ABSTR ACT
Additive manufacturing (AM) processes offer versatile
capabilities, but parts are often riddled with defects
due to the inherent variability of process parameters.
This research presents a closed-loop, in situ sensing-
based feedback system for autonomous, real-time
defect correction that actively adjusts operational
parameters by fusing information from multiple
sensor inputs. The approach is integral to a digital
twin, creating a thread between the manufacturing
system, sensing and control data, edge processing,
and deep learning–based dynamic adaptations. To
demonstrate this approach, a commercial polymer
3D printer outfitted with acoustic emission sensors
and optical imaging was used to develop a testbed
for manufacturing process monitoring and real-time
control. Two principal elements were incorporated: a
multimodal deep learning model for heterogeneous
sensor inputs, and a digital thread enabling real-time
use. Both time-series and image data were collected
as exemplary cases of the variety of sensor types
usable in similar monitoring and control approaches,
while common defects—such as under- and
overextrusion—were deliberately introduced into the
G-code. The labeled datasets were used to train the
deep learning models.
KEYWORDS: additive manufacturing, defect correction, deep
learning, closed-loop feedback control
1. Introduction
A polymer additive manufacturing (AM) process, such as
fused deposition modeling (FDM), is commonly used for
both prototyping and near-net-shape production of parts [1].
FDM-type 3D printers use a heated deposition unit to add
layers sequentially to build complex geometries. Several AM
process-induced defects are associated with the printer’s tem-
perature and motion settings. These defects include warping
of parts, under- or overextrusion of material throughout layers,
and misalignment due to external vibrations or slippage of the
printer’s movement axes, to name a few.
Given the resources involved in AM part production,
3D printing technologies would benefit from automated
feedback and assessment of the part as it is manufactured
to address manufacturing defects and thereby improve the
quality of the produced parts. To detect the presence of a
defect, appropriate sensing is needed to capture relevant
information specific to the machine and the part being
produced. Related efforts have been reported to acquire such
data using thermal, vibrational, and acoustic sensors, among
several others [2]. Advanced sensing devices are typically
extrinsic to the built-in capabilities of typical commercial 3D
printers. Some researchers are therefore relying on built-in
sensing technology to detect faults, while others are lever-
aging add-on sensing, such as that provided by a variety of
nondestructive evaluation (NDE)–based monitoring adapted
for AM purposes [3–5, 59, 60]. For example, approaches
involving infrared thermography attempt to extract part-
layer temperature and distinguish between normal and
abnormal states [6]. In this context, material extrusion states
such as loading, normal, and jammed have been differen-
tiated based on the measured current draw of the feeder
mechanism/extruder [7]. In addition, vibrations on the build
platform using accelerometers to gauge proper extrusion
and adhesion have also been proposed [8]. Furthermore,
recent advancements have reported a diverse array of other
sensors used for such process monitoring, including vision-
based, laser surface profilers, and vibrational sensors [9]. This
multi-sensory approach, however, necessitates integrating
various data types, each requiring distinct processing and
classification methods. Overall, the major driver for moni-
toring FDM printer processes, as seen in the literature, has
been to achieve a digital twin approach that accounts for all
relevant process parameters and post-manufacturing qualifi-
cation and certification requirements [2].
ME
|
TECHPAPER
† Theoretical and Applied Mechanics Group, Drexel University, Philadelphia, PA
19104, USA
‡ Digital Engineering Hub, Henry M. Rowan College of Engineering, Rowan
University, Glassboro, NJ 08028, USA
*Corresponding author: kontsos@rowan.edu
Materials Evaluation 84 (7): 32–53
https://doi.org/10.32548/2026.me-04580
©2026 American Society for Nondestructive Testing
A DIGITAL THREAD APPROACH FOR
REAL-TIME DEFECT CORRECTION IN
POLYMER ADDITIVE MANUFACTURING
SARAH MALIK† AND ANTONIOS KONTSOS‡*
32
M AT E R I A L S E V A L U AT I O N • J U LY 2 0 2 6
Additive manufacturing (AM) processes offer versatile
capabilities, but parts are often riddled with defects
due to the inherent variability of process parameters.
