YRS)This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

TIME

•

•

Identify molecular features necessary formodeling different interfaces (melt/solid foramorphous, crystalline or composite
polymeric materials; solid/solid diffuse
interface for SLS, solid and pre-polymer/resin
for SLA, chemical reaction kinetics in in situ
polymerizing/crosslinking situations…)
Develop characterization methods forinterfacial properties, which will inform model
development
Model mechanical properties of interfaces atmultiple scales (atomic, mesoscale, …)Understand aging and evolution of interfaces
during processing and post-processing:characterize and model
Develop models for the mechanical, thermaland optical properties of a final part based oninterfacial properties
Model the effects of additives (e.g., carbon
nanotubes, fibers, nanoparticles, surfactants,
plasticizers, . . . ) on interfacial formation andproperties
Incorporate the effects of phase changes inmaterials on interfaces

MILESTONES••

Sets of variables necessary to model
various interfaces in different AM
processes
Data sets for models to implement andtarget•Interfacial models for mechanicalproperties•

Predictive models of part performancethat incorporates the formation
process and input material properties

STAKEHOLDERS & POTENTIAL ROLES
•
•
•
•
•

Industry/AM Users: Provide input on needs, problems andcurrent state; conduct demos/pilots.
Industry/AM Providers: Provide tool solutions, software,and prototyping.
Academia: Develop software algorithms, learning/adaptive
frameworks, conduct data analysis.
Standards Committees: Develop software standards
certification processes.
Government: Maintain computational repository, cyber
infrastructure, and develop/test algorithms.

22

In situ Process Measurements

PERFORMANCE TARGETS•

Suite of characterization methods
for interfacial properties•

Materials whose overall part
anisotropies are not impacted byinterfacial anisotropies andproperties•

Processes that have fewer or nointernal interfaces

RELATIVE IMPACTS
High

Improves Product Quality: All parts depend on
interfacial properties
Low
Reduces Costs: Not an immediate cost benefit
Medium Accelerates Innovation: Provides flexibility in design
High
Enhances Competitiveness: Better material properties
Medium Reduces Time to Develop Products: By removing a
constraint, development should be faster. Less testing
and more confidence in properties.
Other: N/AFIGURE 3-3. ROADMAP ACTION PLAN:
STANDARDS FOR VALIDATION, CERTIFICATION AND QUALIFICATION OF MODELS AND PARTS
MAJOR BARRIER: Accepted standards to feed and support process models are currently lacking, as are standards to validate processmodels. Insufficient validation of process models and resulting uncertainties in predictive capabilities limits their practical use.

APPROACH: The objective is to standardize and evaluate data to enable development of standards for validation, certification andqualification of process models. The approach will benchmark the state-of-art in process modeling of AM; establish new protocols; and createand demonstrate standards that will feed and validate AM process models and support part certification and qualification.

NEAR (1-2
YRS)

•
•

MID (3-5 YRS)

•
•
•
•
•

LONG (>5
YRS)

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

ROADMAP ACTION PLAN

TIME

•

MILESTONESStandardized/evaluated data for input ofrelevant physical, chemical, and thermal
properties
Inventory existing models, data, tools to help
identify needs (i.e., standardization “GapAnalysis”)•

Develop/revise multi-variable and multiphysics models (using standard input data)
Create standard test protocols specific to AM
parts
Explore rapid qualification/certification routes(ICME)
Establish protocols/procedures to allow data
sharing•

Identify standard components (data format)
needed to deliver validated component(digital thread)
Develop fundamental model (based on
Process – Structure – Property) that allows
transition of certification protocols to otherplatforms as they are discovered

•

•
•

•
•

Established benchmark standards forgeometry and mechanicalperformance for specified materials
Established first stage repository
location for existing shared accesstools, data, and models
Demonstration of revised processmodels utilizing initial standardized
input data
Protocol developed for rapid part
qualification
Evaluation of benchmark case using
ICME model or using AM-Bench
framework

•

•
•
•

Industry/AM Users: (e.g., Boeing) — Provide input onneeds, problems and current state; conduct
demonstrations/pilots.
Industry/AM Providers: (e.g., Stratasys) — Provide tool
solutions, software, prototyping, models, access to processparameters, and adopt established standards.
Academia: Develop software, algorithms, learning/adaptive
frameworks, conduct data analysis.
Standards Committees: Develop software standards,
certification processes, and testing procedures.
Government: Lead effort to establish and periodicallyreview roadmap. Maintain computational repository, cyber
infrastructure, cyber security; develop/test algorithms;
support of fund models.

•
•
•
•

Protocols for rapid qualification of
AM parts and processes
30% reduction in cycle times
Ability for decision making in risky
environs
Central software warehouse(virtual repository with source ofrecord to enable part and modelcertification)

Demonstration of rapid certification ofpart
Validation of a model across multiple
platforms

STAKEHOLDERS & POTENTIAL ROLES•PERFORMANCE TARGETS

RELATIVE IMPACTS

High

Improves Product Quality: Supports
qualification/certification of parts leading to better quality
Medium Reduces Costs: Leads to fewer part defects, certified
performanceHigh
Accelerates Innovation: Speeds up the design processHigh
Enhances Competitiveness: More reliable, proven partsand performance
High
Reduces Time to Develop Products: Speeds up thedesign process

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

23

CHAPTER 4: IN SITU PROCESSING MEASUREMENTS
OVERVIEW OF THE TOPIC AREA

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

In order to manufacture high quality parts using PB AM technology, it is essential to be able to identify and thenmonitor the critical material and process parameters as a part is being manufactured. These in situ
measurements are key to further understanding AM processes, improving process efficiency and quality, andproducing parts with desired qualities. In situ measurements are used to monitor process temperature andvarious material properties, detect defects, and provide feedback for process control. There are, however, many
challenges to effectively obtaining and utilizing needed in situ measurements. In many cases, it is difficult to
integrate required measurement technology into the actual manufacturing equipment and process. Because thegoal is that data from the measurements be used for process control, the measurements must be obtained at avery high speed and the gathered data interpreted in real time. For certain properties of materials adequate
techniques may not yet exist to obtain desired measurements. Thus, further deployment of PB AM requires
continued technology development to improve in situ measurement capabilities.

FUTURE/DESIRED CAPABILITIES
Improved in situ measurement capabilities will be needed to enhance PB AM processes and to produce highquality parts. The desired in situ measurement capabilities and technologies are described briefly below andlisted in Table 4-1.
Monitoring/Controls Technology
There is a need to develop and enhance technologies that allow capture of in situ measurements during PB AM
manufacturing processes. In particular, the speed and resolution of these measurement capabilities needs to be
improved. To capture all needed information, multi-sensor measuring capabilities—instead of technologies
measuring only one variable, such as temperature—need to be developed. More comprehensive measurements
will enable the development of better control systems for the manufacturing processes.
In situ Measurement for Build/Part Quality
For further deployment of PB AM technologies, it is important that part quality and build-to-build consistency isimproved. Enhanced in situ measurement capabilities play a key role in these improvements. Capabilities such as
in-process defect detection, characterization of material properties, and real time layer-to-layer adhesion
strength measurements will lead to enhanced process control and better part quality.
In situ Characterization of Structure, Composition, and Thermal Properties
AM offers great potential to understand and record key information about a part’s micro-level characteristics,
such as molecular structure, fiber orientation, thermal properties, and strain. This type of detailed information
could one day enable manufacturers to build parts to very exact specifications and validate that parts meet thosespecifications. Manufacturers will also be able to provide detailed product maps that include data on the microlevel characteristics of each individual part. Being able to conduct in situ characterization and control will enable
manufacturers to optimize a part’s molecular structure, which will lead to improved part quality. In order togather this micro-level information, technologies need to be available to measure the temperature, geometry,
and chemical characteristics of every deposited layer at high speeds.

