PREFACE

The Roadmap Workshop on Measurement Science for Metal-Based Additive Manufacturing was hosted by the 

National Institute of Standards and Technology (NIST) Intelligent Systems Division (ISD) within the Engineering 

Laboratory. Workshop planning and execution and preparation of this report were conducted under the 

direction of Kevin Jurrens, Deputy Division Chief for ISD. The information contained herein is based on the 

results of the workshop, 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 represent the 

views of the entire AM community. 

 

Disclaimer

This report was prepared as an account of work cosponsored by 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 

Metal-Based Additive Manufacturing 

WORKSHOP SUMMARY REPORT

May 2013

Prepared by 

Energetics Incorporated, Columbia, Maryland 

for the 

National Institute of Standards and Technology, 

U.S. Department of Commerce 
ACKNOWLEDGMENTS 

Many thanks to all those who participated in the Roadmap Workshop on Measurement Science for Metal-Based 

Additive Manufacturing held December 4–5, 2012 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 are extended to the plenary speakers (listed below) and to the many expert participants (a complete list 

is provided in Appendix A). 

Speakers and Panelists 

Dave Bourell, University of Texas at Austin

Gary Fleegle, National Center for Defense Manufacturing and Machining

Ed Herderick, EWI

John Hunter, Carpenter Powder Products, Inc.

Kevin Jurrens, National Institute of Standards and Technology 

Agnes Klucha, Pratt & Whitney 

Tom Kurfess, Office of Science and Technology Policy, Executive Office of the President 

Hank Phelps, Lockheed Martin Aeronautics – Marietta

Dean Robinson, GE Global Research Center

Brent Stucker, University of Louisville 

S. Shyam Sunder, National Institute of Standards and Technology

White Paper Contributors 

Jennifer G. Bryant, John E. Benfer, Anthony B. Petrizzo, U.S. Navy, Naval Air Systems Command

Tom Campbell, Virginia Tech University

Kevin Chou, The University of Alabama

Shane Collins, GROWit3d

Vito Gervasi, Milwaukee School of Engineering

Stacey Kerwien, U.S. Army

Frank Liou, Michael Bytnar, and Joyce Bytnar, Missouri University of Science and Technology

Dan Maas and Jesse Blacker, The Ex One Company, LLC

Jyoti Mazumder, University of Michigan at Ann Arbor

Ken Patton, Dr. Imelda Cossette, Ed Tackett, Frank Cox, ASTM F-42 Educational Working Group

Constance J.S. Philips, National Center for Manufacturing Sciences

Special thanks are due to the Energetics Incorporated team who provided support for workshop planning, 

facilitation, and preparation of the summary report. The workshop report and supporting documents can be 

found on the workshop website at http://events.energetics.com/NIST-AdditiveMfgWorkshop/index.html. 

Further information about this report can be obtained by contacting Kevin Jurrens at kevin.jurrens@nist.gov. 

Cover Photos 

 Photos 1,2, and 4, from top to bottom, NIST 

 Photo 3, from top to bottom, Oak Ridge National Laboratory
Measurement Science Roadmap for Additive Manufacturing v

TABLE OF CONTENTS

Executive Summary ......................................................................................................................................................................... vii

Background .................................................................................................................................................................................. vii

Summary of Results ................................................................................................................................................................... viii

Chapter 1: Introduction...................................................................................................................................................................1

Overview ........................................................................................................................................................................................1

Workshop Scope and Process ..................................................................................................................................................2

Plenary Session .........................................................................................................................................................................2

Industry Panel Discussion ......................................................................................................................................................3

Workshop Process ..................................................................................................................................................................4

Chapter 2: AM Materials .................................................................................................................................................................5

Overview of the Topic area .......................................................................................................................................................5

Future/Desired Capabilities ........................................................................................................................................................5

Technological & Measurement/Standards Challenges ..........................................................................................................7

Roadmap for Priority Research and Development ..............................................................................................................8

Chapter 3: AM Processes and Equipment ................................................................................................................................ 13

Overview of the Topic area .................................................................................................................................................... 13

Future/Desired Capabilities ..................................................................................................................................................... 13

Technological & Measurement/Standards Challenges ....................................................................................................... 16

Roadmap for Priority Research and Development ........................................................................................................... 19

Chapter 4: AM Qualification and Certification ....................................................................................................................... 25

Overview of the Topic Area ................................................................................................................................................... 25

Future/Desired Capabilities ..................................................................................................................................................... 25

Technological & Measurement/Standards Challenges ....................................................................................................... 27

Roadmap for Priority Research and Development ........................................................................................................... 29

Chapter 5: AM Modeling and Simulation .................................................................................................................................. 35

Overview of the Topic Area ................................................................................................................................................... 35

Future/Desired Capabilities ..................................................................................................................................................... 35

Technological & Measurement/Standards Challenges ....................................................................................................... 37

Roadmap for Priority Research and Development ........................................................................................................... 38

Chapter 6: Cross-Cutting Challenges ....................................................................................................................................... 43

Cross-cutting Technical Challenges ...................................................................................................................................... 43

Non-Technical Challenges ....................................................................................................................................................... 44

Appendix A: Participants .............................................................................................................................................................. 47
vi Table of Contents

Appendix B: Agenda ...................................................................................................................................................................... 49

Appendix C: White Papers .......................................................................................................................................................... 51

Appendix D: Acronyms/Abbreviations ..................................................................................................................................... 77

LIST OF FIGURES

Roadmap Action Plans 

Figure 2-1. Characterization Data and Standards for Post Processing 

Figure 2-2. Robust In-Situ Process Monitoring Techniques 

Figure 2-3. Metals Design Allowables Database 

Figure 3-1. NDE Techniques Optimized for Metals AM 

Figure 3-2. Fast In-Situ Measurements 

Figure 3-3. Performance Capability Database for AM Technologies 

Figure 3-4. Sensors for Measuring and Monitoring AM Processes and Products 

Figure 4-1. Closed Loop Process Control 

Figure 4-2. Standard Guidelines and Methods for Qualification and Certification 

Figure 4-3. Shared, Standardized Third Party Data Repository 

Figure 4-4. Standards and Protocols for Round-Robin Build and Material Testing 

Figure 5-1. Expert System for AM Design 

Figure 5-2. Validated Physics- and Properties-Based Predictive Models for AM 

Figure 5-3. Standard Data Structures, Definition, and Metrics for AM Models 

LIST OF TABLES

Table 2-1. Desired Capabilities and Technologies for AM Materials

Table 2-2. Barriers and Challenges for AM Materials

Table 2-3. Key Barriers and Corresponding Priority Roadmap Topics for AM Materials

Table 3-1. Desired Capabilities and Technologies for AM Processes and Equipment

Table 3-2. Barriers and Challenges for AM Processes and Equipment

Table 3-3. Key Barriers and Corresponding Priority Roadmap Topics for AM Processes and Equipment

Table 4-1. Desired Capabilities and Technologies for AM Qualification and Certification

Table 4-2. Barriers and Challenges for AM Qualification and Certification

Table 4-3. Key Barriers and Corresponding Priority Roadmap Topics for AM Qualification and Certification

Table 5-1. Desired Capabilities and Technologies for AM Modeling and Simulation

Table 5-2. Barriers and Challenges for AM Modeling and Simulation

Table 5-3. Key Barriers and Corresponding Priority Roadmap Topics for AM Modeling and Simulation

Table 6-1. Summary of Standards Development Requirements
Measurement Science Roadmap for Additive Manufacturing vii

EXECUTIVE SUMMARY 

BACKGROUND

The U.S. manufacturing sector has a significant impact on U.S. job growth, the country’s overall economy, and 

national security. Additive manufacturing (AM), a process for fabricating parts layer-by-layer directly from a 3-D 

digital model, has tremendous potential for producing high-value, complex, individually customized parts. 

Companies are beginning to use AM as a tool for reducing time-to-market, improving product quality, and 

reducing the cost to manufacture products. Metal-based AM parts have emerged for use in applications such as 

automotive engines, aircraft assemblies, power tools, and manufacturing tools such as jigs, fixtures, and drill 

guides. 

While the use of AM has been growing, a number of challenges continue to impede its more widespread 

adoption, particularly in the areas of measurement science and standardization. These challenges were explored 

in the Roadmap Workshop on Measurement Science for Metal-Based Additive Manufacturing sponsored by the 

National Institute for Standards and Technology (NIST) and held on December 4–5, 2012 in Gaithersburg, 

Maryland. Workshop objectives included the following. 

 Building on prior AM roadmaps and other efforts, gain expert input on: 

o Measurement science barriers, challenges, and gaps that prevent the broad use of metal-based AM 

o Research and development needed to address the priority measurement and standards challenges 

o Future measurement- and standards-related targets and goals for AM

o Action plans to address identified barriers and gaps 

 Provide input for strategic planning by the ASTM International standards organization F421 standards 

committee on Additive Manufacturing Technologies 

 Influence and coordinate with the National Additive Manufacturing Innovation Institute on development 

of the national AM roadmap 

White papers were created by workshop participants prior to the 

workshop to provide additional background and a starting point for 

discussions. The workshop opened with plenary presentations that 

provided context on AM challenges and use cases. About 75 expert 

participants then addressed the technical topics in Figure E-1 during 

breakout sessions. 

This report is based on workshop results. The views presented are 

those developed by the experts that participated and do not 

necessarily represent the views of the entire AM community. In some 

cases, ideas overlap among breakout groups illustrating the cross-cutting nature of some priorities and 

challenges. 

This report provides useful information to public and private decision makers interested in furthering the 

capabilities of AM and accelerating its more widespread use. By focusing on metal-based AM, the workshop was 

able to thoroughly address one portion of the overall AM roadmap. It is hoped that the national research agenda 

for metal-based AM will incorporate the consensus-based needs and priorities established during this workshop 

and presented in this report. 

 

1

“Committee F42 on Additive Manufacturing Technologies,” ASTM International, accessed June 5, 2013, 

http://www.astm.org/COMMITTEE/F42.htm. 

Figure E-1. Workshop 

Breakout Topics

 AM Materials

 AM Processes and Equipment

 Qualification and Certification of 

Materials, Processes, and Products

 Modeling and Simulation of AM
viii Executive Summary

Figure E-2. Important Technology and Measurement Challenges for Additive 

Manufacturing

SUMMARY OF RESULTS

Each breakout group identified future desired capabilities and technologies for AM, and measurement barriers 

impeding advances. 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 the challenges shown in Figure E-2 have elements that cut across all aspects of additive 

manufacturing, from materials and modeling to design and manufacturing processes. Examples of some of these 

include: 

 Standards and protocols for all aspects of AM, from materials design and use to part build and 

inspection 

 Measurement and monitoring techniques and data, from material feedstock through final part 

inspection, including effective process controls and feedback 

 Fully characterized materials properties, which are key to materials development, processing 

effectiveness and repeatability, qualification of parts, and modeling at many levels 

 Modeling systems that couple design and manufacturing, which impacts the development of 

materials as well as new processing technologies 

 Closed loop control systems for AM, which are vital for processing and equipment performance, 

assurance of part adherence to specifications, and the ability to qualify and certify parts and processes 

AM Materials 

Characterization Data 

and Standards for Post 

Processing 

Robust In Situ Process 

Monitoring Techniques 

Metals Design 

Allowables Database 

AM Process and 

Equipment 

Non-Destructive 

Evaluation Techniques 

Optimized for Metals 

AM

Fast In-Situ 

Measurements 

Performance Capability 

Database for AM 

Technologies 

Sensors for Measuring 

and Monitoring AM 

Processes and Products 

AM Qualification 

and Certification 

Closed Loop Process 

Control 

Standard Guidelines and 

Methods for 

Qualification and 

Certification 

Shared/ Standardized 

Third-Party Data 

Repository 

Standards and Protocols 

for Round-Robin Build 

and MaterialTesting 

AM Modeling 

and Simulation 

Expert System for AM 

Design 

Validated Physics- and 

Properties-Based 

Models for AM 

Standard Data 

Structures, Definitions, 

and Metrics for AM 

Models 
Measurement Science Roadmap for Additive Manufacturing 1

CHAPTER 1:INTRODUCTION

OVERVIEW

Additive manufacturing (AM) is a high-priority technology growth area for U.S. manufacturers. Innovative AM 

processes that fabricate parts layer-by-layer directly from a 3-D digital model have great potential for producing 

high-value, complex, and individually customized parts. Companies are beginning to use AM as a tool for 

reducing time to market, improving product quality, and reducing the cost to manufacture products. Metal-based 

AM parts have emerged for use in a number of applications, such as automotive engines, aircraft assemblies, 

power tools, and manufacturing tools such as jigs, fixtures, and drill guides. 

AM offers multiple advantages over traditional manufacturing techniques, including reduced material waste, 

lower energy intensity, reduced time to market, just-in-time production, and construction of structures not 

possible with traditional manufacturing processes. By adding precisely measured layers to create an object, 

wasted materials or by-products are greatly reduced when compared to traditional manufacturing techniques. In 

traditional aerospace manufacturing, for example, titanium parts are machined down to size from larger blocks 

of titanium, which produces up to 90% waste material that 

cannot be readily reused. AM could greatly reduce the waste 

from this process, reducing the energy used in production of 

raw materials and in the processing steps. 

Historically, bringing a part from concept to production has 

required numerous prototyping iterations that include 

machine reconfigurations and expensive and time-consuming 

part tooling and prototype fabrication. Much of the 

prototyping with AM can be accomplished with computer 

models, thereby reducing the time to reach full production. In 

addition, the rapid reconfiguration of AM setups increases the 

economic feasibility of small batch production. 

The unique AM process allows the creation of scaffolding-like 

structures, mimicking materials found in nature. These 

complex structures can reduce weight without compromising 

strength or increasing surface area. The potential applications 

for these structures are wide-ranging. AM can also be used for 

remanufacturing parts that have reached the end of their 

useful life, restoring them to like-new parts. Advanced 

techniques and surface treatment processes result in a part 

that potentially requires 2%–25% of the original energy and 

only a fraction of the material.2

A number of technical issues must be addressed to achieve 

widespread use of additive processes for direct part 

production, and to realize the potential economic benefits. 

Among the issues are gaps in measurement methods, 

performance metrics, and standards needed to evaluate 

fundamental AM process characteristics, improve the performance of AM equipment, improve the accuracy of 

AM parts, and increase confidence in the mechanical properties of parts fabricated using these systems. 

 

2

John Sutherland et al., “A Comparison of Manufacturing and Remanufacturing Energy Intensities with Application to Diesel 

Engine Production,” CIRP Annals—Manufacturing Technology 57, no. 1 (2008): 5-8. 

Materials Standards for AM

The adoption of parts made via AM processes into critical 

applications, such as aerospace engine components, is 

hampered by a lack of consensus properties for AM part 

materials (required for many procurement 

specifications). Industrial round-robin testing, which 

includes both the fabrication and mechanical property 

measurements of test specimens, is a required step for 

developing consensus properties and acceptance into 

authoritative material databases.

The National Institute of Standards and Technology 

(NIST) is currently assessing state-of-the-art testing 

methods for determining properties of bulk metal 

materials and raw metal powders, including mechanical 

testing, non-destructive techniques, and X-ray diffraction. 

Methods will then be evaluated and enhanced for use on 

AM parts and raw additive powder. Direct Metal Laser 

Sintering (DMLS) will be used to make parts and test the 

new methods. NIST will also coordinate an industry 

round-robin testing study of processed-part material 

properties for a selected AM material and AM process, a 

first step for acceptance of material into an authoritative 

database. 

(http://www.nist.gov/el/isd/sbm/matstandaddmanu.cfm)
2 Introduction

WORKSHOP SCOPE AND PROCESS

The National Institute of Standards and Technology (NIST) hosted the Roadmap Workshop on Measurement 

Science for Metal-Based Additive Manufacturing at their Gaithersburg, MD campus on December 4–5, 2012. The 

workshop supported NIST programs in the area of smart manufacturing processes and equipment and addresses 

a high-priority technology growth area for U.S. manufacturers. The workshop brought together over 75 AM 

experts from industry, government, national laboratories, and academia to identify measurement science 

challenges and associated R&D needs for metal-based AM systems. This workshop aimed to accomplish the 

following. 

 Build on prior AM roadmaps and other efforts that have identified technology challenges and R&D 

needs, by developing information on: 

o Measurement science barriers, challenges, and gaps preventing broad use of metal-based AM 

o Research and development (R&D) needed to address the priority measurement and standards 

challenges 

o Future measurement- and standards-related targets and goals for AM

o Action plans to overcome the high priority barriers 

 Provide input for strategic planning by the ASTM International standards organization F423 standards 

committee on Additive Manufacturing Technologies 

 Influence and coordinate with National Additive Manufacturing Innovation Institute (NAMII) on the 

development of a national AM roadmap 

The workshop included a stage-setting plenary session, panel discussions, and extended breakout sessions as 

described below. The full workshop agenda is provided in Appendix B. 

PLENARY SESSION

Presentations from leading industry experts were given to set the stage for the subsequent workshop 

discussions. The full presentations can be downloaded from the workshop website at

http://events.energetics.com/NIST-AdditiveMfgWorkshop/downloads.html. The presentations included the 

following: 

 Workshop Scope and Objectives: Kevin Jurrens, Deputy Chief, Intelligent Systems Division, NIST

This presentation discussed NIST’s unique mission, measurement and standards outputs from NIST 

laboratories, and AM contributions from NIST’s Engineering Laboratory, Materials Measurement 

Laboratory, and Manufacturing Extension Partnership. The workshop scope, process, and desired 

outcomes were also presented. 

 Roadmaps for Additive Manufacturing — Past, Present, and Future: Dave Bourell, Temple Foundation 

Professor, University of Texas at Austin, Co-Chair 2009 Roadmap for Additive Manufacturing 

A review and comparison of previous AM roadmap efforts was presented, including the 1998 National 

Center for Manufacturing Sciences Roadmap Study4, 2009 National Science Foundation/Office of Naval 

Research Roadmap Study5, and 2010 U.S. Naval Air Systems Command Direct Digital Manufacturing of 

Materials6. 

 Additive Manufacturing: Standards and Other International Trends: Brent Stucker, Professor, 

University of Louisville, Chair of ASTM F42 

 

3 ASTM F42: Committee F42 on Additive Manufacturing Technologies, http://www.astm.org/COMMITTEE/F42.htm 

4

1998 Industrial Roadmap for the Rapid Prototyping Industry”, National Center for Manufacturing Sciences, Report 0199RE98

5 Roadmap for Additive Manufacturing: Identifying the Future of Freeform Processing, prepared by University of Texas at Austin, Missouri 

University of Science and Technology, and Georgia Institute of Technology, sponsored by the National Science Foundation and Office of 

Naval Research. 2009 http://wohlersassociates.com/roadmap2009.html 

6 Direct Digital Manufacturing of Metallic Components: Affordable, Durable, and Structurally Efficient Airframes, NAVAIR, ONR, 2012. 
Measurement Science Roadmap for Additive Manufacturing 3

Information was provided on the role of the University of Louisville in AM, ASTM F42 scope and 

accomplishments, various AM processes (e.g., vat photo-polymerization, material jetting, powder bed 

fusion), the output and parameters of AM, current and future AM standards, and a strategic approach 

for AM standards development. 

 NAMII Priority Applied Research Needs: Gary Fleegle, Vice President and Chief Operating Officer, National 

Center for Defense Manufacturing and Machining, NAMII Acting Deputy Director for Technology Development 

This presentation illustrated how NAMII works toward goals by fostering strong public-private 

collaboration, using an integrated technology plan, and involving industry through open membership, 

project calls for key topics, and other initiatives. 

 Manufacturing – the Road to Success: Tom Kurfess, Assistant Director for Advanced Manufacturing, 

Office of Science and Technology Policy, Executive Office of the President

This presentation stressed the importance of U.S. manufacturing to the economy. Conducting 70% of 

U.S. R&D and producing $1.40 for every $1.00 of manufacturing output, the U.S. manufacturing industry 

drives jobs throughout the economy. Advanced manufacturing initiatives, such as the National Robotics 

Initiative, the Materials Genome Initiative, and the White House’s initiatives encouraging science, 

technology, engineering, and mathematics (STEM) careers, were cited as greatly benefiting the AM 

industry. 

INDUSTRY PANEL DISCUSSION

Following the opening presentations, a moderated panel 

session was held to discuss the current state and needs of 

the AM industry. The moderator and panelists are listed in 

Figure 1-2. The key points that emerged are summarized 

below, and reflect the unique opinions of the panelists and 

their respective fields of endeavor. Many of the panel themes 

were echoed during the breakout sessions and further 

expanded upon. 

Important overall goals for AM include: 

 Reducing risk to stakeholders in the community and 

supply chain 

 Transitioning AM into production applications 

 Building the AM capabilities needed to successfully 

move forward and appropriately insert technology 

Standards are important to moving AM forward. Some key standards-related issues include: 

 Consistent machine calibration standards for prototyping or production beyond machine variability, this 

is especially important for the aerospace industry where standards are nonexistent 

 Benchmarking studies to determine the frequency of machine calibration 

Process monitoring is important for quality and production throughput. Key capabilities needed include: 

 Real-time process monitoring techniques to increase knowledge base 

 Data that will help develop feedback systems, predictive processing, and modeling capability 

 Process sensing and control to improve the accuracy and repeatability of AM 

Knowledge of AM raw materials is inadequate and requires work in these important areas: 

 Output quality as a function of material input quality (e.g., composition, chemistry, morphology, and 

particle size distribution of the feedstock affects the final AM product) 

 In depth understanding of raw materials (e.g., test for laser powder deposition conducted with virgin and 

recycled powder, the recycled performed better, why?) 

Figure 1-1. AM Panelists

Moderator, Ed Herderick, EWI, Chair of Additive 

Manufacturing Consortium (AMC)

 Agnes Klucha, Program Manager, Engineering 

Innovation Center, Pratt and Whitney

 John Hunter, Director of Marketing & Sales, 

Carpenter Powder Products, Inc.