This research presents a closed-loop, in situ sensing-
based feedback system for autonomous, real-time
defect correction that actively adjusts operational
parameters by fusing information from multiple
sensor inputs. The approach is integral to a digital
twin, creating a thread between the manufacturing
system, sensing and control data, edge processing,
and deep learning–based dynamic adaptations. To
demonstrate this approach, a commercial polymer
3D printer outfitted with acoustic emission sensors
and optical imaging was used to develop a testbed
for manufacturing process monitoring and real-time
control. Two principal elements were incorporated: a
multimodal deep learning model for heterogeneous
sensor inputs, and a digital thread enabling real-time
use. Both time-series and image data were collected
as exemplary cases of the variety of sensor types
usable in similar monitoring and control approaches,
while common defects—such as under- and
overextrusion—were deliberately introduced into the
G-code. The labeled datasets were used to train the
deep learning models.
KEYWORDS: additive manufacturing, defect correction, deep
learning, closed-loop feedback control
1. Introduction
A polymer additive manufacturing (AM) process, such as
fused deposition modeling (FDM), is commonly used for
both prototyping and near-net-shape production of parts [1].
FDM-type 3D printers use a heated deposition unit to add
layers sequentially to build complex geometries. Several AM
process-induced defects are associated with the printer’s tem-
perature and motion settings. These defects include warping
of parts, under- or overextrusion of material throughout layers,
and misalignment due to external vibrations or slippage of the
printer’s movement axes, to name a few.
Given the resources involved in AM part production,
3D printing technologies would benefit from automated
feedback and assessment of the part as it is manufactured
to address manufacturing defects and thereby improve the
quality of the produced parts. To detect the presence of a
defect, appropriate sensing is needed to capture relevant
information specific to the machine and the part being
produced. Related efforts have been reported to acquire such
data using thermal, vibrational, and acoustic sensors, among
several others [2]. Advanced sensing devices are typically
extrinsic to the built-in capabilities of typical commercial 3D
printers. Some researchers are therefore relying on built-in
sensing technology to detect faults, while others are lever-
aging add-on sensing, such as that provided by a variety of
nondestructive evaluation (NDE)–based monitoring adapted
for AM purposes [3–5, 59, 60]. For example, approaches
involving infrared thermography attempt to extract part-
layer temperature and distinguish between normal and
abnormal states [6]. In this context, material extrusion states
such as loading, normal, and jammed have been differen-
tiated based on the measured current draw of the feeder
mechanism/extruder [7]. In addition, vibrations on the build
platform using accelerometers to gauge proper extrusion
and adhesion have also been proposed [8]. Furthermore,
recent advancements have reported a diverse array of other
sensors used for such process monitoring, including vision-
based, laser surface profilers, and vibrational sensors [9]. This
multi-sensory approach, however, necessitates integrating
various data types, each requiring distinct processing and
classification methods. Overall, the major driver for moni-
toring FDM printer processes, as seen in the literature, has
been to achieve a digital twin approach that accounts for all
relevant process parameters and post-manufacturing qualifi-
cation and certification requirements [2].
ME
|
TECHPAPER
† Theoretical and Applied Mechanics Group, Drexel University, Philadelphia, PA
19104, USA
‡ Digital Engineering Hub, Henry M. Rowan College of Engineering, Rowan
University, Glassboro, NJ 08028, USA
*Corresponding author: kontsos@rowan.edu
Materials Evaluation 84 (7): 32–53
https://doi.org/10.32548/2026.me-04580
©2026 American Society for Nondestructive Testing
A DIGITAL THREAD APPROACH FOR
REAL-TIME DEFECT CORRECTION IN
POLYMER ADDITIVE MANUFACTURING
SARAH MALIK† AND ANTONIOS KONTSOS‡*
32
M AT E R I A L S E V A L U AT I O N • J U LY 2 0 2 6