24

In situ Process Measurements

Knowledge and Data
Improved in situ measurement capabilities will create a vast amount of new data, which will be both anopportunity and a challenge. The new data being generated will enable improvements on multiple fronts, from
AM modeling and process improvements to machine learning and being able to provide a detailed, informative
record for every part that is manufactured. When the data is combined with new real-time data interpretation
capabilities, in situ process control becomes feasible, which will lead to improved part quality and consistency of
product. However, improvements in data processing power, efficiency and speed are needed in order to fully
realize these benefits.
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Table 4-1. Desired Capabilities for PB AM In situ Measurement
Monitoring/Controls Technology•

Capability for in situ monitoring while processing during small and large scale productiono High speed, high resolution in situ measurements (optical, IR thermography, x-ray, ultrasound, vibrational
spectroscopy)o 3D sensors with high speed and precisiono Multi-sensor, multiplex monitoringo Monitoring stress with high speed and full field capabilityo Engineering control within a given AM technology to obtain a range of properties from a single feedstock

In situ Measurement for Build/Part Quality
•
•
•
•
•
•
•
•
•
•

Build-to-build consistency: material properties and part tolerance (99% acceptance)
In situ scans of evolving shape over the build duration (i.e., more than just layer) for all processes
Accurate, reproducible repeatable, and fast builds
Automated in-process defect detection
Online defect distribution (voids, particles or secondary matter)
Layer to layer adhesion strength measurements in real time
New sensing technology for in situ characterization of material properties
Quantitative imaging of the build process at the layer level; dynamic deformation (Doppler) imaging
In situ measurements for rheology (either directly or by correlation of inference)
In situ measurements for tack (either directly or by correlation of inference)

•
•
•
•
•
•
•
•
•

Ability to measure the geometry of every layer in situ to 5 µm accuracy at high speeds (in real time)
Optimization of molecular structure via in situ characterization and control
Real-time validation of micro structure
In situ characterization and control over graded composition: fiber orientation, reactive materials, suspension
Ability to monitor surface roughness with high speed and in full field
Strain field mapping for entire part
Measures for online polymer entanglement
Measurement of temperature and chemistry distribution of every layer at high speeds in situ (in real time)
Capability for in situ monitoring of thermal profiles and molecular orientation interface properties and profiles in polymers
extrusion (FDM)
Imaging through “dense” liquids in stereolithography (SLA); real-time in situ viscosity characterization in SLA (for resins)

In situ Characterization of Structure, Composition, and Thermal Properties•

Knowledge and Data••••

Machines that produce an informative record of in situ monitoring data with every part; machine learning, i.e., evolving
process controls from aggregated build data sets
Real-time data interpretation to support in situ process control
Ability to obtain and interpret feedback during processing
AM machines that use feedback from property sensing monitors for control

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

25

TECHNOLOGICAL & MEASUREMENT/STANDARDS CHALLENGES
The barriers and challenges currently impeding large scale deployment of PB AM in situ measurement
technologies are presented in Table 4-2, categorized by common themes. In each category, specific barriers orchallenges are prioritized based on expert and stakeholder input.

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

In the area of monitoring and control technology, one of the most significant challenges is the inadequate speed
and resolution of current measurement technologies. Existing equipment is not capable of measuring thermaland chemical distribution of each deposited layer accurately and in real time during the AM process. In addition,
most current imaging technologies are not easily integrated into AM printers. There is need for imaging
solutions that are effective through scattering media, unobtrusive, and tailorable to different printer geometries.
Other major barriers concern knowledge and data. There is a lack of comprehensive models that can be used tointerpret in situ data and develop automated control systems. To develop such models, there is need for in situ
measurement data to inform the models. Fast and accurate big data analytical methods for handling in situ
measurement information and to develop physics-based models are also lacking.

Table 4-2. Barriers and Challenges for PB AM In situ Measurement
Monitoring/Controls Technology•

High Priority

Medium Priority

Low Priority•

Time scale of measurement compared to print speedo No existing equipment capable of measuring the thermal and chemistry distributions of each layer to satisfactory
resolution in real time during the AM process
o Measurement acquisition speed and incorporation on commercial instrumentso Spatial resolution response time
Imaging and measurement modalities through scattering mediao Interfacing a printer with an x-ray beam or next generation focused neutron beamo X-ray flux in lab based sources is too low for process control
o Analysis of materials below surface (ex. SLS, Vat polymerization)o In-situ spectroscopic measurements

•
•
•
•

Monitoring/imaging equipment not designed to accommodate needed temperature and size requirements
Lack of in situ controls on OEM equipment
Cost benefit ratio of measurement to reliability
Computer processing power needed for real-time data analysis and feedback••

Lack of large area, small-size sensors for fast high resolution measurement
Ability for 3D sensor (triangulation) - size 10 µm , 1 MHz rate )o 3D sensing microscope precision; sensing volume X mm3; speed 20 kHz for small volume
Sensor simplification: simple interpretation; data reduction )Lack of sensors that do not affect part performance (i.e., non-intrusive)
Sensor interference – information from one sensor can invalidate information/guidance from others•••

Measurement Techniques•

Accuracy and precision of measurements, impacted by temperature fluctuations, environmental changes (stress or
strain)o Inability to register true temperature or rheology for a material that is changing temperature, density and surface
structure (real-time)••

Limited technology for in situ non-contact measurement of mechanical properties
Lack of high throughput characterization; variability among applications

High Priority

Medium Priority

26

In situ Process Measurements

Table 4-2. Barriers and Challenges for PB AM In situ Measurement
Knowledge/Data•

High Priority
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5•

Medium Priority
Low Priority

Lack of process structure property (PSP) models to interpret in situ data; full understanding of what parameters aremost important is lackingoNo capability for automated feedback into system and self-correctionoLimited process simulation technology (models)oLack of design tools for structural compositional heterogeneous materialso
Inadequate/limited upstream part layout simulation toolso Lack of validated sintering models for polymer and polymer composite powders
Lack of big data analytics methods that can be employed for in situ measurement data analysis in conjunction withphysics-based modeling; challenges to address include:oData volume, processing capability, storageoComplexities of real-time analysis for multi-sensors ( both speed and accuracy)oLimited big data computer science researchers engaged in AM•

Layer by layer interactions that bring very large variations, and lack of fundamental models to formulate•••

Consensus on coherent set of needs
Ability to detect breaches in data integrity
Dynamic chemistry not completely controlledoHow to define standards for different raw materials; machine vendor, machine specs, operator environment

ROADMAP FOR PRIORITY R&D
A roadmap action plan was developed to enumerate the activities and approaches for overcoming the mostcritical barriers identified in Table 4-2. Roadmaps action plans are outlined in more detail in Figures 4-1
through 4-4. The key barriers being addressed and the corresponding priority roadmap topics are summarizedin Table 4-3.

Table 4-3. Key Barriers and Corresponding Priority Roadmap Topics for PB AM In situ
Measurement
High-Priority Challenge/Barrier

Priority Roadmap Topic

There is a lack of in situ imaging modalities that are
effective and can be easily incorporated into AM
printers.

