Towards a Digital Twin Simulation for Cycle Times Analysis in a

Cyber-Physical Production System

Vinicius Barbosa

1 a

, Rui Pinto

2 b

, Jo

ao Pinheiro

2 c

, Gil Gonc¸alves

2 d

and Anabela Ribeiro

Department of Electrical and Computer Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal

SYSTEC-ARISE, Faculty of Engineering of the University of Porto, Porto, Portugal

Continental Advanced Antenna, Sociedade Unipessoal, Lda., Vila Real, Portugal

Keywords:

IEC 61499, Function Block Programming, Digital Twin Simulation, Cyber-Physical Production Systems.

Abstract:

The Digital Twin concept refers to the virtual representation of physical assets and is an emerging technology

in the I4.0 paradigm for digital transformation. Digital Twin integration with discrete-event simulation mod-

els is the key enabler to create digital models of real dynamic manufacturing systems. Usually, simulation

alone does not support optimization and advanced analytics, especially considering the lack of real-time data

from the physical system. One of the biggest challenges for manufacturers is to enable integration between

simulation models and Digital Twin technology for real-time data exchange, such as monitoring and optimiza-

tion of cycle times and reducing waste. The lack of standards to build the Digital Twin concept explains this

issue. This study addresses this problem by proposing a communication interface between a Python-based

Digital Twin (DINASORE) and a Java-based AnyLogic simulation model. DINASORE supports Function

Blocks compliant with the IEC 61499 standard and external communication using OPC UA. Cycle time data

is collected automatically by the Digital Twin in the Edge layer of the Cyber-Physical Production System and

made available to the simulation model via OPC UA. Results show that it is possible to analyse the production

process and propose optimizations in real-time.

1 INTRODUCTION

Within the Industry 4.0 (I4.0) paradigm, simulation is

a powerful tool that can help manufacturers optimize

their production processes, reduce costs, and improve

product quality (Zhang et al., 2019). One known use

case is process optimization, where simulation can be

used to optimize manufacturing processes, such as as-

sembly lines or material handling systems. By iden-

tifying bottlenecks and inefﬁciencies, manufacturers

can make changes to improve productivity and reduce

waste.

Simulation tools enable manufacturers to test dif-

ferent scenarios and make data-driven decisions with-

out the need for costly and time-consuming physical

experiments. This can help to speed up innovation

and improve competitiveness in the manufacturing in-

dustry. In this context, simulation can be used to:

https://orcid.org/0009-0009-5832-5039

https://orcid.org/0000-0002-0345-1208

https://orcid.org/0000-0003-4522-1002

https://orcid.org/0000-0001-7757-7308

• Identifying bottlenecks: Simulation can help to

identify bottlenecks in the production process by

modelling the ﬂow of materials, workers, and

equipment. Manufacturers can identify areas of

the process that are slowing down production and

make changes to improve efﬁciency.

• Reducing waste: Simulation can help to reduce

waste in the production process by identifying op-

portunities for process improvement. Manufac-

turers can identify areas where materials are be-

ing wasted or processes are inefﬁcient and make

changes to reduce waste.

• Improving quality: Simulation can help to im-

prove product quality by identifying potential

quality issues early in the production process.

Manufacturers can identify areas of the pro-

cess where defects are likely to occur and make

changes to prevent them from happening.

• Optimizing resource allocation: Simulation can

help to optimize the allocation of resources, such

as workers and equipment, in the production pro-

cess. Manufacturers can identify the most efﬁ-

Barbosa, V., Pinto, R., Pinheiro, J., GonÃ

galves, G. and Ribeiro, A.

Towards a Digital Twin Simulation for Cycle Times Analysis in a Cyber-Physical Production System.

DOI: 10.5220/0012123100003546

In Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2023), pages 369-376

ISBN: 978-989-758-668-2; ISSN: 2184-2841

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

369

cient way to allocate resources to maximize pro-

duction output.

• Testing new processes: Simulation can help to test

new manufacturing processes before they are im-

plemented in the real world. Manufacturers can

identify potential issues with the new process and

make changes before implementing it in the phys-

ical world.

Another important technology within I4.0, often used

in industrial settings nowadays, is the concept of Dig-

ital Twin (DT), originally used to create virtual mod-

els of aircraft components and systems to test and

optimize performance (Glaessgen and Stargel, 2012).

