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Digital Twin: Manufacturing Excellence through Virtual Factory Replication

Authors:
  • Digital Twin Institute

Abstract

This paper introduces the concept of a " Digital Twin " as a virtual representation of what has been produced. Compare a Digital Twin to its engineering design to better understand what was produced versus what was designed, tightening the loop between design and execution.
Digital Twin: Manufacturing
Excellence through Virtual
Factory Replication
A Whitepaper by Dr. Michael Grieves
This paper introduces the concept of a
“Digital Twin” as a virtual
representation of what has been
produced. Compare a Digital Twin to
its engineering design to better
understand what was produced versus
what was designed, tightening the loop
between design and execution.
Page 1 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
Introduction
The concept of a virtual, digital equivalent
to a physical product or the Digital Twin1
was introduced in 2003 at my University
of Michigan Executive Course on Product
Lifecycle Management (PLM). At the time
this concept was introduced, digital
representations of actual physical products
were relatively new and immature. In
addition, the information being collected
about the physical product as it was being
produced was limited, manually collected,
and mostly paper-based.
In the decade that has followed, the
information technology supporting both
the development and maintenance of the
virtual product and the design and
manufacture of the physical product has
exploded.
Virtual products are rich representations of
products that are virtually
indistinguishable from their physical
counterparts. The rise of Manufacturing
Execution Systems on the factory floor has
resulted in a wealth of data collected and
maintained on the production and form of
physical products. In addition, this
collection has progressed from being
manually collected and paper based to
being digital and being collected by a wide
variety of physical non-destructive sensing
technologies, including sensors and gauges,
Coordinate Measuring Machines, lasers,
vision systems, and white light scanning.
1 I introduced the term “Digital Twin” in
Virtually Perfect: Driving Innovative and
Lean Products through Product Lifecycle
Management (pg. 133). I attributed it to
John Vickers of NASA whom I work with.
We have subsequently used this term in
current projects.
In light of these advances, it is timely to
explore how the Digital Twin can move
from an interesting and potentially useful
concept that aids in understanding the
relationship between a physical product
and its underlying information to a critical
component of an enterprise-wide closed-
loop product lifecycle. These tasks will
both reduce costs and foster innovation in
the manufacture of quality products.2
Digital Twin Concept
Model
The Digital Twin concept model is shown
in Figure 1. It contains three main parts: a)
physical products in Real Space, b) virtual
products in Virtual Space, and c) the
connections of data and information that
ties the virtual and real products together.
In the decade since this model was
introduced, there have been tremendous
increases in the amount, richness, and
fidelity of information of both the physical
and virtual products.
On the virtual side, we have improved the
amount of information we have available.
We have added numerous behavioral
characteristics so that we can not only
visualize the product, but we can test it for
performance capabilities.
We have the ability to create lightweight
versions of the virtual model. This means
2 While the focus of this paper is on the
manufacturing phase, the use of the Digital
Twin extends throughout the product’s life
to provide value to its user and
information on how it actually performed
to its manufacturer. This larger use is
described in Virtually Perfect.
Page 2 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
that we can select the geometry,
characteristics, and attributes that we
require without carrying around
unnecessary details. This dramatically
reduces the size of the models and allows
for faster processing.
These light-weight models allow today's
simulation products to visualize and
simulate complex systems and systems of
systems, including their physical behaviors,
in real-time and with acceptable compute
costs.
These lightweight models also mean that
the time and cost of communicating them
electronically is substantially less. They
now can be shared not only with the
organization but also throughout the
supplier network. This enhances
collaboration in both reducing time to
understand and enhancing both quality and
depth of understanding of product
information and changes.
As importantly, we can simulate the
manufacturing environment that creates
the product, including most operations,
both automated and manual, that constitute
the manufacturing process. These
operations include assembly, robotic
welding, forming, milling, and other
manufacturing floor operations.
On the physical side, we now collect more
and more information about the
characteristics of the physical product. We
can collect all types of physical
measurements from automated quality
control stations, such as Coordinate
Measuring Machines (CMMs). We can
collect the data from the machines that
perform operations on the physical part to
understand exactly what operations, at
what speeds and forces, were applied. For
example, we can collect the torque
readings of every bolt that attaches a fuel
pump to an engine in order to insure that
each engine/fuel pump attachment is
successfully performed.