New In situ Imaging Modalities (Figure 4-1)
New and improved in situ imaging technologies that are effective andunobtrusive need to be developed.

Existing equipment is not capable of measuring
thermal and chemical distribution of each depositedlayer accurately and in real time during the AM
process.

Real-Time Process Measurement at Required Spatial Temporal
Resolution (Figure 4-2)
Next generation high performance modular sensing technologies are needed inorder to verify part quality and inform closed loop feedback.

Comprehensive models that can be used to interpret insitu data and develop control systems are lacking.

In situ Control and Model Integration (Figure 4-3)
The relevant process parameters that have significant impact on finishedmaterial properties need to be identified and control algorithms to makeprocess adjustments need to be developed and validated.

Fast and accurate big data analytical methods forhandling in situ measurement information anddeveloping physics-based models are lacking.

Big Data Analytics for AM (Figure 4-4)
New methods to analyze big data sets from in situ measurement need to be
developed and incorporated into process models.

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

27

FIGURE 4-1. ROADMAP ACTION PLAN:
NEW IN SITU IMAGING MODALITIESMAJOR BARRIER: Current imaging technologies are not easily integrated into 3D printers. Technologies must cover a large spatial area,
very quickly, and preferably be hyperspectral. In addition, they must be cost effective and easily integrated.

APPROACH: Imaging modalities are needed that are unobtrusive and customizable to the type of printer geometry. Technology will need
to be either reflectance or transmittable through material or equipment, and have the capability to be integrated with other measurement
technologies. The approach is to understand gaps in imaging and develop technologies with desired capabilities.

NEAR (1-2
YRS)

•
•

Inventory of imaging types with strengths andweaknesses as well as needs for integration
Understand gaps within as well as between
each technology type

MILESTONES•

MID (3-5 YRS)••

Develop technologies to close these gaps
Develop integration of imaging technologies
with printer systems

•
•

•
•
•

Provide integration to modeling
Provide data for active feedback efforts

Gaps have been identified andquantified••

Gaps closed by developing novel
imaging sources and better detectors
Requirements developed for industry
for integration
Prototype printer and imaging system
integration
Data provided for modeling,simulations, and instrumentation
feedback
Validation of imaging measurements
Real-time closed-loop software

STAKEHOLDERS & POTENTIAL ROLES
•
•
•
•
•

Industry/AM Users: Set requirements for performance; e.g.
frame rate, cost, resolution.
Industry/AM Providers: Open architecture of integration ofimaging systems; open hardware and software; printer andimaging manufacturers.
Academia: Demonstrate the integration, software control,
feedback to printer conditions, develop imaging
technologies.
Standards Committees: Set imaging standard, develop
calibrates that are amenable to 3D printer environments.
Government: Provide user facilities with novel light sourcesand detectors that are multimodal.

28

In situ Process Measurements

PERFORMANCE TARGETS••

LONG (>5
YRS)

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

ROADMAP ACTION PLAN

TIME

•
•
•
•
•

Integration of printer vendors,
OEM imagers and softwaredevelopers
Simplify data analysis
Data decision tools
Reduce variation in printed parts;
reduce defects
Develop unobtrusive imaging
technologies in line with printercapabilities
Develop cost effective
technologies

RELATIVE IMPACTS
High

Improves Product Quality: Reduces defects andvariation by identifying their causes
Medium Reduces Costs: Identifies anomalies, reduces waste
before post validation
Low
Accelerates Innovation: Little benefit to innovation but
improves quality of product
High
Enhances Competitiveness: Feedback for highperformance and certification
Medium Reduces Time to Develop Products: Eliminates
problems and makes model development quicker
Other: N/AFIGURE 4-2. ROADMAP ACTION PLAN:
REAL-TIME PROCESS MEASUREMENT ATREQUIRED SPATIAL TEMPORAL RESOLUTION
MAJOR BARRIER: Existing measurement techniques lack the required speed, resolution, and instrument footprint to enable and verify
build-integrity throughout part volume.

APPROACH: Next generation high performance modular sensing is needed to verify part quality and inform closed loop feedback. The

ROADMAP ACTION PLAN

TIME

•
•

Conduct inventory of sensing technologies
and determine what process variables need
to be measured for each additive technology
Understand gaps in existing capabilities andcreate plan for development
Start developing sensor capabilities andcategorize cost benefits

MID (3-5 YRS)NEAR (1-2
YRS)

•

•

Down-select and validate for specific AM
process
Conduct data flow interface integration for
closed-loop control and data visualization.
Collaborate with other AM initiatives

LONG (>5
YRS)

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

approach will investigate existing technologies and capabilities, understand critical parameters to be measured, and develop sensor
capabilities to fill gaps. Sensor technology would then be tested at pilot scale, and then optimized/integrated into AM processes.

••

Optimize and integrate sensing technologies
Evaluate next generation control systems•

MILESTONES••

List of critical parameter for each
additive process
Initial sensor technique development
as a stand-alone prototype

•
•
•
•

•
•
•

•
•

•

Integration of sensor technique into
prototype AM equipment
Successful proof-of-concept of in situ,
real-time process measurements

Completion of demonstrations forpractical applications

STAKEHOLDERS & POTENTIAL ROLES•PERFORMANCE TARGETS

Industry/AM Users: Early technology adopters and provide
feedback.
Industry/AM Providers: Integration of sensors into
commercial equipment.
Academia: Fundamental sensor development andequipment integration.
Standards Committees: Define/recommend part quality.
Government: Define requirements for certification andqualification.

••

Void content detection of
10 micronTemperature gradientVoxel based material property
build history throughout thevolumeMeasurement resolution
throughout build– Local – 10 micron– Thermal– Chemistry– Density– Dimensional measurement
Data exchange to big data cloud
for analysis

RELATIVE IMPACTS
High

Improves Product Quality: Verifying quality, six-sigma
improved reproducibility
Medium Reduces Costs: Less material waste, higher equipment
cost
Medium Accelerates Innovation: Reduces time to validate
material properties for new products
High
Enhances competitiveness: Part quality in-line withconventional manufacturing
High
Reduces Time to Develop Products: Reduced part
testing
Other: N/A

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

29

FIGURE 4-3. ROADMAP ACTION PLAN:
IN SITU CONTROL AND MODEL INTEGRATION
MAJOR BARRIER: There is a lack of multivariate modeling that can account for material and process variability in real time. In situ

measurement is lacking but necessary to inform those models. Models and measurements can be integrated to drive closed loop control.

APPROACH: The objective is to reduce waste, improve quality, and have greater real-time control of finished parts through better

ROADMAP ACTION PLAN•

NEAR (1-2
YRS)

TIME

•

MID (3-5 YRS)

•
•
•
•

•

LONG (>5
YRS)

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understanding and sensing of controllable parameters. The approach is to identify the measurable and controllable process parameters thatmake an impact on or influence the properties of final desired product. Next-generation non-linear control algorithms are needed to makeprocess adjustments based on the complex, coupled physical-chemical interactions at relevant processing speeds.

