ISO/ASTM DTR 52958

ISO/TC 261
Secretariat: DIN
Date: 2025-12-082026-xx
Additive manufacturing of metals — Powder bed fusion (PBF) — In-
situ coaxial photodiode monitoring for lack of fusion flaw detection
in PBF-LB
Fabrication additive de métaux — Fusion sur lit de poudre — Bonnes pratiques pour — Surveillance par
photodiode coaxiale in situ pour la détection et l'analyse desde défauts in situ pour lede fusion en PBF laser-LB
FDIS stage
TThhiiss dr draaft ift iss su subbmmiittetted to d to aa p paaralrallelel vl voteote iinn IS ISOO, C, CEENN.

© ISO/ASTM International 2025 2026
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© ISO/ASTM 2026 – All rights reserved
ii
Contents
Foreword . iv
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Significance and use . 2
5 Design of coupons . 3
5.1 General. 3
5.2 Intentionally seeded flaws. 4
5.3 Randomized/stochastic flaws . 6
6 Sensor description . 6
7 Flaw detection algorithms . 7
7.1 General. 7
7.2 Statistical algorithms . 8
7.3 Machine-learning approach: clustering algorithm . 13
8 Implementation of customized algorithms to components with randomized flaws and the
associated voxelization thereof . 16
9 Case study: procedural implementation and validation results . 19
Bibliography . 24

© ISO/ASTM 2026 – All rights reserved
iii
Foreword
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This document was prepared by Technical Committee ISO/TC 261, Additive manufacturing, in cooperation
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Manufacturing, and in collaboration with the European Committee for Standardization (CEN) Technical
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© ISO/ASTM 2026 – All rights reserved
iv
Additive manufacturing of metals — Powder bed fusion (PBF) — In-
situ coaxial photodiode monitoring for lack of fusion flaw detection in
PBF-LB
1 Scope
This document provides a workflow comprising experimental procedures and flaw detection algorithms
aimed at locating flaws in parts produced during the powder bed fusion-laser-based (PBF-LB) process of
metals. It emphasizes the use of coaxial photodiode-based in-situ monitoring and statistical and clustering
machine learning algorithms, particularly for detecting lack of fusion-induced flaws. The workflow delineates
setting thresholds for statistical detection and determining the number of clusters for machine learning
algorithms, utilizing intentional seeded flaws in parts. Validation procedures are provided through computed
tomography scanner data. Hardware limitations and considerations for multi-laser processes are addressed,
with attention to potential issues.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content constitutes
requirements of this document. For dated references, only the edition cited applies. For undated references,
the latest edition of the referenced document (including any amendments) applies.
ISO/ASTM 52900, Additive manufacturing — General principles — Fundamentals and vocabulary
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/ASTM 52900 and the following
apply:
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— — ISO Online browsing platform: available at https://www.iso.org/obp
— — IEC Electropedia: available at https://www.electropedia.org/
3.1 3.1
clustering algorithm
unsupervised machine learning methods with unlabelled input data is grouped by similarity
3.2 3.2
coaxial sensorphotodiode arrangement
type of sensor arrangement on the powder bed fusion-laser-based machine aligned with the laser beam path
3.3 3.3
computed tomography (
CT)
non-destructive examination technique capturing radiographic projections of an object at various rotational
angles followed by mathematically reconstructedreconstruction to produce a three-dimensional volume data
set or one or more two-dimensional cross-sectional images
[SOURCE: ISO/ASTM TR 52906]
© ISO/ASTM 2026 – All rights reserved
3.4 3.4
data alignment
process of transforming different sets of geometrically or temporally related data into a single, global
coordinate system
3.5 3.5
data registration
procedure of data alignment and assigningassignation of a persistent identification to the aligned data set
3.6 3.6
ex-situ analysis
measurement procedure performed after the completion of the build cycle
3.7 3.7
flaw indicator
indicator corresponding to the location of flaws predicted by the flaw detection algorithm
3.8 3.8
lack of fusion
type of process-induced porosity with not fully melted or fused powder particles onto the previously
deposited substrate.