A DT can be used to optimize production processes,

monitor equipment performance, and predict mainte-

nance needs. The DT concept refers to a virtual rep-

resentation of a physical facility such as a machine, a

production unit or line, a department or a human oper-

ator. A DT is a computer model that is connected to a

real-world object or system through sensors and other

data collection devices, usually, in the Edge layer of

a Cyber-Physical Production System (CPPS) (Park

et al., 2019).

On the one hand, a DT can be online, which allows

for real-time monitoring, analysis, and optimization

of the system, processes and production results. On

the other hand, a DT can be ofﬂine for ﬁnding opti-

mum set points, end-points and other control param-

eter settings to conﬁgure the system. This is possible

because the DT and the physical system are connected

through Internet of Things (IoT) devices, smart sen-

sors and actuators (Qi et al., 2021).

One of the key beneﬁts of using a DT is the mon-

itoring of cycle times, energy usage, or other key per-

formance indicators (KPIs) in real-time, allowing or-

ganizations to identify trends and issues before they

become major problems. Thus, the integration be-

tween DT and a simulation tool enables powerful

decision-making about process improvements and re-

source allocation. On the one hand, the DT is con-

nected to the physical system through sensors and

other data collection devices, allowing the collection

and analysis of data in real-time. On the other hand,

the simulation model can simulate the impact of pro-

cess improvements, test changes to a production line,

and analyze different scenarios without impacting the

real system.

In certain scenarios, to highlight the importance

of intelligent simulation modelling, simulation mod-

els need to be empowered with external DTs to sup-

port advanced calculation, optimization, or evalua-

tion. This means the simulation models need to be

connected with DTs, usually developed with a pow-

erful programming language such as R, Python, or

MATLAB. One active research area is the usage of

DTs for automatic data logging, i.e., using sensors or

other automated tools to collect cycle time data, and

its integration with simulation tools to improve simu-

lation accuracy and reduce the time required for man-

ual data entry. By connecting the DT to the simulation

tool, data can be automatically collected and fed into

the simulation model, allowing for real-time analysis

and optimization of the production process.

This study proposes an approach to integrate

the Dynamic INtelligent Architecture for Software

and MOdular REconﬁguration (DINASORE) frame-

work (Pereira et al., 2020; DIGI2-Lab, 2023), used

to create virtual replicas of physical systems that are

connected to the real-world system through sensors,

with a simulation model executed in the AnyLogic

simulation tool. This simulation model represents a

simple production line process following a Discrete

Event Simulation (DES) approach. By enabling a

communication interface between the AnyLogic sim-

ulation and DINASORE, which is collecting cycle

time data automatically from the production equip-

ment, the simulation model can use real-time data col-

lected from the physical process. On the one hand,

this avoids error-prone manual data logging and entry

in the simulation. On the other hand, it is possible to

analyse the production process and propose optimiza-

tions in real time.

The paper is organized into four more sections.

Section 2 provides a technological context and a com-

prehensive analysis of the state of the art. Section 3

describes in detail the case study to be addressed and

the proposed approach. Section 4 describes the exper-

imental methodology and results achieved while fur-

ther discussing the proposed approach. Finally, Sec-

tion 5 concludes the paper, stating ﬁnal remarks about

the study performed.

2 STATE OF RESEARCH

The DT and simulation are closely related concepts

that are often used together in the context of manu-

facturing and engineering. However, there is some

confusion about the concept of a digital model, where

simulation models are seen as DTs and vice-versa.

According to (Kritzinger et al., 2018), a simulation

model is a digital model of a physical system and, de-

pending on the level of data integration between the

physical and digital counterparts, a digital model can

also be a digital shadow or a Digital Twin.

Some DT proposals are not supported with simu-

lation features or are built based on simulation mod-

els. These DTs can be executed in the Edge layer of a

SIMULTECH 2023 - 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

370

CPPS and are used mainly for physical object digital-

ization, data collection, external data communication,

and data-driven decision-making for optimized pro-

cess control. They are basically a software wrapper

that introduces agent-oriented features to the phys-

ical entities that are being virtualized (Pinto et al.,

2016).(Longo et al., 2021) discuss the potential of

DTs in manufacturing and logistics systems and the

readiness of simulation practice to implement DTs.

The authors argue that while DTs have the potential

to improve system design and optimization, simula-

tion practice may not be ready to fully implement this

technology due to a lack of standardization and clear

deﬁnitions of DTs.