•

Understand the relevant process parametersthat have significant impact on finishedmaterial properties
Identify the strongly coupled physicalchemical interaction
Identify relevant sensors/transducers formeasuring significant physical-chemicalinteractions
Identify appropriate validation tools
Test and validate process models
Incorporation of empirical process variableproperty control
Integrate process models and in situ sensors
into control scheme for validation acrosstechnology platforms
Iterating printing technology based on modeldriven process control

MILESTONES
•
•

•
•
•

•

Inventory of controllable parameterswith highest variance
Determination of speed-accuracyproperty relationships

Integration of relevant sensors into
process
Dissemination of signal/sensor
validation
Proof-of-concept of empirical processcontrol••••

Industry/AM Users: Provide input on needs, problems, andcurrent technologies.
Industry/AM Providers: Provide tool solutions, capabilities.
Academia: Develop and validate process models throughtheory and experiment.
Standards Committees: Develop software standards andcertification processes.
Government: Validate sensor capabilities, maintain model
database.

30

In situ Process Measurements

•
•
•
•
•

Reduce waste (failed prints)
Improved quality (precision andaccuracy) across technologies
Intelligent decisions on speedaccuracy-property trade-offs
Realization of voxel-level controlof material properties
Measurement, analysis, andcontrol techniques

Completion of validation anddemonstration experiments

STAKEHOLDERS & POTENTIAL ROLES•PERFORMANCE TARGETS

RELATIVE IMPACTS
High
High
High
High
High
High

Improves Product Quality: Improved precision andaccuracy in shape and composition
Reduces Costs: Reduction of waste/failed prints
Accelerates Innovation: Ability for increased complexity
Enhances competitiveness: Enables production ofqualified parts
Reduces Time to Develop Products: Reduced iteration
time
Other: Increased workforce effectiveness anddevelopment

FIGURE 4-4. ROADMAP ACTION PLAN:
BIG DATA ANALYTICS FOR AM
MAJOR BARRIER: There is a lack of fast and accurate big data analytics methods for in situ measurement data and physics-basedmodel predictions.

APPROACH: New methods must be developed for analyzing big data sets from in situ measurement. This will enable better quality control

ROADMAP ACTION PLAN

TIME
NEAR (1-2 YRS)•••

MID (3-5 YRS)

•
•
•
•

LONG (>5
YRS)

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and ability to optimize processing parameters. Measurement data can be combined with process model predictions to generate robust quality
measures. The approach is to catalogue current big data methods, translate/refine for AM applications, then establish a repository for open
source AM big data software.

••

Review of state-of-the-art big data analytics
methods applicable to in situ AM
measurements
Generate big AM in situ measurement datafrom optical thermal images, make them anopen source database
Initial development of big data analytics
methods for AM quality measure
Refine the development of big data analytics
methods for AM quality measure
Define the desired output of physics-basedmodels based on in situ measurement
capabilities
Establish open source software repository ofdeveloped big data analytics methods
Benchmark different developed big data
analytics methods and software
Validate the model predictions with in situ
measurement data for AM part qualification
Expand the big data analytics methods for
new in situ measurement data, such as
chemical, x-ray, etc.

MILESTONES•

Established repository for data setsfrom in situ measurement•

Established repository for open
source AM big data analytics
softwareDemonstration of open source AM big
data analytics software for part quality
measure

•

•

•
•
•
•

Industry/AM Users: Provides input on quality requirements,
problems, and test cases.
Industry/AM Providers: Provide tool solutions, software.
Academia: Develop process models, big data analytics
algorithm, conduct data analysis.
Standards Committees: Develop software standards.
Government: Establish repository for data sharing andopen source software.

•••

Real-time conversion of in situ
measurements of quality
Enable process enhancement
through validated process models
and in situ measurements
Certify quality of AM parts with insitu measurements

Fully validated and expandable open
source AM big data analytics
software for part quality measure

STAKEHOLDERS & POTENTIAL ROLES•PERFORMANCE TARGETS

RELATIVE IMPACTS
High
MediumMediumHigh
Low

Improves Product Quality: Produce defect free parts
Reduces Costs: Increase yield
Accelerates Innovation: Enable process improvement
Enhances competitiveness: Improve quality: reduce
cost
Reduces Time to Develop Products: Not much impact
Other: N/A

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

31

CHAPTER 5: PERFORMANCEOVERVIEW OF THE TOPIC AREA
The viability of PB AM technologies will ultimately depend on the ability of the technologies to produce parts
that meet their end use requirements. This will require PB AM parts to meet, or exceed, the performance of
conventionally manufactured products. Thus, part performance is where all aspects of PB AM—materials,
printer technology, processes, and post-processing—come together.
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Output repeatability and part durability are important elements of PB AM performance. In order to achieve highlevels of reproducibility, qualification and certification processes for each element of PB AM technology (i.e.,materials, equipment, processes, performance) need to be developed. The development of standards for
qualification and certification is complicated by the numerous permutations of machines, materials, processes,
and techniques and the absence of a central repository of PB AM data.

FUTURE/DESIRED CAPABILITIESParts produced using PB AM technologies will need to meet product performance expectations of end users.
The desired performance capabilities and expectations are described briefly below and listed in Table 5-1.
Reliability/Repeatability
When it comes to PB AM part performance, the reliability and repeatability of produced parts is very important.
There also needs to be reduced variance in the material properties of the printed parts. To produce suchconsistently high-quality parts, PB AM printers themselves must become more reliable and have better
tolerances, and used material feedstocks need to be more consistent and better controlled. The mechanicalproperties of produced parts must be equivalent or superior to traditionally manufactured parts.
Product Lifetime
Parts produced must not only be of high quality, but they also need to provide reliable functionality over an
extended time-period. The expected product lifetime and required level of performance over that time-periodvaries greatly depending on the application.
Post-Processing
In many PB AM technologies, post-processing steps, such as removal of support material and surface finishing,
are used to produce high quality parts. For many products, there is also a need to conduct testing of the finishedparts and establish traceability for used materials and process parameters. To ensure that PB AM technologies
will be economically viable, it is important to reduce these post-processing costs and time needed for those
steps.
Material/Part Design and Functionality
As PB AM technologies are used for an increasing number of applications, there is a need to be able to produce
more complicated parts consisting of multiple materials. Some of the parts will also need to be multifunctional,
where each component of a part performs a different role to achieve synergistic goals. To produce suchcomplex parts, printing methods that use multiple materials in one build need to be developed. Adequate
material libraries and catalogues are also needed.

32

PerformanceProduct Characteristics
Characteristics of parts that are produced with PB AM techniques need to meet the expectations of the market.
For example, printed materials and surfaces are expected to be void-free and their mechanical properties must,at a minimum, be equivalent to injection molding. Flexibility will continue to be a distinct advantage of AM, ascapabilities to produce parts with tailored properties for specific applications will be further enhanced.
Medical Applications

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The medical market is an area where PB AM technologies show significant future growth potential. The potential
to efficiently and quickly produce customized and unique parts and devices is particularly important for medical
applications. In many cases, medical parts and devices must meet very stringent criteria for accuracy,
performance, and safety. Issues such as biocompatibility are of utmost importance in this market.