[SOURCE: ASTM E3166-20, 3.4.7]
3.9 3.9
reference datum feature
notch, groove, or similar feature added to the geometry in a seeded flaw coupon to ease data alignment and
registration of porosity locations for CT-scan ex-situ analysis
3.10
direct in-situ measurement
Commented [eXtyles1]: The term "direct in-situ
measurement" has not been used anywhere in this document
measurement procedure performed during the powder bed fusion-laser based manufacturing process
3.10 3.11
intentionally seeded flaw
act of intentionally creating flaws through computer-aided design or manipulation of designated processing
parameters, resulting in the placement of the anticipated flaw or the act of intentionally creating a flaw
through the insertion of an artificial object
[SOURCE: ISO/ASTM TR 52906]
4 Significance and use
A workflow for indirect flaw detection and analysis documentation during PBF-LB is provided by using the
signals received from a coaxial photodiode that can detect flaws, including lack of fusion, in fabricated
components.
These flaws may have detrimental effects on the mechanical performance of fabricated parts. The workflow
of this document provides a procedure to identify the range of upper and lower thresholds required for the
statistical detection algorithms to effectively identify stochastic lack of fusion defects induced during the
process. It provides a procedure to identify the number of clusters required for machine learning detection
algorithms. In the validation procedure, the datasets collected from a CT scanner that are registered and
voxelized can be used. It has to beis noted that the size of detectable flaw, as determined by the procedure
outlined in this standarddocument, is contingent upon the resolution and frequency of the hardware
employed, specifically a co-axial photodiode and its associated data acquisition card. For instance, when
© ISO/ASTM 2026 – All rights reserved
utilizing a commonly available photodiode with a frequency of 60 kHz, the procedure and algorithms
prescribedspecified by this standarddocument are unable to detect flaws smaller than 100 micronsμm.
In general, an in- situ photodiode installed coaxially provides information from the process signature and
flaws. However, the recorded in- situ data needs to be corrected to remove chromatic and monochromatic
distortion. The corrected data analyse by two main algorithms to identify flaws:
a) 1. statistically, and
b) 2. by machine learning.
These algorithms can be systematically optimized and customized to detect lack of fusion flaws. To this end,
intentionally seeded flaws are first added to the computer-aided design (CAD) of coupons to tune the
parameters of the algorithms. Then, the customized algorithm is tested by detecting randomized/stochastic
flaws created by powder bed fusion-laser based with intentionally decreased energy density. The comparison
of detection results could be analysed by algorithms with the CT data applied through a volumetric approach
to identify the randomized/stochastic flaws. A flowchart illustrating the progression in this document is
shown in 0Figure 1.
52958_ed1fig1.EPS
Figure 1 — Schematic for calibration flow needed for detecting the lack of fusion flaw
5 Design of coupons
5.1 General
To customize and calibrate the detection algorithms systematically, two sets of coupons are suggested.
Reference datum features can also be added to the geometry to ease data alignment and data registration of
porosity locations in the ex- situ analysis that is CT-scan in this practice. 0Figure 2 represents some
suggestions for registry notches/grooves.
© ISO/ASTM 2026 – All rights reserved
52958_ed1fig2b.EPS
52958_ed1fig2a.EPS
a) Added vertical and horizontal registry grooves b) Added inclined notches
Figure 2 — Addition of registry grooves and inclined notches to the geometry of the coupon
5.2 Intentionally seeded flaws
The effect of the lack of fusion flaw can be mimicked by embedding intentional seeds/voids in the coupons.
[ [1]]
According to ISO/ASTM TR 52906 Error! Reference source not found., , for creating these seeds, various
sizes, distributions, and geometries of seeds can be added to the computer-aided design. Two forms of
spherical and cylindrical intentional seeded flaws can be considered where the size of spherical flaws is
identified by their diameter and the size of cylindrical flaws is identified by their cross-sectional diameter and
height. Note that the minimum size of the intentional flaw is dictated by the powder bed fusion-laser based
machine restrictions. It is, however, recommended that the feature size of flaws is set in the computer-aided
design model to three different classes:
The minimum size possible to be made by the powder bed fusion-laser based machine (for example, 100 µm
for the diameter of spheres and 100 µm for height and diameter of cylinders) depending on the resolution and
laser spot size;:
a) 1. the minimum value plus 50 μm (for example, 150 µm for the above-mentioned parameters);
and
b) 2. the minimum value plus 100 µm (for example, 200 µm for the above-mentioned parameters).