 Dean Robinson, Manager, Model-Based 

Manufacturing Lab, GE Global Research Center

 Hank Phelps, Senior Engineer, Technology 

Development & Integration, Airframe Design, 

Lockheed Martin Aeronautics
4 Introduction

 Chemistry of the power and potential changes during AM processing (e.g., many powders are not 

designed specifically for AM processes) 

 Clarify intended use and specifications for powder suppliers 

 Openly accessible high-fidelity material property databases 

Higher quality products require improvements to AM processes, including: 

 Methods for improving as-built surface finish 

 Predictive models for distortion correction such as process modeling, brute force methods, or reverse 

engineering (e.g., review work done for predictive modeling castings/forgings for heat treatment 

distortion); Dante software is commercially available and could have applicability for AM-produced metal 

parts 

 Tools to exploit AM design capabilities, such as better geometrical design tools 

 Incorporating lessons from historical development of parts or other manufacturing process 

WORKSHOP PROCESS

The workshop was designed to include four breakout groups, each focusing on a different technical aspect of 

metal-based AM, as shown in Figure 1-2. White papers were created and submitted by workshop participants 

prior to the workshop to provide additional background and a starting point for discussions; these are provided 

in Appendix C. Within each breakout group, several key questions were posed to gain insights on the important 

challenges and pathways to address them, including: 

 What are the ideal targets for technology, process, 

performance, or capability that we want to achieve for AM (in 

the breakout topic area)? 

 What are the technology, measurement, and standards barriers 

that keep us from achieving the desired future end state why? 

 Are there any additional barriers limiting the broad, more 

widespread use of additive manufacturing and why? 

The results of these discussions are summarized in the following report chapters, which are organized by 

breakout topic. Each breakout group used a simple 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 R&D, standards development, and other future efforts for AM. The results of these indepth examinations can be found in the Roadmap for Priority R&D section of each chapter. A variety of acronyms 

unique to this topic are noted throughout the report and listed with explanations in Appendix D. 

Note that the ideas presented here are a reflection of the expert participants and not necessarily the entire 

metal-based AM industry. As such, they should be viewed as a snapshot of the important perspectives, but not 

all-inclusive. In some cases, ideas overlap among breakout groups, illustrating the cross-cutting nature of some 

priorities and challenges. 

This report provides useful information to both public and private decision-makers interested in furthering the 

capabilities of AM and accelerating its more widespread use in the industrial sector. In addition, this report will 

be contributed for integration with the NAMII national AM roadmap and will provide a foundation for 

development of the ASTM F42 Strategic Plan. By focusing on metal-based AM, the workshop was able to 

thoroughly address one portion of the overall AM roadmap. It is hoped that the national research agenda for 

metal-based AM will incorporate the consensus-based needs and priorities established during this workshop and 

presented in this report. The workshop report and supporting documents can be found on the workshop 

website at http://events.energetics.com/NIST-AdditiveMfgWorkshop/index.html. 

Figure 1-2. Workshop 

Breakout Topics

 AM Materials

 AM Processes and Equipment

 Qualification and Certification of 

Materials, Processes, and Products

 Modeling and Simulation of AM
Measurement Science Roadmap for Additive Manufacturing 5

CHAPTER 2: AM MATERIALS

OVERVIEW OF THE TOPIC AREA

Metal materials currently in use in additive manufacturing (AM) include steels (tool steel and stainless), pure 

titanium and titanium alloys, aluminum casting alloys, other alloys (nickel based and cobalt-chromium), gold, and 

silver. The range of metals available for use in AM continues to grow as new technologies and applications 

emerge. 

AM materials have many implications for a wide range of applications in the areas of national security, energy, 

photonics, micro fluidics, and semiconductor manufacturing. To utilize the full potential of AM, the materials 

used will need further development. Although significant progress in new AM materials has been possible, a 

number of challenges remain. For example, AM parts must meet desired specifications for toughness, fatigue, 

strength, and other properties. Currently, such understanding is not available due to lack of sufficient testing, 

lack of shared knowledge and materials test results across the AM community, and lack of standardized test 

methods for AM materials. 

FUTURE/DESIRED CAPABILITIES

In the future, AM materials will facilitate more direct part production and shorter lead times and production 

runs. Designers will find a greater variety of product capabilities to satisfy their form, fit, and function needs. 

Table 2-1 provides further details on the desired capabilities and technologies for AM materials. Many of the 

capabilities shown take advantage of additive manufacturing to provide new material functionalities or to utilize 

materials in unique ways. 

Materials 

Raw materials characteristics are critical to AM product quality. As a result, improvements to feedstock 

materials or development of new materials could have a direct impact on facilitating more widespread use of 

AM. A comprehensive understanding of the material properties that most affect micro-structure and those that 

can cause material deviation is essential to designing new materials to take full advantage of the AM process (e.g., 

faster solidification rates with new high-temperature alloys). Better understanding of AM materials properties, 

characteristics, and performance parameters is also desired to enable better utilization in applications. Greater 

knowledge in these areas would improve not only the ability to design new materials and products, but also 

enable better control of the AM process and the quality of the final products. 

New materials could include multiple capabilities or smart technology (e.g., sensors built-in to material 

structures) that lead to new applications of AM. The ability to utilize multiple materials in the same part, or to 

change out single materials to provide unique characteristics, would open new opportunities for AM products. 

Materials Process Control 

In-process material data acquisition would enable closed loop control, measurement of in-situ material 

properties, allow for property predictions, and provide micro-structural control. Improved understanding of inprocess material performance would advance the ability to control residual stress distortion and in-process 

material consistency, issues that are both important to product quality. 

Supply Chain Capabilities 

Reliable, available, and consistent powders and wire are essential for quality AM products, reducing production 

risks, and gaining wider adoption of AM technology. Greater availability of materials is needed in general. 

Innovative methods for materials production, such as directly from ores, could be one approach for increasing 

materials availability. There is also a shortage of material recycling services and means for reusing AM materials. 
6 AM Materials

Metallurgical / Powder Properties Knowledge 

Greater understanding and characterization of AM materials is needed, such as powder size, shape, and 

chemistry. The ability to tie surface characteristics to effects on fatigue properties would enable design of 

materials with improved performance. Similarly, defect type, frequency, and size quantification could facilitate the 

understanding of property impacts on product quality and performance. 

Standards Specification Database 

Databases and standards or specifications for all aspects of AM materials were identified as an important 

foundational aspect. Databases would include alloy specifications, as well as information on AM material 

properties and micro-structural characteristics at all stages of processing and use. AM material properties should 

be housed in a publically accessible database that includes common alloy specifications, machine independent 

material properties for deposited metals, micro-structural characteristics, and images (for virgin and recycled 

feedstocks, in-situ processes, finished build, etc.). 

Table 2-1. Desired Capabilities and Technologies for AM Materials

Materials by Design 

 Materials designed for “build-ability,” chemistry, and physical form

 Materials engineered to take full advantage of AM functional materials, such as smart materials (e.g., sensor built-in to material 

structure where the material is the sensor) and new alloys (e.g., faster solidification rates with new high-temperature alloys)

 Multiple material capabilities (i.e., ability to print multiple powders in varying concentrations in a single build, ability to change 

out one part material)

 New materials (functionally-grade, metal/ceramics, metal matric composites, new AM classes not derived from history or 

wrought, graded material properties within structures, location-specific materials and microstructures)

 Utilization of materials that cannot be converted into products via traditional processes (e.g., new materials enabled by AM 

processes)

 AM parts as “drop-in” replacements for conventional manufactured parts (e.g., castings or powder metal)

Material Process Control 

 Advances in reducing/controlling residual stress distortion

 Knowledge of parameters (e.g., upper and lower limits) that can cause material deviation in a process 

 Consistent material properties based on process control

 Understanding the parameters that most affect micro-structure

 Measurement of material properties in-process:

o Real-time data from AM processes to enable property predictions (and process relationships) and micro-structural control

o In-situ data acquisition, including temperature management, to enable closed loop control

Supply Chain Capabilities 

 Reliable, available, consistent powders and wire for AM

 Ability to create feedstock for AM from ore elements

 Greater availability of materials

 Ability for recycling and reuse of AM materials

Metallurgical / Powder Properties Knowledge 

 Quantification of defect types, frequency, size and impact on properties

 Established surface characteristics and effects on fatigue properties

 Better understanding and characterization of AM materials and input materials, including powder size, shape, and chemistry

Standards Specification Database

 Strong database of material properties

o Public access to common alloy specifications for AM processes

o Machine-independent material properties for deposited metals

o Micro-structural database with images at each step, including (a) feed stocks, both virgin and recycled; (b) in-situ process;

and (c) finished build
Measurement Science Roadmap for Additive Manufacturing 7

TECHNOLOGICAL & MEASUREMENT/STANDARDS CHALLENGES

A number of technical and measurement challenges to continued advances in AM materials were identified. A 

major issue is insufficient understanding of post-processing, which involves the removal of the part from the 

platform or the removal of support structures for the purpose of finishing the part. Another major issue is the 

inability to monitor and detect anomalies during the additive manufacturing process. This capability would allow 

the manufacturer to make appropriate adjustments to laser power, processing speed, or other parameters in 

real-time to improve the final product. A complete list of the challenges identified is provided in Table 2-2. 

Table 2-2. Barriers and Challenges for AM Materials

(● = one vote)

Materials Processing

High Priority

 Limited understanding of post-processing issues for AM materials ●●●●●●

o Thermal post-processing behavior (not similar to wrought or forged materials)

o Hot isostatic pressing (HIP), heat treatment, welding, etc. are not well-characterized

Low Priority

 Lack of control over microstructure gradients in as-built parts 

 Insufficient methods to manage AM process and technology risks

 Challenging materials data storage and retrieval solutions, image analysis, and modeling

o Challenges in handling massive amounts of process data

o Insufficient methods for process analysis and statistics

Measurement and Monitoring of Materials In-Process

High Priority

 Lack of robust in-situ monitoring and measurement for materials performance ●●●●●

o Lack of capability for high-speed video and high-speed thermograph (e.g., for deposition of materials 

on windows)

o Lack of real-time materials monitoring and measurements 

o Inability to perform in-situ detection of processing anomalies leading to discontinuities: thermal 

gradients, voids, and inclusions

o Ineffective feedback control for material composition and microstructure; sensor integration is 

unattainable with current black box controllers

Medium Priority  Lack of external measuring, understanding, and ability to control residual stress ●●

Low Priority

 Developing alternative in-situ instrumentation technologies (e.g., process monitors)

 Lack of robust, effective, affordable external non-destructive evaluation (NDE) methods

 Lack of validated characterization/selection criteria for powder or wire feedstock used in AM production

Materials Standards

Medium Priority  Lack of microstructure standards to enable evaluative criteria/judgments ●●●

 Limited certified measurement methods/specifications for powder chemistry and size distribution ●●●

Low Priority

 Incomplete understanding of how to characterize AM surface roughness ●

 Lack of AM materials standards to build customer confidence in AM ●

 Lack of standards for utilizing sensor technology/tools for open system in-process materials control ●

 Incomplete standards for collecting, mining, and combining data on materials ●

Materials Data 

Medium Priority

 Lack of AM material data sharing across companies due to intellectual property (IP) restrictions, unique

data types, and other factors ●●●

 Limited metallurgical/powder properties knowledge ●●●●

o Variation of part properties when made on different machines

o Limited availability of reference material/data 

o Lack of a fixed-source metals database (e.g., standard protocols and data for AM materials, x-y-z 

specimen preparation, etc.)

Low Priority  Compatibility of AM processes with regulatory certification requirements based on statistical process 

control (SPC)
8 AM Materials

ROADMAP FOR PRIORITY RESEARCH AND DEVELOPMENT

A roadmap action plan was developed to enumerate the activities and approaches for overcoming the most 

critical barriers identified in Table 2-2. Roadmap action plans are outlined in more detail in Figures 2-1 through 

2-3. The key barriers being addressed and the corresponding priority roadmap topics are summarized in Table 

2-3. 

Table 2-3. Key Barriers and Corresponding Priority Roadmap Topics

for AM Materials

High-Priority Challenge/Barrier Priority Roadmap Topic

Understanding of post-processing issues for AM 

materials is currently limited, making it difficult to 

design AM hardware and effectively control 

materials properties.

Characterization Data and Standards for Post-Processing 

(Figure 2-1)

o Models, standards, and datasets are needed to improve postprocessing control of material properties and ultimately the final 

product. 

In-situ monitoring and measurement for materials 

performance is limited and lacks robustness (e.g., 

real-time measurements, deposition of materials, 

detection of anomalies, feedback control).

Robust In-Situ Process Monitoring Techniques (Figure 2-2)

o Robust, innovative inspection and control technologies are needed 

to enable effective in-situ monitoring of materials processing and 

allow for detection and/or mitigation of anomalies.

Metallurgical/powder properties knowledge and 

reference data is currently limited, particularly 

understanding how part properties vary by machine.

Metals Design Allowables Database (Figure 2-3)

Open databases of material properties need to be developed to 

enable determination of product design and testing is needed to 

establish material design allowances. 
Measurement Science Roadmap for Additive Manufacturing 9

BARRIER: Thermal post processing requirements for AM components are different from thermal processing for 

wrought materials due to how the feedstock is manufactured and intrinsically due to the AM process itself. The 

effects of these factors on final product characteristics are poorly understood. Thermal post-processing behavior, 

HIP, and heat treatment of AM parts are not well-characterized. 

APPROACH SUMMARY: Build on conventional metallurgical understanding of AM processes and materials, and 

develop requirements specific for AM processes.

FIGURE 2-1. ROADMAP ACTION PLAN: CHARACTERIZATION DATA AND 

STANDARDS FOR POST PROCESSING

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Provide input on 

requirements, issues, needs

 Industry/AM Providers: Provide solutions, tools, 

techniques

 Academia: Develop model, frameworks, data and 

software

 Standards Committees: Create guidance, 

standards, specifications, documents

 Government: Provide infrastructure and test 

methods

RELATIVE IMPACTS

LOW —HIGH

 Accelerates innovation: Engineers have 

valid information to design parts

 Enhances industry competitiveness:

Industry becomes viable

 Faster product development time:

Industry becomes viable

 Improves product quality: Improved 

part/quality and consistency

 Reduces costs: Reduces scrap and reduces 

redundant databases

 Other: Improves manufacturing methods

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS

1-2 

years

 Conduct industry survey of AM 

feedstock vendors to collect critical 

parameter information (e.g., 

chemistry, microstructures)

 Understand gaps in existing versus 

required process

 Begin to build repository of process 

parameters and existing controls, e.g., 

for non-destructive inspection (NDI)

 Consolidation of critical material and 

process parameters

 Published gaps in existing parameters 

and updated roadmap

 Established a database with key process 

parameters

 First-time, quality 

aerospace metal AMbuilt parts

 Validated and specified 

post process work for 

the first time on AM 

built parts

3–5 

years

 Define process standards and 

measurements

 Establish public access for AM 

microstructure database for 

aerospace alloys

 Integrate computational modeling of 

post AM material with database (e.g., 

for NDI)

 Published new/updated process and 

measurement standards

 Open access to alloy database

 Published model detail and preliminary 

comparison to micro-structural 

database

5+ 

years

 Conduct demonstration to validate 

post AM process models and 

standards

 Update post process requirements 

and standards as required

 Develop NDI techniques and 

protocols

 Models and standards validated or 

revised

 Machine-independent properties for 

deposited metals

 Improved surface finish
10 AM Materials

BARRIER: In-situ process monitoring techniques for material and product defects are currently not robust and 

lack key capabilities (e.g., high-speed video and high-speed thermograph for deposition of materials, real-time 

measurement, and in-situ detection of processing anomalies leading to discontinuities, such as thermal gradients, 

voids, and inclusions). Feedback control for composition and microstructure, and sensor integration is not 

attainable with current black box controllers.

APPROACH SUMMARY: Identify, develop, and implement process monitoring, NDE, and in-process measurement 

techniques to enable maximum detection of material defects.

FIGURE 2-2. ROADMAP ACTION PLAN: ROBUST IN-SITU PROCESS 

MONITORING TECHNIQUES

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Aerospace, biomedical, oil 

and natural gas industry: Identify needed material 

and mechanical properties

 Industry/AM Providers: Open up software and 

collaborate with researchers to implement and 

support these techniques

 Academia: Conduct basic research and analysis

 Standards Committees: Evolve standards along 

with the technology

 Government: Support standards development; 

Coordinate and facilitate cooperation among NIST, 

Oak Ridge, NASA, DOE, DOC, NSF, NIH, DARPA; 

Resources (i.e., neutral source)

RELATIVE IMPACTS

LOW—HIGH

 Improves product quality: Eliminate defects; 

Have an intimate and unprecedented 

understanding of component quality

 Reduces costs: Scrap, raw materials; Reduce 

capital investments in forming/shaping

 Accelerates innovation: For example, making 

available the data needed to develop techniques 

for designing micro-structuring 

 Enhances industry competitiveness: Lower 

lead times (e.g., batch size one production)

 Faster product development time: Eliminate 

need for tooling

N/A Other: Needed for AM to be a manufacturing 

tool

1–2 

years

 Identify and implement existing process 

monitoring technologies, identify 

constraints and limits, and resolve 

measurement capabilities

 Collect and analyze critical data

 Correlate process monitoring data to 

NDE measurements

 Implementation of process monitors on 

existing AM platforms

 Identification of limits of existing 

sensor/process monitoring equipment

 Correlation of NDE and mechanical 

testing to determine if sensor resolution 

is adequate

 Maximized 

detection 

capabilities to 

qualify production 

with batch size of 

one

3–5 

years

 Correlate NDE data with destructive 

testing

 Identify existing, alternate, and inprocess measurement techniques not 

being investigated that are capable of 

scaling with AM processes

 Identify and develop techniques for realtime and long-term collection, analysis, 

and storage of massive data sets

 Identification of alternatives that address 

the gaps of existing process monitoring 

technologies

 Implementation of these new technology 

detection limits 

 Correlation to NDE and mechanical 

testing

 Demonstration of the ability to collect 

and store the pertinent data

5+ 

years

 Use data to drive modeling efforts

 Correlate modeling with process 

measurement to enable robust process 

control (e.g., vision system identifies a 

defect/pore, process control system 

corrects and eliminates defects)

 Demonstration of direct correlation 

between process monitoring, control, 

and NDE

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS
Measurement Science Roadmap for Additive Manufacturing 11

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Provide feedstock 

specifications for evaluation, data mining, and 

determining priorities; part production

 Industry/AM Providers: Identify parameters that 

significantly affect variability

 Academia: Determine upper and lower limits of 

parameters

 Standards Committees: Publish initial metrics, 

set material/machine standards

 Government: Support and facilitate cooperation

BARRIER: No public database exists to derive materials properties for design allowables for specific processes. 

Acquiring data is difficult due to the number of AM machine manufacturers, the evolution of machine control software/ 

hardware versions, and the lack of standard protocols for AM materials (e.g., x-y-z specimen preparation).

APPROACH SUMMARY: Undertake collaborative effort to test specific materials and processes and establish 

databases. Three aspects of this activity are (1) Feedstock (e.g., powder, wire); (2) Manufacturing Platform (i.e., Electron 

Beam (ARCAM 5-12, A-1, A-2, A-2x, A-2xx) or Laser Beam (EOS – M270, M280; Concept laser – M2, M2ab; Renishaw –

AM125, AM2510; ; Phoenix Systems - PXL, PXM, PXS, PXS & PXM dental; SLM Solution – SLM 280, SLM 250 

Realizor) (3) Testing Protocol

FIGURE 2-3. ROADMAP ACTION PLAN: METALS DESIGN ALLOWABLES

DATABASE

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: Improves part 

quality and consistency, understanding

 Reduces costs: Reduces scrap; Reduces 

redundant databases

 Accelerates innovation: Engineers have 

valid information to design parts, promotes 

use of AM

 Enhances industry competitiveness:

Industry becomes viable

 Faster product development time:

Industry becomes viable

1-2 

years

 Feedstock

o Identify alloy testing priorities

o Develop material specification for 

each machine

 Manufacturing platforms

o Prioritize processes/materials

o Develop standards for initial 

machine metrics

o Identify parameters that significantly 

affect material properties

o Determine upper and lower limits 

for parameters that affect properties

 Testing Protocol

o Design testing protocol (i.e., x, y, z 

build location); begin testing on high 

priority pairs (materials/processes)

o Identify facilities where specimen 

building will occur

o Qualify machines

 General

o Documentation on data reporting

o Initial database/repository 

established

 Feedstock

o Materials/alloys identified

o Alloys acquired

o Material specification published

 Manufacturing Platforms

o ASTM standard published for initial 

machine metrics

o Process parameters identified for 

priority platforms

 Testing Protocol

o Test specimens built and tested for 

top 2 alloy types

 Low cost, flexible 

database that can be 

updated as technology 

changes

 Industry acceptance of 

data

 Greater use of AM

 Feedstock

o Standardized 

feedstock 

properties

 Manufacturing 

Platforms

o Published standard 

for determining 

machine platforms 

suitable for 

processing 

materials

 Testing Protocols

o Robust testing 

protocol that can 

accommodate the 

various 

technological 

differences

3-5 

years

 Testing Protocol 

o Produce test specimens

o Test specimens and analyze results

o Expand materials testing to cross 

process barriers

o Compare results for models

o Incorporate flexibility (database 

changes as more information 

becomes available from sensors)

 Feedstock

o Alloys acquired

 Manufacturing Platforms

o Machine standard to which machine 

manufacturer must comply

 Testing Protocol

o Test specimens built and tested for 

top 10 alloy types

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS

Measurement Science Roadmap for Additive Manufacturing 13

CHAPTER 3: AM PROCESSES AND EQUIPMENT

OVERVIEW OF THE TOPIC AREA

Additive Manufacturing (AM) technologies use metal, polymer, or composite materials to develop a threedimensional “print” of a given part. The two processes commonly employed for metal AM materials (the focus 

of this report) are powder bed fusion and directed energy deposition. Direct energy deposition potentially has 

the capability to use multiple materials (combination of two or more powders). Other metal-based processes in 

use, but not as common, include material jetting and ultrasonic bonding. Most AM systems melt the metal 

materials and produce parts with close to 100% density, with properties that can match or exceed those of a 

cast part. 