Table 5-1. Desired Capabilities for PB AM Performance
Reliability/Repeatability
•
•
•
•
•
•
•
•
•

Reliable/repeatable, better tolerances on printers
System output repeatability; reliability of the end product
Reduced variance in material properties (as printed)
Consistent or controlled feedstock for machines
Predictive properties of final parts based on input process/material parameters; improvement in predictability/repeatability of “as built”
parts
Full bonding across layers – equivalent properties in all directions
Mechanical properties equivalent or superior to those traditionally manufactured
Ideal mechanical properties (e.g., modulus, failure mode) – based on the intended application
Performance tied to version of the process (e.g., lot number)

Product Lifetime••••

Reliability of functionality over an extended period
Maintenance of initial properties over time vs. changing properties (e.g., degradation kinetics)
Chemical reactivity, including intentional degradation capability (e.g. biodegradation)
Chemical stability: 10 years; human implant; 50 years

Post-Processing•••

Reduced post processing time/cost
Finished part Particle Generation Test Method
Electronic pedigree traceability (from model-based definition to inspection)

Material/Part Design and Functionality
•
•
•
•
•
•

Designer multi-functional parts through print design and multiple materials
Multifunctional – composites with each component performing different roles to achieve synergistic effects
Validation of the usefulness of composite lamination theory (CLT) in modeling polymers extrusion (FDM) structures
Design tailor-ability towards lightweight structures
Printing with multiple materials in one “build”
Expanded “library” of materials

Product Characteristics
•
•
•
•

•

Void free (reliable surface characteristics)
Silicone-like material to color (prostheses, simulation)
Mechanical properties equivalent to injection molding
Tailored properties for applications (e.g., patient-specific, geometry, property topography, etc.)
Fatigue criteria for polymers extrusion (FDM) printed polymers (considering current standards)

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

33Medical Applications
•
•
•
•
•

•

Performance tied to large validation data sets: pictures, videos
Speed with accuracy for medical products
Analysis of surface porosity for medical devices (e.g., to enable cleaning)
Application-dependent performance requirements for biomedical (biocompatible, biodegradable)
Analytical methods to verify biocompatibility
Application-dependent performance requirements – bio-printed organs (in vitro diagnostics, research purposes, actual implants)

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

TECHNOLOGICAL & MEASUREMENT/STANDARDS CHALLENGES
The barriers and challenges currently impeding enhancements in the performance of PB AM technologies andparts are presented in Table 5-2, categorized by common themes. In each category, specific barriers orchallenges are prioritized based on expert and stakeholder input.
One of the most significant challenges to be overcome is the lack of fundamental understanding of PB AM
processes. Research is needed to understand complex non-equilibrium systems, interfacial layer-by-layer
adhesion of AM materials, and the process changes and other factors that have significant impact on variance.
Once the science of PB AM is better understood, enhanced process models can be developed. Accurate andvalidated process models, in turn, will result in better PB AM technologies and high-performing parts.
Standards development is another priority area where further work is needed to help ensure consistency inperformance. Developing standards for feedstock materials and tolerances is particularly important. In addition,adequate safety standards for PB AM processes, and manufacturing facilities, are needed. Addressing potential
safety concerns will help support acceptance and the future growth of this emerging industry.

Table 5-2. Barriers and Challenges for PB AM Performance
Fundamental Understanding of PB AM Processes•••

High Priority

Lack of understanding and (and subsequently performance modeling) of PB AM processes
Poor interfacial adhesion – layer by layer – how does understanding of bulk materials translate to AM process?
Lack of focus on how fundamental science fields are juxtaposed in a complex, non-equilibrium system

Inspection and Certification
Medium Priority•••

Lack of accepted/validated inspection methods to detect voids
Certification and inspect-ability (e.g., complete geometries)
Burdensome testing to verify cleaning/biocompatibility

Stock Materials and Standards
High Priority•

Lack of standards for feedstock materials tolerances

Medium Priority••

Undisclosed material composition
Lack of nomenclature for polymers

Low Priority•

High sensitivity to material/molecular weight/polydispersity changes

Variable/Parameter Impacts on Part Properties
High Priority••

High sensitivity to small process changes
Designing experiments to identify significant variables and those with high impacts on variance

Medium Priority•••

Black boxing of parameters and profile setting
Understanding/identifying critical variables for process control
Understanding how material properties are altered as a result of printing

34

PerformanceTable 5-2. Barriers and Challenges for PB AM Performance
Transparency in PB AM
Medium Priority•••

Closed ecosystems (e.g., narrowly designed machines)
Lack of choice of machine vendors
Minimal/limited stated performance requirements for a given application: materials, design, medical•

Lack of/insufficient safety standards (facility standards – none are federally recognized)

Safety Standards
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5High Priority

ROADMAP FOR PRIORITY R&D
A roadmap action plan was developed to enumerate the activities and approaches for overcoming the mostcritical barriers identified in Table 5-2. Roadmaps action plans are outlined in more detail in Figures 5-1 through5-5. The key barriers being addressed and the corresponding priority roadmap topics are summarized in Table
5-3.

Table 5-3. Key Barriers and Corresponding Priority Roadmap Topics for PB AM
Performance
High-Priority Challenge/Barrier

Priority Roadmap Topic

Lack of standards for feedstock material tolerances.

Information on Stock Materials and Standards for Polymer Formulation
and Tolerances (Figure 5-1)
The standardization needs of stakeholders are identified, information
gathered to formulate standards, and predictive models and design tools
developed.

Lack of transparency in PB AM and related sciences.

Increasing Transparency in PB Materials and Processing (Figure 5-2)
Gaps in industry transparency need to be assessed, needed standards
developed, and results disseminated.

Limited understanding of the full set of parameters andvariables impacting the properties of the finished part.

Critical Control Parameters, Variables, Measurements, and Concepts
for PB AM (Figure 5-3)
Critical process variables need to be identified, measurements developed,
impact on final product properties understood, and control mechanisms
developed.

Lack of understanding and modeling of PB AM
processes, where many types of fundamental sciencefields are juxtaposed in a complex, non-equilibrium
system.

Knowledge and Data to Improve Performance Modeling of PB AM
Processes (Figure 5-4)
Critical gaps in existing materials and process knowledge need to be
identified, research efforts directed to address identified gaps, andinfrastructure and framework for sharing information established.

Details about polymer powder volatiles, dust and otheremissions are unknown; potential health impacts andother risks from the emissions are not understood.

Environmental, Health and Safety Factors for Polymer AM Materials(Figure 5-5)
Methods to quantify fugitive polymer particles need to be developed, risks
related to the emissions understood, and acceptable emission levels andmitigating technologies identified.

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

35

FIGURE 5-1. ROADMAP ACTION PLAN:
INFORMATION ON STOCK MATERIALS AND STANDARDS FOR POLYMER FORMULATION AND
TOLERANCESMAJOR BARRIER: Multiple instances of material formulation and processing (e.g., raw materials, polymer formulation, feedstock to AM

equipment, and post printing processing) limit understanding and clarification of composition and properties. It is unclear what the appropriate
level of material composition and properties specifications are at these various levels of material integration.

APPROACH: The objective is to establish a framework (standards, data, tools) needed to support availability of stock polymer materials for

ROADMAP ACTION PLAN•

NEAR (1-2
YRS)

TIME

•

MID (3-5 YRS)•••LONG (>5
YRS)

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AM. The approach is to identify the needs of stakeholders for standardization, gather information to formulate standards, and develop
predictive models and design tools.

