ASTM E3353-22

Standard Guide for In-Process Monitoring Using Optical and Thermal Methods for Laser Powder Bed Fusion

SIGNIFICANCE AND USE
4.1 Metal additive manufacturing has broadened design space, enabling production of more complex and customized products. Additive technology along with the broadened design space is pushing the limits of inspection capabilities and has led to challenges in process and product qualification, verification, certification, etc. In-process monitoring technologies have been developed to help address these challenges.  
4.2 In-process monitoring in AM is emerging from the realm of Research and Development (R&D). As such, there are not yet well-established procedures for incorporating AM process monitoring within a qualification or certification framework outside of a specific company or institution’s internal use. Practical application of in-process monitoring data spans multiple disciplines and parts of the production cycle, each with well-established practices, terminology, expectations, etc. This guide draws on these where appropriate.  
4.3 Inspection and Statistical Process Control (SPC)—A primary motivation for using in-process monitoring technologies is to aid in process and product qualification, verification, certification of AM components that are increasingly difficult to inspect. AM process monitoring functions can be broadly separated into two categories of application: in-process inspection and process control. In-process inspection refers to the identification of in-process signatures that correlate to the formation of physical flaws and defects in additively manufactured component. This is discussed further in 5.2 on Flaw Detection. Statistical Process Control (SPC) encompasses measurement or observation of process signatures or metrics associated with the stability or repeatability of the additive manufacturing process. This is discussed further in 5.3 on Statistical Process Control (SPC). Real-time feed-forward or feed-back control methods and techniques may be considered subcategories under process control, and can make use of the same in-process moni...
SCOPE
1.1 This guide provides information on emerging in-process monitoring sensors, sensor configurations, sensor data analysis, and sensor data uses for the laser powder bed fusion additive manufacturing process.  
1.2 The sensors covered produce data related to and affected by feedstock, processing parameters, build atmosphere, microstructure, part geometry, part complexity, surface finish, and the printing equipment being used.  
1.3 The parts monitored by the sensors covered in this guide are used in aerospace applications; therefore, their final inspection requirements for discontinuities are different and more stringent than for materials and components used in non-aerospace applications.  
1.4 The metal materials under consideration include, but are not limited to, aluminum alloys, titanium alloys, nickel-based alloys, cobalt-chromium alloys, and stainless steels.  
1.5 This guide discusses sensor observation of parts while they are being fabricated. Sensor data analysis may take place concurrently or after the manufacturing process has concluded.  
1.6 The sensors discussed in this guide may be used by cognizant engineering organizations to detect both surface and volumetric flaws.  
1.7 The sensors discussed in this guide may be used by cognizant engineering organizations to detect process stability or drift, or both.  
1.8 The sensors discussed in this guide are primarily configured in staring, co-axial, or mounted configurations.  
1.9 This guide does not recommend a specific course of action, sensor type, or configuration for application of in-process monitoring to additively manufactured (AM) parts. It is intended to increase the awareness of emerging in-process sensors, sensor configurations, data analysis, and data usage.  
1.10 Recommendations about the control of input materials, process equipment calibration, manufacturing processes, and post-processing are beyond the scope of this guide and are und...

General Information

• Status: Published
• Publication Date: 30-Jun-2022
• ICS: 25.030 - Additive manufacturing
• Technical Committee: E07 - Nondestructive Testing
• Drafting Committee: E07.10 - Specialized NDT Methods

Relations

• Refers: ASTM E1316-24 - Standard Terminology for <?Pub Dtl?>Nondestructive Examinations
• Effective Date: 01-Feb-2024
• Refers: ASTM E1934-99a(2024) - Standard Guide for Examining Electrical and Mechanical Equipment with Infrared Thermography
• Effective Date: 01-Feb-2024
• Refers: ASTM E3166-20e1 - Standard Guide for Nondestructive Examination of Metal Additively Manufactured Aerospace Parts After Build
• Effective Date: 01-Feb-2020
• Refers: ASTM E1316-19b - Standard Terminology for Nondestructive Examinations
• Effective Date: 01-Dec-2019
• Refers: ASTM E1316-19 - Standard Terminology for Nondestructive Examinations
• Effective Date: 01-Mar-2019
• Refers: ASTM E1934-99a(2018) - Standard Guide for Examining Electrical and Mechanical Equipment with Infrared Thermography
• Effective Date: 01-Nov-2018
• Refers: ASTM E1316-18 - Standard Terminology for Nondestructive Examinations
• Effective Date: 01-Jan-2018
• Refers: ASTM E1316-17a - Standard Terminology for Nondestructive Examinations
• Effective Date: 15-Jun-2017
• Refers: ASTM E1316-17 - Standard Terminology for Nondestructive Examinations
• Effective Date: 01-Feb-2017
• Refers: ASTM E1316-16a - Standard Terminology for Nondestructive Examinations
• Effective Date: 01-Aug-2016
• Refers: ASTM E1316-16 - Standard Terminology for Nondestructive Examinations
• Effective Date: 01-Feb-2016
• Refers: ASTM E1316-15a - Standard Terminology for Nondestructive Examinations
• Effective Date: 01-Dec-2015
• Refers: ASTM E1316-15 - Standard Terminology for Nondestructive Examinations
• Effective Date: 01-Sep-2015
• Refers: ASTM E1256-15 - Standard Test Methods for Radiation Thermometers (Single Waveband Type)
• Effective Date: 01-Jul-2015
• Refers: ASTM E2587-15 - Standard Practice for Use of Control Charts in Statistical Process Control
• Effective Date: 01-Apr-2015

Overview

ASTM E3353-22, titled "Standard Guide for In-Process Monitoring Using Optical and Thermal Methods for Laser Powder Bed Fusion," is a critical standard developed by ASTM for the additive manufacturing (AM) industry. This document provides foundational guidance on using emerging sensor technologies for in-process monitoring within the laser powder bed fusion (LPBF) process. As metal additive manufacturing increases production complexity and customization, conventional inspection methods face notable limitations. In-process monitoring addresses these challenges by providing real-time data acquisition and analysis, thereby supporting quality assurance, process qualification, verification, and certification of intricate AM components.