Much progress has been made in the development of AM processes and equipment, but some significant 

challenges must be addressed to move AM more into the mainstream. Improving product quality, increasing the 

efficiency of production, gaining the ability to rapidly produce larger and more diverse parts, and lowering 

production costs are a few of the important challenges that are directly impacted by processing techniques. 

Overcoming these barriers in many cases requires approaches that are unique to the type of processes and 

equipment employed, and account for the materials being used. 

FUTURE/DESIRED CAPABILITIES

In the future, new capabilities are needed to improve equipment efficiency, enable more rapid production at 

larger scales, and ensure repeatable product quality. The capabilities identified to achieve this are described 

briefly below and listed in Table 3-1. 

AM Process Technology 

Improved equipment efficiencies and finishing processes are needed. Ideally, overall equipment efficiencies (OEE) 

of greater than 70% are desired (i.e., world-class automotive production has OEE of roughly 85%). The AM 

community would benefit from technology that effectively utilizes multiple material types to create products 

with increased functionality. This includes, for example, the ability to facilitate composite fabrication (i.e., nonmetal and metal composites) to produce multi-functional composite materials. More efficient powder 

distribution systems (i.e., cleaner, targeted, and recycle control) can lessen the waste of powder inputs. In 

addition, better control and capabilities to build microscale features (i.e., less than 200 microns) are needed, 

along with commercially viable equipment that can be used across industry platforms. 

Production Speed and Scale 

High-speed production equipment is needed for high-volume manufacturing, potentially at a lower cost. 

Achieving a level of affordable, small-lot production could provide a range of users (i.e., supply chain companies 

and end-use consumers) with access to designer materials and a network of AM part producers. Continuous 

processing and distributed manufacturing would also allow for more rapid and customized production of parts. 

Product Quality 

Producing higher quality parts is a key capability identified by experts and stakeholders in the AM community. 

Minimal post-AM processing can help achieve defect-free parts that meet technical requirements at costcompetitive rates of production. Issues with product quality have emphasized the need for stress-relieved, 

geometry- and composition-correct, homogeneous deposited materials that will enable production of higher 

quality parts. Broad deployment of machines and processes capable of producing parts at the level of quality 

seen in the aerospace industry, for example, would greatly benefit the growing AM industry. 
14 AM Processes and Equipment

Standards and Protocols 

Development of standards and protocols for metal-based AM parts can help improve the quality and 

repeatability of results from AM processes. Developing standards will require determining what factors need to 

be controlled to ensure part consistency. The objective is to develop standard methods for qualification and 

certification that will increase the numbers of certified AM parts in service in the U.S. economy. ComputerAided Design (CAD) approaches and best practices must be developed that take advantage of unique AM 

capabilities. This highlights the need for an improved common file format for use in AM equipment and 

processes. The goal is transforming CAD into a computer-aided manufacturing tool for AM technologies. New 

AM file formats would need to be standardized through the ASTM F42 committee. 

The large amounts of data being generated and made available to the AM community continue to grow, driving 

the need for data analysis tools and definition of part properties to enable appropriate archiving. Techniques and 

standards are also needed to characterize multi-material parts. 

Process Measurement, Monitoring, and Control 

Improved sensors and controls used in AM equipment and processes can enhance monitoring and control 

capabilities to provide real-time visibility and control of the build environment. Optimal systems for process 

monitoring and control will help AM systems limit the risk of cross-contamination of metal powders. Real-time 

inspection and material property determination during the deposition process can improve production of 

qualified parts directly from the AM machine. Process monitors and controls should be fully integrated with the 

AM process. This would enable a number of improvements to monitoring and control capability, such as the 

ability to monitor part geometry layer-by-layer for real-time part qualification, and the ability to control or 

mitigate distortions and residual stress in parts. In the future, machines that are self-monitoring and selfcalibrating could self-correct and control important equipment performance parameters. 

Measurement methods and tools are critical to monitoring and control on many levels. Benchmarking of 

experience is needed to improve various aspects of AM equipment and processes. This would include, for 

example, benchmarks related to the design of AM parts, part geometry, and measurements and accuracies of 

AM processes. Environmental conditions and impacts are also important, including time, temperature, humidity, 

dew point, re-use of materials or parts, recyclability, virgin material and re-use ratios, thermal cycling, and 

oxidation. Non-destructive evaluation (NDE) techniques are needed to enable validation of process 

performance. Measurement methods before, during, and after the production process should include feedstock 

“qualification” in the process loop (i.e., before building the part) as well as the ability to quickly and easily 

validate achievement of desired output, including part microstructures, geometry, and quality. 

Process Models 

Accessible, user-friendly, high-fidelity modeling tools would benefit all aspects of the metal-based AM 

community. Modeling and simulation of AM processes can help in assessing the costs and design of parts, 

thereby improving the final products. Deterministic modeling of the inputs (materials), the transformation 

process, and final outputs can be used to better predict processing results. Integration of the design function—

also known as design for additive manufacturing (DFAM)—with optimization software would lead to more 

predictable processes and higher quality products. 

Knowledge and Data 

A greater understanding of properties of materials and AM parts, process parameters, performance, and 

relationships is needed to improve the predictability of both models and results. A life-cycle knowledge base that 

captures the evolution of AM technology is an essential component of building this understanding. AM machines 

vary greatly and have unique life-cycles, from the generation of feedstocks to the end of the process. Capturing 

data on process performance and parameters to feed life-cycle analysis will require collaboration between the 

equipment industry and end users. 
Measurement Science Roadmap for Additive Manufacturing 15

Table 3-1. Desired Capabilities and Technologies for AM Processes and Equipment

AM Technology

 Open atmosphere processing 

 Overall equipment efficiencies (OEE) of greater than 70% (world class automotive production has an OEE of roughly 85%,

typical OEE is about 30%)

 Automated, non-geometry-specific finishing processes (tumbling, shot peening, machining, etc.)

 Production of net-shape parts that are ready-to-use

 Technology to enable use of multiple materials in the same part

o Multi-functional composite materials

o Facilitation of composite fabrication (non-metal and metal composites)

 Process to produce isotropic material properties

 More efficient powder distribution system (i.e., cleaner, targeted, with recycle control)

 Machine self-validation of maintenance status/build readiness 

 Better control and ability to build micro-scale features (i.e., less than 200 microns), including large scale parts (e.g., good 

properties and surface) 

Production Speed and Scale

 Distributed manufacturing (i.e., Kinko’s-type AM, iTunes download files)

 Affordable, small-lot production

 Continuous processes for AM

 High-speed AM production equipment for high volume/mass production, faster manufacturing of lower cost parts

 Big working envelope

 Series production, not rapid prototyping or rapid manufacturing; fast cycles and minimized post-processing

Product Quality

 Aerospace production-quality-capable machines and processes

 Scrap rate of less than 1,000 parts per million

 Defect-free parts that meet technical requirements at cost-competitive rates in production

 First-part correctness with minimal post-AM processing (as good as wrought metal)

 Stress-relieved, geometry-correct, composition-correct, homogeneous deposited materials that meet high reliability standards

 Application of statistical process control (SPC): Process capability, Six Sigma (values and distribution) for quality control

Standards and Protocols 

 Certified parts in service

 File format commonality (CAD leading to computer-aided manufacturing) synchronized with the ASTM format

 Knowledge of factors that need to be controlled to ensure part consistency

 Standards to ensure repeatability of results

Process Measurement, Monitoring, and Control

 In-process, real-time sensing/monitoring, measurement, and control:

o Integrated with AM process 

o Intelligent and calibrated decisions and control

o Contamination-free monitoring capabilities

o Optimized sensors for process monitoring and control for high-volume production

o Monitoring geometry of the part layer-by-layer for real-time part qualification

o Closed-loop process control specifically for metals AM

o Control/mitigation and measurement of distortion and residual stress in parts

 Self-monitoring, self-diagnostic, self-calibrating machines

 Smart systems with integrated sensors, systems, and software

 Benchmarking of design, part geometry, measurements, accuracies, and number of tests can aid in validating models

 Knowledge of environmental impacts (time, temperature, humidity, dew point, reuse, recycle, and virgin and reuse ratios, 

thermal cycling, oxidation, etc.)

 Process validation through NDE techniques

 Technician-friendly control part that quickly and easily validates achievement of desired output

 Rapid feedstock “qualification” in the loop just before building the part
16 AM Processes and Equipment

Table 3-1. Desired Capabilities and Technologies for AM Processes and Equipment

Process Models

 Integrated modeling and simulation platform with atomistic informed meso-scale capability

 Integration of the design function (i.e., design for additive manufacturing - DFAM) with process optimization software

 Design allowable guidelines integrated with design modeling and simulation tools

 Physics- and chemistry-based cost estimation models 

 Deterministic models and predictive capabilities for input transformation and outputs

Knowledge/Data

 Life-cycle knowledge base (i.e., expert systems, artificial intelligence)

 Understanding of properties, process parameters, performance, and relationships to improve predictability

 Recording of material transformation physics data in real time, creating a part history database as the part is being built

 Understanding capabilities (design, materials, cost, and properties) via knowledge a database to identify the best 

process/application match

 Knowledge of parts where AM could improve the cost-effectiveness of production

 Open architecture for hardware and software

 User friendly hardware, software, maintenance/repair, training, and segue to education

TECHNOLOGICAL & MEASUREMENT/STANDARDS CHALLENGES

The barriers and challenges currently impeding large-scale deployment of AM processes and equipment are 

presented in Table 3-2 and are categorized by common themes. In each category, specific barriers or challenges 

are prioritized based on expert and stakeholder input. 

The challenges identified revolve primarily around the development of advanced technology; the ability to better 

monitor and control processes and outputs; the standardization of all aspects of AM, from materials inputs 

through products; and the enhancement of the knowledge base to support process improvements. The lack of 

real-time sensors in all the areas critical to process monitoring and control was identified as a major challenge. 

Closely related to this is the absence of non-destructive evaluation techniques that could be utilized in-situ to 

monitor and calibrate performance. 

The lack of standards was identified as a challenge in a number of areas, since standards are particularly critical 

to ensure machine-to-machine consistency and provide guidelines for test parts and sensors. Standards 

development is necessary to overcome the lack of accurate, achievable material datasets for design allowable 

and control samples. Converting high-fidelity, academic modeling tools into easy-to-use tools for engineers with 

desktop machines was also noted as a major challenge. While such models exist, they are not comprehensive, 

readily available, or usable by the AM community at large. 
Measurement Science Roadmap for Additive Manufacturing 17

Table 3-2. Barriers and Challenges for AM Processes and Equipment

(• = one vote)

Technology Challenges

High Priority  Limited availability of design tools optimized for AM ●●●●●●

Low Priority

 Powder process technologies that yield reproducible feedstocks ●

 Achieving a desired surface finish on the internal geometry of complex shapes ●

 High machine cost, metals are high (~$750,000) compared with plastics

 Machine control software that inhibits real-time parameter changes using sensor-derived inputs

 High-resolution, high-speed, affordable, high-power computed tomography or alternative technology for

complex geometry

 Limited availability of mature technologies

Process Measurement, Monitoring, and Control

High Priority

 Lack of sensors for monitoring and in-situ control ●●●●●●●●

o Development of real-time sensors and measurement methods for:

 Dimension, including geometry, roughness, and low-cost residual stress measurements

 Microstructure and mesostructure

 Surface finish

 Defects, porosity, and flaws

 Feedstock tolerance

 Energy source, including laser beam and electron beam

 High-speed imaging and non-imaging

o Complete process and system sensor schemes (real-time, continuous), knowing what to measure 

and how to measure it

o Low cost, high-resolution thermal sensing

 Sensors for melt pool, total part, large areas in harsh environments

 By type of AM process/scenario

 Temperature range/accuracy

 Calibration (bench and installed)

 Errors and uncertainty

 Input/output, display, archives, and controls

 Lack of measurement methods for in-situ process control ●●●●●●●

o In-situ distortion monitoring (including inside the powder bed)

o Active process measurement and control

 Optimizing NDE techniques for metals AM ●●●●●●●●●● 

o Combining NDE techniques to better assess quality via an integrated approach

o Adapting existing NDE techniques to AM, especially parts, and characterizing defects

o Lack of affordable quality inspection tools for direct metal parts 

Medium Priority  Lack of applied statistical process control ●●●

Low Priority

 Lack of predictive feed-forward models and descriptive process control and sensing measurements ●

 Understanding how to measure complex internal/lattice structures (dimension, roughness) ● 

 Laser/electron beam quality and spot location monitoring

o Spot, shape, and size

o Z-shift (process control parameter)

o Mode

o Power and scatter

o Beam diagnostics (real-time) 

o Spot location monitoring, both lateral (x-y plane) and vertical

 Laser/energy beam standards for quality and characterization

 Understanding/measuring differences in machines

 Sensors that integrate AM system and material standards

 Developing high-speed feedback control for Direct Metal Laser Sintering, Selective Laser Melting, and 

Electron Beam Melting

 Lack of low-cost solutions for dynamically controlling laser beam diameter, power location, and shape
18 AM Processes and Equipment

Table 3-2. Barriers and Challenges for AM Processes and Equipment

(• = one vote)

Standards 

High Priority

 Lack of metrics and standards for measuring and characterizing AM processes and artifacts ●●●●●●●

o Lack of standards for geometric accuracy, material properties (spatial), defects, surface 

characteristics, and variation in these characteristics

o Establishing standards for “boundary conditions” 

o Lack of standards for microstructure quantification

 Lack of standards for ●●●●●●●

o Machine-to-machine consistency

o Measurement test pieces

o Sensors

Medium Priority

 Lack of techniques/standards to characterize multi-material parts (characterize before qualify) ●●●

 Lack of calibration parameters for processes and ASTM F42 equipment (existing and future) ●●●

 Lack of standards for creating, reporting, and storing AM test data ●●●

 Complexity of standards needed to characterize process and system capabilities ●●

Low Priority

 Developing standardized calibration/qualification protocol for machines and equipment

o Standards for prototyping, production-quality-parts-capable machines

o Requirements (features and properties)

o Calibration frequency

 Lack of measurement standard for total energy input and loss

 Determining when a process/product standard is needed (given the dozens of technologies) ●

 High cost associated with development of measurement standards (too many processes and metals)

 Leveraging the harmonization of International Organization for Standardization (ISO) and ASTM 

standards 

o Reaching out to small and medium-sized enterprises (SMEs) and new AM entrants

 Lack of a timely and adaptable standards process to keep up with rapidly shifting or new AM 

technologies

 Developing “lot-based” testing standards

 Lack of standards for presenting/reporting data and findings, no credible methods (e.g., fatigue 

plot)/database for comparisons

 No standards for disciplined technology delivery (key parameters, traceability, and documentation)

Knowledge/Data

High Priority  Lack of accurate, achievable material dataset (design allowable and control samples) ●●●●●●●●●●

 Lack of standards and protocols for datasets and data transfer ●●●●●●

Medium Priority

 Incomplete knowledge capture, organization, and codification for life-cycle; challenges in changing

paradigm from experimental base to smart machine/smart production; open architecture is not available

(i.e., proprietary) ●●●

Low Priority

 Understanding the relationship between raw material, build process parameters, and post-processing

 Understanding the interplay between multiple disciplines (e.g., design, manufacturing, materials, 

thermodynamics, mechatronics, etc.) 

 Storing and scrubbing huge quantities of data

Models

High Priority  Converting high-fidelity, academic modeling tools into easy-to-use tools for engineers with desktop 

machines (or on machine control computer for process/control) ●●●●●●●

Low Priority

 Lack of model integration throughout the digital thread ●

 Insufficient as-built material properties, based on type of process

 Lack of predictive distortion models in real-time and inability to measure residual stress 

 Incompleteness of physics and chemistry models, atomistic mesoscale simulations are lacking

 Lack of integration of models and process knowledge to enable qualification of processes that are 

inherently transient with many control variables
Measurement Science Roadmap for Additive Manufacturing 19

ROADMAP FOR PRIORITY RESEARCH AND DEVELOPMENT 

A roadmap action plan was developed to enumerate the activities and approaches for overcoming the most 

critical barriers identified in Table 3-2. Roadmap action plans are outlined in more detail in Figures 3-1 through 

3-5. The key barriers being addressed and the corresponding priority roadmap topics are summarized in Table 

3-3. 

Table 3-3. Key Barriers and Corresponding Priority Roadmap Topics for AM Processes 

and Equipment

High-Priority Challenge/Barrier Priority Roadmap Topic

Current NDE techniques are not optimized for 

metals AM and adaptation of existing technology can 

be challenging. 

NDE Techniques Optimized for Metals AM (Figure 3-1)

o New and improved imaging and processing NDE techniques are

needed to enable optimization of in-situ non-destructive inspection

(NDI) and post-process part inspection.

In-situ measurement methods are currently lacking 

to enable monitoring and control of critical process 

and material parameters, such as microstructure and 

mesostructure, surface finish, defects, porosity, 

online convection, powder bed distortion, and 

numerous others.

Fast In-Situ Measurements (Figure 3-2)

o In-situ measurement methods are needed that can be utilized in 

production environments to assess key parameters and optimize 

part-to-part reproducibility. 

Metrics and standards for measuring and 

characterizing AM processes and artifacts are lacking 

(e.g., geometric accuracy, material properties 

(spatial), defects, surface characteristics, 

microstructure, and machine-to-machine 

consistency). Standards and protocols for datasets 

and data transfer are also lacking.

Performance Capability Database for AM Technologies

(Figure 3-3)

o Protocols, standards, and procedures are needed to collect AM 

process and equipment capability data and establish a performance 

database. 

Sensors are currently lacking for process monitoring 

and in-situ control, including real time sensors and 

measurement methods, integrated process and 

system sensor schemes (real-time and continuous), 

and low-cost, high-resolution thermal sensing

technology. Easy-to-use modeling tools for 

engineers with desktop machines or machine 

control computer for process/control are also 

lacking or too complex.

Sensors for Measuring and Monitoring AM Processes and 

Products (Figure 3-4)

o Standardized, repeatable, and reliable sensor technology and 

validated models are needed that can be applied across multiple AM 

platforms. 
20 AM Processes and Equipment

BARRIER: Existing NDE techniques are not optimized for AM processes, materials, or parts. Techniques are 

lacking for in-situ, NDI, and post-process AM part inspection.

APPROACH SUMMARY: Evaluate existing AM processes and adapt or develop, as appropriate, for AM processing 

or parts. Develop new techniques and address both in-situ and post-process applications.

FIGURE 3-1. ROADMAP ACTION PLAN: NDE TECHNIQUES OPTIMIZED 

FOR METALS AM

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Collect a history of data, track 

process capabilities, participate in NDE development 

efforts, use new techniques and provide feedback, 

and investment

 Industry/AM Providers: Implement NDE 

techniques, enhance equipment, and provide 

capability and access

 Academia: Help with software and technology 

development and algorithms for image processing

 Standards Committees: Document standard 

inspection processes and define NDE attributes and 

limits

 Government: Define programs to develop NDE to 

meet government (military) requirements

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: Enables 

needed inspection techniques and ensures 

product quality

 Reduces costs: Minimizes inspection 

requirements, reduces destructive testing 

requirements, allows earlier detection of 

problems

 Accelerates innovation: Shorter time 

to market

 Enhances industry competitiveness: 

Enhances traceability and ensures product 

quality

 Faster product development time:

N/A

1–2 

years

 Identify in-situ imaging techniques and 

image processing approaches; evaluate

the best technologies

 Evaluate existing post-process NDE 

techniques and identify approaches for 

AM

 Identify opportunities to develop new 

techniques for in-situ and post-process 

NDE; plan for research and development 

(R&D)

 Identification of techniques that apply 

to AM

 Identification of a few candidate 

techniques

 Plan for development of new 

techniques

 Build confidence in 

AM processes

 Find defects and 

processing issues early 

in the process

 Develop more 

efficient and effective 

inspection schemes

 Understand processes 

enough to reduce 

inspection 

requirements 

 Use in-situ techniques 

for closed-loop 

diagnostics

3–5 

years

 Develop selected imaging and processing 

techniques for in-situ AM processes

 Develop and adapt existing NDE 

techniques for AM

 Conduct R&D for new NDE in-situ and 

post-process techniques

 Proven techniques

 Plan for implementation

 List of potential techniques for further 

development

5+ 

years

 Plan for and accomplish implementation 

of successful new techniques 

 Transition technologies, commercialize 

as appropriate 

 Evaluate and plan for the next round of 

development

 Development of available techniques 

 Techniques incorporated into AM 

machines

 Ability to collect supporting data for 

inspection reports and traceability

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS
Measurement Science Roadmap for Additive Manufacturing 21

BARRIER: Systems are lacking to measure parameters that are key to part quality and reproducibility, including 

online convection, density of powder bed, distortion/residual stress, composition, absorbed power, cracking, and 

porosity. The relationship between part quality and in-situ thermal management is poorly understood.

APPROACH SUMMARY: Develop pilot system to measure and control key parameters.

