Digital Twin Concept for a Novel Aerosol-on-Demand Jet-Printing

System

Hanna Pfannenstiel and Ingo Sieber

Institute for Automation and Applied Informatics, KIT, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen,

Germany

Keywords: Digital Twin, Modelling, Simulation, Additive Manufacturing, Aerosol Jet-Printing System, Machine

Learning.

Abstract: In this article, we present the concept and architecture of a digital twin (DT) used for the development and

subsequent control and operation of a novel aerosol-on-demand (AoD) jet-printing system. Since the process

of aerosol generation used in the AoD printing process has many complex interactions that can hardly be

described by established theories, this paper develops an architecture that enables the digital image to learn

from its physical counterpart. Conventional DT architectures only allow the use of digital twins if they can

mimic their physical counterpart accurately. Our approach overcomes this limitation by enabling the digital

twin to learn empirically and thereby improve its models by using a data loop.

1 INTRODUCTION

Digital twin (DT) technology is commonly described

in both academia and industry as a combination of a

physical entity, a virtual counterpart, and a data link

between the two. In this context, the digital twin is a

digital representation of a system in operation,

consisting of its main characteristics, attributes and

behaviors. (Stark & Damerau, 2019). Digital twins

have many definitions depending on the context they

are used in (Glaessgen & Stargel, 2012; Grieves,

2014; Grieves & Vickers, 2017; Rosen et al., 2015;

Tao et al., 2018; Kritzinger et al., 2018; Autiosalo et

al., 2020). DT is of great importance in the production

context as it offers the possibility to improve

production processes, adjust production processes to

other, new means of production, confirm settings and

find new operating points and simulate situations to

predict performance (Roy et al., 2020).

The development of printed electronics is based

increasingly on the functional printing of novel

nanomaterials (Das & He, 2021; Suganuma, 2014;

Wu, 2017; Choi et al., 2015, Magdassi & Kamyshny,

2017), as the use of inks with special chemical,

physical, or optical properties enables the production

of novel functional structures (Sirringhaus &

https://orcid.org/0000-0003-2811-7852

Shimoda, 2003; Sieber et al., 2020; Sieber et al.,

2021; Magdassi, 2010). Printed functional elements

such as conductive tracks or electronic components

like resistors and transistors require high quality in

terms of line width, edges, and layer thickness to

ensure reproducible electrical properties

(Subramanian et al., 2008). One promising concept in

this context is aerosol jet printing, where functional

ink is atomized into a fine spray and then transformed

into a stable and over several millimeters well-

collimated aerosol jet through hydrodynamic

focusing by a sheath gas flow (Ganz et al., 2016,

Gupta et al., 2016). Due to this collimation range, the

distance between the substrate and the print head can

be varied without significantly changing the line

width. This is also why aerosol-based printing

processes, unlike inkjet printing, are suitable for

printing on 2.5D and 3D components (Neotech,

2025).

Recently, the authors presented a new principle

for aerosol-jet-on-demand printing, the core of which

is an atomization unit integrated directly inside the

printhead (Ungerer et al., 2023a). This enables a

compact system design, printing operation in all

spatial directions, widely tunable distance between

printhead and substrate, as well as jet-on demand

mode of operation (Sieber et al., 2022). This new

Pfannenstiel, H. and Sieber, I.

Digital Twin Concept for a Novel Aerosol-on-Demand Jet-Printing System.

DOI: 10.5220/0013459200003970

In Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2025), pages 201-208

ISBN: 978-989-758-759-7; ISSN: 2184-2841

201

method of achieving an AoD printing process exhibits

many complex interactions that are difficult to

describe with conventional theory. Insights into the

function of atomization require physical tests on the

laboratory setup. These are difficult to perform and

interpret due to the complex flow conditions in the

AoD printhead. Previous attempts to measure the

aerosol flow field using Particle Shadow Velocimetry

(PSV) have shown that the small droplet size

combined with a highly complex velocity field makes

the measurements inaccurate (Pöppe, 2023; Ungerer

et al., 2022).