This standard is particularly relevant for aerospace applications, where stringent requirements for flaw detection and product integrity are essential. While the guide does not prescribe specific tools or configurations, it highlights practical considerations for integrating optical and thermal sensors, as well as effective strategies for analyzing and utilizing process monitoring data.

Key Topics

• Sensor Technologies and Configurations
  • Overview of in-process monitoring sensors (optical, thermal)
  • Sensor configurations: staring, co-axial, and mounted setups
• Process and Quality Monitoring
  • Real-time identification of potential flaws and defects (e.g., porosity, lack of fusion, cracking)
  • Statistical Process Control (SPC) for monitoring process stability and repeatability
• Data Capture and Analysis
  • Practical methods for sensor data acquisition during fabrication
  • Strategies for data alignment, reduction, compression, and visualization
• Material Coverage
  • Applicable to various metals: aluminum alloys, titanium alloys, nickel-based alloys, cobalt-chromium alloys, stainless steels
• Practical Constraints
  • Focus on in-process monitoring; post-processing recommendations are excluded
  • Highlights the need for further standards regarding input material and post-manufacturing operations

Applications

In-process monitoring using optical and thermal methods in LPBF is applicable in several critical areas:

• Aerospace Manufacturing: Ensures the stringent quality standards required for aerospace parts by identifying surface and volumetric flaws as components are built, rather than relying solely on post-build inspections.
• Process Qualification and Certification: Supports manufacturers in qualifying and certifying intricate AM parts, even as complexity increases and traditional inspection reaches its limits.
• Statistical Process Control (SPC): Enables real-time tracking of process consistency, helping to detect process drift or instability before non-conforming products are produced.
• Quality Assurance of Complex Geometries: Provides a practical method to monitor intricate shapes and customized designs that are difficult to inspect with conventional techniques.
• Yield Improvement and Cost Reduction: Early detection of defects leads to reduced scrap rates and improved product yield, offering economic benefits especially for high-value products.

Related Standards

For comprehensive process and product control in additive manufacturing, ASTM E3353-22 should be used in conjunction with other relevant standards, including:

• ASTM E1316: Terminology for Nondestructive Examinations
• ASTM E3166: Guide for Nondestructive Examination of Metal Additively Manufactured Aerospace Parts After Build
• ISO/ASTM 52900: Terminology for Additive Manufacturing Technologies
• ISO/ASTM TR 52905: Guideline for Non-Destructive Testing and Evaluation of Defects in AM Parts
• EN 16714-2: Non-Destructive Testing - Thermographic Testing - Part 2: Equipment
• ASTM E2587: Practice for Use of Control Charts in Statistical Process Control

By referencing and applying ASTM E3353-22 along with these foundational documents, organizations can enhance their LPBF quality management and process monitoring strategies, ensuring compliance, reliability, and performance in advanced additive manufacturing environments.

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Keywords: ASTM E3353-22, in-process monitoring, laser powder bed fusion, additive manufacturing, LPBF, optical monitoring, thermal monitoring, process control, statistical process control, aerospace additive manufacturing, quality assurance, NDT, AM parts certification.

Standards Content (Sample)