Extending Model
Lifespans – A Matter of
Unifying the Virtual and
Real Worlds
The amount and quality of information
about the virtual and physical product
have progressed rapidly in the last decade.
The issue is that the two-way connection
between real and virtual space has been
lagging behind.
Global manufacturers today either work
with the physical product or with the
virtual product. We have not developed
the connection between the two products
so that we can work with both of them
simultaneously.
The typical way we do this is to develop a
fully annotated 3-D model. We then
develop a manufacturing process that will
realize this model with a Bill of Process
(BOP) and Manufacturing Bill of
Materials (MBOM). The more
sophisticated and advanced manufacturers
then simulate the production process
digitally.
Page 3 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
However, at that stage, we then simply
turn over the BOP and MBOM to
manufacturing and leave the virtual
models behind. In many cases currently,
we even dramatically water down the
usefulness of the model by producing 2-D
blueprints for the factory floor.
There are manufacturers who are bringing
3-D models to the factory floor by way of
terminals stationed in the work cells.
However, even here there is not real
integration and connection between the
virtual model and the physical product
taking shape on the factory floor. The
terminal model merely serves as a
reference, and a human has to perform the
connection between the virtual and the
physical product on an ad hoc basis.
As shown in Figure 2, linking the physical
product with the virtual product could take
the form of the 3-D model not only
appearing on the screen but also
incorporating actual dimensions from the
physical product. The information of the
physical product would overlay the virtual
product and highlight differences that
would need to be addressed.
This simultaneous view and comparison of
the physical and virtual product will reap
major benefits, especially in the
manufacturing phase of the product.
Digital Twin Fulfillment
Requirements
In order to deliver the substantial benefits
to be gained from this linkage between
virtual and physical products, one solution
is to have a Unified Repository (UR) that
will link the two products together.
Both virtual development tools and
physical collection tools would populate
the Unified Repository. This would enable
two-way connection between the virtual
and physical product.
On the virtual tool side, design and
engineering would identify characteristics,
such as dimensions, tolerances, torque
requirements, hardness measurements, etc.,
and place a unique tag in the virtual model
that would serve as a data placeholder for
the actual physical product. Included in the
tag would be the as-designed characteristic
parameter.
When the design was released for
production, these tags would be collected
from the virtual product model and used to
create the UR. A lightweight model with
the tags and their characteristics and
geometrical location would also be created.
On the physical side, these tags would be
incorporated into the MES in the Bill of
Process creation at the process step where
they will be captured. As the processes
were completed on the factory floor, the
MES would output the captured
characteristic to the UR.
The final step would be to incorporate this
back into the factory simulation. This
would turn the factory simulation into a
factory replication application. Instead of
simulating what should be happening in
the factory, the application would be
Page 4 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
replicating what actually was happening at
each step in the factory on each product.
The factory replication application would
be in constant communication with the UR,
picking up the latest data from actual
production and displaying it in the virtual
factory.
Users could see in near real-time or even
real time, what actually was occurring on
the factory floor and view the actual
product characteristics as they were going
through production cells.
There are a significant number of use
cases that can be envisioned from having
such a capability.
Digital Twin Model Use
Cases
The digital twin capability supports three
of the most powerful tools in the human
knowledge tool kit. These three tools are:
conceptualization, comparison, and
collaboration. Taken together, these
attributes form the foundation for the next
generation of problem solving and
innovation.
Conceptualization
Unlike computers, humans do not process
information, at least not in the sense of
sequential step-by-step processing that
computers do. Instead, humans look at a
situation and conceptualize the problem
and the context of the problem.
Humans take in all the data about the
situation there interested in. They then
conceptualize the situation, seeing in their
mind's eye its various aspects. While they
can do this looking at tables of numbers,
reports, and other symbolic information,
their most powerful and highest bandwidth
input device is their visual sight.
What currently happens is that humans
take visual information, reduce it to
symbols of numbers and letters, and then
re-conceptualize it visually. In the process,
we lose a great deal of information, and
we introduce inefficiencies in time.
The capability of the digital twin lets us
directly see the situation and eliminate the
inefficient and counterproductive mental
steps of decreasing the information and
translating it from visual information to
symbolic information and back to visually
conceptual information.
