DEGREE PROJECT IN TECHNOLOGY,
FIRST CYCLE, 15 CREDITS
STOCKHOLM, SWEDEN 2021
A feasibility study of Digital Twin
for additive manufacturing
From the perspective of resource efficiency,
smaller companies, and the future of industry
TOR FAGLE SHIMAMURA
DOUGLAS TIMPER
KTH ROYAL INSTITUTE OF TECHNOLOGY
SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
A feasibility study of Digital Twin for additive manufacturing
From the perspective of resource efficiency, smaller companies, and the future of industry
Tor Fagle Shimamura & Douglas Timper
MG110X Examensarbete inom Industriell Produktion 2021
KTH Industriell teknik och management Industriell produktion
SE-100 44 STOCKHOLM
Abstract: The Digital Twin in additive manufacturing has become a topic of interest as
the method promises to be the next step in its development as well as an essential
component of Industry 4.0. However, the area is as of now largely undefined since both
Digital Twin and additive manufacturing are terms that encompasses several methods
and techniques as well as to it being relatively novel. A Digital Twin is as defined a
digital representation of a physical system with real-time flow of data between them.
Due to its simulation capabilities, the method is seen as the future of additive
manufacturing. Additive manufacturing today is hindered by its unpredictability. Prints
often fail due to a lack of ability to accurately predict, monitor, and control the printing
process, an area where the Digital Twin excels. The question is not if the Digital Twin
will be a part of the future of additive manufacturing but rather how that relationship
will look like. This report focuses on the integration of the Digital Twin and additive
manufacturing by looking at a smaller additive manufacturers shopfloor to generate an
understanding of what the next step towards integration should be. The companies that
currently put great effort into developing and integrating their Digital Twins are mostly
the large ones with plenty of resources to spend. However, this report still finds that
there are benefits that could be gained by smaller companies if they were to invest into a
Digital Twin. This is especially true if they have intentions to automate their business in
the future. A small company may not be able to benefit as much as a larger one and the
initial investment could prove to be an expensive endeavor for a relatively small
increase in efficiency. Developing their business with Digital Twins in mind is however
a good idea to prepare for the future as the method become more commercially
established. There are however some aspects of the Digital Twin that can be made
inexpensive while still providing great value to the shopfloor as well as future-proofing
the manufacturing processes.
Sammanfattning: Digital Tvilling inom additiv tillverkning har blivit ett ämne av stort
intresse eftersom att det ses som det nästa steget i dess utveckling och som en viktig
komponent i industri 4.0. Området är dock fortfarande mestadels odefinierat i och med
att både Digital Tvilling och additiv tillverkning är termer som innefattar många olika
metoder och tekniker samt att Digital Tvilling inom additiv tillverkning är ett relativt
nytt koncept. En Digital Tvilling är enligt definition en digital representation av ett
fysiskt system med ett realtidflöde av data mellan dem. På grund av dess möjlighet att
simulera så ses metoden som en del av framtiden för additiv tillverkning. Anledningen
till detta är att den additiva tillverkningens största problem i dagsläget är dess
oförutsägbarhet. Utskrifter misslyckas ofta på grund av en brist i förmåga att förutspå
och övervaka och kontrollera utskriftsprocessen, vilket är områden som den Digital
Tvillingen utmärker sig i. Frågan är inte om den Digital Tvillingen kommer vara del av
additiv tillverkning, utan snarare hur deras förhållande kommer se ut. Denna rapport
fokuserar på integrationen av Digital Tvilling och additiv tillverkning genom att se på
en mindre additiv tillverkares fabriksgolv för att skapa en förståelse av hur nästa steg av
integration kommer att se ut. I nuläget är det mestadels stora företag med mycket
resurser som utvecklar och integrera Digital Tvillingar. Denna rapport finner dock att
mindre företag kan tjäna på att integrera en Digital Tvilling. Detta är speciellt sant om
de har intentioner att automatisera sin verksamhet i framtiden. Ett mindre företag lär
inte tjäna lika mycket som ett stort och den initiala investeringen kan bli stor för en
relativt liten ökad effektivitet. Att utveckla sin verksamhet med Digital Tvilling i åtanke
är dock en bra idé för att förbereda för framtiden allt eftersom den blir mer kommersiellt
etablerat. Det finns även en del aspekter av Digital Tvilling som kan utföras med liten
budget och bidra med stort värde till fabriksgolvet samt för att framtidssäkra
verksamhetens tillverkande processer.
Preface: This paper was written as a part of the MG110X “Examensarbete inom
Industriell Produktion” course during the spring semester of 2021 at Kungliga Tekniska
Högskolan, KTH. We would like to thank Tomas Österlind for mentoring us during this
project and Svensson 3D for allowing us to visit their facility and answering our
questions. The authors of this report is Tor Fagle Shimamura and Douglas Timper. Tor
have held the primary focus on the Digital Twin aspect while Douglas have focused
more on the additive manufacturing aspect. The responsibility of all other parts has been
equally shared.
Table of Contents
1
Introduction.............................................................................................................1
1.1
What is a Digital Twin? ....................................................................................1
1.2
What is additive manufacturing? .....................................................................3
1.3
Digital Twin in relation to additive manufacturing ............................................5
1.4
Research questions.........................................................................................5
1.5 Why does the Digital Twin in relation to additive manufacturing needs to
be researched? .........................................................................................................5
1.6
2
3
4
Benefits of Digital Twins .........................................................................................6
2.1
Resource efficiency .........................................................................................6
2.2
Simulation capabilities .....................................................................................7
2.3
Technological advancements and implementations. .......................................9
A study in a modern additive manufacturing company’s shopfloor. ......................10
3.1
About the manufacture ..................................................................................10
3.2
Integrated enabling technologies. .................................................................10
3.3
Is the integration of a Digital Twin a viable solution? .....................................11
3.4
The risk and reward of integration .................................................................11
Implementation of a Digital Twin ..........................................................................12
4.1
5
What has been examined at and how was it studied?.....................................6
Our Digital Twin system proposal for additive manufacturer .........................12
Conclusions ..........................................................................................................14
5.1
Conclusions to research questions ...............................................................15
5.2
Future work ...................................................................................................16
6
References ...........................................................................................................17
7
Appendix A - Abbreviations ......................................................................................