This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the
Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
Designation: E3353 − 22
Standard Guide for
In-Process Monitoring Using Optical and Thermal Methods
for Laser Powder Bed Fusion
This standard is issued under the fixed designation E3353; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision.Anumber in parentheses indicates the year of last reapproval.A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope is intended to increase the awareness of emerging in-process
sensors, sensor configurations, data analysis, and data usage.
1.1 Thisguideprovidesinformationonemerging in-process
monitoringsensors,sensorconfigurations,sensordataanalysis, 1.10 Recommendationsaboutthecontrolofinputmaterials,
process equipment calibration, manufacturing processes, and
and sensor data uses for the laser powder bed fusion additive
manufacturing process. post-processing are beyond the scope of this guide and are
under the jurisdiction of ASTM Committee F42 on Additive
1.2 Thesensorscoveredproducedatarelatedtoandaffected
Manufacturing Technologies. Standards under the jurisdiction
by feedstock, processing parameters, build atmosphere,
ofASTM F42 or equivalent are followed whenever possible to
microstructure, part geometry, part complexity, surface finish,
ensure reproducible parts suitable for NDT are made.
and the printing equipment being used.
1.11 Recommendations about the inspection requirements
1.3 Thepartsmonitoredbythesensorscoveredinthisguide
and management of fracture critical AM parts are beyond the
areusedinaerospaceapplications;therefore,theirfinalinspec-
scope of this guide. Recommendations on fatigue, fracture
tion requirements for discontinuities are different and more
mechanics, and fracture control are found in appropriate end
stringent than for materials and components used in non-
user requirements documents, and in standards under the
aerospace applications.
jurisdictionofASTMCommitteeE08onFatigueandFracture.
1.4 Themetalmaterialsunderconsiderationinclude,butare
NOTE 1—To determine the deformation and fatigue properties of metal
not limited to, aluminum alloys, titanium alloys, nickel-based
parts made by additive manufacturing using destructive tests, consult
alloys, cobalt-chromium alloys, and stainless steels.
Guide F3122.
NOTE 2—To quantify the risks associated with fracture critical AM
1.5 This guide discusses sensor observation of parts while
parts, it is incumbent upon the structural assessment community, such as
they are being fabricated. Sensor data analysis may take place
ASTM Committee E08 on Fatigue and Fracture, to define critical initial
concurrentlyorafterthemanufacturingprocesshasconcluded.
flaw sizes (CIFS) for the part to define the objectives of the NDT.
1.6 The sensors discussed in this guide may be used by 1.12 This guide does not specify accept-reject criteria used
cognizant engineering organizations to detect both surface and
in procurement or as a means for approval of AM parts for
volumetric flaws. service.Anyaccept-rejectcriteriaaregivensolelyforpurposes
of illustration and comparison.
1.7 The sensors discussed in this guide may be used by
cognizant engineering organizations to detect process stability 1.13 Units—The values stated in SI units are to be regarded
or drift, or both.
as the standard. No other units of measurement are included in
this standard.
1.8 The sensors discussed in this guide are primarily con-
1.14 This standard does not purport to address all of the
figured in staring, co-axial, or mounted configurations.
safety concerns, if any, associated with its use. It is the
1.9 This guide does not recommend a specific course of
responsibility of the user of this standard to establish appro-
action, sensor type, or configuration for application of in-
priate safety, health, and environmental practices and deter-
process monitoring to additively manufactured (AM) parts. It
mine the applicability of regulatory limitations prior to use.
1.15 This international standard was developed in accor-
dance with internationally recognized principles on standard-
This guide is under the jurisdiction ofASTM Committee E07 on Nondestruc-
ization established in the Decision on Principles for the
tiveTesting and is the direct responsibility of Subcommittee E07.10 on Specialized
Development of International Standards, Guides and Recom-
NDT Methods.
mendations issued by the World Trade Organization Technical
Current edition approved July 1, 2022. Published September 2022. DOI:
10.1520/E3353-22. Barriers to Trade (TBT) Committee.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
E3353 − 22
2. Referenced Documents Distortion (GD) Measurements
2 ISO/IEC15775InformationTechnology—OfficeMachines
2.1 ASTM Standards:
— Method of Specifying Image Reproduction of Colour
E1213Practice for Minimum Resolvable Temperature Dif-
Copying Machines by Analog Test Charts — Realisation
ference for Thermal Imaging Systems
and Application
E1256Test Methods for Radiation Thermometers (Single
ISO/TC 108/SC 5Condition Monitoring and Diagnostics of
Waveband Type)
Machine Systems
E1316Terminology for Nondestructive Examinations
2.4 EN Document:
E1543PracticeforNoiseEquivalentTemperatureDifference
EN 16714-2Non-destructive Testing - Thermographic Test-
of Thermal Imaging Systems
ing - Part 2: Equipment
E1934Guide for Examining Electrical and Mechanical
Equipment with Infrared Thermography
2.5 MIL Document:
E2582Practice for Infrared Flash Thermography of Com- MIL-STD-150APhotographic Lenses (12 May 1959)
posite Panels and Repair Patches Used in Aerospace
3. Terminology
Applications
E2587Practice for Use of Control Charts in Statistical
3.1 Order of Precedence—In order of precedence, the fol-
Process Control
lowing terminologies apply:
E2862Practice for Probability of Detection Analysis for
3.1.1 For terminology related to general NDTpractices, use
Hit/Miss Data
Terminology E1316.
E3023Practice for Probability of Detection Analysis for â
3.1.2 For terminology related to NDT of metal additively
Versus a Data
manufactured parts, use Guide E3166.
E3045Practice for Crack Detection Using Vibroacoustic
3.1.3 For terminology related to AM, use ISO/ASTM Ter-
Thermography
minology 52900.
E3166Guide for Nondestructive Examination of Metal Ad-
3.2 Definitions:
ditively Manufactured Aerospace Parts After Build
3.2.1 build chamber, n—see terminology ISO/ASTM
F3122Guide for Evaluating Mechanical Properties of Metal
52900.
Materials Made via Additive Manufacturing Processes
3.2.2 co-axial configuration, n—a sensor integrated within
2.2 ISO/ASTM Standards:
theopticalpathofthelaser,suchthatthesensor’sfieldofview
ISO/ASTM 52900Terminology forAdditive Manufacturing
is fixed to the moving position of the laser (or other heating
Technologies
sources considered in future versions of this guide).
ISO/ASTM 52921 Terminology for Additive
3.2.2.1 Discussion—Co-axialconfigurationisalsoknownas
Manufacturing—Coordinate Systems and Test Method-
on-axis, down-beam,or on-axial configuration. Refer to Sec-
ologies
tion 7 on Melt Pool Monitoring.
ISO/ASTMTR 52905Additive Manufacturing of Metals —
Non-destructive Testing and Evaluation — Defect Detec- 3.2.3 defect, n—see Terminology E1316.
tion in Parts
3.2.4 flaw, n—see Terminology E1316.
2.3 ISO Standards:
3.2.5 flaw characterization, n—see Terminology E1316.