With the digital twin to build a common
perspective, we can directly see both the
physical product information and the
virtual product information,
simultaneously. Instead of looking at a
report of factory performance and re-
conceptualizing how the product is
moving through the individual stations,
looking at digital twin simulations allows
us to see the progress of the physical
product as it is moving and actually see
information about the characteristics of the
physical product.
Instead of looking at an array of numbers
on tolerance measurements, we can look at
the products lined up in the virtual factory
and see the actual trend lines that indicate
a problem is developing.
Because we have tagged the products with
the designed characteristics, we can select
those tags and see the designed parameters
and the actual parameters simultaneously.
Comparison
The next tool that humans use in assessing
situations is the idea of a comparison. We
compare unconsciously and continuously
our desired result and our actual result in
order to determine a difference. We then
decide how to eliminate that difference.
Page 5 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
Comparison is one of most powerful
intellectual tools that we possess.
When we have the virtual product
information and the physical product
information completely separate, we still
can do that comparison. However, it is
inefficient, as we have to look at the
physical product information, find the
corresponding virtual product information,
and then work out the differences.
With the digital twin model, we can view
the ideal characteristic, the tolerance
corridor around that ideal measurement,
and our actual trend line to determine for a
range of products whether we are where
we want to be. Tolerance corridors are the
positive and negative deviations we can
allow before we deem a result
unacceptable.
Depending on how we implemented this
capability, we could see the differences in
terms of color, with colors progressing
from green, “there is no difference,” to
yellow, “we are in our tolerance corridor,”
to red, “we are beyond the tolerance
corridor.” We can then make
instantaneous decisions about the
differences.
We can do this with measurements, tensile
strength, torque readings, and pretty much
any characteristic where we can define the
desired characteristic in some sort of a
measurement, either quantitative or even
qualitative. We can enable this capability
for a single product or a range of products.
Using the example from above as to trend
lines, we could overlay the ideal trend on
the actual trend lines.
Having this capability, also allows us to do
the comparisons and adjust future
operations. For example, if we were seeing
tolerances on the plus side of our ideal
measurement, we could change parameters
in the operations of cells further down the
line to adjust them to err on the side of
negative tolerances. Instead of
degenerating into tolerance stacking, we
could ensure tolerances were distributed
around a mean.
The last tool we have is collaboration.
Collaboration
The most powerful things that humans do
is collaborate with each other in order to
bring more intelligence, more variability
of perspectives, and better problem
solving and innovation to situations. The
problem with conceptualization as it
occurs without the digital twin model is
that this conceptualization occurs only
within the individual. The digital twin
model allows a shared conceptualization
that can be visualized in exactly the same
way by an unlimited amount of individuals
and by individuals who do not need to
share the same location.
With the digital twin capability, we can
look at any physical product at any stage
on the factory floor and overlay the virtual,
product on top of it. This capability of
virtual products can be extended across
multiple factories. This means that
individuals across the world can not only
looking at the performance of their own
factory, but they can be monitoring how
they are doing against factories in other
parts of the world. A problem that arises in
one factory can be identified and
controlled not only in that factory, but the
solution immediately transferred and
implemented in all other factories across
the globe.
In the past, factory managers had their
office overlooking the factory so that they
could get a feel for what was happening on
the factory floor. With the digital twin, not
only the factory manager, but everyone
Page 6 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
associated with factory production could
have that same virtual window to not only
a single factory, but to all the factories
across the globe.
Instead of simply viewing a factory
simulation of what should take place in the
factory, factory replication means that we
can see what is actually taking place on
the factory floor as parts move through the
various work cells and inspection stations.
However, as Figure 2 illustrates, it is
exponentially better than simply seeing the
progression and completion of products.
We can also see the key design
characteristics that we are most concerned
about, the actual characteristics we have
achieved, and the gap between the desired
and actual.
The digital twin capability with its
conceptualization, comparison, and
collaboration capability frees us from the
physical realm where humans operate
relatively inefficiently. We can now move
to virtual realm where physical location is
irrelevant, and humans from across the
globe can have common visualization,
engage in comparisons identifying the
difference between what is and what
should be, and collaborating together. This
is extremely powerful and only occurs if
we can match the physical product with
the virtual product.
Conclusion
Over the last decade, there have been
dramatic advances in the capabilities and
technologies of both the data collection of
the physical product and the creation and
representation of the virtual product, the
Digital Twin. The issue is that while the
data information of each of these areas has
increased dramatically, the connection
between the two data sources has lagged
behind.