Note that within the layers in which the intentional flaws are made, an optimum down-skin parameter is used
to endure the mechanical integrity of the intentional flaw. The capping layer, however, can have no down-skin
setting.
Two examples of intentionally seeded flaws are shown in 0. 0Figure 3. Figure 3a)) and b) represent two
dimensional cross sections of samples showing the distribution of the intentionally seeded flaws (cylindrical
and spherical), respectively. In 0Figure 3 (a),), six nominally identical sets of three sizes of cylindrical flaws
(Ø, H = = 200 µm, Ø, H = = 150 µm, and Ø, H = = 100 µm are shown; in 0Figure 3 (b),), six nominally identical
sets of three sizes of spherical flaws (Ø = 200 µm, Ø = 150 µm, and Ø = 100 µm) where Ø is the diameter and
H is the height, in microns, are demonstrated; and in 0Figure 3 (c),), the capping layer of spherical flaws are
represented.
© ISO/ASTM 2026 – All rights reserved
52958_ed1fig3a.EPS
52958_ed1fig3b.EPS
a) Cylindrical type b) Spherical type
52958_ed1fig3c.EPS
c) Schematic of spherical flaws showing the capping layer
Key
A sets (clustered intentional voids)
B capping layer
NOTE 1 All dimensions are in SI coordinate system.
NOTE 2 0 Figures 3a)) and 03b)) are published under an open access CC by 4.0 license.
Figure 3 — Two dimensional cross sections of samples showing the distribution of different types of
intentionally seeded flaws
© ISO/ASTM 2026 – All rights reserved
5.3 Randomized/stochastic flaws
Randomized/stochastic flaws can be achieved normally because of process anomalies or by altering process
parameters in which the lack of fusion flaws are created by decreasing the energy density during the
fabrication. For creating randomized lack of fusion flaws, four scenarios are recommended:
a) 1. reducing the laser power;
b) 2. increasing the hatching distance;
c) 3. increasing the scanning speed; and
d) 4. increasing the layer thickness.
These alterations depend on the material. It is, however, recommended that the produced parts with altered
process parameters exhibit a relative density reduction of around 0,5 % compared to parts fabricated with
nominal process parameters.
6 Sensor description
The sensor embodied in this document is a coaxial photodiode arrangement with a sampling frequency of
equal or more than 60 kHz. The coaxial photodiode arrangement is aligned with the laser beam path through
a beam splitter (see 0Figure 4).).
NOTE 1 Photodiodes can capture light intensity signals from the melt pool in different wavelengths; however, the
preferred wavelength range for most metallic alloys to capture melt pool light intensity is normally in the visible and
near-infrared ranges (between 750 nm to 900 nm).
In addition to the light intensity data, laser modulation and XY scanner position are recorded and stored in
the associated personal computer. Intensity and geometry calibrations of dataset are also required.
NOTE 2 Details of the data calibration routines are out of the scope of this practice.
NOTE 3 The intensity correction for the coaxial data can be implemented because of the chromatic aberration
phenomenon. It is initiated because the wavelength of light intensity recorded by the photodiode is not the same as the
wavelength optimized for the scanner mirror and f-theta lens or dynamic focusing units.
NOTE 4 The intensity and geometry corrections are dependent on the commercial system and are normally
implemented by the original equipment manufacturer’s monitoring system.
© ISO/ASTM 2026 – All rights reserved
52958_ed1fig4.EPS
Key
1 laser
2 scanner mirror
3 F-Thena lense
4 roller
5 powder
6 build plate
7 beam splitter
8 inside the optical path
9 co-axial sensor
Figure 4 — Position of the coaxial sensor in the PBF-LB setup
7 Flaw detection algorithms
7.1 General
Two different approaches are recommended for detecting flaws: statistical algorithms and clustering machine
learning-clustering algorithms.