ISO 10878Non-Destructive Testing - Infrared Thermogra-
3.2.6 indication, n—see Terminology E1316.
phy – Vocabulary
3.2.7 lack of fusion (LOF), n—see Guide E3166.
ISO 11146-2Lasers and Laser-related Equipment — Test
3.2.7.1 Discussion—LOF-induced void formation can be
Methods for Laser Beam Widths, DivergenceAngles and
sub-categorized based on their formation directionality: as
Beam Propagation Ratios — Part 2: General Astigmatic
horizontal LOF, in which adjacent scan tracks within the same
Beams
layer have insufficiently melted or fused together, forming a
ISO 12233Photography — Electronic Still Picture Imaging
void, and vertical LOF, in which scan tracks in a new layer do
— Resolution and Spatial Frequency Responses
not fully melt or fuse previous or lower layers.
ISO 13372Condition Monitoring and Diagnostics of Ma-
chines — Vocabulary
3.2.8 layer imaging, n—a process monitoring technology
ISO 17359Condition Monitoring and Diagnostics of Ma-
applied to powder bed fusion where images are captured and
chines — General Guidelines
recorded of the build layers before or after, or both, laser
ISO 17850Photography — Digital Cameras — Geometric
exposure, powder spreading, or material consolidation.
3.2.8.1 Discussion—Layer imaging utilizes one or more
cameras in staring configuration.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
3.2.9 melt pool mode, n—a characteristic of the melt pool
contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM
Standards volume information, refer to the standard’s Document Summary page on size, shape, and dynamic behavior, primarily dependent on the
the ASTM website.
input laser energy density.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
contactASTM Customer Service at [e-mail].
Available from International Organization for Standardization (ISO), ISO
Central Secretariat, Chemin de Blandonnet 8, CP 401, 1214 Vernier, Geneva, Available from www.en-standard.eu.
Switzerland, https://www.iso.org. Available from http://everyspec.com.
E3353 − 22
3.2.9.1 Discussion—Melt pool modes include conduction 3.3.1.13 SPC—Statistical Process Control
mode and keyhole mode. Refer to 7.2.2.
3.3.1.14 TFOV—Total Field of View
3.2.10 melt pool monitoring, n—the continuous measure-
4. Significance and Use
ment of process signatures associated with perturbations,
anomalies, or trends stemming from the laser (or other heat
4.1 Metal additive manufacturing has broadened design
source) induced molten metal pool.
space, enabling production of more complex and customized
3.2.11 porosity (keyhole), n—see Guide E3166. products.Additivetechnologyalongwiththebroadeneddesign
3.2.11.1 Discussion—This guide differentiates from contex- space is pushing the limits of inspection capabilities and has
tual use in Guide E3166 in that it considers the in-process led to challenges in process and product qualification,
keyhole pore formation (as opposed to post-build inspection in verification, certification, etc. In-process monitoring technolo-
Guide E3166). Keyhole porosity is created when the laser gies have been developed to help address these challenges.
energy density is sufficiently high to cause a deep melt pool
4.2 In-process monitoring in AM is emerging from the
resulting in hydrodynamic instability of the surrounding liquid
realmofResearchandDevelopment(R&D).Assuch,thereare
metalandsubsequentcollapse,leavingavoidattherootofthe
not yet well-established procedures for incorporating AM
keyhole. Like generic voids and gas porosity, keyhole porosity
process monitoring within a qualification or certification
causes a part to be less than fully dense.
framework outside of a specific company or institution’s
3.2.12 process signature, n—potentiallyobservablephysical
internaluse.Practicalapplicationofin-processmonitoringdata
phenomenonthatoccursduringtheAMfabricationprocessand
spans multiple disciplines and parts of the production cycle,
is potentially correlatable to part quality metrics.
each with well-established practices, terminology,
expectations,etc.Thisguidedrawsonthesewhereappropriate.
3.2.13 self-healing, n—phenomena where potential defects
are re-melted duringAM fabrication which thus eliminates the
4.3 Inspection and Statistical Process Control (SPC)—A
defect’s existence in the final part.
primary motivation for using in-process monitoring technolo-
3.2.14 solidification crack, n—also known as hot crack, gies is to aid in process and product qualification, verification,
cracks that initiate when rapid cooling at the fusion boundary certification of AM components that are increasingly difficult
of a melt pool causes high thermal strain and separation of to inspect. AM process monitoring functions can be broadly
material that is not adequately filled by molten material. separatedintotwocategoriesofapplication:in-processinspec-
tion and process control. In-process inspection refers to the
3.2.15 spatter, n—particlesejectedawayfromthevicinityof
identification of in-process signatures that correlate to the
a melt pool.
formation of physical flaws and defects in additively manufac-
3.2.15.1 Discussion—Also known as ejecta, spatter or
tured component. This is discussed further in 5.2 on Flaw
ejecta can originate from within the melt pool or from
Detection.StatisticalProcessControl(SPC)encompassesmea-
surrounding powder. It is caused by multiple different physical
surement or observation of process signatures or metrics
phenomena, but can generally be sub-categorized into hot
associated with the stability or repeatability of the additive
spatter or cold/cool spatter. Refer to 4.7.3.1(1).
manufacturing process. This is discussed further in 5.3 on
3.2.16 staring configuration, n—a type of sensor configura-
Statistical Process Control (SPC). Real-time feed-forward or
tionwhereinanon-contactsensorismountedwithinoroutside
feed-back control methods and techniques may be considered
the build chamber, such that its field of view is fixed with
subcategories under process control, and can make use of the
respect to the machine coordinates (see ISO/ASTM 52921).
same in-process monitoring measurement tools. Currently,
3.2.16.1 Discussion—Also known as fixed position, lateral
these concepts and techniques are still largely under research
configuration, off-axial configuration,or paraxial configura-
and development not generally implemented in commercial
tion.
LPBF systems. They are not discussed further in this guide.
3.2.17 voids, n—see Guide E3166.
4.4 Production and Development Uses—Production of fin-
3.3 Abbreviations:
ished components using additive manufacturing requires some
3.3.1 Thefollowingabbreviationsareadoptedinthisguide:
combination of inspection to ensure the component meets
3.3.1.1 CCD—Charge-coupled Device
design requirements for the ultimate product functionality and
3.3.1.2 CMOS—Complimentary Metal-Oxide Semiconduc-
process qualification. Both inspection and process control
tor
applicationsofin-processmonitoringmaybeintegratedintoan
3.3.1.3 CT—Computed Tomography
overall product or process qualification, verification, or certi-
3.3.1.4 FOV—Field of View
fication strategy, or a combination thereof, in the production
3.3.1.5 IFOV—Instantaneous Field of View
environment. In-process monitoring tools are also valuable in
3.3.1.6 LED—Light Emitting Diode
the development both of the additive process and build design,
3.3.1.7 LOF—Lack of Fusion