This paper aims to describe the concept and

architecture for a DT of the AoD, as well as to present

and discuss approaches to model reduction and shows

which subsystems are necessary in the DT and how

they interact.

The structure of this paper is: Section 2 presents

the patented AoD jet-printhead as well as the current

lab setup, acting as part of the physical layer, Section

3 deals with the models describing the functioning of

the printhead as well as approaches to model

reduction. In Section 4 the DT is presented. Here the

concept as well as the specific architecture are shown.

Section 5 closes the paper with conclusions and gives

an outlook over further planned work.

2 SETUP OF THE AoD

JET-PRINTING SYSTEM

2.1 The AoD Jet-Printhead

The primary feature of the novel concept of the AoD-

jet printhead is the integration of the atomization

process into the printhead (Ungerer et al., 2023a).

This enables a compact system design, printing

operation in all spatial directions, widely tunable

distance between printhead and substrate, as well as

jet-on demand mode of operation (Sieber et al., 2022).

Fig. 1 shows a schematic of the printhead with all its

main components. Aerosol generation takes place by

excitation of a fluid inside a glass capillary (2) by

means of a piezo actuator (3). The aerosol spray flows

into the mixing chamber (1) where it is

aerodynamically focused by an inflowing sheath gas

and the nozzle (4). The sheath gas flows into the

mixing chamber by four inlets which are arranged

equidistantly around the circumference. The four

inlet channels, from which only two are depicted in

Figure 1, have a meandering structure (5). The

deflection in the channel bends causes additional

mixing so that the velocity profiles of the sheath gas

are homogenised within the mixing chamber (Sieber

et al., 2022).

Figure 1: Schematic of the principle design of the AoD-jet

print head.

2.2 Lab Setup of the AoD Jet-Printing

System

The existing laboratory setup is still under

development, its current implementation is shown in

Figure 2. It consists of the printhead with atomization

unit and nozzle, a frequency generator, an ink

reservoir with pressure control and mass flow sensor,

a high-power LED, and a high-speed camera.

Pressure control of the ink reservoir is carried out by

an Ultimus II dispenser from Nordson, while the mass

flow of the ink during operation of the printhead is

measured between the reservoir and the printhead by

a mass flow sensor (SLG-0150, Sensirion,

Switzerland). The printhead is connected to a

frequency generator (SDG2000X, Siglent

Technologies CO) to provide the required frequency

for atomization. The piezo actuator is directly

connected to the capillary. Focusing of the aerosol

spray is done by interaction of the inner contour of the

nozzle and the mass flow of the sheath gas. The flow

rate of the sheath gas is controlled by the mass flow

controller (red-y smart controller GSC, Vögtlin). To

monitor the quality of the aerosol jet and the droplet

distribution, the setup includes a high-speed camera

(EoSens 3CXP Mikrotron, SVS-Vistek GmbH) and a

light microscope (VHX-7020, Keyence). This allows

the monitoring and control of the printing process

exclusively by optical methods to ensure the desired

quality is achieved.

SIMULTECH 2025 - 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

202

Figure 2: Laboratory setup of the AoD testbed.

3 MODELLING

In order to develop and design the patented concept

of the AoD printhead, a CFD model of the printhead

is implemented and used for design optimization of

the printhead with respect to its manufacturability in

our in-house workshop (Ungerer et al., 2023b).

Simulation results not only lead to the design but also

gives preliminary operation parameters. Ansys Fluent

in its versions R19.3, R20.1, and 2024 R2 is used for

fluid dynamic calculations. When modelling the ink,

the Euler-Lagrange model is used, which includes a

particle-based approach to the discrete phase (Sieber

et al., 2022).

The generated aerosol is modelled in Fluent using

a cone model, which means that the generated aerosol

is described by its origin, the cone axis, the cone

angle, the radius, the diameter of the droplets, the

diameter distribution, the exit velocity of the droplets

and the aerosol mass flow rate (Ungerer et al., 2023c).