The statistical algorithm works based on the threshold method, and the clustering algorithm distributes data
into different groups. The workflow to detect the flaws was demonstrated in 0Figure 1 and is the following:
Step 1: The preferred algorithm can be applied to the data collected during the printing of the samples with
intentionally seeded flaws.
© ISO/ASTM 2026 – All rights reserved
Step 2: The detection result is compared with the design and CT scan data to customize/calibrate the
algorithm parameters such as moving average windows length and thresholds.
NOTE The setting of CT scan can normally be optimized in terms of resolution.
Step 3: The customized algorithm stemmed from Step 2 can be used for the detection of randomized flaws in
the fabricated samples.
Step 4: The detection results of Step 3 are validated by CT scan through the volumetric approach and
confusion matrix which is explained in 9Clause 9.
As an example, several statistical and machine-learning algorithms that are suitable for this practice are
discussed in 7.27.2 and 7.37.3. Additionally, the volumetric approach is used to compare the result with CT,
which is discussed in 9Clause 9.
7.2 Statistical algorithms
7.2.1 General
Statistical algorithms work based on the selection of threshold levels, and their central feature is the moving
[ [2]]
average. Error! Reference source not found. Thus, it is critical to have an optimized workflow to identify
a threshold range and the window size/length of the moving average by end users. In 0Figure 5,, the
application of the threshold range to the photodiode's signal is shown. As seen, some ripples exist outside the
upper and lower thresholds where each data sample is associated with a geometrical point in the XY cartesian
coordinate. Any processed signal (for example, moving averaged) greater than the upper threshold and lower
than the lower threshold is defined and is called a signal perturbation in this document [0(Figure 5b)).)].
Signal perturbations are associated with an abnormality in the process and are normally corresponding to the
location of flaws. Thus, the perturbation can be mapped to the geometry of samples and visualized with a
different colour ([for example, as a yellow indicator shown in 0Figure 5c)).)]. To map the perturbation with
potential flaws in the fabricated components, a proper detection algorithm is needed to be selected from
absolute limits (AL) and short-term fluctuation (STF). Two statistical algorithms are discussed in 7.2.2 and
7.2.3the following sections.
NOTE The capping layer of intentional seeded flaws [see 0Figure 5 (c)])] is used to optimize the value of upper and
lower thresholds and data window length since it represents higher signal perturbations.
© ISO/ASTM 2026 – All rights reserved
52958_ed1fig5.EPS
Key
X1 time
X2 horizontal size of a layer
Y1 light intensity (a.u.)
Y2 vertical size of a layer
A photodiode time series data along with upper and lower thresholds
B magnified signal representing signal perturbation
C one layer of the sample including indicators
1 photodiode signal
2 magnified signal
3 signal perturbation
4 upper threshold
5 lower threshold
6 detection algorithm (AL, SD, SOM, etc.)
7 a layer
8 flaw indicator
Figure 5 — Correlation of signal perturbation and flaw location after applying the detection
algorithm
7.2.2 Absolute limits (AL)
The algorithm is based on the application of moving the average to the signal. The user can set the window
length of the moving average. The algorithm can be applied to the time-series one-dimensional data while
considering an upper and lower threshold (to be explained in the workflow sectionsubclause) in which any
ripples above the upper threshold and below the lower threshold indicate perturbation/disturbance in the
process as shown in 0Figure 6. The algorithm can normally detect flaws created as a result of a sudden laser
power variation. 0Formula (1) can be used to find the moving average for the data, I :
m,a
(1)
© ISO/ASTM 2026 – All rights reserved
𝑥+𝑛
𝐼 = (𝑖) (1)
∑
𝑚,𝑎
2𝑛+1 𝑗=𝑥−𝑛
where
𝑛 is the number of datapoint;
𝑗 is the number of period;
i is the signal intensity.
NOTE If the number of data points in the last moving window is less than the nominal length of the moving average,
the average value for the last window is determined according to the available number of d
...