providing support for engineering decisions on parameter
3.3.1.8 LPBF—Laser Powder Bed Fusion
selection (for example, laser power, scan speed) for new
3.3.1.9 MPM—Melt Pool Monitoring
materials, scan strategy, part geometry, part placement on an
3.3.1.10 PBF—Powder Bed Fusion AM build platform, etc. A prerequisite to SPC is establishing
3.3.1.11 R&D—Research and Development
the normal variation of the process which can be evaluated
3.3.1.12 SFT—Spatial Frequency Response usingin-processmonitoringtoolsduringprocessdevelopment.
E3353 − 22
4.5 Economic Justification—In-process monitoring can be correlatedtoprocessparameters.Whileprocessparametersare
economically justified through its contribution to cost reduc- generally commanded or set point values, process signatures
tion and yield improvements in addition to its value to the
provide a measured voice of process. Process signatures may
additive manufacturing enterprise as an element of an overall
alsobecorrelatedtopartqualitymetrics,asshowninFig.1.As
process or product qualification, verification, or certification
part of a product inspection and validation strategy, in-process
strategy, or a combination thereof. For high value products,
monitoring aims to utilize the correlation between these
in-process monitoring has been shown to reduce the scrap
process signatures and part quality metrics. In-process moni-
fraction rate by at least 10 % according to recent literature.
toring can thus be used to in conjunction with or in-lieu of
The realization of the cost/part reduction in the scrap fraction
post-process inspection methods (for example, NDE).
rate over time is dependent on the diagnostic capability of the
4.6.1 Process Signature Taxonomy—Many different terms
in-process monitoring strategy as measured in false alarm
have been used in AM to describe process signatures or part
(false positive) and undetected defect (false negative) perfor-
quality metrics in the context of in-process monitoring (for
mance. Further in-process monitoring can produce per part
example, defect, fault, flaw, anomaly, imperfection, etc.). The
cumulative yield improvements through enabling process en-
followingprovidesahigh-leveltaxonomyusedinthisguideto
gineeringdiagnosiscapabilitieswithinpartmanufacturingsuch
further define and categorize deleterious process signatures in
that SPC charts can be tuned to optimize the system’s diag-
AMprocessmonitoring.Asnotedin4.3,in-processmonitoring
nostic performance.
is primarily used as part of an overall quality plan, either as a
4.6 Identifying Part Quality from Process Signatures—
supplement to or replacement of traditional component inspec-
Ultimately,finalpartqualitymetricsandassociatedmechanical
tion methods (for example, NDE) or to enable statistical
or functional performance ofAM parts are of greatest concern.
process control. These two functions are mapped to corre-
Guide E3166, pertaining to ex-situ NDT, identifies two corre-
sponding taxonomies are mapped in Fig. 2.
lations of interest: process-flaw correlation and flaw-property
4.6.2 For the in-process enabled inspection case, this tax-
correlation. In the context of this guide, measurements of
onomy builds upon established standards or work items (see
materialflawsorpropertiesareconsidered part quality metrics.
Terminology E1316, Guide E3166, and ISO/ASTM TR
As noted in Guide E3166, part quality metrics may be
52905).
correlated to the process or process parameters, such as laser
(1) Indication (Terminology E1316): In an in-process en-
power, laser scan speed, etc. as shown in Fig. 1. In-process
abled inspection, a process signature observed from the in-
monitoring pertains to the observation and measurement of
processmonitoringdatathatisevidenceofapotentialmaterial
process signatures,orobservablephenomenathatoccurduring
flaw is deemed an indication (Terminology E1316). As in
theAM process, for example, electromagnetic emissions from
traditional NDE, the indication is subject to interpretation as a
the melt pool, acoustic emissions, etc. Process signatures are
false indication, nonrelevant indication,or relevant indication
(Terminology E1316). A relevant indication (Terminology
7 E1316) is indicative of a material flaw and requires further
Colosimo, B. M., Cavalli, S. and Grasso, M. “A cost model for the economic
evaluation of in-process monitoring tools in metal additive manufacturing,” Inter- evaluationastowhethertheflawisacceptableorthepartmust
national Journal of Production Economics, Vol 223, 2020, 107532, ISSN 0925-
be rejected based on the requirements of the component.
5273.
FIG. 1 General Schematic of AM In-process Monitoring High-level Objectives for Inspection to Identify the Correlations, Through Ana-
lytical or Numerical Methods, that Relate Process Signatures to Part Quality Metrics and Utilize These as Part of a Broader Inspection
or Part Validation Strategy
E3353 − 22
FIG. 2 Description of Higher-level Terms Relating an Observation of Process Signatures From In-process Monitoring for Inspection and
Statistical Process Control (SPC) use Cases
(2) Flaw (Terminology E1316): A flaw is an imperfection defects, but is meant as a guide to better understand how the
or discontinuity, the formation of which may be detectible by mostcommonlyobservedorunderstoodflawsanddefectsmay
in-process monitoring, but is not necessarily rejectable. relate to in-process monitoring. Additional details regarding
(3) Defect(TerminologyE1316):Oneormoreflawswhose in-processdefectandflawformationareprovidedinregardsto
aggregatesize,shape,orientation,location,orpropertiesdonot each measurement system modality discussed starting in Sec-
meet specified acceptance criteria and are rejectable. tion 7.
4.6.3 Statistical process control (SPC) uses statistical meth-
4.7.1 Stochastic versus Systemic Defect Formation—
ods to improve quality by reducing the variability of one or
Systematic defects are voids resulting from input processing
more process outputs. For in-process monitoring enabled
parameters and build plan. In contrast, stochastic flaws result
statistical process control, one or more process signatures are
from conditions that are not systematically controlled (that is,
the outputs of the process to which SPC is applied. Process
areaconsequenceofrandomorstatisticalprocesses),asshown
variation may be classified in one of two categories, common
in Fig. 3.
cause variation or special cause variation.
4.7.2 In-process Defects:
(1) Common Cause Variation (Practice E2587), also re-
4.7.2.1 Void Formation—The term voids (voids in Guide
ferred to as chance variation, is inherent random variation in
E3166, or synonymous with discontinuity in Terminology
the process which is predictable within statistical limits. An
E1316)includesanymaterialdiscontinuitywithinapartthatis
additive manufacturing process may be said to be in a state of
not a designed feature. This includes pores and cracks. While
statistical control when only common cause variation is
the methods of formation of voids is not always discernible in
observed (Practice E2587).
post-process inspection, their formation and corresponding
(2) Special Cause Variation(PracticeE2587),alsoreferred
signatures may be observable and distinguishable via in-
to as assignable cause variation, associated with a process
process monitoring.
disturbanceorupset.Specialcausevariationmaybeassociated