The mesh used for the CFD calculations was

optimised using a mesh independence study resulting

in 2.1 ∙ 10



elements (Ungerer et al., 2023c). Since

the Euler-Lagrange model used is a particle-based

consideration of the discrete phase, it must be ensured

that a particle can, in principle, be completely

contained within a mesh element. To increase the

resolution in the atomization zone, i.e., in the area

between the capillary and the nozzle outlet, while

maintaining this condition with a droplet diameter of

approximately 20 µm, an element size of 33 µm is

used (see Fig. 3) (Ungerer et al., 2024)

Due to the complexity of the model, the CFD

simulations require many hours to a few days for

calculation (the CFD simulations are carried out on a

workstation equipped with AMD Ryzen Threadripper

3970X processor with 32 cores, 64 threads @

3.7 GHz, 128 GB RAM, and an Nvidia Titan RTX

graphics processor with 24 GB). One goal of the DT

is to provide the user with recommendations for

optimal settings. This must be done based on

simulations. For this reason, a computationally

efficient model is required that can represent all

relevant aspects of the underlying CFD model. Since

the DT regularly updates its CFD models when new

data becomes available over the course of the

printer’s lifecycle the algorithm must also be able to

autonomously reduce the models every time they are

updated. For this purpose, various approaches to

model reduction are investigated, namely polynomial

regression (Pfannenstiel et al., 2024a) and the

Gaussian method (Pfannenstiel et al., 2024b), with

the aim of autonomously determining properties of

the aerosol jet solely through its initial injection

parameters. The goal of this ongoing work is to

autonomously create a reduced model that comes as

close as possible to the computationally intensive

CFD simulation and can be used inline by the DT.

Figure 3: Meshed geometry model of the printhead and a

free space area (Ungerer et al., 2023c).

4 DIGITAL TWIN

4.1 Digital Twin Concept

Essential for putting the newly developed concept of

the aerosol printhead into operation is the

determination of the control parameters and an

Digital Twin Concept for a Novel Aerosol-on-Demand Jet-Printing System

203

Figure 4: Three-layer concept of the Digital Twin.

adapted chamber design for a proper function. A

problem herein is the contamination of the inner walls

of the manufactured printhead with ink due to non-

optimal control parameters or non-optimal geometry

of the chamber design of the printhead, both leading

to wall contact of the ink spray (Sieber et al., 2022).

Such contaminations would lead to difficult and time-

consuming cleaning processes on the real component.

Determination of ink-specific operation parameters

as well as an adaptation of the chamber design to

specific ink formulations is one task of our digital

twin concept, which is based on a three-layer design.

The virtual layer (VL) is linked to the physical layer

(PL) by an information-processing layer (IPL) (see

Fig. 1). The bidirectional mapping and

interoperability of the physical and the virtual space

are realized through data interaction (Zheng et al.,

2019).

The VL consists of a simulation model of the

printhead, which is implemented using computational

fluid dynamic (CFD) and a 3D CAD (computer-aided

design)-model. The manufacturing of the printhead as

well as the laboratory setup and the measurement

equipment is located in the PL. Mapping of virtual

and physical layer is conducted by the IPL by means

of data analysis, derivation of control parameters for

the digital and the physical twin, as well as a quality

control. Input of the VL to the IPL are the ink-specific

simulation results like e.g. operation parameters,

design parameters, and control parameters. Analysis

and evaluation of the simulation results take place in

the IPL with respect to the defined criteria, like, e.g.

non-wetting condition of the inner walls of the

printhead, avoidance of turbulences by limiting the

Reynolds number to below 1200 (which is controlled

by the mass flow of the sheath gas used for

aerodynamical focusing), a stable, focused and

collimated aerosol jet, and design rules of the

manufacturing processes used. Based on the analysis

and evaluation results design optimization is

controlled. Once the optimized design together with

the respective control and operation parameters are

achieved, a preprocessed file of the initial design-for-

manufacture of an aerosol-on-demand printhead,

designed for one specific ink formulation is generated

as input to the CAD tool in the VL. Here the final

CAD 3D model is constructed, send to the IPL where

postprocessing is conducted. The postprocessed file

is digital input to the part manufacture in the PL. Up

to this transfer, the printhead is entirely described

digitally. After the manufacturing process, a real part

of the ink specific printhead P

ink

exists. The

manufactured printhead is installed in a laboratory

and measurement setup and set into operation with

the (in the IPL derived) control and operation

parameters. In-operation measurements were carried

SIMULTECH 2025 - 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

204

out with respect to aerosol generation and by

inspection of the printed tracks. The resulting data of

the measurement again are digital descriptions and

input into the IPL. Measurement data are used in two

ways:

1. To control the quality of the printed

structures (and, hence, validate the

simulation model).