1. Introduction
The integration of a Digital Twin in additive manufacturing is an interesting area of
research that promises to be the next step towards a defect-free additive production. To
better understand this area, the terms “Digital Twin” and “additive manufacturing” as
well as their relation to each other needed to be defined. Abbreviations used in this
report can be found in Appendix A.
1.1 What is a Digital Twin?
The term “Digital Twin” was first published as a part of NASA’s technology and
processing roadmap under their technology area 11 in 2010. There the Digital Twin
were to be used to better maneuver a space craft by generating more accurate real-time
simulations. The roadmap states that “A Digital Twin is an integrated multi-physics,
multi-scale, probabilistic simulation of a vehicle or system that uses the best available
physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin”
[1].
The term Digital Twin, DT is today used as a synonym for virtual model, which
depending on its level of integrations can be an unfair comparison. A definition of what
the term DT meant were given by Kritzinger et al. who created three different categories
of virtual models depending of their level of integration or flow of data [2]:
Digital Model
The Digital model is a virtual representation of a physical system or product with a low
level of integration, meaning that there is not any automatic flow of data between the
physical and virtual as illustrated in Figure 1[2]. The Digital Model is often used to
illustrate future products or systems by feeding a selected program data manually to
create the digital representation, if the model where to be updated it will have to be done
manually.
Figure 1. Illustration of the data flow in the Digital Model.
Digital Shadow
The Digital Shadow, like the Digital Model, is a virtual representation of a product or
system but with an automatic flow of data from the physical to the virtual model, note
that the flow of data is a one-way process as presented by Figure 2 [2]. The Digital
Shadow can be used to monitor a production line and in real-time track different
products or activities, it cannot however automatically act on the data it collects.
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Figure 2. Illustration of the data flow in the Digital Shadow.
Digital Twin
The DT is based on the definition of its peers but unlike them the DT is created with the
highest level of integration between the physical and virtual, using automatic flows of
data in both directions, presented in Figure 3 [2]. The DT can gather data and based on
that data make decisions in real-time, decisions that optimize different parameters based
on the product or systems surroundings to increase its quality and/or efficiency.
Figure 3. Illustration of the data flow in the Digital Twin.
DT is today seen as the key enabler for industry 4.0 and as a part of the future of
manufacturing that will be integrated in both a holistic and a local level in the factories
of the future in order to predict, monitor, and control its processes [3].
DT on the holistic level is a tool used to plan and control the layout and the flow of
materials and processes on the shopfloor. The manual gathering of data required to
develop a production layout is estimated to take up 74% of the time during the planning
phase and by introducing a DT that percentage is expected to drop [4]. However, the
required initial investment would limit this method for the larger and wealthier
companies for the near future. As a tool to control the material flow on a shop floor
there are different conceptual methods, Glatt et al. presented a use case which describes
a scalable material handling process build out of a network of connected smart conveyor
belts [5], while Frankó et al. introduced different ways to track autonomous vehicles in
industry 4.0 on a shop floor [6].
DTs on the local level is a tool to optimize the quality of products by gathering data in
real-time from the acting machine(s) on the shopfloor using sensors and using that data
to optimize its various parameters. The research around the implementation of a DT
directly into manufacturing machines is still in its early stage and reports presenting use
cases are sparse, there is however reports presenting concepts. DebRoy et al. presents
that by monitoring and modeling heat transfer and material flow, the inner structure and
the mechanical properties of an additive manufactured products will theoretically be
more accurately predicted [7].
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1.2 What is additive manufacturing?
Additive manufacturing, AM, is a manufacturing technique that produces parts layerby-layer rather than through traditional subtractive manufacturing methods where
material is removed in order to produce the final shape . Materials that can be used in
AM include metallic, ceramic, and polymeric as well as combinations, hybrid, or
functionally graded materials (FGMs) [8].
The additive manufacturing methods are still relatively novel and there are plenty of
challenges left before it can fully replace and complement traditional methods. The
reason that there is a desire to overcome these challenges and further develop AM is its
potential benefits, Tofail et al. summarized the benefits as the following [8]:
1. Direct translation from design to manufactured part, requiring fewer middle
steps in the manufacturing process.
2. Potential for customization without the need for additional tooling or other
manufacturing cost.
3. Accurate control over the component’s internal structure, allowing complex
internal features.
4. Allowing the manufacturing of parts with hollow or lattice structures, increasing
flexibility or making the part lightweight while maintaining functionality.
5. Components can be made into their final, or near final form directly and thus
minimizing the need to additional processing.
6. It has the potential to minimize waste by maximizing material utilization.
7. Overall development and manufacturing time can be greatly reduced. As the
process can go directly from design to manufacturing without any middle steps.
8. Can manufacture a large variety of parts with a relatively small operational
footprint.
9. Parts can be manufactured on demand and making something new require less
planning beforehand.