One example of a DT approach using standards

is the DINASORE framework (Pereira et al., 2020;

DIGI2-Lab, 2023). It follows a Model-based Engi-

neering approach for CPPS design and implementa-

tion since it is compliant with the IEC 61499 stan-

dard (Lyu and Brennan, 2021) for Function Block

(FB) design. It also enables communication interfaces

using the OPC UA protocol (Schwarz and B

orcs

ok,

2013). Finally, DINASORE enables FBs creation

using Python coding language, which makes eas-

ily available scientiﬁc computing, Machine Learning

(ML), optimization, data science, and big data tools

within the Edge layer.

Including simulation features in DTs can be

achieved by the integration between the DT and ex-

ternal simulation tools, i.e., sensor data collected by

the DT is made available as inputs in the simulation

model, while the simulation outputs close the loop by

activating actuation actions in the DT level. This in-

tegration would enable manufacturing process opti-

mization and efﬁciency improvement. From the sim-

ulation perspective, the use of DTs can help to im-

prove the accuracy of simulation models and enable

more sophisticated analysis and optimization.

Coupling these simulation tools with external DT

offers additional advanced analytical analysis, inter-

active visualizations and optimization. This integra-

tion makes the simulation modelling more intelligent

and extends its applicability to a broader range of

problems. Thus, by combining these two tools, manu-

facturers can create a virtual representation of a man-

ufacturing system, monitor its performance in real-

time, and make data-driven decisions to improve per-

formance and efﬁciency. There are several commer-

cial professional simulation software that can be used

to study process behaviour:

• Simulink (Shukla et al., 2019): Simulink is an ex-

tension of MATLAB that allows users to model,

simulate, and analyze dynamic systems and pro-

cesses using a graphical interface.

• Arena (Rossetti, 2015): This tool is commonly

used for DES in manufacturing and logistics. It

allows users to create detailed models of produc-

tion systems and test different scenarios to opti-

mize performance.

• Simul8 (Elder, 2014): Another simulation tool

that can be used for DES in production processes.

It is a powerful and user-friendly platform for sim-

ulating and optimizing complex systems and pro-

cesses, allowing users to make informed decisions

and improve efﬁciency and productivity.

• FlexSim (Nordgren, 2002): FlexSim is designed

for DES in manufacturing and logistics. It enables

building 3D models of systems, allowing users to

visualize the operation and behaviour of systems

in a realistic environment. Also, it includes a pow-

erful optimization engine that enables users to ex-

plore different scenarios and strategies to optimize

their systems and processes.

• Simio (Vik et al., 2010): Another example of a

simulation technology that can be used for a wide

range of applications, including manufacturing,

logistics, and healthcare. Its advanced features

include object-oriented modelling, 3D animation,

and optimization capabilities.

• AnyLogic (Borshchev, 2013): A simulation soft-

ware tool that allows users to create models of

complex systems using a variety of simulation

methods, including DES, continuous and Agent-

based simulation. It can be used for production

process optimization, as well as other applications

such as transportation and logistics.

The simulation software tools mentioned have their

own advantages and disadvantages. This study fo-

cuses on AnyLogic since it presents multi-method

modelling capabilities and scalability. It can also sup-

port, besides DES, Agent-based Modelling, system

dynamics and other multi-approach modelling (Bor-

shchev, 2014). Moreover, it can be integrated with

other software tools built in Python, which can be use-

ful for importing/exporting data from the simulation

models. A key advantage of AnyLogic is its capabil-

ity to integrate with Python packages supported by the

Pypeline library (Wolfe-Adam, 2023).

(Damiani et al., 2018) presents a case study on the

design of a production line using simulation and DT

technology. The authors propose a solution that com-

bines simulation models in AnyLogic with a DT to

improve the design process and optimize the perfor-

mance of the production line. The proposed solution

includes improved design accuracy, reduced develop-

ment time and costs, and enhanced production line

Towards a Digital Twin Simulation for Cycle Times Analysis in a Cyber-Physical Production System

371

Figure 1: Laboratory case Study.

performance. However, it is not clear how the integra-

tion between the DT and the AnyLogic is achieved,

and if there is real-time data integration.

(Ait-Alla et al., 2019) present a simulation model

that integrates a real-world production system with a

DT, which represents a virtual representation of the

physical system. The simulation model is used to ana-

lyze the interaction between both for production con-

trol and to optimize the production system’s perfor-

mance. Both DT and simulation consist of two mod-

els implemented using AnyLogic and interlinked us-

ing a Java-based TCP/IP interface. However, it is not

clear how the DT collects production and system data.