(1) Pores (Guide E3166)—Pores are material discontinui-
with a spike, shift, trend, or change in variability of the
ties that are distinguishable from cracks, but may similarly act
in-process signal.
as stress concentration or crack initiation sites. Cracks, viewed
4.7 Additive Manufacturing Flaws and Flaw Formation in 2D, are a discontinuity with an extremely low aspect-ratios.
Mechanisms—Understandinghowin-processflawsanddefects Pores and cracks may be surface-connected. In the context of
formduringfabricationiscriticaltotheinstrumentdesign,data this guide, pores are further sub-categorized from description
analysis or interpretation, and general application of AM in Guide E3166 based on their formation mechanisms and
in-process-monitoring. The following describe flaws that may potential signatures:
exhibit in-process, and may be targeted for observation by (a) Keyhole Porosity (Guide E3166 and ISO/ASTM TR
in-process monitoring instruments. The following is not a 52905)—Keyhole porosity is related to instability in the liquid
comprehensive list or categorization of in-process flaws or melt pool, and typically occurs under relatively high laser
E3353 − 22
NOTE 1—Reprinted from Additive Manufacturing, Vol 36, Snow, Z., Nassar,A. R., and Reutzel, E. W., “Review of the formation and impact of flaws
in powder bed fusion additive manufacturing,” 2020, 101457, https://doi.org/10.1016/j.addma.2020.101457, with permission from Elsevier.
FIG. 3 Example Organization and Categorization of Some Flaws Observable in a Laser Powder Bed Fusion (LPBF) Process, Catego-
rized by ’Systematic’ or ’Stochastic’ Formation
energy density (7.2.2). Observation of keyhole porosity gener- (4) Hatch-contour Overlap and Short-hatch Flaw—Ahori-
ally requires melt pool monitoring to capture a keyhole event, zontal LOF stemming from incomplete melting and wetting at
or related melt pool signature (7.2.2). This can be generally the intersection of a contour and infill laser scan tracks.
(but not directly) related to observation of a deeper, wider, or 4.7.2.2 Cracking:
brighter melt pool. Individual keyhole pores are roughly an (1) Delamination Cracking—Delamination occurs when
order of magnitude smaller than the melt pool, or approxi- layers within anAM build separate from one another forming
mately the scale of typical LPBF powder (for example, 10’s of acavityorcrack,oftenduetoexcessiveresidualstressbuildup
µm). Specific instrument design criteria, and statistical corre- during fabrication in conjunction with poor design of the part
lationbetweenin-processmonitoringobservationsandkeyhole or support materials, or both, or selection of appropriate AM
pore formation are still a matter of research and development. build parameters. This most often occurs at the interface
(b) Gas Porosity (Guide E3166)—Gas porosity, thought between a solid part structure and support structure, support
to result from gas entrapped within a powder particle during and substrate, or the solid part and substrate. During AM
manufacturing of the powder or interstitial gases released due fabrication,delaminationcrackingmaybeobservedasincreas-
to reduced solubility upon solidification, is generally not ingelevationofthepartabovenewpowdersurface,oracoustic
considered to be observable via current in-process monitoring signatures that occur during cracking events.
techniques, since the pores are incorporated into the powder (2) Solidification Cracking (or Hot Cracking)
material and do not typically reach the surface. —Solidification-cracking occurs when rapid cooling at the
(2) Lack of Fusion (LOF) (Guide E3166 and ISO/ASTM fusion boundary of a melt pool causes high thermal strain and
TR 52905)—LOF pore formation can be subcategorized as separation of material that is not adequately filled by molten
eitherhorizontalLOForverticalLOF(ISO/ASTMTR52905). material. Solidification cracks may occur during solidification,
Generally, only horizontal LOF pores or events are observable or very shortly after, and can be enlarged or exacerbated by
on the top surface of the fabricated layer via in-process subsequent heating and cooling cycles. Certain materials are
monitoring. However, observation of multiple LOF events more susceptible to hot cracking than others, and various filler
within the same region over multiple layers may be indicative materials may be introduced to the alloy to reduce susceptibil-
of formation of vertical LOF pores. ity.Combinationofprocessparameters,andtheireffectonmelt
(3) Hatching LOF—A horizontal LOF stemming from pool shape and resultant thermal gradients in and around the
incomplete melting and wetting of adjacent scan tracks. melt pool, can contribute to the likelihood of solidification
E3353 − 22
cracking. Solidification cracks may be observable via acoustic may be targeted by process monitoring instruments (Fig. 4)as
signatures, but are generally too small and occur for indication an indication of flaw or defect formation, or deleterious
via optical means. fabrication quality.
4.7.3 In-process Flaws: 4.7.3.2 Powder Layer or Recoating Flaws—Improperappli-
cation of metal powder layers during LPBF fabrication can
4.7.3.1 Overheating, Overmelting, or Thermal
result in part defects.Anumber of in-process flaws associated
Heterogeneity—Due to the dynamically moving heat sources
with insufficient or improper powder layer formation are
used duringAM processing, some regions of a fabricated part
known, and are generally easily observed and interpreted.
can experience excessive heat accumulation and elevated
Generally, the source of these flaws can be categorized as
temperatures relative to the rest of the part volume. This can
stemming from the erroneous recoating process (for example,
generally be attributed to one or two factors: (1) combination
skipping, scraping, insuffıcient powder delivery, part strikes),
of scan-strategy and layer geometry which causes excessive
part formation errors (distortion, humping, balling,or superel-
laser exposure over a confined area within the layer (Fig. 4);
evation). While many of these flaws may be observable
(2) laser exposure over a confined region, where the relatively
through multiple process monitoring modalities, they are
low thermal conductivity of the surrounding powder inhibits
primarily observed through Layer Imaging processes. Refer to
conductionofheatawayfromthemeltpool.Localoverheating
Section 8 on Layer Imaging for detailed description of powder
can be observed via several process signatures: (1) Increased
layer flaws.
size, temperature, or brightness of a melt pool (see 7.2.5 on
4.7.4 Speed, Resolution, and Data Considerations—Speed,
Melt pool ‘intensity’); (2) discoloration or ‘scorching’ of the
resolution, and data considerations specific to each sensor
overheated region, and (3) humping, elevation, abnormally
modalitywillbediscussedstartinginSection7.Generally,data
smooth/fluid, or generally different surface structure and to-
rate and storage requirements for process monitoring are
pography in the overheated region (see Section 8 on Layer
relatively high, which largely stems from the multi-scale
Imaging).
(1) Excessive Spatter/Ejecta—At the LPBF melt pool physics of the AM fabrication process, and the necessity to
adequately resolve signatures spatially or temporally.
scale, many particles can be observed escaping (or ejected)
