=Paper=
{{Paper
|id=Vol-3628/short18
|storemode=property
|title=Digital Twins for Optimisation of Industry 5.0 Smart Manufacturing Facilities
|pdfUrl=https://ceur-ws.org/Vol-3628/short18.pdf
|volume=Vol-3628
|authors=Sergiy Fedak,Yuriy Skorenkyy,Marco Dautaj,Roman Zolotyy,Oleksandr Kramar
|dblpUrl=https://dblp.org/rec/conf/ittap/FedakSDZK23
}}
==Digital Twins for Optimisation of Industry 5.0 Smart Manufacturing Facilities==
https://ceur-ws.org/Vol-3628/short18.pdf
Digital Twins for Optimisation of Industry 5.0
Smart Manufacturing Facilities
Sergiy Fedak1, Yuriy Skorenkyy1, Marco Dautaj2, Roman Zolotyy1 and Oleksandr Kramar1
1
Ternopil Ivan Puluj National Technical University, 56 Ruska St, Ternopil, UA46001, Ukraine
2
The Polytechnic University of Milan, Via Raffaele Lambruschini, 4/B, Milan, 20156, Italy
Abstract
Wide adoption of human-centered digital platforms may lead to novel collaborative business
models promoting sustainable development. The paper proposes a procedure for optimizing
the production process using the digital twin technology and considers a food packaging line
as an example. To assure that implementation of digital twins contribute to growth of
sustainable businesses by optimizing use of raw materials and energy, the modeling of
production cycle is to be done with focus on the essential production data and human-friendly
information representation.
Keywords 1
Digital Twin, Industry 5.0, Smart Manufacturing, Process Optimisation
1. Introduction
Environmental hazards and pressing societal risks make it vital to apply every effort pursuing the
global development goals to ensure sustainability of production. Current stage of industrial
development is known as Industry 4.0, with characteristic features being digitization of every sphere,
automation of the manufacturing, omnipresence of the Internet of Things (IoT) devices. This allows
efficient management of the mass production in changing external conditions, reducing production
costs and increasing profits. However, Industry 4.0 fails to address the societal aspects of production
therefore, conceptually new approaches for production systems design and management were
proposed, known as Industry 5.0. Extensive literature is devoted to the refinement of the principles
and features of Industry 5.0 and its relation to the Society 5.0 concept (see [1, 2] for reviews). For
purposes of this study human-centricity of tech innovations (well-being of workers, human
creativity role, collaborative robots) as well as mass customization and sustainability of
manufacturing cycles are to be noted as key distinctions of the Industry 5.0 model.
Among many complex and distributed industries, food packaging stands out due to its wide
presence, variety of product types and raw material types used. Predominantly, small or medium
enterprises (SMEs), involved in food packaging, can benefit greatly from transformation of their
businesses and such a transformation can be also beneficial for national economies. However, small
enterprises often lack the financing and incentive for upgrading their manufacturing lines and need
external support for the transitioning to a circular economy practices, reducing waste and making the
manufacturing smart and flexible or for optimizing production processes. At the same time, for these
small-scale factories the digital twin [3, 4] approach may show its efficiency providing real-time data
on resource consumption and waste generation for analysis and informed decision-making to optimize
performance and improve sustainability. Digital twins may become the driving force and an enabler
for smart and sustainable manufacturing.