(Singgih, 2021) proposes a method for analyzing

the production ﬂow in a semiconductor fabrication

plant using ML techniques, to classify different types

of processing steps and to identify bottlenecks in the

production ﬂow. The data collection scheme involved

the collection of real-time data from various sensors

and control systems in the fab that has been stored

in the fab’s Manufacturing Execution System (MES)

and used to train and validate the ML models.

On the other hand, (Kassen et al., 2021) proposed

a generic simulation model of the production system

using the AnyLogic simulation tool, which can be

used as a digital shadow to optimize production pro-

cesses. The data is obtained from the Enterprise Re-

source Planning (ERP). However, both of these pro-

posals collect aggregated data from a manufacturing

management system, such as MES and ERP, and not

directly from a DT located in the Edge layer.

Overall, in the related work, it is not clear how

the DT implemented collects production and system

data from the shop ﬂoor, or how the integration be-

tween DT and simulation model for data exchange is

achieved.

3 PROPOSED APPROACH

With this study, we intend to propose an approach

to integrate automatic data logging, using the DINA-

SORE framework as a DT for device digitalization,

with the AnyLogic simulation tool. On the one hand,

this enables improved simulation accuracy while re-

ducing manual data entry. On the other hand, the in-

tegration can then enable powerful optimization and

analytics to support the simulation, considering the

Python-based FBs supported by DINASORE.

3.1 Case Study Description

We propose a new case study that takes inspiration

from a real shop ﬂoor manufacturing system. The

analysed manufacturing company is Continental Ad-

vanced Antenna (CAA) (Continental, 2023), a car

antenna manufacturer located in Vila Real, Portu-

gal. CAA production process includes Printed Circuit

Boards (PCB) and electronic component assembly,

which consists mainly of semi-automatic tasks, such

as the insertion of cables, the screwing of different

components, or the coupling of the module electronic

connection to the plastic structure of the antenna. The

operator performs these tasks in a given workstation

with the help of large equipment. In a laboratory en-

vironment, we emulate a similar production line, with

3 workstations, as represented in Figure 1.

Between workstations, manufactured goods can

either move forward or be discarded, according to

their quality when leaving the workstation. The raw

material input into Station 1 is processed and, if the

quality is ok, it moves to Station 2 and so on, until a

ﬁnished product at the end of the line is achieved. All

workstations are controlled by a Siemens Logo PLC

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372

and since there isn’t actual industrial equipment to be

controlled by the PLC in the laboratory setup, opera-

tors interact with the system through industrial button

consoles.

Considering the cycle time analysis and bottle-

neck optimization problem, an Edge device is con-

nected to the PLC, to execute the DINASORE frame-

work for cycle time automatic collection, storage in

a database and visualisation using a KPI web dash-

board. The automatic cycle time data logging process

was reported before by (Pinheiro et al., 2023). In this

case, T1 represents the cycle time in Station 1, T2 for

Station 2 and T3 for Station 3. Figure 2 represents

the dashboard with the KPI visualization for Station

1. The KPIs represented are: I) the number of pieces

OK and NOK at the end of the workstation; II) the

cycle time of the last piece; and III) the average cycle

time in that workstation.

3.2 Bridging DINASORE and AnyLogic

As stated before, the main objective of this study is to

integrate the data collected by the DINASORE frame-

work into an AnyLogic model. Since AnyLogic is

a Java-based software tool and DINASORE is built

in Python, the communication between both environ-

ments is not straightforward. To address this lim-

itation, we used the Pypeline library (Wolfe-Adam,

2023) that connects any AnyLogic model to a local

Figure 2: Visualization of KPIs in the Dashboard.

Python instance and allows to read, import and ex-

ecute any Python code directly from the simulation

model.

In this case, a theoretical average cycle time is

given as input in the AnyLogic model (as Delay time

parameters), which returns as output the simulated

throughput time. On the other hand, DINASORE is

used to collect in real-time the actual cycle times of

workstations and feed the simulation model with up-

to-date time cycles. This data exchange will enable

the identiﬁcation of bottlenecks and potential prob-

lems for further optimization of the production sys-

tem’s performance.