This white paper has proposed that the
connection between the data about the
physical product and the information
contained on the virtual product be
synchronized. This will open up an entire
new set of use cases.
Specifically by merging the virtual product
information as to how the product is to be
manufactured and the information about
how the product is actually being
manufactured, we can have an
instantaneous and simultaneous
perspective on how the manufactured
product is meeting its design specification
goals.
By using this information, we can change
digital factory simulation, which attempts
to predict how the product is to be
manufactured, into a digital factory
replication, which shows how the product
is actually being manufactured. We can
then compare it against the design
specifications. This can occur in real time
or near real-time. This provides a window
onto the factory floor for anyone at any
time from any place.
Focusing on the connection between the
physical product and the virtual product
enables us to conceptualize, compare, and
collaborate. We can conceptualize visually
the actual manufacturing processes. We
can compare the formation of the physical
product to the virtual product in order to
ensure that what we are producing is what
we wanted to produce. Finally we can
collaborate with others in our organization
and even throughout the supply chain to
have up-to-the-minute knowledge of the
products that we are producing.
Page 7 of 7 Digital Twin White Paper
Copyright © Michael W. Grieves, LLC 2014
Focusing on this connection between the
physical product and the virtual product
will improve productivity, uniformity of
production, and ensure the highest quality
products.
About Dr. Michael Grieves
Dr. Michael Grieves is a world-renowned
authority on Product Lifecycle
Management (PLM). Dr. Grieves has
written and lectured extensively on the
topic and is a frequent keynote speaker on
PLM. Dr. Grieves’ works include the
seminal work on PLM, Product Lifecycle
Management: Driving the Next Generation
of Lean Thinking (McGraw-Hill, 2006)
and Virtually Perfect: Driving Innovative
and Lean Products through Product
Lifecycle Management (SCP, 2010)
Dr. Grieves consults with a number of
leading international manufacturers and
governmental organizations such as
NASA.
Dr. Grieves is the Co-Director of the
Center for Lifecycle and Innovation
Management (CLIM) at the Florida
Institute of Technology and is a Research
Professor in the College of Business and
the College of Engineering.
Dr. Grieves is Chairman Emeritus of
Oakland University’s School of Business
Board of Visitors. He has taught in the
United States, China, and Europe at the
university senior undergraduate, and
graduate school levels and has authored
and taught executive education courses. Dr.
Grieves is a Professor at CIMBA
University, Asolo, Italy with an
appointment at the University of Iowa.
Dr. Grieves has over forty-five years
experience in the computer and data
communications industry. He has been a
senior executive at both Fortune 1000
companies and entrepreneurial
organizations during his career. He
founded and took public a national
systems integration company and
subsequently served as its audit and
compensation committee chair. Dr.
Grieves has substantial board experience,
including serving on the board of public
companies in both China and Japan.
Dr. Grieves has a BSCE from Michigan
State University and an MBA from
Oakland University. He received his
doctorate from the Case Western Reserve
University Weatherhead School of
Management.
About the Sponsor
Dassault Systèmes, the 3DEXPERIENCE
Company, serves 190,000 customers
across 140 countries, providing virtual
universes for sustainable innovation.
Dassault Systèmes’ DELMIA brand offers
products that connect the virtual and real
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... However, the recipient of the information from the virtual asset is not always automated control systems but can be humans who can then make the decision on how to act on the physical asset. These elements create an up-to-date digital representation of a physical asset capable of informing processes that shape and build the material object, creating a two-way relationship between the physical and the virtual [66]. Some sources will recognize an extended definition of the physical asset to a potential or a past object [65]. ...
... This was long before the advent of computers with drafting and 3D rendering abilities. In 2004, Dr. Michael Greives, while working with NASA, published a white paper that would take this original physical twin into the digital realm, which he named DTs [66]. This idea would remain unused for a decade because of computing limitations, but interest was aroused again, predominantly among Chinese factories in the mid-2010s [64]. ...
... A grain elevator can be seen as a manufacturing process; there are inputs of materials, throughputs that change properties of materials, and outputs of a final material with specific properties. Historically, DTs have been theorized to be applied to manufacturing processes to help optimize and track individual products, identify anomalies, and allow corrective/remedial measures to be taken in real-time, all of which greatly enhance traceability of the processes [66]. Models are components of a DT that are used to describe processes in a system that forecast future states and help to interpret the implications of current states. ...