FIGURE 3-2. ROADMAP ACTION PLAN: FAST IN SITU MEASUREMENTS

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Input on priorities

 Industry/AM Providers: Implementation and 

collaboration

 Academia: Innovation in sensing and modeling

 Standards Committees: Standards for in-situ 

measurement

 Government: Infrastructure support and

consensus building

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: Improved 

process control

 Reduces costs: Tradeoff between capital 

cost and part repeatability

 Accelerates innovation: Improved 

understanding of process dynamics opens new 

possibilities

 Enhances industry competitiveness:

Reduces lead time 

 Faster product development time:

Reduces cycle and qualification time

 Other: Removes operator variability

1–2 

years

 Measure and analyze the role of 

thermal sensing on part quality 

 Survey and evaluate existing in-situ 

measurement techniques for distortion, 

surface roughness, cracking, and 

porosity

 Demonstration of in-situ detection of 

cracking

 Improved measurement of heat flow  Improved confidence 

level of model input 

 Part-to-part 

reproducibility using 

in-situ sensing

 Optimized throughput

 Improved distortion 

control

 Improved surface 

finish

 Identification of 

porosity and cracking

3–5 

years

 Fundamentally understand the 

relationship of part quality to spatially 

absorbed power, temperature 

distribution, fluid flows, and 

composition 

 Develop control methods and systems 

for production environments

 Demonstration of viable control 

methods

5+ 

years

 Transition to production of control and 

in-situ measurement techniques

 Develop techniques for in-situ 

measurement of composition and 

microstructure

 Demonstration of phase and 

composition detection

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS
22 AM Processes and Equipment

BARRIER: There is currently a lack of process performance capability data relevant to AM. Standards and protocols 

to support such characterization (e.g., dimensional and mechanical) are also lacking.

APPROACH SUMMARY: Create protocols, standards, and procedures for establishing a performance database and 

involve ASTM F42 and ISO committees in activities.

FIGURE 3-3. ROADMAP ACTION PLAN: PERFORMANCE CAPABILITY 

DATABASE FOR AM TECHNOLOGIES

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Provide guidance and 

feedback for establishing priorities in terms 

of processes and requirements

 Industry/AM Providers: Process 

knowledge and sharing

 Academia: Process understanding and

R&D advancements

 Standards Committees: Establish 

procedures, standards, and engagement

 Government: Provide oversight and 

resources and serve as a repository

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: Establishes baseline, 

leading consistency, and understanding

 Reduces costs: Reduces testing costs for 

individual stakeholders

 Accelerates innovation: Establishes a knowledge 

baseline to leverage R&D, rapid qualification, and 

feedback controls

 Enhances industry competitiveness:

Accessible to the public and SMEs and larger 

enterprises 

 Faster product development time: Rapid 

qualification

N/A Other (Education): Accessible knowledge base

1-2 

years

 Determine direction for ASTM 

F42/ISO/ASTM

 Complete current round-robin and 

initial mechanical property programs

 Pick materials, processes, and tests 

 Identify and substantiate key 

input/output characteristics

 Set-up national repository

 Start specification writing

 Industry standards developed for 

process capability

 Established protocols

 Identification of key process 

characteristics

 Creation of national repository

 Established 

procedures for 

characterizing process 

and outputs, including 

reporting, sharing, and 

accessing

 Viable, robust 

database accessible to 

the public 

 Correlation to 

predictive process 

modeling, sensor 

feedback and control, 

and process outputs, 

 Enabled rapid 

qualification

3-5 

years

 Expand round-robins to additional 

sites, processes, and materials

 Expand input/output parameters and 

parameter ranges

 Publish a database on metallic materials 

properties development and 

standardization 

 Begin including life-cycle data

 Development of a broader, more 

robust database

5+ 

years

 Establish a relationship between 

modeling, process monitoring and 

control, and rapid qualification

 Substantiate/develop relationships 

between inputs and outputs

 Established relationships and tie-in to 

modeling, control, and rapid 

qualification

 Rapid qualification capability

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS
Measurement Science Roadmap for Additive Manufacturing 23

BARRIER: The challenge is to define measuring and monitoring requirements for AM processes, component 

geometries, and materials.

APPROACH SUMMARY: Develop sensors and standards, as well as validated models for data to enable

standardized, repeatable, and reliable measurement and monitoring of processes and products.

FIGURE 3-4. ROADMAP ACTION PLAN: SENSORS FOR MEASURING 

AND MONITORING AM PROCESSES AND PRODUCTS

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Provide input, 

needs/requirements, and current state; and

conduct demonstrations/pilot initiatives

 Industry/AM Providers: Establish partnering 

between industry and AM equipment providers

 Academia: Develop software, algorithms, and

enhanced learning

 Standards Committees: Software and hardware 

standards, certifications, and calibration

 Government: Computational legacy and uniform 

test/development algorithms

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: Sensor 

controls lead to predictable material 

properties

 Reduces costs: Improves production yield

 Accelerates innovation: Higher quality 

encourages uses in a broader range of 

markets

N/A Enhances industry competitiveness:

N/A

 Faster product development time:

Supports a range of innovation

N/A Other: N/A

1–2 

years

 Establish a baseline of sensor 

technology state-of-the-art for key 

parameters

 Identify gaps

 Establish working groups (e.g., with 

industry, university, national labs, and 

government) for sensor development

 Consolidated sensor database 

information

 Assembled stakeholders 

 Evaluated sensor adequacy

 Standardized sensors 

across multiple AM 

platforms

 Process control 

software to take 

advantage of sensor 

measurements

 Sensor monitoring to 

accelerate process 

qualification

 Sensor monitoring 

control to improve 

reliability and 

repeatability

 A Moore’s Law for 

sensor monitoring 

and control of AM 

processes

3–5 

years

 Work with original equipment 

manufacturers (OEMs) on details of 

sensor needs and plans

 Benchmark data/test sets for model 

validation

 Develop sensors to fill gaps and 

accelerate critical technologies.

 Validate sensor performance to 

provide data to monitor and control 

the AM process

 Establish widespread use of validated 

models

 Global OEM participation in sensor 

development (needs and plans kickoff)

 Multitude of sensor development 

initiatives bearing fruit

 Validated and qualified sensor 

technologies

5+ 

years

 Establish standards for successful 

approaches

 Keep development open for 

contributions from new technologies

 Publish standards (i.e., ASTM, ISO)

 Continued and expanded 

collaborations

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS

Measurement Science Roadmap for Additive Manufacturing 25

CHAPTER 4: AM QUALIFICATION AND 

CERTIFICATION 

OVERVIEW OF THE TOPIC AREA

Each element of additive manufacturing (AM) technology (i.e., materials, equipment, processes) must be qualified 

and certified to reproducibly manufacture high-quality parts. Non-standardization of materials, process, and 

resulting products makes the manufacturing of a high-quality part difficult on the first run. 

The development of AM 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 AM 

data or authority on AM methodologies. Further deployment of AM technology will require standards 

development to facilitate quicker and more cost-effective certification of all materials, processes, and products. 

FUTURE/DESIRED CAPABILITIES

In the future, improved capabilities will be needed to certify that parts meet the desired specifications, 

performance, and quality. Table 4-1 provides further details on the desired capabilities and technologies for 

qualification and certification of AM materials, processes, and parts. 

Qualification and Certification Methods 

Affordable and rapid qualification and certification of parts and processes is an important desired capability and 

would facilitate more widespread use of AM. Predictive models, standardized testing, and development of 

performance metrics and measurement methods are all key aspects in developing this capability. Methods must 

be flexible and adaptive (rather than prescriptive) to allow for the broadest possible use. The ability to certify 

and qualify parts to existing specifications (e.g., aerospace grade), in the field and after secondary processing is 

also important. 

Standards and Protocols 

Standardization is an important underlying foundation for qualification and certification as it provides the basis 

for consistent part validation and processing, supports model development, establishes repeatability, and 

provides guidelines for repair/maintenance. Standards will also enable consistent handling, storing, and disposal 

of AM feedstock materials in the workplace. 

Reliability and Repeatability 

Being able to produce parts with repeatable quality and specifications is critical. Robust methods and inspection 

techniques will be needed to certify reproducibility of parts and ensure that process performance is repeatable. 

Another key issue is qualifying that parts are true to the computer-aided design (product specification), and that 

the first part produced is repeatable throughout the process. 

Qualification and certification of repeatable processes will require better machine and system in-process 

feedback and control. Closed-loop control systems and other means are needed to enable quantification of 

process variability and performance. Quantifying part dimensioning and tolerances will also be critical to ensure 

parts meet specifications after repeated builds. 

Data 

Additional datasets and data collection capabilities will be needed to support qualification and certification of AM 

parts and processes. This includes standardized methods for capturing manufacturing data as well as part history. 
26 AM Qualification and Certification

An open source of statistical data on material and other properties would facilitate the ability to qualify and 

certify both parts and processes. 

Table 4-1. Desired Capabilities and Technologies for AM Qualification and Certification

Qualification and Certification Methods

 Affordable, rapid, and well-documented parts and product certification and qualification

o Adaptive qualification for products (e.g., non-prescriptive and flexible)

o Independent physics-based simulation for part certification against specifications

o Certification for aerospace-materials-specification-grade AM parts 

o Field process for part verification against expected specifications

o Dashboard visibility (e.g., AS 9100 aerospace specification for e-data for parts)

o Certification of high-value, high-reliability parts (e.g., models to relate process parameters to parts)

 Qualification for material feedstocks: supply management, quality, and storage (e.g., contamination)

o Predictive models and computationally-assisted material qualification 

o Standardized tests for qualification of materials

o Measuring percent of new/used material

o Material qualification that is not platform-specific

 Affordable, rapid, and well-documented machine and process certification and qualification

o Post-process specification (e.g., downstream processes such as hipping, annealing)

o Secondary process certification and guidelines

o Performance-based approval process for non-critical parts (i.e., A- and B-level parts)

Standards and Protocols

 Part qualification/certification

o Guidelines for how to build parts (e.g., orientation), reducing the number of iterations to final part production

o Repair standards for parts

o Guidelines to enable freedom of design/custom designs (e.g., homogeneous parts, consistent parts)

o Standard validation tools for parts

 Materials qualification/certification

o Standards for handling, storing, and disposing of AM feedstock materials in the workplace 

 Equipment/process qualification/certification

o Guidelines for the number of trials that are needed to verify the properties of each build

o Reliability and maintainability guideline adoption (e.g., SAE International (SAE) M110.2, ISBNo-7680-0473-X)

o Single independent source for process standards

o Guidelines for increased mean time between failure

Reliability and Repeatability

 Robust methods for establishing and predicting process repeatability, including

o Build-to-build

o Machine-to-machine

o Operator-to-operator

o In-process parameter sensing and feedback control

o Robust modeling and simulation tools to validate process parameters

o Underwriters Laboratory type approval (trusted third party)

 Repeatable original equipment manufacturers (OEM) equipment performance and consistency

o Trusted third-party approval

o Inspection techniques for internal dimensional verification for critical dimensions (e.g., for injector) and to verify part 

geometry (on-machine verification of geometric dimensioning and tolerance)

 Fidelity of parts to computer drawing

o First part produced is validated against all specifications; all production parts that follow are the same as the first part 

o Trusted process generates valid component (e.g., models to enable verification, validation, and certification)

 Evaluation and non-destructive evaluation (NDE)

o Fast, automated 3-D NDE

o In-situ beam path verification
Measurement Science Roadmap for Additive Manufacturing 27

Table 4-1. Desired Capabilities and Technologies for AM Qualification and Certification

o In-process, in-situ NDE/closed loop control (e.g., infrared and computed tomography) with traceability

 Independent artifact that evaluates a machine’s X-Y-Z accuracy for feature geometry and fidelity

 In-situ characterization and data mining software; models use data to provide feedback for certification

 Consistency in engineered materials and mechanical properties

 Closed-loop control system for machine visibility into variance, system health, and in-situ modifications

o Quantification and reduction of process variability

 Build chamber environmental monitoring for traceability

 Ability to consistently quantify dimensioning (e.g., z direction) and tolerance capability of an AM machine

Data

 Database for functionally gradient materials

 Anonymous pool of open source data, including statistical and historical performance

o Known materials properties database

o Set build model

o Sufficient information to determine performance parameters

 File format interoperability with computer aided design system 

 Informatics for capturing manufacturing data

 Pedigree data collection, including known thermal history, process history, statistical process control, and feedstocks

TECHNOLOGICAL & MEASUREMENT/STANDARDS CHALLENGES

The barriers and challenges currently impeding large-scale deployment of AM processes and equipment are 

presented in Table 4-2, categorized by common themes. In each category, specific barriers or challenges are 

prioritized based on expert and stakeholder input. 

One of the major challenges identified is the lack of standardized guidelines for conducting qualification and 

certification of parts and processes. While a common industry standard is desired, developing such guidelines is 

made difficult by the wide variations in machines and end uses. 

A number of identified gaps in standards point to the immaturity of metal-based AM systems (e.g., some are 

more R&D than production-focused). A high priority is achieving technology maturation (i.e., reliable machines), 

which will inherently result in repeatable and predictable processes and products. 

Another key challenge is the lack of feedback sensors and measurement methods for acquiring the data needed 

for qualification and certification. The closed loop control systems that could help provide data are lacking, as 

are the capabilities for in-situ measurements. Ideally, data collected could be housed in a third-party repository 

that could be accessible and shared by the AM community. Developing a repository of this nature is problematic 

as some data is proprietary, not available in consistent formats, and/or not collected so that it is usable or 

comparable by others. 

Standards for round-robin testing are also lacking for AM and are needed to enable independent testing of 

processes and equipment. Test protocols currently do not exist for AM materials or processes (and 

technologies vary widely). Consequently, the reporting of results is inconsistent and difficult to compare. 
28 AM Qualification and Certification

Table 4-2. Barriers and Challenges for AM Qualification and Certification

(● = one vote)

Standard and Protocols

High Priority

 Non-standard guidelines for qualification and certification ●●●●●●●●●●●● 

o Defining sufficient type and quantity of guidelines

o Wide variations in machines and end users

o Limited ASTM qualification and certification guidelines for AM machine components

o Adherence to standards and proof of compliance are inconsistent 

o Addressing unique specifications with industry-wide common standards

 Lack of standards based on round-robin testing (how to build) ●●●●●●●●●●●

o No test protocol for materials testing and inconsistent reporting of results

o Incomplete part inspection and test standards

o Non-standard ratio of new/used powders and associated test protocols 

Medium Priority  Lack of standard build parameters on different machines; no adoption of a baseline set of standard 

and open application programming interfaces (APIs) and file formats among vendors ●●●

Low Priority

 Complicated part repair due to complexity of materials and geometry, inconsistent automation, and 

lack of ASTM part certification standards

 Lack of vector hatch and mask standards (e.g., ASTM, descriptive standards)

 Difficulty of standards development (e.g., long, costly, technical work, volunteer work, intellectual 

property limits) ● 

o AM is not fully commercialized (i.e., not all technology is mature enough for standards)

o Non-standard AM systems hardware

 Incomplete post-process standards/specifications (e.g., internal, surface roughness, and 

measurements) ●

 Lack of top level standards for dimensioning across machines ●

 Lack of standards for AM materials linked to process (e.g., TI-624)

 Updating the OSHA material safety data sheet materials standards for AM

 Lack of industry standard materials specifications due to limited access to vendor data, no 3rd party 

ratings, no means to create reliability certification scorecards

Inspection, Test, and Measurement Methods

High Priority

 Inadequate feedback sensors and data acquisition/measurement methods ●●●●●●●●●●●●

o Lack of in-situ measurements for real-time process control

o Lack of open loop/closed loop control systems for AM

o Inability to modify AM systems and materials due to IP ownership and need to adhere to 

maintenance and warranty requirements

o Creating methods to produce actionable information from data

Medium Priority  Deficient feedstock-specific standards/specifications (e.g., vaporization and properties) ●●●

o Lack of standard artifacts to test microstructure (e.g., custom-design microstructure)

Low Priority

 Limited methods for characterization of complex materials, including multi-materials, multicomponents, multi-functional materials, orientation-specific factors, and functions of process and

build parameters ●

Data Challenges

High Priority

 Lack of shared, usable data in a 3rd party repository ●●●●●●●● 

o Using parts improperly based on limited data, history, and/or metadata

o New data is not collected or shared

o Inability to correlate data

o Ability to produce materials based on geometric differences

Low Priority  No central standard database for materials properties (e.g., high temperatures and mixed materials)
Measurement Science Roadmap for Additive Manufacturing 29

ROADMAP FOR PRIORITY RESEARCH AND DEVELOPMENT 

A roadmap action plan was developed to enumerate the activities and approaches for overcoming the most 

critical barriers identified in Table 4-2. Roadmap 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 summarized in Table 

4-3. 

Table 4-3. Key Barriers and Corresponding Priority Roadmap Topics for AM 

Qualification and Certification

High-Priority Challenge/Barrier Priority Roadmap Topic

Today’s feedback sensors and data acquisition/ 

measurement methods are inadequate for AM. Systems 

are lacking in-situ measurements to support real-time 

process control/closed-loop control. 

Closed-Loop Process Control (Figure 4-1)

Control technology development will allow for real-time process 

measurement and control, enable the use of closed-loop 

controls, and lead to reliability and repeatability in AM parts.

Guidelines for qualification and certification are 

currently not standardized. Some of the challenges 

include the ability to define the type and quantity of 

guidelines and dealing with wide variations in machines 

and end users. ASTM qualification and certification 

guidelines for AM machine components are currently 

lacking or inadequate.

Standard Guidelines and Methods for Qualification and 

Certification (Figure 4-2)

A standard taxonomy that encompasses all AM methods and 

technologies needs to be developed and adopted by AM 

stakeholders. Standardized methods are also needed for AM 

part and process qualification, certification, and design.

The development of AM certification processes are 

hindered by the lack of valid data in standard formats 

available in an open, web-based, 3rd party database 

repository. The lack of data leads to using parts 

improperly based on limited data, history, and/or 

metadata. New data is not collected or shared and 

existing data is difficult to correlate or compare.

Shared, Standardized Third-Party Data Repository 

(Figure 4-3)

Standard methods for data formats and analysis for AM materials 

are needed to support an open web-based capability for 

publishing, clearing, and sharing data. Analytical methods could 

include, for example, property extrapolation by statistical means. 

The repository would support proper choice and use of AM 

materials.

Standards and protocols are lacking for round-robin 

testing of AM materials and parts.

Standards and Protocols for Round-Robin Build and 

Material Testing (Figure 4-4)

The development of protocols for round-robin build and 

material testing will enhance repeatability and enable 

documentation of part history and test results.
30 AM Qualification and Certification

BARRIER: AM processes are inhibited by poor process control and inadequate measurement methods and 

technologies. Systems are lacking for in-situ measurements to support real-time process control and openloop/closed-loop control.

APPROACH SUMMARY: Foster closed-loop control technology development via AM industry adoption and 

technology advancement.

FIGURE 4-1. ROADMAP ACTION PLAN: CLOSED-LOOP PROCESS 

CONTROL

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Provide input on needs,

conduct demos/pilots, evaluate/produce 

components/parts using closed-loop control

 Industry/AM Providers: Commit and implement 

closed-loop controls

 Academia: Develop technology advancements,

collaborate with AM software and hardware 

providers

 Standards Committees: Modify standards to 

incorporate closed-loop control

 Government: Programmatic support, technology 

development

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: Greater 

process control

 Reduces costs: Reduction of post process 

inspection, higher yield of parts, rapid 

qualification

 Accelerates innovation: Enabling complex 

parts with certainty and create graded 

structures and different materials

 Enhances industry competitiveness:

Use of AM parts industry expands customer 

base

 Faster product development time: Use 

of AM parts industry expands customer base

 Other: Reduces variability

1–2 

years

 Provide feedback to equipment 

manufacturers to enable development 

of adequate closed-loop control

 Organize and execute a process 

control workshop to develop 

collaboration opportunities

 Evaluate current research and inprocess control and establish programs 

for additional needs/requirements

 Develop in-situ measurement for realtime process control

 Approved equipment modification for 

research system manufacturers to 

adopt process control methods

 Comprehensive understanding of 

efforts in closed-loop control

 Annual programs announced for 

process control

 New sensors for closed-loop control

 Reliability and 

reproducibility in AM 

parts

 Six sigma production 

parts

3–5 

years

 Achieve early integration of process 

control into equipment

 Modify standards based on technology 

development

 Use layer-specific information for rapid 

part qualification

 Advanced strategies for real-time 

measurement and control

 Demonstrated layer-specific 

information can be used for NDI/NDE

5+ 

years

 Continue process control development  Reliable and reproducible critical 

advanced AM parts

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS
Measurement Science Roadmap for Additive Manufacturing 31

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Assist in documenting 

current baseline for standards and provide input 

on needs

 Industry/AM Providers: Assist in documenting 

current baseline for standards

 Academia: Enable workforce development and

validate standards 

 Standards Committees: Develop qualification, 

materials, characterization, and testing standards

 Government: Ensure applicability across 

agencies, support/develop database infrastructure, 

and test algorithms

BARRIER: Standard guidelines for qualification and certification are lacking. Challenges include the ability to define 

the type and quantity of guidelines and wide variations in machines and end users. ASTM qualification and 

certification guidelines for AM machine components are currently lacking or inadequate.

APPROACH SUMMARY: Develop uniform standards and a taxonomy that encompasses all AM methods and is 

flexible to accommodate new technologies as they emerge.

FIGURE 4-2. ROADMAP ACTION PLAN: STANDARD GUIDELINES AND 

METHODS FOR QUALIFICATION AND CERTIFICATION

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: Greater 

certainty of process and products

 Reduces costs: Gives a single source/method 

for reducing trial and error waste and reduces 

investments for qualification

N/A Accelerates innovation: Supports design 

freedom and reduces barriers to new 

technology

 Enhances industry competitiveness:

Standardization empowers the suppliers and 

OEMs

 Faster product development time:

Streamlines the selection of processing 

methods

1–2 

years

 Collect existing worldwide standards and 

guidelines currently in use for AM

 Develop common baseline and taxonomy

 Coordinate with AM process models and 

properties development (see Figure 5-2)

 Develop and apply standard validation 

procedures to computational tools (e.g., 

process models and design tools, see 

Figure 5-2)

 Framework begun for ASTM 

standard document

 Modularity incorporated into the 

standard

 Established database (standards, 

supporting data, and generalized 

process models) is available to the 

AM community

 ASTM standard for 

qualification and 

certification

 Layer by layer 

qualification of parts

3–5 

years

 Draft standards framework documents

 Develop standard methods for rapid 

qualification of processes

 Incorporate data from models and 

control systems

 Revised draft standards framework 

document

 Validated models that support 

qualification and certification (see 

Figure 5-2)

5+ 

years

 Write ASTM standard for AM process 

qualification, ensure the standard is 

flexible and supports new technology 

development

 Integrate virtual testing standards with 

experimental testing standards

 Revised standards as technology 

progresses

 Rapid qualification of processes and 

parts

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS
32 AM Qualification and Certification

BARRIER: The lack of valid data in standard formats and a centralized data clearinghouse inhibits the development 

of certification processes for AM.