•

Determine when it is appropriate to identify
feedstock material composition
Identify appropriate mechanisms tounderstand stakeholder concerns about raw
materials
Determine how to use information obtained
from stakeholders to develop a framework forstandards
Identify application areas, the environment inwhich part is used, and expectation on its
useful lifetime
Work with stakeholders to determine
appropriate material composition informationand material properties

Use modeling to design AM parts based on
knowledge of material composition

MILESTONES••

Stakeholders convened to discuss raw
material concerns
Identification of attainable consensus
standards topics•••

Materials standards
Information gathered for framework
development (key applications, data
and materials information required)•

Design tool for material composition
selection to achieve performancestandards

STAKEHOLDERS & POTENTIAL ROLES••

Industry/AM Users: Input on material uses and concerns
Industry/AM Providers: Input on material properties andformulations where possible
Standards Committees: Input to standards development
Government: N/A

36

Performance•

Materials properties andcomposition specifics atappropriate levels needed forformulation and processing
Usable and accessible predictivemodels and design tools thatcover full range from raw materials
to formulation and integrate with
AM equipment and processing

RELATIVE IMPACTS
High

•
•

PERFORMANCE TARGETS

Improves Product Quality: Validated modeling tool for
selection of material composition would decrease numberof development cycles, leading to accelerated innovation,
faster product cycles and subsequently achieving cost
reductions
Medium Reduces Costs: N/A
High
Accelerates Innovation: N/A
Medium Enhances Competitiveness: N/A
High
Reduces Time to Develop Products: N/A
Other: N/AFIGURE 5-2. ROADMAP ACTION PLAN:
INCREASING TRANSPARENCY IN PB MATERIALS AND PROCESSING
MAJOR BARRIER: There is a general lack of transparency in polymer-based AM and related science, ranging from raw materials

composition to formulation, processing, equipment, and finished parts and quality. Materials and products are often seen as a ‘black box’
without sufficient understanding of fundamental science.

APPROACH: There is a need to improve the transparency of materials and processing to enable better design and performance of PB AM

ROADMAP ACTION PLAN

MID (3-5
YRS)

NEAR (1-2
YRS)

TIME

LONG (>5
YRS)

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parts. This effort will assess the gaps in industry transparency by using a systems approach to analyze projects and refine standards. The
approach will include gathering sufficient information from stakeholders to develop, prove and disseminate results via publication of standards.

MILESTONES••

Conduct gap analysis oftransparency in basic polymer
science in AM•

Document the process science ofadvancing from a known state toan unknown (in a specific PB AM
technology)•

Conduct system analysis of arange of projects••

PERFORMANCE TARGETS

1 year - develop survey, forum or meeting toaddress the basic science needs of polymer AM
1 year-18 months – Identify correct stakeholders
and owners: government, academia and industry
18-24 months – Formalize gap analysis
document and disseminate

•
•

•
•
•

Ability to move from a known
scientific state to an unknown
scientific state in the science ofpolymer AM technologies– per type of PB AM technology(FDM, SLS, etc.)

Implementation of testing at multiple sites
Verification of results and publication into
standard(s)STAKEHOLDERS & POTENTIAL ROLES•••

For each technology• Material – Subset of standard materials• Hardware – References program• Software – References program

Industry/AM Users: Year 1 survey of industry.
Industry/AM Providers: Drive gap analysis, offer insight
into documentation, focus on known to unknown paths.
Academia: Test process controls (3-5+ years).
Standards Committees: Compile results and formalize
document.
Government: Decree standards, hold industry
certifications/regulations.

RELATIVE IMPACTS
High

Improves Product Quality: Falls into manufacturing andassociated QA science
Med/High Reduces Costs: Can lower the cost in the long run
High
Accelerates Innovation: Lets academia document basescience, which can then translate into innovation.
High
Enhances competitiveness: Lets any supplier or OEM
or channels enter the market.
High
Reduces Time to Develop Products: Product is nolonger starting from the First Principles.
High
Other: Education across the community of interest.

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

37

FIGURE 5-3. ROADMAP ACTION PLAN:
CRITICAL CONTROL PARAMETERS, VARIABLES, MEASUREMENTS, AND CONCEPTS FOR PB AM
MAJOR BARRIER: Limited understanding of the full sets of parameters and variables impacting the ultimate part properties creates
uncertainties in part performance and quality and impacts the ability to effectively control the process.

APPROACH: The approach includes four components: 1) identify critical variables in manufacturing process; 2) develop (or modify

ROADMAP ACTION PLAN

TIME
NEAR (1-2
YRS)

•
•

MID (3-5 YRS)•LONG (>5
YRS)

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existing) measurements that provide full history of each critical variable through the building of the printing process; 3) understand howchanges in these variables impact desired final part properties; and 4) active control to maintain critical variables with an acceptable window
for all properties.
Conduct inventory of software, tools,
materials and existing measurements acrossall AM platforms
Define all parameters that are changing
during build process
Determine how to measure andcontrol/influence the defined variables•••

Down-select appropriate control methods
Test controls that are defined for variables
Confirm/identify any remaining variables•

Integrate controls on a representative set ofmachines
Define variables that are a result of machineinconsistency and method to control•

MILESTONES
•
•
•

•
•

•

Clear inventory of what is measured
by platforms
Expanded inventory to include newly
identified parameters
Identification of technical gaps
between current measurements andparameters that impact part properties
Proof of concept of controls on build
variables
Reduced variability of part quality
through control of identified
parameters

•
•
•
•

Industry/AM Users: Provide input on needs, problems andcurrent state; conduct demos/pilots.
Industry/AM Providers: Provide tool solutions andintegrated solutions.
Academia: Develop measurement control algorithms andconduct data analysis.
Standards Committees: Develop measurement standards,
certification of process.
Government: Maintain repository, and develop/test
correlations between advanced measurements and thosemore applicable to in situ control.

38

Performance

•
•
•
•
•

Reduction in quality deviation
Reliable parts quality
Improved in situ measurement
Improved active control of processStandardized measurements tocontrol the printing process

Method to apply controls andadjustments for machine variability byplatforms

STAKEHOLDERS & POTENTIAL ROLES•PERFORMANCE TARGETS

RELATIVE IMPACTS

High
Medium
High
High
Low

Improves Product Quality: N/A
Reduces Costs: N/A
Accelerates Innovation: N/A
Enhances competitiveness: N/A
Reduces Time to Develop Products: N/A
Other: N/AFIGURE 5-4. ROADMAP ACTION PLAN:
KNOWLEDGE AND DATA TO SUPPORT BETTER MODELING OF PB AM PROCESSESMAJOR BARRIER: Understanding and modeling AM processes, where many types of fundamental science fields are juxtaposed in acomplex, non-equilibrium system, represents a difficult challenge.

APPROACH: The objective is to improve performance (predictability, repeatability, etc.) through a better understanding of the fundamental
and applied science behind PB AM. The approach will assess existing research knowledge to determine the gaps in materials, processparameters, printing methods, and other factors that influence printed part quality and performance.

NEAR (1-2 YRS)•••

MID (3-5 YRS)•••LONG (>5
YRS)

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ROADMAP ACTION PLAN

TIME

•
•
•
•
•

•
•

Engage community of users, researchers,
manufacturers (materials and equipment) totruly identify field/refine understanding of fieldthat needs to be surveyed for gaps
Conduct survey of above-mentioned field to
identify gaps
Create infrastructure/framework for public
sharing of fundamental material properties(rheology, thermal, surface, etc.)
Establish working groups to explore theimpact of fundamental science as it pertains
to method, material, application, etc.
Develop in situ meta-analytical tools to:
systemically correlate process andfundamental properties with product; autoarchive data; identify dominant processparameters, identify feedback loop needs
Demonstrate integrated measurement
science techniques in hardware/softwaresystems
Create linking activity with design roadmap
and modeling roadmap
Ideally, these analytical tools should
inform/influence/drive the design tools

MILESTONES
•

•
•

•
•
•

PERFORMANCE TARGETS

Development of a series ofrecommendations/justifications forinvestment in the fundamental
science which is indispensable forbroader application of polymer AM
Establishment of database forproperties, processes, etc.
Publish findings/reflections/recommendations to reach wider
community, which is usuallycommercially driven
Be able to calibrate/correlate/design
process based on desired
product/properties
Prototype of tools••

Predictability, repeatability, quality
and inspect-ability of printed
products
Enhanced education/communication/collaboration ofcommunity, i.e., via international
standards

Prediction of properties in printed
parts (so that eventually, predictability
starts at the design phase)o creation of software that would
allow robust, feasible design
flexibility

STAKEHOLDERS & POTENTIAL ROLESRELATIVE IMPACTS

Industry/AM Users: Identify needs, provide support,
perform applied research.
Industry/AM Providers: Link users and researchers,
including applied researchers.
Academia: Conduct fundamental research, identify
knowledge gaps.
Standards Committees: Establish best practices.
Government: Facilitate conversations/collaborations,provide support, perform basic research.