10. It can achieve excellent scalability due to small footprint of each machine.
There are several different categories that count as additive manufacturing. The
International Organization for Standardization (ISO) and the American Society for
Testing and Materials (ASTM) have created a standard (52900:2018) where AM is
divided into seven different categories:
1. Binder Jetting, BJT
2. Direct Energy Deposition, DED
3. Material Extrusion, MEX
4. Material Jetting, MJT
5. Powder Bed Fusion, PBF
6. Sheet Lamination, SHL
7. Vat photopolymerization, VPP
This report has primarily included MEX, PBF, and VPP in its research as they are the
most used among smaller manufacturers. MEX is a category that includes Fused
Deposition Modelling, FDM, that is synonymous to Fused Filament Fabrication, FFF,
and Fused Layer Modelling, FLM. Machines using this technology has a nozzle or
orifice where material is selectively extruded, building the part onto a build plate as
presented by Figure 4. This technology is relatively inexpensive, scalable, and the most
widespread of all AM technologies. Printed parts have good mechanical properties. The
downsides are that it is limited to polymers and composite material, printed parts have a
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vertical anisotropy, step-structured surface, and the method is not great at fine details.
Complex structures can require supports to achieve desired results, however clever
designs can lessen the need for support and thus reduce material waste. Its primary
usage is for prototypes, toys, or low load parts.
Figure 4. MEX-printer using FDM. [9].
PBF is a technology that can be used with metals, ceramics, polymers, composites, and
hybrid materials. Examples technologies for PBF are Electron Beam Melting (EBM),
Direct Metal Laser (DMLS), and Selective Laster Sintering/Melting (SLS, SLM). It
generally works by having a powder bed of the chosen material that gets selectively
melted into a solid before a new layer of powder is put on top as illustrated in Figure 5.
This has the benefits of being relatively inexpensive, the powder base acts as integrated
support structure, reduces the material wasted, and it has a larger range of material
options compered to MEX. Unfortunately, they are relatively slow, parts lack in
structural integrity, produces higher energy costs, and the finish is depending on the
precursors powder size.
Figure 5. Generic PBF-printer. [10].
VPP uses liquid polymer in a vat, where the model is constructed layer by layer. An
ultraviolet light is used to harden specific parts of the liquid to produce the desired
model a variant of this is presented in Figure 6. Stereolithography (SLA) and Digital
Light Processing (DLP) are some examples of such technologies. These technologies
allow for the manufacturing of parts with high accuracy, surface finish, and details.
However, it is limited to photopolymers which has poor mechanical properties that are
also relatively expensive. The build process is however quite slow[8].
4
Figure 6. VPP-printer using SLA. Adapted from [9].
1.3 Digital Twin in relation to additive manufacturing
A DT in relation to AM has several important points of interest. Depending on the
material used, the purpose, and final use of the system the DT will be slightly different.
But the most important aspect is making simulations easier and more accurate [11].
Normally in order to reach the optimal settings for any given product, several
experiments are required to fine tune all variables to ensure a good product [7]. This
procedure can take a lot of time and cost a lot of material, making trial and error very
resource inefficient [12]. The purpose of a DT in this case is to gather data, that later
can drive simulations and make calculations and predictions more reliable as well as
less expensive [11]. An example can be found in metallic AM where the parameters
transient temperature fields, cooling rates, and solidification are needed to simulate the
process and accurately predict the outcome [12]. Metallic AM is however not seen as
widely used with smaller manufactures and is there for not a method that has been
highlighted in this report. Due to the vast range of different types of AM, what
parameters are necessary to analyze varies from case to case. DTs in additive
manufacturing is also still in its infancy and there is a lot of development needed to
fully evaluate its uses [11].
1.4 Research questions
This research presents the potential benefits a DT could provide in AM by answering
the questions:
Is the DT a promising method for manufacturers?
Does an integrated DT show promise in AM especially?
How would the integrated DT effect resource efficiency?
How does a DT relate to the size and resources of the manufacturer?
1.5 Why Digital Twin in relation to additive manufacturing needs to be
researched?
A DT could prove the solution to overcome many of the issues found in manufacturing
today such as long and expensive trial and error process, long paths to product
qualification, and an abundance of defects [11], [12]. To overcome these issues data
needs to be gathered during the planning and manufacturing phase to identify different
parameters and optimize them according to the product and its environment to reach a
satisfactory result. The data gathered will also have to be acted on in real-time to not
5
just predict and monitor but also control the process. The DT, which connects the
physical with the virtual to predict, monitor, and control the processes to achieve a
desirable outcome. Such capabilities seem to be is needed to overcome the issues
presented in additive manufacturing today and develop the practice further.
In regards to how a DT will be built and implemented with additive manufacturer there
is today no consensus on how a the framework of a DT will look like [13]. Therefore, a
study regarding the implementation of a scalable DT with a small additive manufacturer
could provide an insight on how the framework could be constructed. The framework
could then act as a guide towards the creation of DT by presenting areas of interest.
1.6 What has been examined at and how was it studied?
The purpose of this project was to find out how a Digital Twin System, DTS could be
implemented into a small company that manufactures plastic parts using additive
manufacturing. Some things that were investigated is how legacy machines can be
integrated into a DTS, how it can be used to help with the implementation of new
production methods, tracking assets, improving print quality, and efficiency as well as
how it can open the path to automatization. The primary focus of this project was on
how these methods can improve resource management and efficiency. Areas that will be
investigated is material usages, energy use and machine efficiency as well as the
parameters that are connected to these areas.
The research consist of a literature study were different important parameters and
methods was identified. The literature study was conducted by identifying key search
words such as “Digital Twin” and “Additive manufacturing” that were then used to
identify relevant papers and studies that could be used in this report. A visit at an
additive manufacture where their needs were through interviews identified and a
conceptual DT will be connected to and then presented as a solution to those needs.
2. Benefits of Digital Twins
An integrated DT can collect and process data and with that data make real-time
decisions. Different situations require different methods for processing data such as
machine learning, create simulations, gathering of data throughout the process to
accurately predict the outcome. The benefits of DTs are vast but have been categorized
into three categories: resource efficiency, simulation capabilities and technological
advancements and implementations.