APPROACH SUMMARY: Collect crowd-sourced data, create standards formats, and develop a database.

FIGURE 4-3. ROADMAP ACTION PLAN: SHARED, STANDARDIZED

THIRD-PARTY DATA REPOSITORY

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Contribute data and adopt 

crowd source model

 Industry/AM Providers: Contribute data

 Academia: Develop statistical models using the 

data and develop a scientific base for methods

 Standards Committees: Develop standards for 

reporting format

 Government: Programmatic supports and

oversight

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: Reliability and

consistency

 Reduces costs: Less money spent generating 

data

 Accelerates innovation: Part iterations 

faster

 Enhances industry competitiveness:

Precompetitive/transparency

 Faster product development time:

Reduce number of iterations

N/A Other: N/A

1–2 

years

 Use the ASTM standard for reporting 

format

 Create licensing model for common 

data

 Design a method of execution and 

collection for limited processes

 Establish governance for materials, 

processes, and parties

 Establish a web front-end platform

 ASTM standard developed

 Data reporting standard and structure 

(i.e., format)

 Open web-based capability for 

publishing, clearing, and sharing data

 Results from 1–2 materials to validate 

database

 Standardized 

methodology for AM 

materials

 Property 

extrapolation by 

statistical means

 Proper use of process 

and material based on 

performance

 Reduction of 30% or 

more in cycle time 

and materials waste

3–5 

years

 Start building statistical model/link to 

integrated computation materials 

engineering (ICME)

 Grow invested stakeholders and data 

sets

 Define database growth strategy

 Utilized on a platform

 Crowd-sourced data

 Captured all publically available 

government-funded data

5+ 

years

 Govern and evolve the database

 Link statistical database to ICME for 

alloys

 Mature database

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS
Measurement Science Roadmap for Additive Manufacturing 33

BARRIER: No documented standards and protocols exist for round-robin build and materials testing for AM.

APPROACH SUMMARY: Create a set of protocols for round-robin testing, beginning with a single source powder and 

going through part production, process, build, and inspection.

FIGURE 4-4. ROADMAP ACTION PLAN: STANDARDS AND PROTOCOLS

FOR ROUND-ROBIN BUILD AND MATERIAL TESTING

1–2 

years

Powder Metal (new powder only)

 Using a single-source powder, develop written protocols

o Composition (scanning electron microscope/energy dispersive 

spectrometer and x-ray diffraction)

o Size measurement

o Morphology

o Flow-ability/sifting mesh

 Develop report templates and define test protocol

 Definition of 

test protocols 

for powder

 Develop pedigree 

parts

 Enhance repeatability 

and uniformity of 

terms in the AM 

community

 Deliver to the ‘owner’ 

a part with a 

documented history 

and test results

 Maintain a library of 

parts and test 

documentation 

(including timing for 

acceptance) 

 Maintain a 

library/database 

(defined by the 

purpose of the 

specification)

2

years

Part (using same powder/material)

 Select Part A, process 1, Part B, process 2 and describe

o Centerline part

o Inspection datum and features

 Build in supports and allowances for certification as determined by the 

process

 Protocols for 

parts by 

process

 Design guide

2-3

years

Build

 Calibrate machine to OEM standards

 For both Part A and Part B define

o X, Y, and Z orientation to blade, and orientation to material deposition

o Decide where in build volume should it be specified (cold bed or heated 

bed)

o Number of parts in build volume

o Removal method

o Post-processing (order of method of parameters)

o Specific post-processing machining

o Heat treatment/hipping

o Blasting, cleaning, and powder removal

Inspection

 Specify inspection approach

 Specify accuracy of coordinate measuring machine (CMM) and calibrate CMM

 Implement inspection plan and report on results

 Build 

protocols

 Part inspection 

protocols

3 

years

Mechanical Testing

 Develop protocols for testing at a certified testing laboratory, defined by part 

and application

 Define statistical analysis

o Tensile, yield strength, density/porosity, temperature, and other necessary 

parameters

 Generate statistical reports

o Findings by part 

o Variability through the entire process

o Variability of entire parts (A, B, etc.) and other findings

 Protocol for 

mechanical 

testing

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Provide input on protocol 

development

 Industry/AM Providers: Use protocols

 Academia: Validate models and assist in the updating 

process

 Standards Committees: Develop a report template

 Government: Maintain the database

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: Lessons learned

N/A Reduces costs: N/A

N/A Accelerates innovation: N/A

 Enhances industry competitiveness: Useful in a 

design guide

N/A Faster product development time: N/A

 Other: Uniformity of AM terms, database

ROADMAP ACTION PLAN MILESTONES OVERARCHING 

TARGETS
34 AM Qualification and Certification
Measurement Science Roadmap for Additive Manufacturing 35

CHAPTER 5: AM MODELING AND SIMULATION

OVERVIEW OF THE TOPIC AREA

Development of accurate modeling and simulation tools is an important fundamental building block as additive 

manufacturing technologies are being developed and deployed. The availability of good validated modeling and 

simulation capabilities decreases the need for real-world testing of technologies and processes and gives product 

designers a predictive capability to optimize part designs. Models are also the basis for developing the required 

control technologies and software for additive manufacturing (AM) production processes. They will also provide 

support for standards development as well as qualification and certification methods. 

Accurate modeling and simulation tools cannot be developed without comprehensive and validated data on 

materials and processes. These tools also require an excellent understanding of the fundamental processes and 

physical phenomena that underlay AM feedstock inputs, approaches, and technologies. Note that the modeling 

and simulation needs outlined in this chapter are relevant to AM processes and production, but may also 

broadly encompass other aspects of modeling and simulation. 

FUTURE/DESIRED CAPABILITIES

Table 5-1 includes a summary of the desired future modeling and simulation capabilities that were identified. 

These illustrate the range of impacts that modeling and simulation have on the use of AM. 

Process and Materials Models 

AM modeling and simulation solutions are moving toward increased accuracy, with the eventual goal of 100% 

fidelity between modeling and simulation and reality. Increased computational capabilities will make models fast 

enough for real-time applications. Future models will use in-situ measurements to be able to predict structure 

properties and defects. Increased computational capacity will enable the development of real-time models that 

can be used for process control. Desired capabilities include the ability to model residual stress direction, grain 

size distribution, spatial and temporal homogenization, complex lattice design, and surface finish solutions. 

Future models will be multi-scale in nature and could support AM process selection based on key standards. 

Model Inputs 

Model improvement, validation, and verification will benefit from enhanced in-process measurement and nondestructive inspection (NDI) methods. To attain real-time functionality, sensing and feedback capabilities for insitu measurement throughout AM processes will be critical. Increased understanding of thermal properties and 

the capability to measure data file transfer accurately are also critical. 

Standards and Metrics to Support Modeling 

Standards for data inputs to modeling and simulation systems are needed to improve characterization of 

dimensional accuracy, process capability, distortion, surface finish modeling, density, micro properties, and 

verification and validation (V&V). Standardization of these data inputs and key process parameters will enable 

development of more accurate, reliable models that can be used in real-time process applications.

Model Accessibility/Usability 

Greater balance between open source and proprietary modeling and simulation would aid technology 

advancements and may increase simulation access for small- and medium-sized organizations. In addition, 

increased user education may lead to better technical understanding of model and simulation behavior, which 

has been lacking in the industry. 
36 AM Modeling and Simulation

Table 5-1. Desired Capabilities and Technologies for AM Modeling and Simulation

Process and Materials Models

Accuracy

 One-hundred percent fidelity between modeling/simulation and reality

 High-accuracy, fast converging models for AM processes

 Accurate operational and sustainment models, enabling model-based lifecycle control

Fast/Real-Time Capabilities

 In-situ measurement of composition, temperature, cooling rate, dimension, and distortion leading to the ability to predict 

dimensions, surface characteristics, material properties spread, and defects

 Real-time computation

 Real-time modeling to enable process adjustments

 Rapid-cost and capacity modeling, ideally physics-based

 Rapid process qualification leading to a decreased need for testing

 Capability to measure the accuracy of data file transfers

Improved Capabilities

 Residual stress direction and magnitude

 Grain-size distribution

 Spatial and temporal homogenization

 New design capabilities, such as complex lattice structures or alloy transitions 

 Solutions for distortion and surface finish modeling to optimize build orientation, including supports

 Increased energy input efficiency (e.g., loss via reflection and conduction)

Compatibility

 Compatible models that support understanding and linking of all transformations

 Supplier independent process models that can predict outcome from programmable attributes

Comprehensive Models

 Generalized modeling that combines high- and low-fidelity and continuum laws 

 Integrated multi-scale, multi-physics models from complex to dimensionless numbers

 Technologies to model design reproducibility for AM

o Repeatable for a minimum feature size 

o Modeling to support selection of AM processes based on key standards

Model Inputs

 Sensing and feedback to provide data for process control models

 NDI methods for complex parts to provide inputs for model development and validation

 Better knowledge of the thermal properties of materials from loose powder to dense metal

Standards and Metrics

 Validation and verification (V&V) metrics for models and simulation tools

 Standards for modeling dimensional accuracy and process capability

 Standards for distortion and surface finish modeling to optimize build orientation, including supports 

 Criteria for convergence time, accuracy calibration, and applicability for various materials and processes

 Modeling to compare the performance of one machine to another

 Transferability studies and metrics, taking into account intellectual property (IP) rights

 Consistent and increased security of data file contents

Model Accessibility/Usability

 Access to simulations for small- and medium-sized enterprises

 Complete technical understanding by user of model and simulation behavior and outputs

 Balance between “open source” and proprietary modeling and simulation
Measurement Science Roadmap for Additive Manufacturing 37

TECHNOLOGICAL & MEASUREMENT/STANDARDS CHALLENGES

Table 5-2 provides a list of barriers and challenges identified during the workshop. A major challenge today is 

the lack of accurate physics-based predictive models for various AM processes. Deficiencies in current models 

stem from incomplete understanding of all physical phenomena related to AM processes, as well as the inability 

to conduct fast and accurate in-situ measurements for model validation. 

Computational speed and model run time greatly affects the usability of models available today. Older, outdated 

computer processing units (CPUs) are still sometimes used, although faster systems may be available. The 

increasingly large quantity of data that must be handled also creates computational challenges and potentially 

leads to longer model run times. 

Development of good models is hindered by a lack of standards, metrics, and sensors for measuring and 

characterizing AM processes and end products. Practical limitations, such as the limited availability of researchoriented AM machines for model validation, also impact modeling efforts. 

Table 5-2. Barriers and Challenges for AM Modeling and Simulation

(● = one vote)

Process and Materials Models

High Priority

 Lack of validated physics-based, multi-scale predictive models for AM that account for geometric accuracy, 

spatial material properties, defects, surface characteristics, and variations ●●●●●●●●●●●●●●● 

o Lack of extensive property characterization research and inconsistency in material properties

o Lack of experimental methods for defect characterization and the ability to predict defects

o Inability to understand in detail chemical and physical transformations and raw material changes

o Deficient understanding of heat transfer fluid flow, including geometric structure, residual stresses, 

surface finish, diffusion, and other transport phenomena

o Lack of models for phase transformation and nucleation/growth 

o Lack of technical- and physics-based cost modeling

o Lack of data on thermo-physical conditions, including boundary condition data, diffusivity data, and 

continuous cooling curve transformation data 

o Inability to obtain high-accuracy dimensional measurements of internal features (cavities) 

 Lack of AM modeling systems that couple design and manufacturing ●●●●●●●●●●

 Inadequate in-situ measurement capabilities to support model data inputs and validation, including real-time 

NDI and measurement of real-time temperature and heat transfer/cooling rate ●●●●●●●●●

Medium Priority

 Slow model execution time (experiments can be faster than models) and increasing use of large data sets

●●●●●

o Continued use of CPU when faster options are available

o Increasing data size needed for AM processes

Low Priority

 Lack of non-destructive evaluation transducers beyond 200-500 megahertz for online grain size 

distributions ●

 Inadequate model security and encompassing all aspects(e.g., integrity, loss of, and misuse of models) ●

 Limited number of facilities for measurement of high-temperature properties ●

 Lack of models and methods to enable computer-based qualification/certification (as an alternative to

extensive mechanical/machine testing)

 Lack of an AM version of commercial software similar to ABAQUS or ANSYS modeling and analysis 

software

Standards and Metrics

High Priority

 Lack of standards to support consistent data inputs to modeling and simulation, such as dimensional 

accuracy, process capabilities, distortion and surface finish modeling to optimize build orientation, criteria 

for convergence time, accuracy calibration, comparison of machine performance, etc. ●●●●●●●●

 Lack of validation and verification metrics for models and simulation tools●●●●●●

Medium Priority  Inaccessible research-oriented or test bed machines to validate models ●●●●

 Inability to group all process parameters in one dimensionless number for benchmarking ●●●
38 AM Modeling and Simulation

ROADMAP FOR PRIORITY RESEARCH AND DEVELOPMENT 

A roadmap action plan was developed to enumerate the activities and approaches for overcoming the most 

critical barriers identified in Table 5-2. Roadmap action plans are outlined in more detail in Figures 5-1 through 

5-3. 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 AM Modeling 

and Simulation

High-Priority Challenge/Barrier Priority Roadmap Topic

Modeling systems available today lack the capabilities to

couple AM design, materials selection, and manufacturing

processes. 

Expert System for AM Design (Figure 5-1)

A significant expansion of validated AM design rules is 

needed to build expert model-based design systems that can 

be used to select optimum AM materials and processes and 

exploit unique AM capabilities.

Validated physics-based, multi-scale predictive models that 

account for geometric accuracy, spatial material properties, 

defects, surface characteristics, and other variations

(temperature-dependent properties) are not available for 

AM. 

Validated Physics- and Properties-Based Predictive 

Models for AM (Figure 5-2)

Validated, physics-based, multi-scale models that 

incorporate multiple characteristics and properties need to 

be developed. Effective models will optimize part fabrication 

processes and reduce cost, performance, and schedule risks.

Standards are lacking to support consistent data inputs to

modeling and simulation, including key process and product 

parameters. Validation and verification metrics are also 

lacking for AM models and simulation tools (both design and 

process). The definition of common data standards and 

metrics for AM is complicated by the diversity of AM 

equipment and process types.

Standard Data Structures, Definitions, and Metrics 

for AM Models (Figure 5-3)

The attributes of multiple additive processes and supporting 

models need to be identified, defined, and classified in a 

consistent way to support effective modeling and simulation 

tools. Standard data structures need to be established to 

enable collection of needed model inputs while protecting 

IP.
Measurement Science Roadmap for Additive Manufacturing 39

BARRIER: Expert systems for AM design do not exist, and there are few known/validated design rules for AM design. Tools 

are lacking to help select optimum AM materials and processes, or a design for a given AM process (e.g., feature capabilities, 

surface finish/post-treatment, and tolerances).

APPROACH SUMMARY: Capture design rules for various AM processes and materials and create design advisors, tools, and 

expert systems linked to computer-aided design (CAD) tools.

FIGURE 5-1. ROADMAP ACTION PLAN: EXPERT SYSTEM FOR AM

DESIGN

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS

1–2 

years

 Capture available design rules from 

vendors for various metal-additive 

processes and materials 

 Survey industry and academia for metal 

AM design best practices

 Conduct targeted R&D to address gaps 

in design rule coverage

 Evaluate/create CAD-like infrastructure 

(computer-aided AM with properties) 

with linkable expert system design 

advisor software, reformulate best 

models, and then pilot test and release

 Year 1: Design rules/best-practices 

captured from vendors and users

 Years 1–2: New design rules developed 

to address gaps

 Years 1–2: CAD-linkable expert system 

design advisor software developed and 

pilot-tested, models validated through 

specific case applications

 Formalized design 

rules and expert 

advisor models and 

software for AM, 

widely disseminated 

in the United States

 Innovative parts 

design via use of 

comprehensive 

models and 

understanding

 Accelerated industry 

adoption of AM 

capabilities

 Design teams able to 

exploit unique AM 

capabilities 

 Design rule database 

easily extensible by 

users (e.g., for 

company-specific 

applications)

 Determine 

ownership, 

maintenance, and 

distribution model 

for expert advisor 

software

3–5 

years

 Validate best models using highperformance computing and 

experiments

 Refine and update software and expert 

advisor rules 

 Create techniques for efficiently 

mapping entire design space

 AM Human Genome-like process 

established

 Comprehensive map for computer-aided 

AM with properties and the ability to 

rapidly map properties and validate 

 Mechanism in place to capture and 

incorporate user-developed design rules

 Design rules and best practices for 

emerging AM processes and materials

5+ 

years

 Integrate ‘AM Genome’ capability into 

computer-aided AM with properties 

 Develop accessible user interface for 

engineers and other users in 

commercial space

 Incorporate emerging AM processes 

and materials

 Real-time performance achieved

 Continued capture and incorporation of 

new user-developed design rules

 Optimized design of parts and 

microstructures

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Provide metal AM design 

best practices and case studies, conduct 

collaborative R&D on design rule gaps

 Industry/AM Providers: Provide known design 

rules for various metal-additive processes and 

materials, conduct collaborative R&D on design 

rule gaps, input on machine capabilities for 

Genome

 Academia: Provide metal AM design best 

practices, conduct collaborative R&D on design 

rule gaps

 Standards Committees: Create taxonomy and 

terminology, certification/validation methods

 Government: Conduct collaborative R&D on 

design rule gaps, support programs, serve as 

repository for AM Genome

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: Designs optimized 

for reproducibility and quality control

 Reduces costs: Reduced product development 

time and cost, maximizes efficient use of AM for 

production

 Accelerates innovation: Enables design teams 

to exploit the unique capabilities of AM and

streamlines transfer of models to industry

 Enhances industry competitiveness: 

Accelerates adoption of AM capabilities by U.S. 

industry and positions industry to compete

 Faster product development time: Fewer 

design iterations and integrate disparate tools 

 Other: Accelerates knowledge transfer, bestpractice sharing, and on-shoring of manufacturing, 

also spawns new companies and products
40 AM Modeling and Simulation

BARRIER: There is a lack of validated physics- and properties-based predictive models for AM that incorporate 

geometric accuracy, material properties, defects, surface characteristics, residual stress, microstructure properties, 

and other characteristics.

APPROACH SUMMARY: Apply fundamental physics principles, material property measurements, and model 

validation methods to create multi-scale models. Apply standardized verification, validation, and uncertainty 

principles.

FIGURE 5-2. ROADMAP ACTION PLAN: VALIDATED PHYSICS- AND 

PROPERTIES-BASED PREDICTIVE MODELS FOR AM

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS

1–2 

years

 Survey universities and labs for 

o Appropriate material property data 

o Physics-based models 

o Defect characterization

o Temperature-dependent properties of 

AM materials (density, conductivity, and 

absorption)

 Do a gap analysis on surveyed data

 Generalize the process for developing 

optimized models

 Prioritize material properties list, the 

models to be enhanced, and defects 

requiring greater research 

 Prioritized lists of material 

property data, models, and defects

 Properties and model database 

open to AM community

 Generalized models  “Batch of One” (i.e., 

the ability to 

economically and 

correctly fabricate a 

unique part with a 

batch size of one) 

 Reduced cost, 

performance, and 

schedule risks 

 Ability to generate 

controlled 

microstructures

3–5 

years

 Generate platform-independent, generalized 

models incorporating all variables

 Carry out round-robin evaluation of models 

via standard part application (e.g., evaluate 

dimensions, distortion, and surface finish) 

 Generate material data supporting 

prioritized material list

 Identify multiple machines, carry out model 

validations, compare machine-to-machine, 

day-to-day, and platform-to-platform 

variations

 Validated, generalized process and 

part models

 Identification of prioritized model 

and source variability 

 Material database

 Process database

5+ 

years

 Expand material property data to new 

materials 

 Commercialize model software

 Expand models to include multiple scales

 Development of new models and

tools

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Provide input on needs

 Industry/AM Providers: Provide an open 

architecture machine, improve machine 

specifications to support models (e.g., beam 

diagnostics and infrared imaging)

 Academia: Provide models, trained personnel, 

and data analysis

 Standards Committees: Develop standard test 

articles and software standards

 Government: Support for database infrastructure 

and develop and test algorithms

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: N/A

 Reduces costs: Achieving “Batch of One,” 

has a high impact on cost

 Accelerates innovation: Reduces barriers 

to new technology and products

 Enhances industry competitiveness:

Enables better design and quality at lower 

cost

 Faster product development time:

Determine best processing parameters
Measurement Science Roadmap for Additive Manufacturing 41

BARRIER: Differing AM processes and equipment types with different attributes complicate the creation of 

common standards and metrics definitions. Foundational data and knowledge for standard models is also lacking.

APPROACH SUMMARY: Identify, classify, and group the attributes of additive processes and supporting models to 

the nearest “generic” level. Define useful standards and metrics that apply to the groups and can be extended to 

specific applications while building the foundational data, information, and knowledge.