After installation, the Pypeline library can be

added to the AnyLogic, which is ready to connect

with a Python instance by adding an object called

pyCommunicator to the main model. pyCommunica-

tor enables a two-directional communication to send

commands and arguments to Python and receive re-

turns based on Python calculations. These commands

can be statements, variable assignments, and function

calls. Figure 3 represents the simulation model cre-

ated for the case study considered.

Now that the pyCommunicator object is running

within the simulation model, we need to specify the

Python ﬁle that will do the interface with DINASORE

in this speciﬁc case study. This ﬁle contains all the

necessary parameters to connect to the DINASORE

using the OPC UA communication protocol. It ﬁrst

checks whether a connection to the OPC UA server is

possible since every DINASORE instance is an OPC

UA server. If the connection is successfully estab-

lished, it returns to AnyLogic the values stored as

OPC UA variables, which contain the updated cycle

times collected from the physical system. Otherwise,

if the connection fails, it returns the default theoret-

ical cycle time values that are already in use by the

Figure 3: AnyLogic model for the case study.

Towards a Digital Twin Simulation for Cycle Times Analysis in a Cyber-Physical Production System

373

simulation model.

Once Anylogic obtains this data through the py-

Communicator within the simulation model, the val-

ues are subsequently transformed into parameter ob-

jects and then used as Delay time on each delay mod-

ule (which represent real workstations). Finally, af-

ter we have completed the full virtualization of the

real production line, the simulation model output

(throughput time) can be analysed with precision with

some powerful tools within the AnyLogic IDE, as

ﬁnding the optimal solution for a given linear pro-

gramming model, or even make use of data analysis.

4 TESTS AND RESULTS

In this section, the testing methodology is explained

and the results obtained from the validation per-

formed are reported and discussed.

4.1 Testing Methodology

To collect cycle time data from the test case infras-

tructure and share it in real-time with the AnyLogic

model, a simple 3-step production process is emu-

lated. The process turns a paper sheet into a 2-folded

circle. In Station 1, the operator draws a circle on the

paper sheet. In Station 2, the next operator cuts the

circle, and, in Station 3, the circle is folded twice, as

presented in Fig. 4. Note that the cycle times of each

workstation are collected using the console buttons,

which are used by the operators to deﬁne the begin-

ning/end of a task.

Figure 4: Testing Methodology or the 2-folded paper circle

production line.

To analyse the cycle times, we execute a ﬁrst trial

run by producing 30 folded circles in total, in an at-

tempt to provide an easy-to-follow benchmark pro-

cess. The trial run takes about 45 minutes including

material preparation, system preparation and execu-

tion of the tasks in each workstation. Technically,

these cycle times were retrieved with a disjoint FB

pipeline, which connects to the database and prepro-

cesses the data to extract the mean cycle time for each

workstation. Then, the collected data was shared with

the AnyLogic model to identify possible bottlenecks

and suggest a performance improvement strategy for

reducing throughput time. A second trial run was exe-

cuted, considering a performance improvement strat-

egy. This second trial run is similar to the ﬁrst one

in terms of duration and tasks. Next, we discuss the

results achieved before and after the optimization.

4.2 Results

The Yamazumi chart of the ﬁrst trial run is presented

in Figure 5. From the analysis of the chart, we can

identify a bottleneck in Station 2.

Figure 5: Pre-Optimization Yamazumi Chart of the process.

The system calculates a throughput time of 36 sec-

onds, which is limited by the cutting task in Station

2. During the ﬁrst trial run, the authors noted a large

Work-in-Progress (WIP) between Station 1 and Sta-

tion 2. This WIP is explained by the cycle time in Sta-

tion 2 being much larger compared with other work-

stations, which means the line is not balanced. DI-

NASORE collect the cycle times of each of the work-

stations, which became available for analysis in the

AnyLogic for process optimization.

4.2.1 Optimization Strategy

The identiﬁed bottleneck may be caused by two main

issues:

1. The throughput time is limited by the maximum

of cycle times;

2. The difference in cycle times between Station 1

and Station 2 implies that, if Station 1 is running

at 100% of its capacity, there will be a growing

queue of parts waiting to be processed between

these stations, which is unfeasible.

To optimize this process we ﬁrst run the original pro-

cess and conﬁgure the workstations through the Any-

Logic simulation model using the cycle times col-

lected previously in the physical trial run. Before

the optimization process, the simulator predicted a

throughput time of 36.14 seconds, congruent with the

measurements obtained in the physical trial run.