High
Improves Product Quality: N/A
Low/High Reduces Costs: Research costs, but eventually people
will benefit; cannot achieve this now.
High
Accelerates Innovation: N/A
Medium Enhances competitiveness: Depends on adoption ofideas/collaboration
Medium Reduces Time to Develop Products: Not until
analytical tools are integrated with design.
High
Other: Without the fundamental science, AM will not
grow quickly.

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

39

FIGURE 5-5. ROADMAP ACTION PLAN:
ENVIRONMENTAL, HEALTH AND SAFETY FACTORS FOR POLYMER AM MATERIALSMAJOR BARRIER: The amount, size, distance, etc. of powder, volatiles, dust, etc. within and escaping from current polymer AM
machines is not known.

APPROACH: Methods and technologies are needed to quantify ‘fugitive’ polymer particles and set levels (acceptable levels to which safety
standards could be written). The approach will assess existing quantification technology, current/potential risks, identify needed technologies
for mitigation, and provide input for future standards and regulations.

NEAR (1-2
YRS)

•
•
•

MID (3-5 YRS)•••LONG (>5
YRS)

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ROADMAP ACTION PLAN

TIME

•
•

Identify current/potential risks (health, fire,
explosion, etc.)
Develop a “systems” approach to theintegration and use of existing technologies
Assess/validate existing “fugitive” polymer
quantification technology
Research and develop new technologies
Further mitigation of technologies throughcollaborations (OEMs, supplies, industry,
government)
Evaluate effect of cross contamination
between machines (of different processes)

Develop regulations and standards as needed
Enforce as needed

MILESTONES
•
•
•
•
•
•

•
•

STAKEHOLDERS & POTENTIAL ROLES
•
•
•
•
•

Industry/AM Users: Lead role with OEMs
Industry/AM Providers/Suppliers: Lead role inunderstanding how what they supply contributes to risks.
Academia: Lead R&D role in “quantification” in all aspects
as well as new preventative methods, maximum limits, etc.
Standards Committees: Create of standards.
Government: Enforce, regulate standards.

40

Performance

PERFORMANCE TARGETS

Known “efficacy” of existing
technologies
Integration of existing technologies
and “efficacy” of “systems” approach
known
Identification of any risks
New technologies developed/ tested
and deployed
Partnerships established (e.g.,. usergroups) working together
Effects known and better understood••

Safe, transparent, growing
industryIssues addressed/resolve rather
than holding back growth of futureindustry

Regulations and standards that help
propel growing AM industry
Unsafe practices stopped/eliminatedand minimized

RELATIVE IMPACTS
Medium Improves Product Quality: Eliminates crosscontamination
Low
Reduces Costs: Not likely
High
Accelerates Innovation: Won’t seem like it at first but
over longer termHigh
Enhances competitiveness: Safe/transparent
Reduces Time to Develop Products: N/AHigh
Other: Safety – less/mitigation of EH&S impacts

CHAPTER 6: CROSS-CUTTING CHALLENGES
A number of technical and non-technical challenges were identified that cut across materials characterization,
process modeling, in situ measurements, and performance. These are described below.

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5●

Multi-disciplinary collaboration – Establishing a better programmatic structure to support multidisciplinary collaboration was identified as a high priority. A variety of scientific and engineering disciplines
are needed to accelerate advances, from computer science to mathematics, chemistry, physics, engineering,and the manufacturing sciences.

●

Business models – A good business case and economic models are lacking for PB AM in general and thiswas identified as a priority to address. These would help to promote more widespread acceptance. Models
are needed to demonstrate that AM is good, fast, and cheap – and comparable to conventional technologies.

●

Infrastructural issues – Challenges to maintaining a 30 % CAGR (compound annual growth rate), includingbalancing infrastructure requirements, materials, and how to best utilize. Predictable IT infrastructure
requirements need to be established to support AM industry growth overall.

●Life cycle and sustainability – Not enough is known about the life cycle of PB AM parts, particularly ability
for recycle and reuse and aging. Ensuring sustainability via materials and processes is an objective but not
attainable at present. There is a need to understand and predict value creation over a part’s entire lifecycle,
which requires understanding and characterization of materials and parts from cradle to grave. Recyclable
and/or reusable materials for PB AM are also currently lacking.

●Printer and equipment variability – A universal challenge is the variability between printers andprocessing equipment. This issue is exacerbated by the ‘black box’ approach taken by equipment suppliers,
i.e., there are many unknowns about equipment parameters, variability, and inner workings.

●

Surpassing conventional manufacturing capabilities – The ability to surpass machining and otherconventional parts processing techniques in 10 years is a challenge that should be met to ensure morewidespread acceptance of polymer-based additive manufacturing.

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

41

APPENDIX A: AGENDA
Thursday, June 9, 2016

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

7:30 am

Registration

8:30 am

Opening Plenary Session
Portrait Room
Moderator: ZJ Pei, National Science Foundation► Welcome ~ Mike Molnar, Advanced Manufacturing Program Office, NIST
► Workshop Scope and Objectives ~ Kalman Migler, NIST
► Current Status of Polymers Roadmapping ~ Rob Gorham, America Makes

9:00 amPanel Session: Characterization of Materials Throughout Their Lifecycle
Moderator: Mark Dadmun, University of Tennessee-Knoxville► Matthew Di Prima, Food and Drug Administration► Angel Yanguas-Gil, Argonne National Laboratory► Abraham Joy, University of Akron

10:15 amBreak

10:30 amPanel Session: Process Models
Moderator: Kalman Migler (NIST)► Slade Gardner, Slade Gardner Advanced Manufacturing and Materials, LLC► Peter Olmsted, Georgetown University► David Roberson, University of Texas-El Paso

11:45 amLunch and Posters

1:00 pm

Panel Session: In situ Processing Measurements Portrait Room
Moderator: Robert Maxwell, Lawrence Livermore National Laboratory► Miriam Rafailovich, State University of New York at Stony Brook► Jon Seppala, NIST
► Chris Williams, Virginia Tech

2:15 pm

Instructions for Breakout Sessions► Joan Pellegrino, Energetics Incorporated

2:20 pm

Move to breakout rooms

2:30 pm

Breakout Session I: Targets/Capabilities for Polymer-based Additive
Manufacturing
This round of concurrent breakout
sessions will look at the envisioned
future: desired capabilities,
characteristics, and performance.

3:15 pm

NIST Cafeteria

Topic

Room

Materials
Process Modeling
In situ Measurements
Performance

Heritage RoomPortrait Room
Lecture Room C
Lecture Room D

BreakMeasurement Science Roadmap for Polymer-Based Additive Manufacturing43

3:30 pm

Breakout Session II: Challenges and Barriers to Achieving Targets/Goals
Participants will return to their breakout session to consider barriers limiting the broad use of polymerbased additive manufacturing and measurement and standards barriers, challenges, and gaps, and identify
priority topic areas for the measurement roadmap.