FIGURE 5-3. ROADMAP ACTION PLAN: STANDARD DATA 

STRUCTURES, DEFINITIONS, AND METRICS FOR AM MODELS 

ROADMAP ACTION PLAN MILESTONES AND RESULTS OVERARCHING 

TARGETS

1–2 

years

 Establish and prioritize process and 

equipment groupings

 Establish a standard data structure that 

assures needed input while protecting 

IP

 Collect data and define initial metrics 

and standards for priority processes 

and equipment

 Definition of families of processes, 

equipment, and models

 Architecture and standard data 

structure

 Initial data collection using standard 

structure

 Ability to accurately 

model and verify the 

transformation of raw 

material to products

using standard data, 

information 

structures, 

measurements, and 

methods and extendable models (i.e., 

models that address 

defined categories and 

are extended to 

detailed applications)

3–5 

years

 Conduct controlled experiments to 

verify initial metrics and standards

 Extend metrics and standards from 

initial sets to broad coverage of AM

 Provide the structure to unify modeling 

and simulation systems with standards 

to support the evaluation of system 

performance (inputs to transfer 

function provides expected output)

 Pilot implementation and verification of 

metrics and standards

 Metrics and standards for a “basic” set 

of AM processes and equipment

 Completed evaluation of standard 

model representation and defined 

performance attributes (i.e., metrics) of 

the models

5+ 

years

 Provide integrated capability

 Standards and metrics to accurately 

model the transition of raw materials 

to product

 Integrated modeling and simulation 

demonstrated across AM processes 

and equipment

STAKEHOLDERS & POTENTIAL ROLES 

 Industry/AM Users: Provide data and test case 

definition, provide V&V criteria, and support V&V 

testing

 Industry/AM Providers: Provide data and data 

access to support process modeling, actively engage 

in equipment modeling and standards development

 Academia: Conduct research to develop models 

that enable and implement standards

 Standards Committees: Work with ASTM 

Committee F42 on Additive Manufacturing 

Technologies to assure that the standardization 

process is followed, including buy-in from all 

stakeholders and delivery of authorized standards

 Government: Infrastructure capability; Provide 

unbiased coordination of standards and 

measurement development, facilitate 

implementation

RELATIVE IMPACTS

LOW —HIGH

 Improves product quality: V&V 

performance assures product quality

 Reduces costs: Eliminate trial-and-error,

reduce iteration

 Accelerates innovation: Robust 

models enable cost-effective modification 

and “what if” scenarios

 Enhances industry competitiveness:

Reduces failure and enables useful product 

production with less risk and lower cost

 Faster product development time:

Enables rapid error correction

 Other: Lowers the risk of AM 

deployment and opens new opportunities 

and applications
42 AM Modeling and Simulation
Measurement Science Roadmap for Additive Manufacturing 43

CHAPTER 6: CROSS-CUTTING CHALLENGES

CROSS-CUTTING TECHNICAL CHALLENGES

A number of technical challenges were identified that cut across materials, processing and equipment, 

qualification and certification, and modeling and simulation. 

Standards and protocols for all aspects of AM: Standards, protocols, and guidelines are a pervasive need 

for additive manufacturing (AM) and impact all aspects, from materials design and use to part build, inspection, 

and certification. Standards are not only critical to processing and control, but also impact the ability to qualify 

and certify parts and process performance, as well as the development of effective models for design and AM 

operation. The time and cost to develop standards was noted as a continuing challenge, particularly to keep up 

with rapidly shifting or emerging AM technologies. The key standards needs are summarized in Table 6-1. 

Table 6-1. Summary of Standards Development Requirements

Part Quality, 

Consistency, 

and 

Conformance to 

Specifications

 Standards and guidelines for part conformance, qualification, and certification, including:

o Geometric accuracy, material properties (spatial), defects, distortion, surface 

characteristics, and variation in these characteristics

o Microstructure quantification, artifacts to test microstructure

o Characterization of multi-material parts

 Standardized measurement test pieces/specimens and lot-based testing standards

 Standardized part validation tools

 Post-process standards and specifications for parts (e.g., internal, surface roughness, 

measurements)

 Standards for building (e.g., orientation) and repairing of parts

o Round-robin testing for building and testing parts

o Test protocols for materials testing and reporting of results

o Repair protocols

Materials 

Specifications

 Specification standards (machine-independent) for all aspects of materials (e.g., 

microstructure, vaporization, properties, powder chemistry, size distribution), including 

alloys, deposited metals, virgin and recycled feedstocks, and finished build

 Standardized tests and protocols for design and qualification of materials

 Standardized ratios for new/used powders and associated test protocols

 Standards for materials that are linked to processing (e.g., TI-624)

 Handling, storing, and disposal of AM feedstock materials

o Update of OSHA / material safety data sheet materials handling standards for AM 

materials

Process and 

Control 

Standards and 

Metrics

 Standards and metrics for measuring and characterizing AM processes and artifacts to 

support qualification, certification, modeling, and data collection, including:

o Machine-to-machine consistency

o Standard build parameters among machines

o Dimensioning across machines

o Repeatability of results

o Machine metrics and standards for design allowables

o Guidelines for “boundary conditions” 

o Utilization of sensor technology and open system in-process materials control

o Guidelines for trials needed to verify the properties of each build

o Reliability and maintainability guideline adoption (e.g., SAE MI 10.2)

o Mean time between failure

o Descriptive vector hatch and mask standards
44 Cross-Cutting Challenges

Table 6-1. Summary of Standards Development Requirements

 Common, baseline set of standard and open application programming interfaces and file 

formats among vendors (aligned/reconciled with ASTM International formats)

 Standards for AM system hardware

Machine and 

Equipment 

Calibration

 Consistent machine and equipment calibration standards (beyond machine variability), 

including:

o Prototyping, production-quality-parts-capable machines

o Features and properties requirements

o Calibration frequency 

 Calibration parameters for processes and equipment (existing and future)

Data collection 

and Storage

 Standards for mining, creating, reporting, storing, and comparing data on AM materials, 

processes, equipment, and testing

 Data file format compatibility protocols

 Standardized databases for materials properties

Measurement and monitoring techniques and data: Current technologies and techniques for 

measurement, monitoring and control are inadequate and can significantly impact part quality, functionality, and 

performance. New sensors, integrated models, and measurement methods will be needed to enable integration 

of materials and processing control and feedback. 

Modeling systems that couple design and manufacturing: Modeling systems for AM do not integrate 

design and manufacturing aspects which impacts development of materials and products as well as new 

processing technologies. The ability to understand and predict outcomes would greatly contribute to better 

designed parts with fewer defects in production. 

Closed loop control systems for AM: These systems are vital for processing and equipment performance, 

assurance of part adherence to specifications, and the ability to qualify and certify parts and processes. 

Deficiencies in modeling systems are integrally linked to and impact monitoring, control and feedback systems. 

Data from control systems can feed model development and good models can help to provide higher levels of 

sophistication and utility for control systems. 

Insufficient research on AM materials: Metallurgical research on materials for AM is lacking, materials 

manufacturing research is lagging overall. Insufficient resources are being applied to grow the knowledge base, 

and coordination of resources and priorities is lacking. 

NON-TECHNICAL CHALLENGES

Bias toward conventional manufacturing: Product designers in the industry are familiar with conventional 

manufacturing technologies and are naturally biased toward them. As a result, most parts are optimized for 

conventional manufacturing. 

Difficult business case: The business case for AM can be unfavorable, partially due to high capital and material 

costs, as well as parts optimized for conventional manufacturing. Technologies in some cases are not 

demonstrated at scale, which creates additional uncertainty about potential return on investment and hinders 

investments in research and development. Further education of managers and company decision makers about 

AM technologies may alleviate some hurdles. The industry is also characterized by vast but diverse product and 

market niches which can limit interest in investments. 

IP rights vs. open source: Company protection of intellectual property is important to recuperate investments 

made in the development of AM technologies. “Black box” solutions are often the result, precluding open 
Measurement Science Roadmap for Additive Manufacturing 45

source code for software programs. While this approach by individual companies is understandable, it can 

hinder broader efforts to further develop and improve the technologies. Additive manufacturing would ideally 

allow for precision in the reproducibility of a range of metal parts, but robust, comprehensive, and pro-active 

anti-piracy and counterfeit protections and safety mechanisms and protocols would be required. Such measures 

would help ensure ethical use of the technology and can allow for competitive markets to lower the price of AM 

parts and allow this technology to diffuse into the marketplace. Whether anti-counterfeiting measures or 

systems are embedded into AM parts or otherwise, it is important to protect these issues that are uniquely 

relevant to metal-based AM technologies. Unique identifiers such as Radio Frequency ID tags or embedded 

serial numbers have emerged as one approach. Such tags could also provide life-cycle part history. 

Educational challenges: Within the AM sector, lack of interdisciplinary knowledge and research is a barrier. 

AM technologies involve a variety of disciplines, including modeling, laser and electron beam physics, metallurgy, 

and statistics. Thus, it is difficult for one person to have adequate expertise in each of these diverse areas to fully 

understand all aspects of technology development. On the manufacturing side, highly skilled machine operators 

are in demand. Broader concerns about the state of science, technology, engineering, and mathematics (STEM) 

education in the United States can impact the AM industry as well. National technician certification, education, 

and training will be needed for AM across all platforms 

Materials capacity, cost and supply base. In terms of material inputs, the availability of reliable sources of 

quality, economically feasible raw materials or feedstocks could pose a significant challenge. Growing suppliers of 

AM technology in the U.S. is also a challenge, and will require focused efforts to develop a domestic AM 

marketplace that encourages development of the technology and incentivizes suppliers to locate in the United 

States. A larger U.S.-based original equipment manufacturer supplier base for technology and equipment is 

needed. AM parts manufacturers would like to have access to AM production machines that are produced in the 

United States. 

Measurement Science Roadmap for Additive Manufacturing 47

APPENDIX A: PARTICIPANTS

David Abbot 

GE Global Research Center 

Michael Anderson 

U.S. International Trade Commission 

James Barkley 

The MITRE Corporation 

Mark Benedict 

Air Force ManTech 

Jack Beuth 

Carnegie Mellon University 

Craig Blue 

Oak Ridge National Laboratory 

Dave Bourell 

University of Texas at Austin

Jesse Boyer 

Pratt & Whitney Aircraft 

Craig Brice 

National Aeronautics and Space Administration Langley 

Research Center 

Jennifer Bryant 

Naval Air Systems Command 

Tom Campbell 

Virginia Polytechnic Institute and State University 

Isaac Chan 

U.S. Department of Energy 

Diane Chinn 

Lawrence Livermore National Laboratory 

Shane Collins 

GROWit 

Jim Craig 

Stratonics, Inc. 

Bryan Crutchfield 

Materialise 

Ryan Dehoff 

Oak Ridge National Laboratory 

Carl Dekker 

Met-L-Flo Inc

Alkan Donmez 

National Institute of Standards and Technology 

Matt Donovan 

UTC Aerospace Systems 

Allan Eustis 

National Institute of Standards and Technology 

Alex Fima 

Directed Manufacturing, Inc. 

Gary Fleegle 

National Center for Defense Manufacturing and Machining 

David Forrest 

U.S. Department of Energy 

Frank Gayle 

National Institute of Standards and Technology 

Howard Harary 

National Institute of Standards and Technology 

David Hardy 

U.S. Department of Energy 

Ola Harrysson 

North Carolina State University 

Ed Herderick 

EWI 

Tim Horn 

North Carolina State University 

Paul Huang 

National Institute of Standards and Technology 

John Hunter 

Carpenter Powder Products, Inc. 

Robert Ivester 

U.S. Department of Energy 

Kevin Jurrens 

National Institute of Standards and Technology 

Shawn Kelly 

EWI 

Stacey Kerwien 

U.S. Army 

Mary Kinsella 

Air Force Research Laboratory 

John Kleek 

Air Force Research Laboratory 
48 Appendix A: Participants

Agnes Klucha 

Pratt & Whitney 

Howard Kuhn 

ExOne Company 

Abhai Kumar 

ANSER 

Thomas Kurfess 

White House Office of Science and Technology Policy 

Frank Liou 

Missouri University of Science and Technology 

Dan Maas 

ExOne Company 

Mick Maher 

Defense Advanced Research Projects Agency 

Jesse Margiotta 

Defense Advanced Research Projects Agency (Strategic 

Analysis, Inc.) 

Jyoti Mazumder 

University of Michigan 

Frank Medina 

University of Texas at El Paso 

Mike Molnar 

National Institute of Standards and Technology 

Ed Morris 

Lockheed Martin Corporation 

Shawn Moylan 

National Institute of Standards and Technology 

Richard Neal 

National Additive Manufacturing Innovation Institute 

Junichiro Nishiwaki

NHK, Japan Broadcasting Corporation 

Malinda Pagett 

Office of Naval Research 

Deepankar Pal 

University of Louisville 

Ken Patton 

RapidTech 

Bill Peter 

Oak Ridge National Laboratory 

Hank Phelps 

Lockheed Martin Corporation 

Connie Philips 

National Center for Manufacturing Sciences 

Joseph Pickens 

Concurrent Technologies Corporation 

Dean Robinson 

GE Global Research Center 

Dave Schwam 

Case Western Reserve University 

Tim Shinbara 

Association for Manufacturing Technology 

Matt Showalter 

National Aeronautics and Space Administration Goddard 

Space Flight Center 

John Slotwinski 

National Institute of Standards and Technology 

Michael Stern 

Massachusetts Institute of Technology Lincoln Laboratory 

John Strenkowski 

North Carolina State University 

Brent Stucker 

University of Louisville 

Shyam Sunder 

National Institute of Standards and Technology 

Ed Tackett 

RapidTech 

Karen Taminger 

National Aeronautics and Space Administration Langley 

Research Center 

Nancy Tolliver 

National Aeronautics and Space Administration Marshall 

Space Flight Center 

Alan Treat 

U.S. International Trade Commission 

Gary Wainwright 

National Aeronautics and Space Administration Langley 

Research Center 

Al Wavering 

National Institute of Standards and Technology 

Harvey West 

North Carolina State University 

Ryan Wicker 

University of Texas at El Paso 

Ray Xu

Rolls-Royce 
Measurement Science Roadmap for Additive Manufacturing 49

APPENDIX B: AGENDA

Tuesday, December 4, 2012

7:30 am Registration and Continental Breakfast 

8:30 am Opening Plenary Session 

 Welcome ~ Shyam Sunder, Director, Engineering Laboratory, NIST

 Stage-setting Presentations 

 Workshop Scope and Objectives ~ Kevin Jurrens, Deputy Chief, Intelligent Systems 

Division, NIST

 Highlights of Prior Additive Manufacturing Roadmaps ~ Dave Bourell, Temple 

Foundation Professor, University of Texas at Austin, Co-Chair 2009 Roadmap for 

Additive Manufacturing

 ASTM F42 Strategic Framework for Additive Manufacturing Standards ~ Brent

Stucker, Professor, University of Louisville, Chair of ASTM F42

 NAMII Additive Manufacturing Pilot: Priority R&D Needs ~ Gary Fleegle, Vice 

President and Chief Operating Officer, National Center for Defense Manufacturing and 

Machining, NAMII Acting Deputy Director for Technology Development

10:00 am Break 

10:30 am Industry Panel

 Moderated session and Q&A on AM user perspectives

 Moderator ~ Ed Herderick, EWI, Chair of Additive Manufacturing Consortium (AMC)

 Panelists ~

Agnes Klucha, Program Manager, Engineering Innovation Center, Pratt and Whitney

John Hunter, Director of Marketing & Sales, Carpenter Powder Products, Inc.

Dean Robinson, Manager, Model-Based Manufacturing Lab, GE Global Research Center

Hank Phelps, Advanced Development Programs, Lockheed Martin Aeronautics – Marietta

11:40 am Morning Wrap up and Instructions for Breakout Sessions

11:45 am Lunch

1:00 pm Breakout Session I: Desired Capabilities for Additive Manufacturing

 Envisioned future: What capabilities do we want and need the most

3:00 pm Break

3:20 pm Breakout Session II: Challenges and Barriers for Achieving the Capabilities

 Barriers limiting the broad use of additive manufacturing 

 Measurement and standards barriers, challenges, and gaps

5:00 pm Adjourn Day 1
50 Appendix B: Agenda

Wednesday, December 5, 2012

8:00 am Continental Breakfast 

8:30 am Breakout Session III: Prioritization of Challenges

 Review, clarify, and vote on the top challenges

 Determine R&D priorities

 Identify standardization priorities for ASTM F42

10:15 am Break 

10:30 am Breakout Session IV: Pathways for Measurement Science Roadmap

 Small groups work to develop roadmap elements:

 R&D, standards, and other approaches for addressing priority challenges

 Next steps and actionable plan

11:45 am Lunch

1:00 pm Breakout Group Reports (Part 1)

1:30 pm Plenary Talk: Manufacturing the Road to Success

 Tom Kurfess, Assistant Director for Advanced Manufacturing, Office of Science and 

Technology Policy, Executive Office of the President

2:00 pm Breakout Group Reports (Part 2)

2:15 pm Workshop Wrap up and Next Steps 

2:30 pm Adjourn Workshop

3:00 pm Interagency Roundtable (government only) ~ Tentative
Measurement Science Roadmap for Additive Manufacturing 51

APPENDIX C: WHITE PAPERS

Participants were invited to create white papers specifically for the workshop to address the following two 

questions: 

1. What are the key measurement science barriers that prevent innovation in metal-based additive 

manufacturing (AM)? 

2. What are the most important areas where research and development (R&D) is needed—particularly in 

measurement and standards—to overcome these barriers and to accelerate innovation in metal-based 

AM? 

Eleven participants created white papers, presenting ideas for the common good. These voluntary submissions 

were much appreciated and used to shape workshop discussions. The complete white papers are listed below 

and contained in this appendix: 

Author Title

Stacey Kerwien

U.S. Army

Using Measurement Science to Drive Design Guide 

Development for Additive Manufacturing

Dan Maas and Jesse Blacker

The Ex One Company, LLC

Measurement Science Barriers and R&D Opportunities

for the NIST Metal-Based AM Workshop

Shane Collins

GROWit

Laser and Electron Beam PBF

Frank Liou, Michael Bytnar, and Joyce 

Bytnar

Missouri University of Science and Technology

Measurement Science Barriers and R&D Opportunities

for the NIST Metal-Based AM Workshop

Jennifer G. Bryant, John E. Benfer, and 

Anthony B. Petrizzo

NAVAIR

Measurement Barriers in the Implementation of Metals 

Additive Manufacturing for Military Aircraft Repair and 

Maintenance

Ken Patton (RapidTech), Dr. Imelda 

Cossette (MatEd), Ed Tackett (RapidTech),

and Frank Cox (MatEd)

ASTM F-42 Educational Working Group

Additive Manufacturing Competencies

Kevin Chou

The University of Alabama

Process Metrology for Metal-Based Additive 

Manufacturing

Jyoti Mazumder

University of Michigan at Ann Arbor

Measurement Science Barriers and R&D Opportunities

for the NIST Metal-Based AM Workshop

Tom Campbell

Virginia Tech University

Measurement Science Barriers and R&D Opportunities

for the NIST Metal-Based AM Workshop

Vito Gervasi

Milwaukee School of Engineering

Measurement Science Barriers and R&D Opportunities

for the NIST Metal-Based AM Workshop

Constance J.S. Philips

National Center for Manufacturing Sciences

Production and Use of Standard Parts and Our 

Lessons-learned Thus Far
52 Appendix C: White Papers

Using Measurement Science to Drive Design Guide Development 

for Additive Manufacturing 

Stacey Kerwien, U.S. Army 

(UNCLASSIFIED)

It is clear that for the U.S. Army to utilize Additive Manufacturing (AM), concise design guides must be 

developed. 

Successful design guides, such as the AWS D1.1 for Welding, require measurement science in the areas of 

machine parameters and feedback, surface preparation, filler metal composition, and joint configurations. 

Thus, for Additive Manufacturing, it is our position that measurement sciences are needed in the areas of: 

 Part Support Protocol 

 Powder Properties (Composition, Size, Morphology) 

 Build Environment (Temperature, Atmosphere, Sensor Feedback) 

 Recoater Blades (Type, Amount of Wear) 

 Build Plate (Material) 

 Part Positioning 

 Part Removal 

 Beam Focus 

 Filters (Amount of clogging) 

 Final Part Dimensions & Roughness (and the correlation to the original 3-D model) 

 Machine Software & Design Software 

Once the part is produced with a known set of parameters, the challenge is to define the mechanical and 

metallurgical properties to be measured and ensure part-to-part and machine-to-machine consistency. 

In developing a design guide, many of the items listed above merely need to be recorded so that lessons can be 

learned for future builds (i.e., Part support protocol, build plate material, part positioning, and design software 

used). Other items need to be measured in a more quantifiable way that NIST may already have experience with 

(i.e., Final dimensions and roughness, amount of clogging on the filters, amount of wear on the recoater blade). 

Finally, new sensors need to be developed/enhanced to provide real-time feedback on the build environment 

and the beam focus to ensure consistent part quality. 

Thus, a combination of generally accepted practices, standard measurement techniques, and sensor development 

is needed to ensure the continued improvement of additive manufacturing. With improvements to AM, the 

industry and government will finally achieve a provisional design guide at the very least. 
Measurement Science Roadmap for Additive Manufacturing 53

Measurement Science Barriers and R&D Opportunities for the 

NIST Metal-Based AM Workshop 

Dan Maas and Jesse Blacker, The Ex One Company, LLC 

1. What are the key measurement science barriers that prevent innovation in metal-based AM? 

Dynamic Measurements and Control: 

 In-process, real-time sensing and control 

 Real-time, model-based feed-forward control 

 Transient changes in process characteristics with real-time dynamic feedback 

 Process mapping—both for control and for process learning 

Scale: 

 Nano/micro/millimeter AM for metals and metal/ceramics 

 Large-scale (measured in meters) AM machines and equipment 

Material Composition (not just metals): 

 Functionally gradient materials, metal matrix composites, ceramic/metal mixtures 

 Embedded nano-particle/nano-fiber structures 

Environmental Impact: 

 Temperature, humidity, and dew point 

 Reuse, recycle, ratios of virgin to reused 

 Thermal cycling, oxidation, and atmospheres 

2. What are the most important areas where R&D is needed—particularly in measurement and 

standards—to overcome these barriers and to accelerate innovation in metal-based AM? 