1
Proceedings ITTAP’2023: 3rd International Workshop on Information Technologies: Theoretical and Applied Problems, November 22–24,
2023, Ternopil, Ukraine, Opole, Poland
EMAIL: srigfd@gmail.com (A. 1); skorenkyy.tntu@gmail.com(A. 2); marco.dautaj@polimi.it (A.3); zolotyy@gmail.com (A. 4);
kramaroitntu@gmail.com(A. 5)
ORCID: 0000-0003-3654-9161(A. 1); 0000-0002-4809-9025 (A. 2); 0000-0002-2132-4448 (A. 3); 0000-0002-9435-264 (A. 4); 0000-
0002-8153-2476 (A. 5)
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
2. Smart Manufacturing Optimisation in Food-Packaging
Industry
One characteristic feature of digital twins, which are digital representations of physical elements,
is the high connectivity of production equipment units. Digital twins make use of IoT sensors to
monitor production lines in real time [5]. A food-packaging enterprise has been selected as a test-bed
for this research (Figure 1). In the first stage, requirement elicitation has been done and domain-driven
design [6] of the digital twin has been performed for the purpose of optimizing the manufacturing
regimes. The factory already possesses a good level of digitisation, with energy consumption data,
quality checks data and raw material usage data available for analysis. Present level of process
automation can provide energy-efficient and on-time delivery of parts and products. However, there is
no implemented decision-making system supporting fast responses of human operators to fluctuating
market conditions and focused on reducing the energy consumption and raw material use
optimization. Improvements of the manufacturing line performance and sustainability were identified
as primary goals in implementation of the industrial digital twin. Development of the digital twin can
allow nearly real-time and holistic assessment of the current energy consumption per unit of
production at every stage of production cycle with factors affecting the productivity and energy
consumption visualized. This may help the decision-maker not just to identify bottlenecks in
production but also to simulate effects of changing regimes and take timely measures to mitigate
risks. The nature of the production process, as can be ssen from Figure 1, requires participance of
human operators in different sections of the manufacturing line. Coordinated actions of these
operators need an intermediary digital platform, provided by the digital twin, to relieve humans of
repetitive procedures control, allow them to focus on the decision making and creativity, let them
promptly access the relevant information and reduce risk of human errors.
(a) (b)
Figure 1. Food packaging producer chosen as a use-case: (a) Manufacturing line
for food packaging; (b) Food packaging device with QR code labeling.
In order to optimize manufacturing line performance we developed a model of material flow.
Data on energy consumption, production, quality and efficiency are indispensable for analysis and
allow smart sensors, meters, and other monitoring devices that will be connected to manufacturing
machinery in order to identify the energy-intensive processes or equipment. The components of a
smart manufacturing line can provide a variety of data summarized in Table 1. Properly performed
data engineering (data collection, storage, and preparation) is a necessary prerequisite for a better
understanding of the data collected [7-9].
For smart manufacturing processes and units it is necessary to develop a model that represents
the current state of the production line. The digital twin platform can process data on-site or
broadcast data to cloud services [4, 5] to support decision-making based on mathematical models
characterizing resource consumption and process output quality and quantity.
Table 1
Data types, relevant for the industrial data platform in food packaging.
Decision- Production process Data type
making level level
Production data Production process Resource consumption data
data Quality checks data
Emission and waste production data
Equipment status Operating mode data
data Failures and downtime data
Energy Grig electricity Consumed power
consumption consumption data Seasonal variations
data Renewable power Consumed power
consumption Seasonal variations
Environmental Factory floor data Temperature
data Surrounding area Humidity
data Air pollution
To determine the most efficient and feasible solution, several production regimes can be
simulated using the digital twin. Digital twin simulates the behavior of the machine in order to predict
potential problems and optimize the manufacturing processes and maintenance but also can trigger
edge-system controls in case of malfunctions. For a specific processing stage, a set of parameters is to
be prioritized in order to have sufficient amount of relevant information.
Figure 2. Product lifecycle stages controlled by the food packaging
manufacturer.
By implementing a digital twin, it is possible to ensure that consistent and accurate data
obtained from the sensors can enable real-time monitoring and analysis of a manufacturing facility’s
performance and energy consumption to optimize processes for a more sustainable lifecycle [10-11].
3. Smart Manufacturing Optimisation in Food-
Packaging Industry
Based on the data obtained from the manufacturing line, a reference matrix would be developed
consisting of the set of minimum parameters needed to mathematically describe each process and its
dependencies, limits and boundary conditions. This would serve as a blueprint for further analysis and
finding areas and methods for energy optimization and control. Target functions built on this dataset
would be defined for each process that would serve as a tool for optimization of the process and
effectively the entire system. The optimization diagram shown in Figure 3 illustrates a general
approach that can help solve the optimization problem effectively. The inputs x and the outputs y are
quantitative characteristics of the material and information flows that the production system receives
from external entity and sends respectively to the same or different external entity.
Figure 3. Optimisation diagram for the manufacturing process.