2.1 Resource efficiency
A DT, once implemented in a manufacturing setting, can identify important parameters
across a system and then optimize them to improve the quality according to the
organizations predetermined key performance indicators (KPIs). An example of this can
be found with consumer-goods producer Unilever and Microsoft, they implemented a
DT pilot in one of Unilever’s factories in Brazil that produces among other wares soap
under the brand name Dove. It took 3-4 weeks before the DT was fully implemented,
the system was used to among other things optimize the temperature of which soap
were pushed out. Those changes led to company generating 2.8 million USD in savings
6
at the tested factory by reducing energy expenditure and increasing productivity (1% to
3%) [14].
Another example of the resources saved by implementing DT is presented by Erol et al.
They state that because of the implementation of a DT during development of a
Maserati car there was an increase in productivity [15]. In a collaboration between
Maserati and Siemens, a DT was implemented and utilized during the development of
the Maserati Ghibli. Because of the use of the DT fewer prototypes needed to produced
witch among other factors led to a 30% drop in both time and costs during development.
Another example of what have been gained by implementing Siemens’s software can be
found in the collaboration between Siemens and battery manufacturer Rosendahl
Nextrom GmbH [16]. The implanted DT were able to half Rosendahl’s time-to-market
by eliminating the need for real-life testing.
According to Teng et al. Using data-driven improvements via a DT is expected to
increase energy and resource efficiency by 18% [17]. Another way a DT could increase
an organizations resources efficiency is by enabling recycling, recovery, and
remanufacturing of waste electrical and electronic equipment by tracking the produced
product throughout its lifecycle and thereby use appropriate recycle or remanufacturing
according to the data collected at its end-of-life phase [18].
A case study by Liu et al. of a hollow glass manufacturing company showed a very
promising result. While operating their system using a DT their furnaces reached an
average utilization rate of 93,6% compared to the previous average of 70% [19]. They
also concluded that the DT could avoid potential design errors and inefficiencies. The
DT verifies the dynamic execution of new systems early in deployment. By using online
data, the DT also allows for dynamic optimization to improve the operation efficiency
of the manufacturing system. However, they also noted that some decisions should be
left to experiences engineers and therefore the process cannot yet be fully automated.
2.2 Simulation capabilities
DTs shows a great improvement in simulation capabilities in many potential areas.
Ferreira et al. lists seventeen industry 4.0 design principles, them being: Vertical
integration, Horizontal integration, End-to-end engineering integration, smart factory,
interoperability, modularity, real-time capability, virtualization, decentralization,
autonomy, optimization, product personalization, and corporate and social
responsibility. The DT fully captures most but at least partially capture all of these
principles, especially if combined with other methods of simulation [20]. The DT can
therefore be seen as an essential part of industry 4.0 as illustrate in Figure 7.
7
Industry 4.0
The 17 design
principles
Digital Twin
Figure 7. Digital Twin as the first step towards industry 4.0. [20].
Liu et al. proposes a Configuration-Motion-Control-Optimization, CMCO architecture
based on a DT to simulate flow-type manufacturing systems. The quad-play CMCO
model is presented by Liu et al. is illustrated in Figure 8. They present a DTS based on
the Unity3D engine, which is a physics engine often used for game development, that
could collect, gather, and utilize real time data to simulate a manufacturing process in an
efficient and accurate manner. This data can then be used in an iterative optimization
process to generate improved design schemes [21].
Figure 8. Structure of the quad-play CMCO model. Adapted from [21]
Another use of simulations based on DTs is to combine it with big data learning and
analysis, BDLA. As DTs consistently and synchronously reflect the physical entities in
both geometry and behavior it is suitable to gather a lot of data from it. This data would
normally not be feasible to gather as directly from the physical world as it would require
a lot of sensors that can be expensive and add a lot of potential errors. The system still
requires sensors to make the DT accurate, however, many parts can be simulated.
BDLA is good method to increase simulation capabilities to make them match reality as
closely as possible. It can also more effectively gather experience and knowledge about
the process and therefore provide guidance for subsequent production [22].
8
Connecting a DT with a physics simulation was tested by Glatt et al. in a material
handling scenario [5]. The DT was used in a Cyber-physical system, CPS to predict,
monitor, and diagnose the process. The prediction part ran several simulations
automatically to test different speeds and, in their case, it led to a 27% reduction in
transportation time. These physics simulations enable repeated predictive and shortterm simulation of different unique material flows with varying physical attributes. This
shows to be more promising than older methods to the modularity and adaptability of
this model that is not available in old systems.
The monitoring aspect allowed them to notice in the simulation if something may have
gone wrong, send notifications to operators and saves the data about the incident for
study. The DT could in this case act as a virtual or soft sensor, detecting disturbances
that would otherwise be very difficult to measure and analyze.
The diagnostic part included taking real world disturbances and applying the data
gathered from such and inputting them into the simulation to gain improved parameters
for the simulation. These improved parameters then allow the simulation to better help
the management of such disturbances or detect other unknown but potential problems.
2.3 Technological advancements and implementations.
A factory wide integrated DT will act as a core component in and the first step towards
the implementation of a cyber-physical production system, CPPS, the DT would in this
case act as a platform which would let different virtual systems to collaborate in realtime. CPPS is today referred to as the core component of 4th industrial revolution,
industry 4.0 [4], [23], the DT could therefore be seen as the key to this revolution.
Once a DT is in place it could with additional technologies identify and integrate newly
arrived machines or assets. Sommer et al. presents a way towards the automated
generation of a DT which uses machine vision to scan newly implemented assets [24].
A possible area where DT could be applied in a company is digitalization which could
mean the reduction of processes requiring human contact [25], a field that has gained
interest as a result of the COVID-19 pandemic. The DT could help a company better
digitally communicate with their customers as well as minimizing the required manual
work across the shop floor by enabling CPPS which would lead to a higher rate of
autonomation.