Atomic-scale to meso-scale modeling and simulations: 

 An integrated modeling platform addressing atomistic-informed meso-scale modeling has 

applications for functionally gradient materials, ceramic/metal interface structures, metal matrix 

composites, etc. 

o “Atomistic-informed meso-scale modeling: Interfaces and their interactions with defects 

influence a wide range of behaviors from crystal reorientation, slip, twinning, boundary 

sliding, migration, phase stability, etc. Little of this can be predicted by treating only one 

type of defect/interface interaction alone. Models are packed with information. For one 

model to ‘inform’ another means that the transfer of knowledge of dominant mechanisms, 

phenomena, or physical properties, at the lower scale measurably transforms the way in 

which the higher scale is modeled and/or performs. In this regard, scientific expertise has 

to be exercised to determine what atomic scale information is useful and applicable to the 

rate conditions applied at the meso-scale.” Irene J. Beyerlein, Office of Science, U.S. 

Department of Energy. 
54 Appendix C: White Papers

o NIST has already started the Materials Genome Initiative (MGI) to develop a materials 

innovation infrastructure. 

3. Some comments/suggestions for near-term consideration regarding the current Artifact 

Standard. 

 Provide guidelines for the inspection methodology 

o Example: should the artifact sit upon 3 spheres to establish a plane? 

o Example: explain the purpose of the steps/incline/ramp 

 Use the artifact to produce specimens for mechanical property testing 

o Provide guidelines for compression, tensile, fatigue, impact, etc. 

 Consider several size artifacts for micro and meter size AM build boxes 
Measurement Science Roadmap for Additive Manufacturing 55

Laser and Electron Beam PBF 

Shane Collins, Director, Additive Manufacturing Technologies, GROWit3d 

Note: Included in this article is background information on powder bed fusion. The chart at the end lists 

what I believe are the necessary parameters that need to be measured and monitored for powder bed 

fusion. Standardization, measurement, and monitoring of these parameters are necessary for a robust 

powder bed fusion process. 

Powder bed fusion (PBF) is the ASTM-accepted term for an additive manufacturing process where a point heat 

source selectively fuses or melts a region of a powder bed. In the United States, the metal powder bed fusion 

processes are known by the trade names SLM® and DMLS® for the laser beam process and EBM® for the 

electron beam processes. Curiously, there are no machines that perform the PBF process manufactured in the 

United States. That fact notwithstanding, PBF has become a popular method of creating high-value medical and 

aerospace prototype components as well as production components in safety critical applications. This article 

compares the electron beam and laser beam technology while taking a look at the practical aspects of the two 

systems for future PBF process development. 

My first home computer was a 386 PC clone running Windows 3.1. For the time it was pretty fast and had 

enough power to run my wife’s CAD program, CADKEY. What made it fast for the time was the upgraded 

video bus from the normal ISA to the faster VESA local bus that moved data from the microprocessor back and 

forth to the video card. It was fast enough to run a 2D CAD program, but displaying video on the CRT was not 

possible. That was what TVs were for. 

About that same time in my professional career I was involved in the digital imaging revolution that paved the 

way for image processing, calibrated measurements, and digital image archiving. However, before digital cameras 

existed, digital imaging involved the acquisition of video signals where NTSC or PAL video was captured with a 

computer board called a frame grabber. The frame grabbers for the aforementioned 386 PC cost $12,000–

$20,000 because of buffering circuitry necessary for displaying the video on the computer’s CRT. With the 

introduction of the 486 and the PCI bus the data transfer rate was significantly improved and uncompressed 

video signals were easily transferred to the video card for display on the CRT. The cost of the frame grabbers 

plunged to a few hundred dollars while the image capture quality greatly improved. The 486 PC computer with 

the speed of the PCI bus facilitated a revolution in video image capture and digital image analysis. 

Fast forward to 2004 and a similar revolution can be seen in the field of metal laser sintering with the 

introduction of the solid-state Yb-doped fiber laser that replaced the ubiquitous CO2 lasers. The advantages of 

the fiber laser over the gas laser were the low cost of ownership, better absorption due to the emission 

wavelength, continuous wave nature of the beam, and fine focus capability of 100 μ and lower beam diameter. 

This intensity produced 25 kW/mm2 and allowed for 20 μ powder layers to be completely melted several layers 

deep on each pass of the laser beam. The development and introduction of the fiber laser was an enabling 

technology for metal laser sintering and will be discussed in more detail, but first a look at the early years. 

Laser beam PBF systems have their roots in the 1990s from technology developed and commercialized by the 

Fraunhofer Institute, Trumph, EOS, Concept Laser, and Fockel and Schwartz, all from Germany. These early 

systems used gas or disk lasers and processed primarily bronze-based composite materials. One of the first fully 

dense alloy (55% Au-28.5% Ag) systems was introduced by Bego at the 2003 IDS conference as a solution for 

making dental copings for porcelain fused to metal restorations. Shortly after the 2003 IDS, EOS and Sirona 

Medical Systems were working in a cooperation to commercialize the manufacture of fully dense CoCr dental 

materials using laser beam PBF. Due to the high cost of gold powder becoming entrapped inside the machines, it 

would be many years later that sealed machines would make gold alloy processing feasible, whereas lower-cost 
56 Appendix C: White Papers

CoCr (ASTM F75) processing found many applications in general industry and led to other alloy processing, 

including 316L and 17-4 stainless steels. 

About the same time that laser beam PBF systems adopted the fiber laser and started processing true ISO and 

ASTM alloys, Arcam from Sweden was processing fully dense titanium components in its electron-beam-based 

PBF system. Although internal tests proved it possible to process most electrically conductive metal powders, 

Arcam concentrated on titanium, particularly Ti 6Al-4V. 

Both the laser and electron beam systems have nearly a decade in manufacturing and marketing commercial 

systems. Today, there are about a half dozen companies selling laser-beam-based PBF machines with a 

worldwide installed base of nearly 900 systems. Arcam is the only commercial electron-beam-based PBF system 

with an installed base of 100 systems worldwide. So, in about the same amount of time there are multiple 

manufacturers of laser-beam-based systems with an installed base nearly 9 times greater than electron-beambased systems. 

Having more manufacturers and significantly more machines installed, one might draw the conclusion that laserbeam-based systems are superior to electron-beam-based systems. If this is the case, what is it about laserbeam-based systems that make them highly accepted and what is it that makes electron-beam-based systems less 

prevalent? Although this article is not meant to be a feature-by-feature comparison, the fundamentals presented 

help to explain the current status. 

Laser Beam System Overview 

As previously discussed, the laser-beam-based PBF systems use a fiber laser as the fusion heat source. The two 

manufacturers that supply fiber lasers to the laser beam PBF machine manufacturers are U.K.-based SPI, which 

was founded in 2003 and acquired by Trumph in 2008, and Oxford, MA, IPG, which boast sales of more than 

40,000 fiber lasers since 1990. The engines that power the laser beam PBF systems are supplied by competent 

companies that also supply lasers for laser drilling, laser ablation, laser cutting, laser marking, and laser cladding 

where PBF is a small percentage of their overall business. 
Measurement Science Roadmap for Additive Manufacturing 57

Optical diagram of laser beam PBF system 

Next to the heat source, the most important component of the laser beam PBF systems is the beam deflection 

optics that provide the scanning capability for selectively melting areas of the powder bed. With scan speeds up 

to 15 m/s, the scanning mirror must be fast, accurate, and reliable. Most of the laser-based PBF machine 

manufacturers use scanning optics from Scanlab of Germany. Scanlab manufactures a wide variety of 2D and 3D 

scanning systems for OEM applications including micro-machining, DNA Sequencing, laser cutting, and additive 

manufacturing. Again, as in the fiber laser business, the PBF component of Scanlab’s sales is small in comparison 

to the total market for these devices. 

The final significant optical element is the correction lens that ensures the beam is round as it traverses the build 

platform and keeps the beam velocity proportional to the angular velocity of the scanning mirror. Most of the 

systems use the f-Theta lens design with anti-reflection coatings to help prevent damaging laser reflections back 

into the laser. There are a number of f-Theta lens manufacturers that sell off-the-shelf solutions as well as 

custom OEM applications. However, there appears to be a limit to the intensity of laser power that is possible 

when employing the f-Theta lens design. Somewhere in the 300 W laser power range the f-Theta lens heats up 

and causes optical distortions as the lens changes temperature. In order to overcome this shortcoming and to 

meet the increased power needs of laser-based PBF systems, Scanlab recently introduced varioSCAN focusing 

units that dynamically vary the focal length in conjunction with the scanning mirrors. With the varioSCAN units 

installed, f-Theta lenses are no longer needed and this optical layout supports 1 kW laser power as well as 

multiple laser inputs for increased scan speeds or multiple laser power modes. Another recent development 

with the scanning optics is the linear beam intensity profile. Unlike the typical Gaussian distribution beam profile 

where the beam intensity decreases from the beam center to the outside circumference, it is now possible to 

have a nearly equalized beam intensity across the entire profile. This has a profound effect on the overlap of the 

hatch spacing necessary to ensure fully melted surfaces. It is like the difference in mowing your lawn with a 
58 Appendix C: White Papers

lawnmower having a blade that is all the same distance above the ground, versus mowing your lawn with a lawn 

mower where the blade curves up at the wheels. In the latter example it would be necessary to overlap your 

rows quite a bit to cut the grass all the same length, whereas in the former example the wheels only have to be 

overlapped to achieve the same length. The equalized beam intensity profile has the potential to improve surface 

finish, decrease scan time, and reduce subsurface porosity when fully implemented. 

To summarize the laser-based PBF systems, the lasers and scanning optics are supplied by companies that 

manufacture many times more ship-sets than what are used for laser-based PBF machines. This means the heart 

of the systems can be acquired with off-the-shelf items and to some degree the development of laser-based PBF 

technology is paced by IPG, SPI, and Scanlab. To be sure there is much more work to integrate the optics, 

electronics, and electro-mechanical bits, not to mention the man-years in process development, but it is more 

execution rather than development. 

Electron Beam System Overview 

Powerful electron beams used for welding have their roots back in the late 1950s from the German physicist 

Karl-Heinz Steingerwald. Today, two of the oldest electron beam welding machine manufacturers claim to have 

combined machine installations of over 1,800 systems worldwide. The main benefit to the electron beam over 

the laser beam in welding is in the higher beam energy density without effects due to reflectivity. 

Arcam adapted the electron beam technology for freeform fabrication in 1997 with sights on building net shape 

plastic injection mold tooling using steel alloy powders. By 2003, Arcam had 4 electron beam PBF machines in 

house and another 4 machines at external installations. After a few years working with steel powders, Arcam 

turned its focus to titanium alloys and that remains its most widely used alloy today, both internally and at user 

installations. Arcam currently supplies machines, materials, and parameters for Ti CP, Ti 6Al-4V, Ti 6Al-4V ELI, 

and CoCr (ASTM F75), but electron beam PBF machine users have successfully processed many more alloys 

including high-nickel and intermetallic compounds. 

In the electron beam PBF system the electrons are generated from the filament and attracted toward a 

positively charged anode where a beam is formed. The focusing coil produces a converging, Gaussian beam and 

the deflection coil directs the scanning of the beam. The focusing and deflection coils are the electronic 

counterparts of the scanning and f-Theta optics of the laser beam systems. Since there are no moving parts in 

the electron beam PBF system, the scan speed can approach 3,000 m/sec (compared to 15 m/sec with lasers) 

with usable beam power in the several kW range. 

The physics of the electron beam interaction with the powder bed is complex. In addition to the kinetic energy 

from the electrons irradiating the surface, there are four other forces at play that have been modeled by 

Christian Eschey (Technische Universitaet Muenchen): pulse transmission, hermodynamics, electrostatics, and 

electrodynamics. These forces can create unwanted consequences during the electron beam PBF process and 

are dependent on the powder morphology and chemical composition due to different electrical resistance from 

powder particle to powder particle. One such unwanted consequence is the powder’s propensity to disperse 

upon contact by the electron beam. Powder dispersion, aka “smoke” usually results in a total build failure.

As a means of mitigating the powder dispersion failure, Arcam learned to partially sinter the powder layer prior 

to selective melting. This caused the powder layer to adhere to the previous layer and also to itself, thus 

preventing powder dispersion. The elegant solution for the heat needed for sintering the powder came directly 

from the electron beam, which was possible due to the high beam energy and fast scan speed. 

Whether it was intentional or not, the need to sinter the powder prior to selective melting meant the powder 

bed had to be heated to very high temperatures, approaching 650°C to 700°C for titanium and up to 1100°C 

for intermetallic compounds. This created a difficult operating environment for electro-mechanical components 

in the build chamber, but it had advantageous affects compared to a cold process on component microstructure 

and was self-annealing, which reduced the requirement for unwanted support contacts. 
Measurement Science Roadmap for Additive Manufacturing 59

The electron beam PBF machines require a vacuum in the build chamber in order to have a focused beam. The 

added complexity from the vacuum pumps, chamber reinforcement, and seals necessary to maintain 1X10-4

mbar vacuum adds a level of machine integration difficulty not required for the laser-based PBF systems. In 

addition to the vacuum requirement, the electron beam interaction with the powder makes the electron beam 

PBF machines more difficult to develop and optimize. 

Electron beam PBF diagram 

Comparison between laser and electron beam PBF 

Visual inspection of as-built components made on the electron beam PBF machines shows a much rougher 

surface finish and less accuracy to the CAD model than components made with laser beam PBF machines. This is 

due to coarser powder, thicker layers, and a larger melt pool in the electron beam PBF machines. There is 

nothing inherent with the electron beam technology that would prevent the same or better surface finish for the 

electron-beam-based system. It has been the historical implementation of the technology for freeform 

fabrication that created this disparity, specifically the trade-off between build speed and surface finish. 

However, surface finish alone is not a sufficient reason that in the same amount of time there are 9 times more 

laser-beam-based PBF systems installed as there are electron-beam-based PBF systems. Some of the difference 

can be attributed to the higher cost of the electron-beam-based machines and the marketing efforts of multiple 

laser beam machines, but the underlying advantage of the current state-of-the-art laser-beam-based PBF systems 

is higher levels of successful first-time component builds. This supports the job shop prototype business model, 

while having to build a component several times in order to dial-in the build parameters for success is relegated 

to the production business model. To date, we are seeing more demand for prototype components than 

demand for production components and while the ratio of prototype to production components will likely shift 

to production in the coming years, much of the process development and certification work is currently being 

done on the laser beam systems. 

Another contributor to the success of the laser-beam-based PBF machines is their ability to routinely process 

many different alloys including maraging steel; high-nickel super alloys; 316L, 17-4, and 15-5 stainless steel; 

CoCrMo; and aluminum, to name the most popular. Because of the complexity of the electron beam interaction 

with the powder surface and the need for high-temperature processing, optimization of build parameters is 

tedious and time consuming on the electron beam PBF machines. A build failure entails a several-hour cool 

downtime followed by a lengthy restart process. Compare that to a build failure on a laser-based PBF machine, 

where the operator immediately opens the chamber door; pounds down a high spot, for example; closes the 

chamber door; and restarts the build with modified parameters. The iterative process is much faster and allows 

a higher degree of experimentation by general users. On the electron beam PBF machine side, sophisticated 

modeling that accounts for feedstock particle size, shape, and composition along with electron beam dynamics 

needs to be developed to qualify interesting alloys. 
60 Appendix C: White Papers

Conclusion 

Intellectual property rights and patents held by Arcam could help to explain why there is only one electron 

beam PBF machine manufacturer. However, one only needs to look at the litigation in the laser beam PBF 

market along with the various distribution agreements that have emerged to understand this is not the whole 

story. Usually, when markets are viable, competition finds a path around and forward. The primary reasons why 

there are multiple laser-beam-based PBF machine manufacturers are: ease of acquiring off-the-shelf components 

to manufacture the machines, ability to process in a cold build chamber, relative ease of qualifying new materials, 

and demand for components made from the process. Having said that, the technology that has the most upside 

in terms of additive manufacturing of metals in a powder bed is the electron beam process. This is due to the 

electronic control of the beam diameter and deflection that can scan so fast it appears to have multiple beams 

hitting the surface at once. As long as the scanning optics in a laser-based system have mass, it won’t be possible 

to meet the scan speed or beam dynamics of the electron beam system. 

Other than beam dynamics, features that create the total PBF solution can be implemented with either electron 

beam or laser beam systems. Both types of processes can have vacuum, can have heated powder beds, can have 

thin or thick layers, can be scaled up or down for component size (electron beams have an advantage for sub 

micron spot sizes), and can utilize different powder morphology and composition (laser beams have an 

advantage on non-conductive powders). In fact, we are already starting to see thicker layers to speed up 

processing on laser-based machines as the 400 W and now 1 kW lasers are available from IPG and SPI. We find 

vacuum pumps installed on a laser-based machine from at least one machine manufacturer, and the surface finish 

on electron-beam-based PBF components has improved significantly with the introduction of thinner layers and 

multi-beam contour scanning. In short, we are witnessing a convergence of the two technologies. 

As the two PBF technologies converge, there is a need for measurement and control of the process 

fundamentals in order to produce safety critical components. Here is a comprehensive list of those parameters: 

Parameters Measurement EB PBF LB PBF Requirements

Powder Flow H H S, I, MP

Chemical Composition H H S, I, MP

Particle Size H H S, I, MP

O2 H L-H MP

Spread Coherence H H

Power Density at Part 

Bed

Beam Diameter H H

Beam Profile H H

Consistency of Part Bed Area H H

Consistency of Build Time H H

Process

Melt Pool H H

Hatch Space H H

Contour Space L-H L-H

Part Bed Temp Build Platform H H

Top Layer H L-H

Machine

Z axis movement H H

Recoater Contamination H H

Build Atmosphere H H

Gas Flow L H

Component

Mechanical Properties H H

Porosity H H

Microstructure H H

Surface Finish H

Internal Stress L H-L

Dimensions H H

Remaining Powder H H

Notes: EB PBF and LB PBF: H=High; L=Low Requirements: S= Supplier; I= Incoming Inspection; MP=Required in Manufacturing Plan
Measurement Science Roadmap for Additive Manufacturing 61

Measurement Science Barriers and R&D Opportunities for the 

NIST Metal-Based AM Workshop 

Frank Liou, Director, Manufacturing Engineering Program Missouri University of Science and 

Technology 

Michael Bytnar Professor, Mechanical Engineering Department 

Joyce Bytnar Professor, Mechanical Engineering Department 

1. What are the key measurement science barriers that prevent innovation in metal-based AM? 

 Quantify the dimensioning and tolerancing capabilities of an AM machine. AM parts can be 

used for functional models and for fit and assembly, thus it is important to quantify the dimensioning 

and tolerancing capabilities of an AM machine. This capability may be complex due to possible 

shrinkage issues in the AM build process. In some AM processes, shrinkage may be a function of the 

part geometry.

 Comprehensive material specifications and standards for each AM process. Without these 

specifications and standards, engineers will not consider AM as a method of manufacturing.

 Measurement of material properties at high temperatures. Some metal AM processes build 

metals in high temperature that is above the melting point of the metal. In order to fully understand 

these processes and predict the resulting microstructure, the material properties at elevated 

temperatures are needed. The measurement of these properties is expensive. Since each material is 

different, a lot of effort is needed.

 Measurement of the key process parameters at high temperatures. In some metal AM 

processes, measurement of the key process parameters at extreme environment, such as high 

temperature, may be needed to ensure quality and reliability of the parts.

2. What are the most important areas where R&D is needed—particularly in measurement and 

standards—to overcome these barriers and to accelerate innovation in metal-based AM? 

 Open-architecture controllers and reconfigurable machine modules for integrated 

processes. Just like CNC machining centers, AM machines need to take advantage of the openarchitecture controllers and machine modularity so that more innovative processes can be created.

 Creation of comprehensive material specifications and database. Research and development 

is needed to find the material properties at elevated temperatures. This should also include the mixing 

of materials in high temperatures for applications in functionally graded material (FGM).

 Better understanding of the processing-structure-property relationships of materials. It 

will involve physics-based modeling and accurate measurement of the key parameters involved in the 

process. This will help establish the ability to accurately fabricate complicated shapes with a minimum 

number of experiments. 

 In-process measurement and feedback control to help improve reliability, repeatability, 

and uniformity across machines. Nondestructive evaluation technology should be developed to 

enable early defect detection, which is important.

 Machine qualification standards to ensure part-to-part consistency. The standards will 

include all areas such as material input, preparation, processing, and post-processing if applicable.

 Repair qualification standards. If a part or product can be repaired and reused for its initial 

product function, not only will the material waste and amount of landfill be reduced, but also energy 

and matter consumption during manufacture will be reduced because existing components are utilized. 
62 Appendix C: White Papers

However, some companies have a need for parts that require repair or replacement frequently, but 

without a robust qualification process, repair may not be an option such as in the aerospace industry. 

The lack of standards in repair impedes the use of AM for parts remanufacturing.

 Updated AM software research and development. AM processes can potentially produce very 

innovative materials that could not be made before, thus updated AM software to support such 

capability should be researched and developed. For example, functionally graded material (FGM) may 

be characterized by the variation in composition and structure gradually over volume, resulting in 

corresponding changes in the properties of the material. The current standard CAD models do not 

have such capability.

 Technologies to improve dimensional accuracy and surface finish. All techniques to improve 

accuracy of AM processes, either in-processing or post-processing, are needed to widen the 

applications and the market for AM processes.
Measurement Science Roadmap for Additive Manufacturing 63

Measurement Barriers in the Implementation of Metals Additive 

Manufacturing for Military Aircraft Repair and Maintenance 

Jennifer G. Bryant, John E. Benfer, Anthony B. Petrizzo, NAVAIR 

Additive manufacturing (AM) of metals (e.g., titanium, nickel, aluminum) has many potential benefits in numerous 

industries, particularly in the field of aerospace engineering. Titanium alloys (i.e., Ti-6Al-4V) are of specific 

interest because their high cost could be mitigated by AM’s low “buy-to-fly” ratio. Of particular interest to 

NAVAIR Jacksonville (FRCSE) is implementing direct digital manufacturing (DDM) to produce parts for legacy 

aircraft from drawings or reverse engineering when replacements parts are no longer commercially available. 