... The concept of digital twin is retroactive to 2003, when Professor Grieves of the University of Michigan introduced the concept in a course on total product lifecycle management. The four stages of digital twin development are shown in Figure 2. The digital twin was defined as physical products, virtual products and the convergence between the two [32,33]. Glaessgen et al. [34] raised digital twin as an integrated multi-physical, multi-scale, high-fidelity twin system through physical models, sensor upgrades, and historical data, as shown in Figure 3. Abdulmotaleb et al. [35] proposed digital twin as a virtual copy of an organism or non-organism physical entity, while allowing information interoperability between the physical and virtual entities. ...
... One of the features of digital twin is to create virtual models of physical objects digitally to simulate the behavior of physical objects [32]. Virtual models can understand the state of physical entities by sensing data to predict, evaluate and analyze the dynamic changes of simulated objects. ...
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Traditional design, manufacturing and maintenance are run and managed independently under their own rules and regulations in an increasingly time-and-cost ineffective manner. A unified platform for efficient and intelligent design-manufacturing-maintenance of mechanical equipment and systems is highly needed in this rapidly digitized world. In this work, the definition of digital twin and its research progress and associated challenges in the design, manufacturing and maintenance of engineering components and equipment were thoroughly reviewed. It is indicated that digital twin concept and associated technology provide a feasible solution for the integration of design-manufacturing-maintenance as it has behaved in the entire lifecycle of products. For this aim, a framework for information-physical combination, in which a more accurate design, a defect-free manufacturing, a more intelligent maintenance, and a more advanced sensing technology, is prospected.
... Digital Twin (DT) is defined as a physical and/or virtual machine or computer-based model that simulates, emulates, mirrors, or twins the life of a physical entity, which can be a process, an object, a human, or a human-related feature Kiritsis (2011); Grieves (2015). In essence, each DT is linked Journal of Intelligent Manufacturing Fig. 16 Generic framework for a Digital Twin to its physical twin through a unique key that first identifies the physical twin and then establishes a bijective relationship (i.e., one-to-one correspondence) between the DT and the physical asset. ...
... It has been argued that a DT is more than a simple model or simulation Grieves (2015); Kritzinger et al. (2018); Boschert and Rosen (2016). Rather, it is an intelligent, living, and evolving model, being the virtual counterpart of a physical entity or process. ...
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Demands for more accurate machine learning models have given rise to rethinking current modeling approaches that were deemed unsuitable, primarily due to their computational complexity and the lack of availability and accessibility to representative data. In Industry 4.0, rapid advancements in Digital Twin (DT) technologies and the pervasiveness of cost-effective sensor technologies have pushed the incorporation of artificial intelligence, particularly data-driven machine learning models, for use in smart manufacturing. However, the persistent issue with such models is their high sensitivity to the training data and the lack of interpretability in the outcomes, at times generating unrealistic results. The incorporation of knowledge into the machine learning pipeline has been earmarked as the most promising approach to address such issues. This paper aims to answer this call through a Knowledge-embedded Machine Learning (KML) framework for smart manufacturing, which embeds knowledge from experience and, or physics information into the machine learning pipeline, thus making the outcomes from these models more representative of real applications. The merits of KML were then presented through comparative studies showing its capability to outperform knowledge-based and data-driven models. This promising outcome led to the development of frameworks that can potentially incorporate KML for smart manufacturing applications such as Prognostics and Health Management (PHM) and DT, further supporting the usefulness of the proposed KML framework.
... With the advances of technology in areas such as the Internet of Things, Big Data Analytics, Artificial Intelligence, and Cloud Computing, the convergence of the physical and virtual worlds becomes increasingly palpable (QI et al. 2021). The term Digital Twin (DT) was first introduced by Grieves in 2003 at a time when those technologies were still in development (Grieves 2014). ...
Conference Paper
A farm generates a lot of data from various systems, which is then stored in a distributed manner, usually in non-standardized formats, which bears the risk of data inconsistencies. This work addresses this issue by using business process management (BPM) to demonstrate that the use of digital twins (DTs) can improve interoperability between services in the agriculture domain. Steps from the BPM lifecycle were applied to a farming use case in Germany. First, the as-is business process model was discovered and modeled without DTs, analyzed and then redesigned into the to-be model according to the DT integration. The to-be model showed a reduction in the number of tasks needed to be performed by the farmer as well as an improvement of process data quality, interoperability, and efficiency. Finally, a comparison of the' average processing times of both models with the help of process simulation revealed improvements in the to-be process.