The optimal control signal u* is a solution to an optimization problem to achieve the goals of a
particular smart manufacturing use case. This optimal u* should be obtained according to the method
described in this section (examples of individual use cases are presented elsewhere) to improve the
initial control signal u based on the model process description and standard methods. The peculiarity
of the proposed approach is the improvement of the model, which is an integral part of solving
optimization problems.
The general optimization problem involves functional relations of various types for a wide range
of variables, making it very difficult to solve. The optimization recipe can be created as a
superposition of partial solutions for different subsystems and the equations describing specific
processes only contain variables of that subsubsystem (for discussion on this approach applicability
see [3, 4]). Additionally, this strategy significantly lowers the optimization engine's processing power
needs. The optimal solution for the particular subsystem may also be inconsistent with the key
performance indicators of the entire system if various partial optimization problems are self-
contained, which may be the case with distributed manufacturing systems. As a result, no decisions
can be made and no corrective actions can take place until the full optimization solution is discovered
for all systems at once.
Process state vector ⃗x =( x 1 x 2 ⋯ x n ) and control vector u⃗ =( u1 u 2 ⋯ un ) are tuned with the
measured perturbations to choose the model and refine it. The optimization problem
N N
max ∑ F i ( ⃗
x i , u⃗i ) at conditions ⃗
x i=∑ c ij ⃗ y j= ⃗
y j, ⃗ x i , u⃗i ) , for variable control vectors u⃗ i , input
f i( ⃗
i=1 j=1
vectors ⃗x i, output vectors ⃗y i related by the matrix c ij for all subsystems.
4. Results and Discussion
In a general case, the fine optimization result is quite difficult to obtain. However, as the equation
for ith subsystem contains only the variables for this subsystem, the decomposition of the problem is
quite straightforward and the overall control vector can be constructed as the cross-product of partial
control vectors for all individual subsystems. The decomposition [12, 13] of the general process onto
the component subprocesses allows to tackle problems of lower dimensionality either ignoring mutual
influences or taking these into account as perturbations. Hierarchical approaches to optimisation
problem may be applied for the optimal control of quasistatic processes. With decomposition onto N
partial processes every individual problem is specified by its on equation of state gi ( x i , ui , π i ) =0,
i=1,2 ⋯ N . Vectors π i contain relations for the constituent subsystems and may be approximated as
N
linear π i=∑ С ij x j . Then the general target function is represented by the decomposition
j=1
N
f ( X ,U )=∑ f і ( x i ,u i , π i )❑ . The corresponding Lagrange polynomial for ith subsystem has the form
i=1
N N N N (1)
R ( x i , ui , π i , λi , μ i )=∑ f і ( xi , ui , π i ) + ∑ μ
i=1 i=1
i
T
( j=1 i=1
)
π i − ∑ Сij x J + ∑ λiT gi ( x i ,ui , π i ) .
First-order optimality conditions read as
∂R ∂fi ∂ gi
T N
T
(2)
= + λi − ∑ C ij μJ =0,
∂ x i ∂ xi ∂ xi j=1
∂R ∂f i ∂ gi
T (3)
= +λ + μi =0,
∂ πi ∂ π i i ∂ πi
∂R ∂fi ∂ gi
T (4)
= + λi =0,
∂ ui ∂ ui ∂ ui
∂R (5)
=gi ( xi , ui , π i ) =0
∂ λi
∂R T
N (6)
=π i − ∑ C ij x J =0
∂ μi j=1
and the system of equation (2)-(6) is solved with the corrected parameters iteratively. The iterative
procedure is interrupted when the desired tolerance is reached. This method is suitable for on-line
regime due to the possibility of interruption in arbitrary approximation and the obtained sub-optimal
solution is nevertheless better compared to the previous one. Thus the general optimisation problem
may be reduced to independent partial problems with π i, i=1,2 ⋯ N , containing only the variables
for the partial problems b i, and global variables а, identified by the supervisor. Tuning of the partial
problem variables to the coordinator variables а, and identification of the partial problems is to be
done in such a way that for a certain value a = a* solutions of the partial problems correspond to the
initial state of the global problem.
The hierarchical optimization is performed as a sequence of the following steps:
a) The initial value a(0) is chosen and the iteration counter l = 0 is set.
l
b) Independent partial problems are solved to determine the local variable value b i at given a(l).