Additionally, DDM could be implemented during aircraft maintenance and repair to improve operational 

availability and cost savings by avoiding long lead times associated with obtaining one-off-repairable parts (OOR) 

from traditional manufacturing methods.1

However, there are still many measurement science barriers that prevent innovation in metal-based AM and 

aspects that must be further evaluated prior to extensive implementation in industries seeking to adopt AM, 

such as the aerospace industry. Primarily, a database of the mechanical properties of materials produced by AM 

must be established. Characteristics (e.g., fracture toughness, yield strength, etc.) from material produced by AM 

must be compared with the current Metallic Materials Properties Development and Standardization (MMPDS) 

data for the same alloy produced by a traditional method. As parts produced by AM are anisotropic and vary by 

fabrication method, careful consideration must be made in measuring and reporting mechanical property values.2

A clear understanding of the mechanical properties of parts produced by AM prior to industrial (e.g., aerospace) 

applications is critical. However, the AM parts do not necessarily have to match or exceed the mechanical 

properties of parts made by traditional manufacturing, because as long as the mechanical properties are known 

and are reproducible the part may still be engineered to specification. For example, if ultimate tensile strength of 

a material is reported in the MMPDS as 100 ksi from a traditional manufacturing method but the ultimate tensile 

strength for the AM part is measured at 80 ksi, a structural engineer can perform a static strength and/or fatigue 

analysis to determine acceptability or make alterations in the design such that the new material is still usable for 

a particular application. However, without a clear understanding of the mechanical properties of metallic 

material produced by AM, it is not known which industrial applications are realistic. Documented databases of 

mechanical properties and the establishment of industry specifications and standards for parts produced by AM 

are crucial. 

Furthermore, advancements in measurement science must address necessary parameters for parts qualification, 

such as statistical reproducibility in AM for process control. Many industries seeking to adopt AM will require 

the ability to reproducibly manufacture drop-in replacement parts or parts from novel designs (e.g., military 

applications of metal alloy AM may utilize new and improved designs, as well as the ability to rapidly manufacture 

and reverse engineer drop-in replacement parts for numerous platforms to ensure operational availability). 

Therefore, the ability to rapidly qualify parts is of key importance. Understanding, measuring, and controlling 

inter-part, inter-machine variation and reproducibility for the implementation of repeatable and predictable 

processes during part manufacturing will be one key aspect of rapid qualification needed for the implementation 

of AM in military aircraft repair and maintenance, as well as in other industries requiring process control. 

Further R&D is required in this area to accelerate innovations in metal-based AM. 

Finally, predictive modeling and simulation tools will be an important aspect of innovative structural design using 

AM. A benefit of AM is design flexibility and alleviation from some traditional manufacturing constraints. 

However, in order for design freedom to be fully realized, predictive modeling and simulation tools are required 

that can calculate mechanical properties in relation to structural variables (e.g., geometry, payload, etc.). Physicsbased models are also needed to predict microstructure, properties, and defects during the AM process.1
64 Appendix C: White Papers

References: 

1. William E. Frazier and Malinda Paget, Additive Manufacturing Direct Digital Manufacturing of Metallic 

Components, 2011 CTMA Symposium, Quantico, VA. 

2. Ian Gibson, David W. Rosen, and Brent Stucker. Additive Manufacturing Technologies. 2010, Springer 

Science + Business Media, LLC, New York, NY. 
Measurement Science Roadmap for Additive Manufacturing 65

Additive Manufacturing Competencies 

Patton, Cossette, Tackett, and Cox, ASTM F-42 Educational Working Group 

Ken Patton, Principal Investigator, RapidTech 

Dr. Imelda Cossette, Principal Investigator, MatEd 

Ed Tackett, Director, RapidTech 

Frank Cox, Director, MatEd 

With ASTM’s establishment of Standard F-42, an Educational Working Group (EWG) was established to develop 

core competencies for emerging student technicians working in the field of additive manufacturing. This effort is 

funded by a National Science Foundation grant instrument to Edmonds Community College (MatEd, the 

National Resource Center for Material Science) in partnership with RapidTech (National Center for Additive 

Manufacturing). The goal of the grant is to develop student core and professional competencies supporting the 

developed global standards for additive manufacturing and to disseminate those competencies to the nation’s 

educational community for inclusion in technician education and engineering programs, helping to ensure that 

manufacturing technicians and engineers are prepared to enter the workforce with the knowledge and skills 

necessary to work in the manufacturing environments of tomorrow. 

As the Task Force develops the various standards, they are supplied to the EWG for transformation into core 

and professional competencies with the STEM area identified for each. 

RapidTech and MatEd synthesize the standards into core and professional competencies, which are then 

distributed, to the EWG as a whole to make recommendations for edit. The final results are then shared with 

the Executive Committee of ASTM F-42, which is chaired by Bret Stucker. 

The “Student Core and Professional Competencies” document is a living document that is modified each time a 

standard is approved or modified. The results are then disseminated nationally to the educational community for 

inclusion in their manufacturing technician and engineering instructional programs. 

Ken Patton, Principal Investigator of RapidTech, chairs the educational working group with membership of over 

25 educational and industry leaders in additive manufacturing. Those members include Ed Tackett, Director of 

RapidTech; Dr. Imelda Cossette, Principal Investigator of MatEd; Frank Cox of MatEd; Tim Gornet and Dr. Bret 

Stucker of U. of Louisville; Dr. David Rosen of Georgia Tech; and many other leaders of additive manufacturing 

education. It should be noted that industry also participates on the EWG and provides excellent guidance in the 

development of the Student Core and Professional Competencies document. 

Skill and Knowledge Methodology 

The underlying methodology behind the core and professional competences is that each individual competency 

can be defined as either a skill or knowledge. A knowledge-based competency is well suited for distance 

education while a skill competency is more tactile in nature, requires a practicum, and would not be generally 

suitable for distance education, but it would be suitable for a hybrid educational model. 

Science Technology Math & Engineering 

Each individual competency is categorized by the major STEM category. This allows educators to measure how 

STEM competency can be defined. 

For more information please visit: www.rapidtech.org or www.materialseducation.org. 
66 Appendix C: White Papers

Process Metrology for Metal-Based Additive Manufacturing 

Kevin Chou, Mechanical Engineering Department, The University of Alabama 

1. What are the key measurement science barriers that prevent innovation in metal-based AM? 

For metal-based additive manufacturing (AM) processes, the key measurement science barriers include process 

variable measurements (i.e., process metrology), e.g., temperatures and metal molten pool sizes and evolutions 

during the process. Meaningful, accurate, and reliable process variable data offers detailed, insightful information 

to understand the process physics and to monitor the process characteristics and performance and part quality 

and consistency. Moreover, process variable measurements often serve as necessary to validate the process 

modeling/simulations, which are also for fundamental process understanding, a key to enhance process 

performance and part quality. 

From literature, for virtually every manufacturing process, process metrology (process variable measurements) 

was widely studied and considered necessary for process advancements. However, process metrology has not 

been seriously addressed in metal-based AM technologies including selective laser sintering/melting and electron 

beam melting, etc. In literature, there is very limited data on process variable measurements of metal-based AM 

in the public domain. 

It is understandable that the process metrology in metal-based AM is very challenging because of extreme high 

temperatures, temperature gradients, complex material states (solid/liquid), and some having unique 

environments such as vacuum. Further, limited accessibility to the process chamber adds additional challenges to 

integrating instruments. 

2. What are the most important areas where R&D is needed—particularly in measurement and 

standards—to overcome these barriers and to accelerate innovation in metal-based AM? 

Current sensor technologies including infrared imagers and high-speed cameras seem to have the capability for 

process variable measurements in AM; for example, the desired temperatures ranges, spatial and temporal 

resolutions, etc. 

Some of the most important areas where R&D is needed may include: 

1) How to integrate sensors with an AM machine. How to be flexible for different machine platforms 

(same of different process principles). 

2) How to process the vast amount of data. How to interpret the data correctly. How to use sensors and 

data analysis to obtain meaningful results. 

The other areas of importance for R&D in AM process metrology, with benefits in process advancements and 

part improvements, may include: 

3) How to apply acquired/analyzed sensor data for process modeling and simulation validations. 

4) How to correlate acquired/analyzed sensor data with AM part properties from other measurements. 
Measurement Science Roadmap for Additive Manufacturing 67

Measurement Science Barriers and R&D Opportunities for the 

NIST Metal-Based AM Workshop 

Jyoti Mazumder, University of Michigan at Ann Arbor 

Metal-based additive manufacturing is almost two decades old and primarily includes two broad types: 1) 

powder bed such as Selective Laser Sintering (SLS) and 2) pneumatic powder delivery such as Direct Metal 

Deposition (DMD). Powder bed has the advantage of support materials and can prototype complicated parts 

relatively easily, but the deposition rate and work envelop are limited. Deposition of multiple materials is also a 

challenge. For DMD, high deposition rate, work envelop, multiple material deposition, and repair and 

reconfiguration of real components are advantages, but surface roughness increases with high deposition rate. 

Presently both processes are evaluated post mortem. In order to promote broad use of additive manufacturing 

(AM), online measurement and control techniques are needed. Moreover, to further enhance the AM 

technology and realize the full potential of AM for fabrication of Meta-materials with properties not normally 

observed in mother nature, complicated online measurement of composition and phase transformation are 

needed. In order to achieve close, near net shape dimension, measurement techniques have to be non-contact 

and fast (~ms). Some of the critical needs for measurement are listed below. 

1. Future measurement and standards for metal-based AM 

Measurement techniques for AM need to be in situ, not post mortem, to control the process to achieve 

desired dimension, structure, and properties. Some of the urgent needs for measurements are: 

i. Deposition layer thickness for micron-level accuracy 

ii. Surface roughness with micron-level accuracy 

iii. Composition of the deposited layer 

iv. Phase transformation during the deposition and solidification process 

v. Detection of defects such as cracks, porosity, undercut/underfill, residual stress, and 

distortion 

vi. Develop the feedback control utilizing the above-mentioned measurement 

techniques to produce the desired properties 

2. Measurement science barriers, challenges, and gaps preventing broader use 

Major scientific barriers and challenges are: 

i. Lack of materials and process database that is universally acceptable. AM-produced materials 

properties are highly dependent on the process parameters. Therefore, drawing any scientific 

conclusion from the literature data becomes extremely difficult. 

Suggested Solution: Develop a group of dimensionless numbers so that data can be extrapolated 

for different conditions (Ref: T Chande and J Mazumder, Metallurgical Transactions B, Vol. 14B, 

181-190, 1983). This is a practice widely accepted in scientific and engineering communities. For 

example, Reynolds’s number for flow, Biot’s number for heat transfer, etc. With a single 

number we will be able to describe the process and properties connection. 

ii. Detection defects resolved within milliseconds or travel time to cross one heat source beam 

diameter. 
68 Appendix C: White Papers

Suggested Solution: Fast data acquisition for non-contact measurement techniques (e.g., Reflective 

topography [U.S. Patent #5,446,549], Spectroscopic characterization [Real time Cr 

measurement using optical emission spectroscopy during direct metal deposition process, IEEE 

Sensors Journal, (vol 12(5) 958-964, May 2012]) with fast feature extraction using machine 

learning algorithm. 

iii. Deploying measured data for process control. 

iv. Integrated simulation and measurement techniques for fabrication of “Designed Materials” with 

unique properties. 

3. ASTM 42 priorities 

i. All the constituent parameters for energy sources (e.g. Laser, Electron Beam) need to 

standardized. Some of it already exists. 

ii. All the constituent parameters for the powder and raw stocks need to be standardized. 

iii. AM-fabricated materials characterization for the certification process need to be standardized. 

4. R&D needs 

R&D needs are described in the “Suggested Solution” next to the scientific barriers. 
Measurement Science Roadmap for Additive Manufacturing 69

Measurement Science Barriers and R&D Opportunities for the 

NIST Metal-Based AM Workshop 

Tom Campbell, Virginia Tech University 

1. What are the key measurement science barriers that prevent innovation in metal-based AM? 

 Powder consistency (shape, properties, agglomerates, etc.) 

 Closed-loop metrology capability within additive manufacturing systems (temperature, pressure, 

raw materials remaining, etc.) 

 Post-build measurements (certifications, calibrations, etc.) 

2. What are the most important areas where R&D is needed—particularly in measurement and 

standards—to overcome these barriers and to accelerate innovation in metal-based AM? 

 Measure effects of aging (e.g., oxidation) of raw materials on process repeatability 

 Full life cycle measurement capabilities must be put in place to ensure consistent and usable metals 

products (see [1] above) 

 Funding increases from NIST, NSF, DOE, etc., to pay for metals measurements research in 

universities and corporate entities 
70 Appendix C: White Papers

Measurement Science Barriers and R&D Opportunities for the 

NIST Metal-Based AM Workshop 

Vito Gervasi, Director R&D, RP-Research, Milwaukee School of Engineering 

1. What are the key measurement science barriers that prevent innovation in metal-based AM? 

a) Detection and characterization of inconsistencies and defects at a reasonable resolution within each layer 

(and perhaps some number of layers combined) of AM parts. 

b) Detecting and predicting final geometry relative to CAD intent, real-time and/or at build completion. 

c) High-resolution metallurgical inspection of quality of metallic AM component using NDA methods on a 

layer-by-layer basis. 

d) Measurement and detection of “grown-in” stress within the component.

e) For Bi-metallic FGM’s the properties of the interphase are important to measure and characterize.

f) Highly complex components are difficult to test and evaluate (i.e., a complex optimized cellular structure). 

Also, statistically, with build history known, the load capabilities of an optimized cellular structure could be 

predicted. 

g) Non-destructive detection and measurement of anisotropic properties. 

h) Monitoring and controlling grain size and direction is critical for some AM metal applications. 

i) For some AM parts, due to variable density, there is some challenge associated with specifying where the 

part begins/ends. For example, intentional or unintentional porosity needs to be detected and characterized 

for some applications. Now, with “variable density steel” available, part inspection presents many new 

challenges not prevalent in wrought or cast materials. Each density region may need to be handled as a 

separate material. 

Note 1: Defects are often created in AM components due to the layer-wise build method of metallic parts. 

The defects occurring within a layer or between layers of AM components are sealed in by subsequent 

layers. One method/opportunity of detection may be real-time layer-by-layer quality inspection and image 

analysis for components. This information should be archived and tied to the specific part throughout its life 

cycle. 

Note 2: The AM community needs to be able to statistically predict the behavior of a component during its 

intended use (especially in critical applications such as aerospace or medicine). 

Note 3: Scanning during AM part growth has huge potential of detecting potentially high-risk parts before they 

are placed in service. This tracking combines with statistics can reduce the risk and liability of using AM-metal 

components. 
Measurement Science Roadmap for Additive Manufacturing 71

2. What are the most important areas where R&D is needed—particularly in measurement and standards—

to overcome these barriers and to accelerate innovation in metal-based AM? 

Challenges abound: 

i) Tools for real-time scanning and evaluation of AM parts during or after being grown. 

ii) Software tools to data-mine the enormous amounts of data from scans and imaging. 

iii) Harsh environment inspection. Inspection during builds will be challenging due to atm and 

temperatures. Optics will become metalized under vacuum or could become clouded depending on the 

process. Protective measures or robust scanners are needed. 

iv) Development of a standard or benchmark tool(s) to easily assess a range of capabilities of a process 

will be key and software to help designers recognize what is and isn’t possible will also be beneficial. 

There are direction-dependent opportunities/challenges that should not be ignored. 

v) Two phase materials present unique and strategically critical opportunities and challenges. The ability to 

use combinations of materials (intermingled in some manner) to realize the best properties of both 

materials in one component has great potential. These high-performance materials will require 

advanced inspection methods to verify the CAD intent was realized and to ensure material density and 

metallurgy is maintained within spec throughout. 
72 Appendix C: White Papers

Production and Use of Standard Parts and Our Lessons-learned 

Thus Far 

Constance J.S. Philips, Sr. Program Manager, RARE Parts Collaborative Program, National Center for 

Manufacturing Sciences 

The collaborative AM program at NCMS has been ongoing for 21 years, and for at least a decade we have used 

a standard part for assessment of AM machine-material capability. Historically, this part was only an assessment 

tool for a user’s determination of his machine’s feature-building capabilities using differing materials. The user’s 

knowledge was then used is his operations and in his AM part design or part redesign for AM. Our part design 

evolved over several years of trying other test part designs coming primarily from machine OEMs and was 

designed to be particularly challenging to any AM system. NASA Marshall Space Flight Center, Ken Cooper 

specifically, designed the NASA Benchmark Part to contain features encountered frequently in our work 

with DoD maintenance and repair depots and the challenges of legacy part replacement. We shared the stl file 

with several others over the years including NIST most recently. 

While our original use of the NASA Benchmark Part was to better understand the feature-building capabilities 

and limitations of the various AM machines and materials we use, we view the NASA Benchmark Part as 

potentially useful in generating a feature database eventually yielding Design Allowables. We are currently in the 

beginning stages of building parts with this hypothesis in mind. Before embarking on this adventure, we inquired 

of NIST if there was an overarching protocol in use in the conducting of such a study. NIST staff shared with us 

the files associated with the building and measurement of its Standard Part under study. Thus far in our study we 

find it necessary to establish a well-defined set of protocols and are only learning now to what degree their 

definition is needed. 

Control Needs: 

Build Documentation Protocol 

In an attempt to be able to attribute feature attributes and measurements to a process and to a specific material 

used, we anticipated that documentation of the build process would be needed. Our first attempt at specifying a 

build documentation sheet and the first article built and submitted for measurement revealed immediately the 

need for additional controls to be specified. Additional facets of the build needing definition and control are 

surely to emerge as we proceed. The latest Build Documentation Sheet is attached as Exhibit 1. You will note 

that the level of fidelity of data is low without having process monitoring and feedback available as an intrinsic 

part of the build processes. 

Feature Measurement and Documentation Protocols 

Anticipating that the measurement of specific features against the CAD file will be another learning process and 

will require additional protocols, our study is not placing the responsibility for these measurements and 

reporting on the Part’s builders at this time. It is our desire to utilize a third party having diverse measurement 

expertise, perhaps NIST itself, to devise a measurement protocol for the diverse set of features contained in the 

NASA Benchmark Part by actually applying the best measurement instrumentation available, measuring the 

features in each of our test articles, and then defining a protocol for the recording of that data. These combined 

protocols would then be available for use in future Part builds and provide the needed control to minimize the 

variations introduced into data via the measurement devices themselves and establish basic rules for 

measurement documentation. 

A picture of the NASA Benchmark Part CAD rendition is attached as Exhibit 2. 
Measurement Science Roadmap for Additive Manufacturing 73

Call for a Set of National Standard Protocols 

We believe if these NASA Benchmark Part protocols or protocols such as these were developed via an 

organization with the capabilities of NIST, we could realize a set of national standard protocols for the 

conducting of Part and feature studies in the future. We need to have the ability to replicate any such study. We 

believe basic tenets must be defined before embarking upon costly process, materials, and Part research and 

development. As users of these AM technologies, we want the best data possible, with the highest degree of 

reliability and repeatability possible within this constantly emerging AM landscape. Having a nationally adopted 

set of protocols would facilitate and contribute to not only the eventual definition of Design Allowables but also 

to the advancements developed by AM OEMs for machines, process controllers, operating systems, software, 

materials, inspection, and process monitoring systems. 
74 Appendix C: White Papers

Exhibit 1
Measurement Science Roadmap for Additive Manufacturing 75

Exhibit 2 

Measurement Science Roadmap for Additive Manufacturing 77

APPENDIX D: ACRONYMS/ABBREVIATIONS

ABAQUS engineering analysis and product simulation software tool 

AI artificial intelligence 

AM additive manufacturing 

AMC Additive Manufacturing Consortium 

ANSYS engineering analysis and product simulation software tool 

API application programming interface 

ASTM ASTM International (standards organization) 

ASTM F42 ASTM Committee F42 on Additive Manufacturing Technologies 

CAD computer aided design 

CMM coordinate measuring machine 

CPU computer processing unit 

CT computed tomography 

DARPA Defense Advanced Research Projects Agency 

DDM direct digital manufacturing 

DFAM design for additive manufacturing 

DM direct metal 

DMLS direct metal laser sintering 

DOC U.S. Department of Commerce 

DOE U.S. Department of Energy 

EB electron beam 

EBM electron beam melting 

EDS energy dispersive spectrometer 

FAA Federal Aviation Administration 

FDA Food and Drug Administration 

FGMs functionally graded materials 

GPUs graphical processing units 

HIP hot isostatic pressing 

HPC high performance computing 

ICME integrated computational materials engineering 

IP intellectual property 

IR infrared 

ISD NIST Intelligent Systems Division 

ISO International Organization for Standardization 
78 APPENDIX D: ACRONYMS

M&S modeling and simulation 

MHz megahertz 

MMCs metal matrix composites 

MSDS material safety data sheet 

MTBF mean time between failure 

NAMII National Additive Manufacturing Innovation Institute 

NASA National Aeronautics and Space Administration 

NAVAIR U.S. Navy, Naval Air Systems Command 

NCMS National Center for Manufacturing Sciences 

NDE non-destructive evaluation 

NDI non-destructive inspection 

NIH National Institutes of Health 

NIST National Institute of Standards and Technology 

NSF National Science Foundation 

OEE overall equipment efficiencies 

OEM original equipment manufacturers 

ONR Office of Naval Research 

OSHA Occupational Safety and Health Administration 

PM powder metal 

ppm parts per million 

R&D research and development 

RFID radio frequency identification 

RM rapid manufacturing 

ROI return-on-investment 

RP rapid prototyping 

SEM scanning electron microscope 

SLM selective laser melting 

SMEs small- and medium-sized enterprises 

SPC statistical process control 

STEM science, technology, engineering, and mathematics 

UL Underwriters Laboratory 

V&V validation and verification