... With the advances of technology in areas such as the Internet of Things, Big Data Analytics, Artificial Intelligence, and Cloud Computing, the convergence of the physical and virtual worlds becomes increasingly palpable (QI et al. 2021). The term Digital Twin (DT) was first introduced by Grieves in 2003 at a time when those technologies were still in development (Grieves 2014). ...
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Full-text available
A farm generates a lot of data from various systems, which is then stored in a distributed manner, usually in non-standardized formats, which bears the risk of data inconsistencies. This work addresses this issue by using business process management (BPM) to demonstrate that the use of digital twins (DTs) can improve interoperability between services in the agriculture domain. Steps from the BPM lifecycle were applied to a farming use case in Germany. First, the as-is business process model was discovered and modeled without DTs, analyzed and then redesigned into the to-be model according to the DT integration. The to-be model showed a reduction in the number of tasks needed to be performed by the farmer as well as an improvement of process data quality, interoperability, and efficiency. Finally, a comparison of the' average processing times of both models with the help of process simulation revealed improvements in the to-be process.
Thesis
Constant evolution in the global manufacturing resulting from various forces like innovation, changing demands, competition, and regulations is forcing manufacturing enterprises towards more digital and smarter operations to stay competitive in their respective markets. This evolution of manufacturing towards digitalisation led to a new paradigm in the last decade, which has been called the “fourth industrial revolution” or “Industry 4.0”. Yet, it is not a trivial matter for manufacturing organisations to deal with the accelerating frequency of radical changes by means of new strategies, methods, and technologies. Evolving dynamic forces have immense impacts on the digital transformation of manufacturing operations as well as the priorities of scholarly works. Nowadays, it is more apparent and easier to comprehend the relation between evolving market dynamics and their reciprocal consequences on products, processes, and manufacturing systems. Accordingly, manufacturing organisations must handle the initiation of change as well as its propagation, which triggers a multitude of unpredictable and complex modifications in production. This challenge is characterised as the concurrent/coordinated evolution of products, processes, and systems, in other words, “co-evolution” in scholarly works. The Virtual Factory (VF), as “an immersive virtual environment wherein digital twins of all factory entities can be created, related, simulated, manipulated, and communicate with each other in an intelligent way”, enables data integration across the manufacturing value chain as well as the integrated use of technologies and methodologies. Therefore, VF is recognised by scholars as a useful and effective solution to deal with the co-evolution paradigm. However, there are still significant gaps in the knowledge domain as well as empirical challenges in the application domain in terms of designing, developing, and utilising the VF concept. Therefore, the purpose of VF research work is to address such gaps and challenges by designing and developing artefacts and frameworks together with empirical evaluations of designed artefacts in the industrial cases. The VF research work presented in this thesis is the final outcome of a three-year- long PhD study conducted as part of a comprehensive research collaboration project named Smart Factories. The thesis on hand is the final effort to frame the three-year-long research aiming to establish a systemic design and development approach for DT-based VF, employing a collaborative virtual reality capability that can integrate product, process, and system models to support the manufacturing enterprises for handling co-evolution problems during their adaptation to evolving environments. Thus, with this final effort, this thesis is aiming to: Establish comprehensive and methodical foundations for the empirical, conceptual, and philosophical discussions supporting the previously discovered and disseminated knowledge on DT-based VF employing a collaborative virtual reality capability that can integrate product, process, and system models.
Thesis
Full-text available
This thesis explains the structure and operation of a multifunctional digital twin for a material extrusion 3D printer. Among its functionalities, the digital twin includes: advanced monitoring of the printing process (also remotely), the ability to collect and sort job data, run simulations, react in case of errors, evaluate geometric accuracy, monitor part wear, and estimate the actual cost of a part. In the first part, the Industry 4.0 principles of 3D printing and associated goals are explained. Next, the hardware and software structure of the printer is showed. Next, the various parts of the digital twin are discussed: the data interface, the GUI, the digital twin core, and the host software. Of each part, the functionality and structure are described. As the last part a test is reported that illustrates the operation of the system and demonstrates its potential
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