from the vicinity of the melt. These particles initiate from 4.7.4.1 For example, assume a typical 250 mm x 250 mm
several phenomena. Melt ejection occurs when evaporation- build area, divided into 0.1 mm x 0.1 mm pixels (2500
induced recoil pressure exceeds the surface tension pressure pixels/layer).Assume a 200 mm build height divided into 0.02
withinthemeltpool,causingmoltendropletstoescape.Spatter mm layers (10000 layers/build). This results in 2500 pixels/
particles also result from powder particle entrainment within layer × 10000 layers/build × 1 byte/pixel = 62.5 GB/build.
the evaporation-induced gas flow. Hot spatter particles are Similarly, in the temporal domain, consider a sensor acquiring
formed due to laser- or vapor-induced heating of entrained data at 100 kHz, over a 36h build. This results in a 10
particles. Relatively frequent, intense, or excessive hot spatter samples/s × 129600 s/build × 1 bytes/sample results in
NOTE 1—Barfoot, M. (2020). Evaluation of In-Situ Monitoring Techniques (Additive Manufacturing Consortium (AMC) Project Final Report, EWI
Project No. 58279CPQ).
FIG. 4 Example From Staring-configuration, Near-infrared (NIR) Spectrum Melt Pool Monitoring Camera. This System Compiles Images
from Multiple Camera Exposures and Processes Them Into a Single Image. Left: Image Data Based on ‘Integrated’ Values, Which High-
light Thermal Heterogeneity Features. Right: Image Data Based on ‘Maximum’ Value, Which Highlight Spatter or Plume Features
E3353 − 22
approximately 13 GB/build. These values are only given as 4.7.6.2 In this manner, the geometric location of those
typical examples, but indicate the relative volume of data that process signatures that may indicate an in-process flaw or
might be expected to be on the order of 10’s of GB per sensor defect can potentially be aligned and correlated to the same
per build. flaw or defect observed via ex-situ methods (for example,
4.7.5 Data Reduction or Compression—Most often, in- X-ray computed tomography (XCT)). For example, see Fig. 6.
process monitoring data size is reduced either in-line during 4.7.6.3 Alignment of in-process measured process signa-
acquisition, or just prior to storage, so that the raw instrument tures with part geometry requires additional measurements to
values are not transferred or stored.This is done by processing obtain information that relates the positioning of the sensor’s
the data into a reduced-dimension parameter (for example, field of view or sensing area to a coordinate system shared by
obtaining a single-value measurand from a 2D image), reduc- the machine or parts. For further description of some of the
ing the indicated or represented resolution (for example, measurement references, refer toASTM subcommittee F42.08
averaging or ‘binning’ pixels in an image), removing unnec- for proposed standards on data alignment and registration.
essarydata(forexample,darkorsaturatedpixelsinanimage), Some examples of accessory measurements for data alignment
employing data compression algorithms (lossy or loss-less), or or registration are as follows:
employing other data reduction methods. (1) Simultaneous Acquisition of Laser/Galvo Position ver-
4.7.6 Data Alignment or Registration—Data alignment, sus Time—Many commercial process monitoring systems
registration, and visualization considerations specific to each enable synchronized acquisition of the laser scan position via
sensor modality will be discussed in Sections7–9. Refer to the galvanometer (galvo) system in parallel with the process
subcommittee ASTM F42.08 for proposed standards on data monitoring instruments. This is done either by reading the
alignment and registration. digital commands (for example, XY2-100 or SL2-100 digital
4.7.6.1 Visualization of in-process monitoring data is typi- command protocol) sent to the galvanometer, or reading galvo
callyrepresentedinthespatialdomain,suchthatsensorsignals feedbackencodersignal,ifavailable.Alignmentorregistration
orprocesssignaturesderivedfromthosesignalsaremappedto of process monitoring instrument signals or images is done by
the spatial position within the 3D part when or where, or both, directly mapping the sensor signal to the synchronized spatial
they were acquired (Fig. 5). Most often, this is represented in location (for example, XY position) where it was obtained
three ways: (1) 3D part representation, where signatures or from the galvo position. This method is widely used for
features are mapped to the 3D location within a part, forming co-axial instrument configurations (for example, melt pool
digital representation of the part(s), but constructed from monitoring, Section 7), or single-element detectors that do not
process monitoring data; (2) 2D layer representation, where provide spatial information (for example, staring configuration
thedataismappedtoaplanenominallycommensuratewithan photodetector or mounted acoustic sensor).
AM fabrication layer (normal to the build direction); or (3) 2D (2) Reference Scan Pattern—Particularly for staring con-
slice representation, where values or data from a 3D part figurationinstrumentsinaLPBFsystem,areferencepatternor
representation are projected onto a planar slice that is oriented grid with known geometry can be scanned on a bare substrate,
in a direction different than the 2D layer representation. initial layers within a build, or during intermediate layers
FIG. 5 Example Registration of 1D Process Monitoring Data (Signal versus Time from Melt Pool Monitoring (MPM) Photodetectors in
Co-axial Configuration) into 3D Representation, Which Can Then be Projected onto Different Planar Slices (a) 2D Layer Representation
(XY Plane), (b) 2D Slice Representation (YZ Plane), (c) 2D Slice Representation (XZ Plane), (d) 3D Part Representation (Orthographic
Projection), Showing Location of the 2D Slice Locations
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NOTE 1—Barfoot, M. (2020). Evaluation of In-Situ Monitoring Techniques (Additive Manufacturing Consortium (AMC) Project Final Report, EWI
Project No. 58279CPQ).
FIG. 6 Example Local Anomaly Observed in Co-axial Configuration, Photodetector-based Melt Pool Monitoring (Left), and Correspond-
ing Observation of a Pore Defect (Right) from XCT of the Fabricated Part
within a build. Measurement via the process monitoring process monitoring, staring configuration, and co-axial
sensors may be conducted synchronously with the scan, or configuration, are shown in Fig. 7.
immediately after completion. Dimensions of the reference 4.8.1.1 Staring Configuration, also known as ‘offline’ or
pattern may be known from the commanded reference pattern ‘fixed position’configuration. This is a non-contact configura-
geometry programmed into theAM machine controller, or via tion where the sensor is placed in a fixed position with respect
ex-situmeasurementbyacalibrateddimensionalmeasurement to the build plane or machine coordinate system (see ISO/
(for example, calipers, optical CMM). Signal or images ac- ASTM 52921). A staring configuration sensor can be fixed
quired from the process monitoring instruments may then be either inside or outside the controlled-environment (build)
mapped or transformed into the coordinates acquired via the chamber. This configuration is typical with single-point
measured reference scan pattern. pyrometer, camera or thermal imager, etc.