In the last few years the amount of research publications for smarter digitalized
manufacturing has increased drastically, demonstrating how it is becoming more and
more relevant [20].
While analyzing different methods of simulation for industry 4.0 Ferreria et al. [20]
propose that a system using DTs and hybrid simulation methods are the main methods
of simulation in industry 4.0. However, they also state that most research currently done
use artificial data or hypothetical cases. A first-generation DT of AM is achievable but
has plenty of challenges.
9
3. A study in a modern additive manufacturing company’s
shopfloor.
In order to better understand what a DT could do in AM and how one could begin to be
implemented a study was conducted at smaller manufacturers facility. The goal of the
study was to generate an understanding of the current situation as well as to identify
what they wanted from a DTS.
3.1 About the manufacture
A visit was conducted at Svensson 3D who manufacture plastic parts using various
printers and materials, they are currently operating from a smaller facility approximated
to be 100 square meters. In this facility they have a SLS nylon powder printer and
several other FDM printers running using PLA and ABS with possibility to use special
filaments as well as an SLA vat printer. The facility also consists of an office space and
a space for research and development. Their clients send in a virtual model which the
company download to the specified printer which then prints the finished product. The
product is sent back out to the client after quality inspection. The models can vary
greatly between clients and thus the range of complexity in designs is very wide. Their
current goal is to fully automate the entire process, from order to packaged finished
product and they are currently working to automate the printing aspect. This will be
done by a frame that host an array of isolated printers that work together with robotic
arms to remove finished prints and set up clean plates that the machine can print on.
These printers do not have a very high energy cost, but the process is currently rather
material inefficient. The company estimated that 15-20% of all bought material goes to
waste, but they did not have any hard data to support this estimation. This issue has two
factors, the first factor is that prints often require a lot of support structures to be built to
keep the model stable. This factor can be improved by smarted designs adapted for AM
but as the company does not design the products, which means that they cannot directly
influence this factor. The second factor is failed prints, and this can be directly
controlled. There are many factors that can result in a failed print, and it may be
unrealistic never have any failed prints.
3.2 Integrated enabling technologies.
All printers are connected to a server and where they share status updates such as the
speed of the tool, temperature of the plate and time to completion according to three
different estimates. Such as layer time, amount of material left and software
calculations. These are not fully centralized nor documented but accessible for upgrades
and could potentially be feed into a DT. Printers that utilize a build plate have sensors to
help with leveling and provides an automatic leveling feature. The building plate being
sufficiently level is a crucial factor for a successful print. These automated features are
generally not perfect and may require some manual adjustments at times.
Their nylon powder printer is very sensitive to temperature and the current settings were
derived experimentally. This printer also keeps track of a lot of data that may be used in
a DT to simulate more optimal settings according to its environment and the product.
If their machines is by their own design, they may have proper 3D models already made
that can be integrated into a holistic DT. The floor plan is very open and accessible,
allowing a digital model of it to be of lower complexity.
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3.3 Is the integration of a Digital Twin a viable solution?
A Digital Twin could aid with the printing process in several ways. A potential
promising method is to utilize augmented reality, AR. An AR camera could be set up to
monitor and compare the print to the sliced model. If they are too different it stops the
print or sends a warning message. Butt et al. Presents an example of such a solution
[26]. As previously identified the best way for this company, and others in similar
situations, to increase resource efficiency is by reducing the number of failed prints.
The three factors that lead to a failed print is according to the manufacturer; the first
were stoppage in the extruder tool caused by high surrounding temperatures or eventual
cooling failures. The second factor is that the product did not correctly adhere to the
base plate and came loose during manufacturing, the cause of this is inaccurate starting
variables such as temperature and bed leveling. The third and final factor depended on
the design of the product were structurally weak and tiny details could brake during the
manufacturing. Third factor relates to the product itself and has to be managed before
the printing process by setting viable variables or doing some redesigns. The other
factors could be avoided with proper predictions, monitoring and control of the printing
process. Environmental factors, such as dust build-up could also create some issues, but
this may be more easily managed through other means.
A system to keep track of different filament rolls, to keep track over how much material
they have left could be implemented. This could work by weighing the filament to see if
there is enough or when it needs to the changed. This could potentially reduce waste by
potentially utilizing the filament to the fullest. Analytics and simulations to keep track
of different processes may be necessary to fully automate the manufacturing and a DT
could be the solution.
3.4 The risk and reward of integration
A DT is a representation of a physical system or product in all its relevant aspects,
however in practice it can prove troublesome to collect and transfer all relevant data
surrounding the product or system. The problem is to know what data is relevant, if
important aspects is not identified the results of the simulations could be
inconsistently deviating from the actual results, rendering the results from the DT
inadequate. The implementation of a DT could be an expensive process and if the
scope of the necessary aspects is not identified early on that project could go nowhere.
A DT could for a small producer be an overcomplicated solution to a simple problem,
the investment of resources to create and integrate a DT in a smaller additive
manufacturing workshop could prove to be expensive and result in little to none
increase in efficiency.
In additive manufacturing one of the largest factors that could lead to an increase
resource efficiency is a more efficient use of the material. With a DT it is expected
that the number of defected products will be reduced by optimizing parameters
including the cooling rate and extrusion speed to adapt to the products thermal
distribution. An integrated DT will also prepare the manufacturer for the transition to
a cyber-physical production system, future-proofing the production system for
competition to come.
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4. Implementation of a Digital Twin
According to Gaikwad et al. there is three ways that the DT significantly can improve
AM: Optimizing different process parameters and provide a framework for the design of
the part and support structure. Monitoring of potential process errors with simulations
and real-time data from sensors, and thirdly storing and processing the data acquired
from different sensors [27]. With the three ways by Gaikwad et al. as well as the
problems and possibilities previously presented a proposal of a DT for a smaller
additive manufacturing company could be complied.