5:00 pm

Adjourn Day 1

Friday, June 10, 2016
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

8:00 am

Early Networking

8:30 amWelcome and Recap of Day 1
Portrait Room

8:40 amPanel Session: Performance
Moderator: Greg Kittlesen, FDA► Courtney Fox, Carbon► Bryan Vogt, University of Akron► Gerald Grant, University of Louisville

10:00 amBreak and Move to Breakouts

10:15 amBreakout Session III: Pathways for Measurement Science Roadmap
Participants will return to their breakout session and review and clarify top challenges and potential
roadmap topics. After consensus on priority topics, small groups will work together to develop priority
roadmap action plans and next steps, which will be presented in the afternoon plenary.

11:45 pm

Lunch and Posters

1:15 pm

Panel Session: Integration and Standards
Portrait RoomNIST Cafeteria

Moderator: Carl Dekker, Met-L-Flo► Scott Fish, University of Texas► Praveen Tummala, 3DSystems► Lyle Levine, NIST
2:15 pm

Breakout Group Reports

2:45 pm

Workshop Wrap-up and Next Steps

3:00 pm

Workshop Adjourns

44

Appendix A: Agenda

APPENDIX B: PARTICIPANTS
Scott Fish

Nicolas Alvarez
Drexel University

University of Texas at Austin

Joseph Bartolai

Elizabeth Fitzgerald

Joe Bennett

Courtney Fox

Jack Beuth

Slade Gardner

David Bigio

Rob Gorham

Daniel Bowen

Gerald Grant

K Chandrashekhara

Scott Grant

Robert Chisena

Robert Hampton

Kevin ChouYong Huang

Daniel Cole

Irada Isayeva

Brian Czapor

Terrence Johnson

Mark Dadmun

Abraham Joy

Anthony Decarmine

Kevin Jurrens

Jeff DeGrange

Sung Hoon KangCarl Dekker

Jennifer Kelly

Matthew Di prima

Matthew Kelly

Chris Dimitriou

Jason Killgore

Chad Duty

Gregg Kittlesen

Penn StateUS Department of Energy (DOE) National Security Campus

This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

NIST

CarbonCarnegie Mellon University

Slade Gardner Advanced Manufacturing and Materials, LLC

University of Maryland

NCDMM

Honeywell

Louisville

Missouri University of Science and Technology
University of Michigan

Autodesk

Center for the Advancement of Science in Space

University of Alabama

University of Florida

US Army Research Lab

US Food and Drug Administration (FDA)

UDRI

Army Research Laboratory

University of Tennessee

University of Akron

Oxford Performance Materials

NIST, Intelligent Systems Division

Impossible Objects LLC

Johns Hopkins University

Met-L-Flo Inc.

FDA

FDA-CDRH

University of Notre Dame

Nike Inc

NIST

University of Tennessee/Oak Ridge National Laboratory(ORNL)

FDA-CDRH

Hilmar Koerner

Air Force Research Laboratory

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

45

Zhenyu Kong
Virginia Tech

Anthony Kotula
NIST

Kirsten Kozlovsky

University of Notre Dame

Mark Kujawski
BASF
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Kenneth Langley
Waters Corporation

Gary Larson
Stratasys Inc.

University of Illinois at Chicago

Elana Polyakova

Graphene 3D Lab Inc

Brian Patterson

Los Alamos National Laboratory (LANL)ZJ PeiNational Science Foundation (NSF)

Amy Peterson

Worcester Polytechnic Institute

Steven Pollack
Carbon 3D

Ming Leu

Missouri University of Science and Technology

James Lewicki

Lawrence Livermore National Laboratory (LLNL)

Joao Maia

Case Western Reserve University

Blake Marshall
DOE

Robert Maxwell
LLNLJames McGuffin-Cawley

Case Western Reserve University

Claire McIlroy

Georgetown University

Kalman Migler
NIST

Mike Molnar
NIST

Stephanie Moran
CarbonRyan Murphy
SolvayTina Tse Nga Ng

University of California, San Diego

Thao (Vicky) Nguyen
Johns Hopkins University

Scott Olig

US Naval Research Laboratory

Peter Olmsted

Georgetown University

46

Yayue Pan

Appendix B: Participants

Timothy Pruyn
AFRL/RX

Miriam Rafailovich
Stony Brook University

Russ Revey

DOE National Security Campus

Rick Ricker
NIST

Laura Ricles

Food and Drug Administration

Mark Robbins

Johns Hopkins University

David Roberson

The University of Texas El Paso

Natalie Rudolph

University of Wisconsin-Madison

Godfrey Sauti
NIA/NASA Langley

Christina Savisaar
FDA

Jay Schieber

Illinois Institute of Technology

Steve Schmid
Notre Dame

Lester Schultheis
University of Maryland

Jon SeppalaNIST

Steven Shpiner

Bryan Vogt

Emilie Siochi

Jonathan Volk

Prescient

University of Akron

Center for the Advancement of Science in Space

Christopher Spadaccini

Lawrence Livermore National Laboratory

Timothy Spahr
Arkema Inc
This publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

Thomas Starr

University of Louisville

Daniel Stolyarov
Graphene 3D Lab Inc

Limin Sun
FDA

Charles Sweeney
Texas A&M

Blake Teipel

Essentium Materials

Mary Toney
NSF

John Tumbleston
CarbonPraveen Tummala
3D Systems

Tony Van Buuren

Center for the Advancement of Science in Space

Katherine Vorvolakos
FDAMark (Xiaoke) Wan
Sync Optics LLC

Todd Weisgraber
LLNL

Chris Williams
Virginia Tech

Buzz Wincheski
NASA LaRC

Kris Wise
NASA

Paul Withereu
NIST

William Yackabonis
Autodesk

Angel Yanguas-Gil

Argonne National Laboratory

Bryan Zahner

Essentium Materials

LLNL

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

47

APPENDIX C: ACRONYMS/ABBREVIATIONSThis publication is available free of charge from: https://doi.org/10.6028/NIST.AMS.100-5

3D

three dimensionalAM

additive manufacturing

ASTM

American Society for Testing and Materials

BAAM

big area additive manufacturing

CAD

computer aided design

COTS

commercial off-the-shelf

DOE

design of experiments, Department of Energy

EHS

Environment, Health and Safety

FAA

Federal Aviation Administration

FDA

Food and Drug Administration

FDM

fusion deposition modeling, used synonymously in this report with FFF and polymers extrusion

FFF

fused filament fabrication

HIP

hot isostatic pressing

HPC

high performance computing

ICME

integrated computational materials engineeringIRinfrared

ISO

International Organization for Standardization

LLNL

Lawrence Livermore National Laboratory

NSF

National Science Foundation

NAMII

National Additive Manufacturing Innovation Institute

NIST

National Institute of Standards and Technology

OEM

Original Equipment Manufacturer

ONR

Office of Naval Research

PB

Polymer-based

PSP

process structural properties

QA

quality assurance

QC

quality control

R&D

research and development

RFID

radio frequency identification

RM

rapid manufacturing

ROI

return-on-investment

SLA

stereolithography

SLS

selective laser sintering

STEM

science, technology, engineering, and mathematics

Measurement Science Roadmap for Polymer-Based Additive Manufacturing

49