2. To derive model parameters of the discrete

phase.

The line morphology of the printed lines is

examined using image processing methods. With the

help of the line width, line density, smoothness of the

edges and overspray, statements are made about the

quality of the printed lines. Based on the

measurement results, a model validation is conducted

in the VL in two respects: On the one side the

inspection of the printed track is used for quality

control and to validate the operation parameter, on the

other side, the measurement data of the aerosol spray

are used to enhance the model of the discrete phase.

This validation step qualifies the CFD model of the

aerosol-on-demand printhead for use as a digital twin.

4.2 Digital Twin Architecture

Based on this concept of a digital twin, an architecture

for a comprehensive DT is being developed, which

clearly defines and structures the individual

subsystems and their interactions. This also takes into

account the manufacturing tolerances of the

individual physical components of the AoD-jet

printing system, as well as the autonomously running

model reduction, which avoids time-consuming CFD

simulations by generating simpler models that

estimate the initial printing control parameters from

input parameter sets of the CFD models (Ungerer et

al. 2024; Pfannenstiel et al., 2024a; Pfannenstiel at al.,

2024b).

The architecture of the digital twin proposed in

this paper consists of two types of digital twins as a

kind of subsystems, following a definition by Grieves

(Grieves, 2023). These are the digital twin prototype

and the digital twin instance. We apply this concept

both to the components that make up the real AoD

setup or are used in the printing process and to the

printing process itself.

Our new principle of the AoD printing process

features many complex interactions that are hardly

describable by established theory. Physical tests

necessary to gain an understanding of the atomization

process have shown to be difficult to perform on the

AoD printhead (Pfannenstiel at al., 2024b).

The comprehensive DT architecture is also based

on a three-layer architecture consisting of a Physical

Layer (PL), a Virtual Layer (VL) and an Information

Processing Layer (IPL) (see Fig. 4) and embeds

definitions of Grieves who has established the use of

DTs in the Product Lifecycle Quality (PLQ) loop

where DTs can be used to replace physical tests

(Grieves, 2023). Grieves introduces the Digital Twin

Prototype (DTP), which contains all information

necessary to produce a product. Before a physical

product is manufactured, tests can be done on this

virtual, idealized version of the product and the

Digital Twin Instance (DTI): When a product is

manufactured, a DTI is created which is tied to that

specific product. The DTI features all the tolerances

like e.g. imperfections and differences from the real

component to the DTP. It also stores information

about the operation of the product it is tied to.

The Physical Layer of our DT architecture

contains the printer setup and the user and is

connected to the Data Storing Layer. The basic

features of the VL are the Part DTP and the Print

DTP. In the Part DTP the information of the

components of which the AoD printing system

consists are stored (e.g. as CAD together with its

fabrication requirements, or specific inks defined by

there parameters like e.g. viscosity, particle load) and

follows very much the definition of Grieves. This

looks different with respect to the Print DTP. Because

of the high flexibility of digital additive processes,

there does not exists one ideal product. Printing

results will be dependent on ink parameters, from the

interaction of the ink and the substrate and so on.

Hence in our architecture, the Print DTP is defined as

the ideal model of the manufacturing process. Here

all the models describing the AoD printing process

are saved. These models include detailed settings for

the CFD simulation, a model that describes the

atomization as well as the recommendation

algorithm.

Combination of this set of initial conditions

simulate the specific process and its result. Since the

proposed architecture allows for model improvement,

the version of the respective model needs to be

tracked also. Instantiation of Part and Print DTP takes

place by the Part DTI and the Print DTI, respectively.