4.1 A proposed Digital Twin system for additive manufacturer
The reduction of faulty prints is the most essential aspect a DT can work with in AM, an
aspect which will lead to a higher utilization of resources increasing the company’s
profit by reducing its cost. For a DT to realize this it needs to be able to monitor and
predict heat distribution under the process. The two main contributors that can be
altered without changing the part is the temperature of the build plate and the
temperature in the nozzle.
The temperature of the build plate should be regulated depending on the material used
which due to human error is a parameter that can be neglected therefor a system that can
receive an order and begin to print it automatically would eliminate that error. The DTS
could start off with a system that monitor when the printers are used and what orders are
begin caried out to centralize that data, the next step would then be to give the system
the ability to suggest printers to different orders according to the amount of filament in
the machine, to increase the utilization of the material used. The next step would then be
to have the system automatically send orders to printers according to amount of filament
and usage, making the system a DT.
As previously presented another way to handle failed prints is to lower their impact by
stopping the printing as soon as a defect is detected. Using a camera with an AR model
of the sliced product overlayed over the build plate of an MEX printer would be capable
of detecting and warning incase the print is failing by comparing and detecting
geometrical deviations, a version of this presented by Ceruti et al. is illustrated in Figure
9. VisionLib has a quality inspection tool using AR called Twyn that may be suited to
be integrate into such a DT. It matches AR models of parts to the real world through a
tablet or phone to see if they are in the right position [28]. It claims to work well with
Unity3D and thus would be possible to adapt into this purpose. The budget for this
project is hard to fully estimate in a general case as there are many unknowns. Some
factors that impact the investment cost is development time, minimum required quality
of camera and software cost. These need to be determined by a case-by-case study and
then be compared with the cost of failed prints to see if it would a useful tool to
minimize waste and time.
12
Figure 9. AR defect detector using SURF. Adapted from [29]
If the investment costs are high, having systems in place to improve start variables to be
as stable as possible may be a better alternative to go for. The problem with these is that
they cannot account for problems that arise later during the printing process and it may
be very difficult to truly know all variables and how they will influence the print. So, a
system like this would always add an additional level of security that can be highly
valuable in time.
A DT could also be programed to increase the speed of the additive manufacturing by
optimizing the parameters: nozzle speed, nozzle temperature, and the rate of extrusion.
To create such a DT, a system where simulations of the thermal distribution are run
throughout the process would be needed and/or a library of previous attempts were
through machine learning those parameters could be optimized.
If the company choses to automize the handling of finished parts, an additional DT
where the part’s mass, and its center is calculated and used to determine the pace of the
handling system would be useful to shorten the travel time. Such DT would with a
virtual model of the finished part be able to calculate the force required to make that
part fall over and adjust the speed to one were a full stop would not generate enough
acceleration to knock it over.
13
5. Conclusions and Discussions
This section presents some suggestions as to how a smaller manufacturing company
could implement DTs to improve their business or work towards the implementation.
The methods will be listed loosely in order of how difficult they are to implement. One
of the simplest and easiest systems to implement is a 3D model of the manufacturer’s
shopfloor an example of which is presented by Figure 10.
Figure 10. Digital Model of the manufacturer’s shopfloor.
Figure 10 is an example for what such a model could look like, inspired by the current
set-up of Svensson 3D by the time of the visit. The different parts are accurate in size,
are color-coded, but are not highly detailed and is currently not connected to any
simulation software. The purpose of this is primarily to allow easy and intuitive
planning of the layout, allowing the company to test and feel out layouts before having
to do any physical work to change it.
This model can answer if important things can be accessed, could things be placed in a
more efficient matter, and if there is space for new machines.
The benefits of using a system like this are not major in a direct sense. However, having
an additional level of control and planning can become highly valuable if it helps
finding some critical error. It may also make it easier to find and perform small
optimizations that grow in value over time. Having a 3D model is also a precursor to
allow the use of more complex and valuable DT methods.
Next method is to gather and organize data from the manufacturing process in a
centralized way. While building a DT, the more data points that are used, the more
accurate the DT will become. It is possible to predict some variables, but even the best
predictions are only truly useful if they can be verified. Because modern AM machines
often has digital components, gathering some data is relatively easy, but adding
additional sensors and manual entries of print quality based on current settings can be
highly valuable for future simulations. Some examples of additional sensors are AR
quality control of the print, available material measurements and temperature
measurements. These have been described in more detail earlier in the report.
14
This data does not necessarily need to be integrated into a 3D model but can exist in a
more abstract data-based form. However, combining it with a physics model would
allow for physics-based simulations. It could also be very useful to create a user
interface utilizing the 3D model to make it clear for operators exactly what machines are
running, their status, and any potential errors they might have.
The next milestone is to start running simulations to optimize the manufacturing
process. This may become far more difficult to do as a small company as such software
can either require a lot of work to set up. Large companies such as Siemens can set up a
department to build systems tailored to their needs and the growing use of it shows that
it has a lot of potential. However, there are some simulations that still could be used
with a lower budget. In relation to AM in particular, DT can be used to find optimized
print variables to ensure quality and reduce resource use. It can also be used to make
machine use more efficient, making sure there is enough material in the printers and
may predict errors.
5.1 Conclusions to research questions
Is the DT a promising method for manufacturers?
DTs shows promise for manufacturers like other simulation technologies that has grown
rapidly under the last few decades as computing power has improved drastically. Using
simulations for the entire manufacturing process seems to us to be a natural next step in
manufacturing. As there are a lot of large companies investing a lot into DT and the
very rapidly growing interest in it in scientific literature seems like proof of it.
However, it worth noting that it is still a relatively novel idea and has yet to truly prove
its value.