Solving these partial problems may be parallelized in time.
c) Value a(l+1) is reset with new b i, i=1,2 ⋯ N .
l
d) If a(l) ≈ a* , the process is interrupted.
f) l = l +1, and control is transferred to step (b).
The coordination procedure is the most effectively used for the model in which the number of
relevant variables is greatly reduced by choosing only the most important ones for a defined goal of
the manufacturing transformation, for example, energy consumption and the recycling rate. Consider
X to be the coordinator of the partial subsystems. The state variables relevant for the relations are to
be put into the coordinating vector, other state variables can be inserted only into the partial problem
vectors. This way the Lagrangian R ( x i , ui , π i , λi , μ i ) is composed of the partial functions
N (7)
( )
Ri ( a , bi ) =f і ( x i ,ui , π i )+ μ iT π i − ∑ С ij x J + λ iT g i ( x i ,u i , π i ) ,
j=1
where from the next N partial problems are obtained. For the fixed Х the minimization with respect to
N
ui , π i is performed with optimality conditions of the first order gi ( x i , ui , π i ) =0 and π i − ∑ CTij x J =0.
j=1
∂R
One can fulfill the condition =0 by resetting the coordinator variables:
∂ xi
df dg
T ( l) (8)
x
( l +1) (l )
=x − α ( dx
+λ
dx
− μC , )
where α is the chosen step magnitude. This equation provides the Lagrange minimization by
sequential resolution of the above equations with interruption condition |x (l +1) − x (l )|< ε , for the desired
tolerance ε that guarantees practical problem solvability.
Using production data from the pilot food-packaging facility, the solution of the partial problem
for the most energy consuming and quality-critical part, which is the forming press, has been
attempted to test the proposed methodology. This allowed identifying the optimal maintenance regime
for particular regimes of the production line, determined by the external demand. Optimization has
been done in two input parameters, which are quantity of the raw material (plastic sheet) per hundred
units of product (x1) and energy used by the forming press (x2). Output parameters for the target
function minimization were the percentage of quality products (y1) and the combined production cost
and energy consumption parameter (y2), both of which are directly related with the sustainability
goals. Controls were specified as maintenance duty cycle (u1) and seasonality of energy consumption
(u2), which is related to availability of the renewable energy for the manufacturing line.
Numerical results show that the decomposition allows to drastically reduce the problem
dimensionality thus reducing requirements to the processing power of edge devices which would be
able to perform the needed calculations. This way the partial optimization problems with short time
cycle can be assigned to the low-resource edge devices which receive streaming data on product fast
quality checks, which are performed at every production stage as well as data about availability of the
renewable (for example, solar) energy. Only the optimization of long-term regimes of the production
line will be escalated to the high-performance processing units (cloud infrastructure), which will also
reduce the information security risks.
5. Conclusions
Wide adoption of digital platforms may lead to novel collaborative business models promoting
sustainable development. A generic model of product lifecycle in the packaging industry is
considered and the procedure of the production optimisation is proposed, which reqiures specific
chouice of the terget function. For the particular case of food-packaging manufacture, the target
function has been built with the overall goal of reducing energy consumption and optimizing the use
of raw material. By prioretizing the desired effects of the production optimization, we make it
possible to split the general model into composite blocks aith their corresponding variable sets. This
dimensionality reduction significantly simplifies data processing thus ensuring that the digital twin
design allows taking timely and efficient decisions regarding the manufacturing process.
Among benefits from the proper decomposition of the model we stress the ability to balance
needs of on-site processing for fast decision-making and proper operation control which importance is
exemplified by the packaging industry use-case. By minimizing the target function on the parameters
of product quality and energy consumption, the smart manufacturing facility simultaneously improves
performance and contributes to the goals of sustainable development. The loss of some information
which may be useful in other respects, like predictive maintenance, may be regarded as disadvantage
of this type of system decomposition, however, properly designed digital data platform may
compensate for such loss and allow failure risk mitigation by storing extra information in external
datalake for separate processing, which will be discussed elsewhere.
6. Acknowledgements
This work was partially supported by the European Institute of Technology through the project
“Smart Manufacturing Innovation, Learning-labs, and Entrepreneurship” (HEI grant agreement No
10044).
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