The instances of the parts of the AoD-jet printing

system consist of tolerances of the real manufactured

part used in the AoD-system and uses the data from

the corresponding Part DTP as basis. The result of

this DTI is a representation of the real component as

detailed as possible. The Print DTI is tied to a specific

printed structure and the respective printing process.

A Print DTI is created when the user initiates a

Digital Twin Concept for a Novel Aerosol-on-Demand Jet-Printing System

205

Figure 5: DT architecture to support the AoD printing process including the necessary structure to enable automated

improvement (Pfannenstiel et al. 2024b).

printing process by defining the printing goals which

are saved as a dataset in the Print DTI. The printing

goal then is input into the recommendation algorithm

(in the IPL) which deduces on basis of simulations the

set of initial parameters. The initial parameters also

take deviations of real parts used in the AoD-system

resulting from the manufacturing process into

account, depicted by the link from the Part DTI in

Figure 5, and are input into the AoD-systems control.

A simulation on basis of these initial parameter set is

carried out which enables a model improvement by a

comparison of the simulation results with the printing

results. This does not need to happen simultaneously

to the physical printing process and can be done when

there is computational power available. If the

simulation is found to be not accurate enough, an

automated method can change model settings in the

simulation in certain capacities (Pfannenstiel et al.,

2024a). Since initial conditions and results are all

saved in a DTI, the simulation can calculate the same

case with different model settings and determine

which model settings better describe reality. Based on

the best simulation model, a model reduction is

automatically initiated, on the basis of which a line

prediction and control is created. The same approach

is used for improving the recommendation algorithm.

5 CONCLUSIONS AND

OUTLOOK

In this article, we present a digital twin architecture

that uses the DTP and DTI proposed by Grieves as

subsystems. We apply this structure both to the

components that make up the printing process and to

the actual product, the printed structure. Since in the

case of highly flexible, digital, additive manufacturing

technologies, there is no definition of a SINGLE

product, but the printed structure/component depends

on a variety of different influencing factors, we use an

ideal model here. This model is constantly validated

and improved through the implementation of a data

loop during the lifetime of the DT. This approach

enables the application of this DT architecture to our

newly developed AoD-jet printing system, as the

actual atomization process is not yet fully understood

and therefore cannot be modelled. However, by

continuously comparing the printing result with its

simulation prediction, a continuous model

improvement is made, which increasingly

approximates the description of the actual effects.

The possibilities offered by the proposed DT

architecture include real-time capability, use in

design optimization, and autonomy. To achieve real-

time capability for in-line control of the printing

process, a transition from computationally intensive

CFD models to less computationally demanding

models is necessary. The basis of in-line control is

that sensor signals are read during the printing

process and input into the simplified model, thus

adjusting the control variables of the printer during

the printing process. Investigations regarding suitable

model reduction have already been conducted using

regression fitting and the Gaussian process. Future

investigations into model reduction will address

approaches that proceed more specifically in data

collection, such as Bayesian optimization.

Once the models are sufficiently trained through

the DT process to provide a detailed description of the

AoD printing process for a variety of different

SIMULTECH 2025 - 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

206

applications, the design can be iterated using the DT.

This means that design optimization of individual

components can be carried out in the virtual layer, and

validation can be performed using digital testing.

Through the interface to the physical layer, the

manufacturing of the optimized component can then

be carried out.

In principle, the presented DT architecture also

provides autonomous control of the AoD-jet printing

process. This requires targeted further development

of the AoD-jet printing system. In this case, user

interaction would be limited to specifying the printing

goal, and the DT would autonomously send the

recommended settings to the printing system and

carry out the printing.

Additionally, further investigations are being

conducted into the model representation of the

atomization process to allow for detailed

phenomenological modelling.

ACKNOWLEDGEMENTS

The authors would like to acknowledge Achim

Wenka (IMVT, KIT) for his continuing support in the

field of computational fluid dynamics, Raissa Stella

Maffo for her work on DT, Martin Ungerer for his

conceptual work on and implementation of the AoD-

setup, Hawo Höfer (IAI, KIT) for his work on model

reduction, and Klaus-Martin Reichert (IAI, KIT) for

soft- and hardware support.

This work was supported by the program Materials

Systems Engineering of the Helmholtz Association.

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