There are two possible optimization strategies to

remove the bottleneck in Station 2: I) Accelerate the

SIMULTECH 2023 - 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

374

cutting process in Station 2; II) Increase the produc-

tion capacity available in Station 2. The cutting task

speed is limited by human capacity and, as such, we

chose to implement the second strategy by adding a

second worker to Station 2. The throughput results of

10 simulations, with the original cycle times and after

optimization, are represented in Table 1.

Table 1: Quantitative analysis of 10 simulations.

Simulation (n)

Throughput time with

original cycle times (s)

Throughput time

after optimization (s)

1 33.82 17.44

2 33.62 17.25

3 37.3 18.11

4 41.27 17.91

5 36.9 18.12

6 34.57 18.4

7 35.38 16.86

8 32.52 17.14

9 37.85 17.97

10 38.22 18.12

Average 36.145 17.732

At the end of all 10 simulations, represented in

the table above, we calculated the mean value and

the standard deviation for each scenario. The original

setup data lead us to a throughput time range between

33.55 and 38.74 seconds with a conﬁdence interval of

99.8%, which covers the previous result and validate

the simulation model.

To validate the feasibility and impact of the op-

timization strategy, we simulate the new scenario by

increasing the delay module capacity corresponding

to Station 2. The simulation model outputs a new

throughput time mean of 17.73 seconds between man-

ufactured goods and, with the same previous conﬁ-

dence interval, leads to a throughput time range be-

tween 17.27 and 18.24 seconds. To conﬁrm this sig-

niﬁcant increase in production capacity a second trial

run is executed, now with two workers on Station 2.

During the second trial run, another 30 pieces

were manufactured. In this trial run, it was notice-

able that the production rate had increased and the

WIP between Station 1 and Station 2 didn’t exist any-

more. The measured throughput time of the system

was been reduced to about 17 seconds, which approx-

imately corresponds to half of the cycle time of the

cutting cycle time. This reduction was possible due

to the duplication of workers in Station 2, as n

workers

reduces the cycle time to

cycletime

workers

. The optimization

process improved the rate of production by 111%,

through a symbiotic relationship between the auto-

matic measurement process and the simulation of pro-

duction lines.

5 CONCLUSIONS

DT simulation in manufacturing has the potential to

improve system design and optimization. However,

the integration between DT technology and simula-

tion tools may not be ready to be fully implemented.

The paper presents a laboratory case study where a

DT was successfully implemented in a manufactur-

ing system to collect data while being integrated with

a simulation model of the physical system. The main

goal was to analyse equipment cycle times and bot-

tleneck optimization. The DT is materialized with the

DINASORE framework and AnyLogic was used for

the simulation model.

The advantage of the proposed solution is the clear

deﬁnition of industrial standards for the digital model,

such as IEC 61499 and OPC UA. Cycle time data is

automatically logged in the simulation, which can be

used to identify bottlenecks in real-time and exper-

iment with optimization approaches. Results show

that DINASORE is suitable to create a DT simula-

tion, since it enables data collection and communica-

tion with AnyLogic. Ultimately, this DT simulation

approach enables the optimization of the production

system’s performance and the detection of potential

problems and bottlenecks in the system.

On the other hand, the laboratory case study may

not represent the complexity of the CAA production

line process, thus the optimization of a larger-scale

manufacturing system may have additional require-

ments. For future work, we intend to create a sim-

ulation model of the actual CAA production process.

Moreover, we intend to use DINASORE to integrate

automatically industrial equipment for cycle time col-

lection, instead of using button consoles.

ACKNOWLEDGEMENTS

This work was supported by multiple funding sources

including the: Base funding (UIDB/00147/2020)

and Programmatic funding (UIDP/00147/2020) of the

SYSTEC – Center for Systems and Technologies and

ARISE - Associate Laboratory for Advanced Produc-

tion and Intelligent Systems (LA/P/0112/2020), both

funded by national funds through the FCT/MCTES

(PIDDAC); and project Continental FoF - Continen-

tal AA”s Factory of the Future, with reference POCI-

01-0247-FEDER-047512, co-funded by the European

Regional Development Fund (ERDF), through the

Operational Programme for Competitiveness and In-

ternationalization (COMPETE 2020) under the POR-

TUGAL 2020 Partnership Agreement.

Towards a Digital Twin Simulation for Cycle Times Analysis in a Cyber-Physical Production System

375

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