Also, the use of DT combined with AR and VR shows a lot of promise to make design
and decision making more intuitive. This has the potential to fuel creativity, allow for
new ideas, and methods to be found and developed.
Does an integrated DT show promise in AM especially?
Additive manufacturing especially using metallic materials still has a lot of problems to
overcome before becoming fully viable as a widespread manufacturing process. A large
amount of current research into improving this field seems to suggest that the DT is the
next step in improving AM technology. AM often being digital by default also make it
lend itself well into DT, making it easier to integrate them into each other.
How would the integrated DT effect resource efficiency?
As shown by case studies and research into the field, DT has a lot of potential in
optimizing efficiency and stability of different manufacturing processes. Errors in a
manufacturing process can waste material resources and inefficient use of machines can
cost a lot of energy. Before simulations became commercially viable, for example the
car industry had to build and crash a lot of cars to test safety as well as wear and tear.
Computer simulations allowed a lot of these tests to be made in the digital space before
the physical and thus saving a lot of physical resources. DT is an expansion of this, and
similar results are reasonable to expect.
15
How does a DT relate to the size and resources of the manufacturer?
DTs has mostly been used by manufacturers with a lot of available resources to invest
into them. As the idea is still rather novel there are not a lot of accessible, ready-made
tools to integrate a DT, and the system may need to be built up from scratch. This will
be limiting for smaller companies that does not have the resources to allocate into these
systems. However, there are some aspects of DT that can be used by smaller companies.
The benefits from this may not be that large, and the situation needs to be examined
case by case to determine if it is worth the investment. More importantly, setting up and
enabling the use of DT into the manufacturing process may be a wise idea to quickly be
able to adopt such methods as they become more easily accessible. This is especially
important if there are ideas to integrate robotics into the manufacturing process as DT
can be highly valuable for this purpose.
5.2 Future work
The next step for DTs in AM is in our opinion the creation of data centralizing tool that
can allow smaller additive manufacturers more control of their operations. The tool
would then act as the foundations of the DT to come by allowing automatic flows of
data from different installed sensors. That data would be used to make predictions that
could allow the system to generate decisions and control processes with controllers
installed where needed. As we see it as of now the additive manufacturers are mostly
utilizing Digital Models and to turn those into DTs, they most first become Digital
Shadows as presented by Figure 11. Most importantly, this area demands experiments
and case studies with additive manufacturers to acquire conclusive results on its
commercial viability, faults, and prospects.
Digital
Model
• Create a
framework for
the storage and
processing of
data
Digital
Shadow
• Install sensors
and automate
the flow of data
from the
physical to the
virtual
Digital
Twin
• Install
controllers and
automate the
flow of data
from the virtual
to the physical
Figure 11. The evolution towards a Digital Twin.
16
6. References
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
M. Shafto et al., “DRAFT Modeling, Simulation, information Technology & Processing
Roadmap - Technology Area 11,” Natl. Aeronaut. Sp. Adm., p. 27, 2010, [Online].
Available: https://www.nasa.gov/pdf/501321main_TA11-MSITP-DRAFT-Nov2010A1.pdf.
W. Kritzinger, M. Karner, G. Traar, J. Henjes, and W. Sihn, “Digital Twin in
manufacturing: A categorical literature review and classification,” IFAC-PapersOnLine,
vol. 51, no. 11, pp. 1016–1022, Jan. 2018, doi: 10.1016/j.ifacol.2018.08.474.
R. Rosen, G. Von Wichert, G. Lo, and K. D. Bettenhausen, “About the importance of
autonomy and digital twins for the future of manufacturing,” in IFAC-PapersOnLine,
May 2015, vol. 28, no. 3, pp. 567–572, doi: 10.1016/j.ifacol.2015.06.141.
T. H. J. Uhlemann, C. Lehmann, and R. Steinhilper, “The Digital Twin: Realizing the
Cyber-Physical Production System for Industry 4.0,” in Procedia CIRP, Jan. 2017, vol.
61, pp. 335–340, doi: 10.1016/j.procir.2016.11.152.
M. Glatt, C. Sinnwell, L. Yi, S. Donohoe, B. Ravani, and J. C. Aurich, “Modeling and
implementation of a digital twin of material flows based on physics simulation,” J.
Manuf. Syst., May 2020, doi: 10.1016/j.jmsy.2020.04.015.
A. Frankó, G. Vida, and P. Varga, “Reliable identification schemes for asset and
production tracking in industry 4.0,” Sensors (Switzerland), vol. 20, no. 13, pp. 1–24,
2020, doi: 10.3390/s20133709.
T. DebRoy, W. Zhang, J. Turner, and S. S. Babu, “Building digital twins of 3D printing
machines,” Scr. Mater., vol. 135, pp. 119–124, 2017, doi:
10.1016/j.scriptamat.2016.12.005.
S. A. M. Tofail, E. P. Koumoulos, A. Bandyopadhyay, S. Bose, L. O’Donoghue, and C.
Charitidis, “Additive manufacturing: scientific and technological challenges, market
uptake and opportunities,” Materials Today, vol. 21, no. 1. Elsevier B.V., pp. 22–37,
Jan. 01, 2018, doi: 10.1016/j.mattod.2017.07.001.
N. Bhattacharjee, A. Urrios, S. Kang, and A. Folch, “The upcoming 3D-printing
revolution in microfluidics,” Lab Chip, vol. 16, no. 10, pp. 1720–1742, 2016, doi:
10.1039/c6lc00163g.
S. Mufti, C. Smith, A. Tennant, and L. Seed, “Selective electron beam melting
manufacturing of electrically small antennas,” Adv. Sci. Technol. Eng. Syst., vol. 2, no. 6,
pp. 70–75, 2017, doi: 10.25046/aj020609.
L. Zhang et al., “Digital twins for additive manufacturing: A state‐of‐the‐art review,”
Appl. Sci., vol. 10, no. 23, pp. 1–10, 2020, doi: 10.3390/app10238350.