(3) Reference Target—Similar to the scanned reference 4.8.1.2 Co-axial Configuration, also known as ‘on-axis’ or
pattern, a calibrated dimensional target or artifact may be ‘inline’. This is a non-contact configuration especially suited
placed in the field of view or sensing area of the process foropticalorradiometricsensors,wherethesensorismounted
monitoringinstrument(s).Forexample,animagermayobserve in an optical path shared by the laser heat source. The field of
a dimensional calibration artifact that has been oriented with view of the sensor is then fixed to the moving reference frame
8). An addi-
the machine or part coordinate system (Section of the laser spot and moves in the same scan trajectories of the
tional step may be necessary to reference the position of the laser throughout the fabrication process.This effectively keeps
artifact with respect to the machine or part coordinates. the melt pool stationary within the sensor field of view.
Examplesensorsincludefilteredradiometers,spectrometers,or
4.8 AM Process Monitoring Modalities—In the context of
high-speed cameras.
this guide, modality describes a group of similar process
4.8.1.3 Other Configurations—A variety of other physical
monitoring technologies, grouped based on similar attributes
instrument configurations can exist that may be unique,
regarding the measured object(s) or phenomena of interest, or
specialized, or not easily described by the aforementioned
the types of measurement instruments employed. In-depth
configurations. For example, an acoustic microphone may be
discussion of different modalities are discussed beginning in
suspended within the build chamber, or an oxygen sensor set
Section 7. Different modalities may be sub-categorized or
withintheinertgasrecirculationsystem(forexample,machine
grouped in different ways. An additional important descriptor
condition monitoring, Section 9).
forprocessmonitoringtechniquesisthe physical configuration
of the sensor(s).
5. Basis of Application
4.8.1 Physical Configurations—Process monitoring sensors
of various types can be fixed to stationary locations onto or 5.1 Rationale for application of AM in-process monitoring
within theAM machine. The same type of sensor can be fixed is varied, and depends on the products being developed and
into different configurations, which will change the position, their qualification or certification requirements (see 4.1 pro-
field of view, or coordinate frame in which the sensor data is duction versus development and 4.5 on Economic Justifica-
defined. The two primary configurations used in LPBF in- tion).
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FIG. 7 Example Schematic of Two Common Instrument Physical Configurations in Laser Powder Bed Fusion (LPBF) Process Monitor-
ing: (a) Co-axial Configuration and (b) Staring Configuration
5.2 Flaw Detection—A primary intended use for AM pro- limits, process capability, out of control action plan systems,
cess monitoring is to identify the formation or existence of
and matching/equivalency testing. Reference Practice E2587.
flaws during the fabrication process, so that it may supplement
5.3.1 Terms:
(or possibly replace) ex-situ part quality measurements. This
5.3.1.1 Control Chart is a trend chart of a measurement or
may be used to direct or inform location-specific post-build
a statistic in time order with control limits and a centerline
NDE, form the basis for accept/reject criteria, or provide
which define ‘usual’ process performance. A control chart is
supplemental characterization of the build process or part
intended to model, partition and monitor the process variation
design. For an AM flaw catalog and a review of relevant
into Common Cause and Special Cause (see Fig. 8).
post-process NDT standards under ISO jurisdiction, refer to
(1) Control Limits and Centerlines define the partitions of
ISO/ASTMTR52905.Alistandreviewoftechnicallyrelevant
Common Cause and Special Cause variation, and are estab-
flaws that are exhibited in the final part and potentially
lished from a reference data set. This reference data set is
observed through ex-situ NDT methods are provided in Guide
assumed to be devoid of any Special Cause variation. Control
E3166. Also, refer to subcommittee ASTM F42.01 for pro-
limitsareestimatedfromthisreferencedata,usuallycalculated
posed standards on test methods for intentionally seeding
as the process mean plus and minus three standard deviations.
flaws. In addition to these ex-situ observed flaws, those
All subsequent data are plotted on the control chart and
exhibited in-process are discussed in context of the different
compared to the control limits. Observations beyond the
measurement modalities beginning in Section 7.
control limits are indicative of out-of-control state caused due
5.2.1 Probability of Detection (POD)—TheconceptofPOD
to Special Cause variation.
analysis may be applied to in-process monitoring, but specific
(2) SPC Challenges in AM Process Monitoring—
examples are limited at the time of publication of this guide.
Identifying and formulating the appropriate control variables
Readers may want to refer to published standards fromASTM
andcontrolstatisticsareoneofthechallengeswithSPCofAM
E07.10 on Specialized NDT Methods regarding POD analysis
in-process measurements. It is undoubtedly preferable for the
(for example, Practice E2862, Practice E3023).
measurements to have strong causal relationships to final
5.3 Statistical Process Control (SPC)—Statistical process product quality, but the relative juvenescence of the industry
control (SPC) is defined as the application of statistical makes many of these relationships unproven, and only esti-
techniques to control a process. This control is achieved by mated from other tangentially-but-incompletely-related
taking action on the partitioning of process variation into technologies, like welding and materials science. If every
common cause variationand special cause variation.Common possiblemetricissubjectedtorigorousSPC,dataoverloadcan
cause variation results from the natural variability of the hamper analysis and improvement efforts. Over time, use
process or “stochastic noise.” Special cause variation results continuous improvement methods to drive for SPC procedures
from deviations that, with appropriate diligence, can be attrib- that elegantly partition variation with the appropriate amount
uted to a specific reason.Akey concept of SPC is to promptly of effort/overhead and directly relate to final product quality.
identify when the process is out-of-control due to the onset of (3) Identifying and Defining Control Limits—Another par-
special cause variation so that timely corrective action can be ticularchallengeoftheSPCofAMin-processmeasurementsis
taken, and not tampering with the background noise of the one of the calculations of control limits for process subgroup-
processresultingfromcommoncausevariation.Thelong-term ings and summarizations where trends, steps, and discontinui-
goal is to continually reduce the common cause variation ties are natural components of Common Cause variation.
through systemic improvements.The term SPC will be used to Limits calculated classically will generally be inappropriate,
include control charts, control limits, specification or tolerance being too loo
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