G. L. Knapp et al., “Building blocks for a digital twin of additive manufacturing,” Acta
Mater., vol. 135, pp. 390–399, 2017, doi: 10.1016/j.actamat.2017.06.039.
F. Tao, H. Zhang, A. Liu, and A. Y. C. Nee, “Digital Twin in Industry: State-of-the-Art,”
IEEE Trans. Ind. Informatics, vol. 15, no. 4, pp. 2405–2415, 2019, doi:
10.1109/TII.2018.2873186.
Jennifer Smith, “Unilever Uses Virtual Factories to Tune Up Its Supply Chain,” THE
WALL STREET JOURNAL, 2019. https://www.wsj.com/articles/unilever-uses-virtualfactories-to-tune-up-its-supply-chain-11563206402 (accessed Apr. 10, 2021).
“Getting to market quickly,” SIEMENS, 2015.
https://new.siemens.com/global/en/company/stories/industry/getting-to-marketquickly.html (accessed Apr. 10, 2021).
“Battery production with digital twin - Machine builder implements digitalization
strategy with solutions from Siemens,” SIEMENS, 2019.
https://press.siemens.com/global/en/news/battery-production-digital-twin-machinebuilder-implements-digitalization-strategy-solutions (accessed Apr. 10, 2021).
S. Y. Teng, M. Touš, W. D. Leong, B. S. How, H. L. Lam, and V. Máša, “Recent
17
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
advances on industrial data-driven energy savings: Digital twins and infrastructures,”
Renew. Sustain. Energy Rev., vol. 135, no. February 2020, 2021, doi:
10.1016/j.rser.2020.110208.
X. V. Wang and L. Wang, “Digital twin-based WEEE recycling, recovery and
remanufacturing in the background of Industry 4.0,” Int. J. Prod. Res., vol. 57, no. 12,
pp. 3892–3902, 2019, doi: 10.1080/00207543.2018.1497819.
Q. Liu et al., “Digital twin-based designing of the configuration, motion, control, and
optimization model of a flow-type smart manufacturing system,” J. Manuf. Syst., Apr.
2020, doi: 10.1016/j.jmsy.2020.04.012.
W. de Paula Ferreira, F. Armellini, and L. A. De Santa-Eulalia, “Simulation in industry
4.0: A state-of-the-art review,” Comput. Ind. Eng., vol. 149, no. September, p. 106868,
2020, doi: 10.1016/j.cie.2020.106868.
Q. Liu et al., “Digital twin-based designing of the configuration, motion, control, and
optimization model of a flow-type smart manufacturing system,” J. Manuf. Syst., vol. 58,
no. PB, pp. 52–64, 2020, doi: 10.1016/j.jmsy.2020.04.012.
P. Wang and M. Luo, “A digital twin-based big data virtual and real fusion learning
reference framework supported by industrial internet towards smart manufacturing,” J.
Manuf. Syst., vol. 58, pp. 16–32, Jan. 2021, doi: 10.1016/j.jmsy.2020.11.012.
L. Monostori, “Cyber-physical production systems: Roots, expectations and R&D
challenges,” Procedia CIRP, vol. 17, pp. 9–13, 2014, doi: 10.1016/j.procir.2014.03.115.
M. Sommer, J. Stjepandic, S. Stobrawa, and M. Von Soden, “Improvement of factory
planning by automated generation of a digital twin,” Adv. Transdiscipl. Eng., vol. 12, no.
July, pp. 453–462, 2020, doi: 10.3233/ATDE200105.
T. Erol, A. F. Mendi, and D. Dogan, “Digital Transformation Revolution with Digital
Twin Technology,” 4th Int. Symp. Multidiscip. Stud. Innov. Technol. ISMSIT 2020 Proc., 2020, doi: 10.1109/ISMSIT50672.2020.9254288.
J. Butt, “Exploring the interrelationship between additive manufacturing and industry
4.0,” Designs, vol. 4, no. 2, pp. 1–33, 2020, doi: 10.3390/designs4020013.
A. Gaikwad, R. Yavari, M. Montazeri, K. Cole, L. Bian, and P. Rao, “Toward the digital
twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics
to detect process faults,” IISE Trans., vol. 52, no. 11, pp. 1204–1217, 2020, doi:
10.1080/24725854.2019.1701753.
VisionLib, “VisionLib Twyn.” 2021, [Online]. Available: https://visionlib.com/enterar/.
A. Ceruti, A. Liverani, and T. Bombardi, “Augmented vision and interactive monitoring
in 3D printing process,” Int. J. Interact. Des. Manuf., vol. 11, no. 2, pp. 385–395, 2017,
doi: 10.1007/s12008-016-0347-y.
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7. Appendix A - Abbreviations
ABS – Acrylonitrile Butadiene Styrene
AM – Additive Manufacturing
AR – Augmented Reality
BDLA – Big Data Learning and Analysis
BJT – Binder Jetting
CMCO – Configuration-Motion-Control-Optimization
CPPS – Cyber-Physical Production System
CPS – Cyber-Physical System
DED – Direct Energy Deposition
DLP – Digital Light Processing
DMLS – Direct Metal Laser Sintering
DT – Digital Twin
DTS – Digital Twin System
EBM – Electron Beam Melting
FDM – Fused Deposition Modelling
FFF – Fused Filament Fabrication
FGMs – Functionally Graded Materials
FLM – Fused Layer Modelling
KPIs – Key Performance Indicators
MEX – Material Extrusion
MJT – Material Jetting
PBF – Powder Bed Fusion
PLA – Polylactide
SHL – Sheet Lamination
SLA – Stereo Lithography
SLM – Selective Laster Melting
SLS – Selective Laster Sintering
VPP – Vat Photopolymerization
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