=Paper=
{{Paper
|id=Vol-3742/paper24
|storemode=property
|title=Development of digital twin interface for Industry 4.0 production line
|pdfUrl=https://ceur-ws.org/Vol-3742/paper24.pdf
|volume=Vol-3742
|authors=Yuriy Skorenkyy,Viktor Voroshchuk,Tetiana Vitenko,Oleksandr Kramar
|dblpUrl=https://dblp.org/rec/conf/citi2/SkorenkyyVVK24
}}
==Development of digital twin interface for Industry 4.0 production line==
https://ceur-ws.org/Vol-3742/paper24.pdf
Development of digital twin interface for Industry 4.0
production line
Yuriy Skorenkyy∗,† Viktor Voroshchuk†, Tetiana Vitenko† and Oleksandr Kramar†
Ternopil Ivan Puluj National Technical University, 56 Ruska St, Ternopil, UA46001, Ukraine
Abstract
Within Industry 4.0, the augmented reality (AR) interfaces may become important components
of industrial digital twins (DT) enabling human operators to manage smart manufacturing
processes. Different aspects of DTs’ design have to be analyzed in context, including relevant data
identification, taking into account AR-interface data flow. Development of augmented reality-
enhanced interface for a DT in the metal processing industry is proposed which takes into
account the data flows specific for IoT devices and DT architecture. An integration of essential
stages of AR-enabled DT development will assure compliance with the concept of Industry 4.0
and allow new innovative solutions.
Keywords
Digital Twin, Internet of Things, Industry 4.0, Augmented Reality 1
1. Introduction
The advent of the Internet of Things (IoT) has paved the way for novel opportunities to
enhance industrial processes through the integration and intricate management of sensor-
equipped machines and actuators [1, 2]. In process operations, the Industrial Internet of
Things (IIoT) empowers manufacturers to gain a comprehensive view of every stage in the
manufacturing process, facilitating real-time adjustments to ensure seamless production
flow and mitigate the probability of defects and failures stemming from human errors, in
accordance with principles of Industry 4.0 [3, 4]. The positive implications of smart
manufacturing transcend the realms of production operation, equipment, and inventory
optimization. Coupled with improved flexibility of highly specialized production adapted
for rapid reconfiguration, this factor offsets the substantial costs associated with creating,
operating, and ensuring the security of IIoT systems [5, 6].
This paper considers a task of development of the intuitive and efficient interface for the
digital twin of the Industry 4.0 compliant production line. The paper is organized as follows.
In Section 2, literature review is presented to substantiate the research topic. In Section 3
CITI’2024: 2nd International Workshop on Computer Information Technologies in Industry 4.0, June 12–14, 2024,
Ternopil, Ukraine
∗ Corresponding author.
† These authors contributed equally.
skorenkyy@tntu.edu.ua (Yu. Skorenkyy); vorosсhuk_v@tntu.edu.ua (V. Voroshchuk); vitenko@tntu.edu.ua
(T. Vitenko); kramar_o@tntu.edu.ua (O. Kramar)
0000-0002-4809-9025 (Yu. Skorenkyy); 0000-0002-8943-1493 (V. Voroshchuk); 0000-0003-4084-0322
(T. Vitenko); 0000-0003-0805-3732 (O. Kramar)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
the methodology of study is described. In Section 4 the results of our study and the interface
development are presented. Section 5 is devoted to conclusions.
2. Related Works
Digital twins (DTs), widely used in Industry 4.0 to model and control production processes
[7-10], are virtual replicas of physical objects or systems that can be created and
manipulated using augmented reality (AR) and virtual reality (VR) technologies [11-15].
These technologies facilitate the creation of realistic 3D models that can interact with the
real world and offer a myriad of functionalities [12, 16].
DTs have emerged as a distinctive hallmark of smart manufacturing [10, 17-21], offering
multiple benefits. In the industrial domain, DTs can be harnessed to simulate, control, and
optimize production processes, enable predictive maintenance of equipment [10, 22-24],
and foster collaboration among workers [25].
The efficacy of augmented reality (AR) assistance frameworks in manufacturing
workflows is contingent upon the underlying software and hardware capabilities, as well as
the system's design attributes that facilitate the seamless integration of human operator
performance across relevant use cases (e.g., assembly/disassembly,
maintenance/inspection) [26-28]. Immersive visualization layered upon physical
environments using AR enables industrial personnel to assimilate and manipulate digital
interactivity with enhanced perceptibility and workflow integration [29]. Associated
improvements in specialized employee training and developmental modes also arise [30,
31], facilitated by tailored simulations of manufacturing operations, including emergency
contingencies – executed safely in virtual factory environments [16, 32]. Through such
immersive learning functionality, the AR-based platforms significantly augment contextual
employee environmental awareness and competencies. Consequently, quantitatively fewer
deviations can be projected, stemming from superior comprehension of workplace
dynamics [33].
AR integration within production environments can enhance real-time recognition of
hazardous situations for industrial personnel, enabling expeditious assessment of probable
risk exposure from discrete processes. Prior investigations [34-36] delineate the capacity
for AR to mitigate error-induced harm on the production floor through sequential
occupational safety, equipment operation, and maintenance instruction mapped to precise
spatial worksite coordinates. Additionally, the AR-enabled warning systems demonstrate
applicability within construction verticals [37] for advance notification of violations of
safety protocols before accidents occur. Workers will be able to determine risks with
improved precision in comparison to conventional techniques, subsequently adjusting
behaviors to amplify on-site protections [38].
However, there are challenges that need to be addressed to fully realize the potential of
AR application in production processes. One of the primary challenges is the presence of
spatial inaccuracies in the binding to markers recognized by computer vision [39]. AR
systems may experience technical glitches, such as network issues, and sometimes have
difficulties in interpreting complex scenarios, which may lead to incorrect information
being provided to staff. Consequently, there is a risk of emergencies if employees rely solely
on AR systems. Therefore, it is crucial not only to improve the tools themselves to support
AR but also to carry out a continuous process of staff training, for whom these tools are
merely one of many tools in the production environment [40]. Another issue with
production-type AR systems is their potential to distract attention and partially block the
field of vision, which can potentially lead to a violation of the safety of production processes.
The solution to such challenges lies in further optimization of the user interface, active
utilization of sound prompts, and the use of tools that will evaluate the priority of
information delivery and ensure the balancing of the AR layer's saturation, enabling
employees to remain focused on their primary tasks.
The adoption of extended reality (XR) technologies in manufacturing offers numerous
potential benefits and additional opportunities for producers and consumers [41] as a tool
for simulations, computer-aided design of systems and components, professional training,
and equipment maintenance. Machine learning algorithms can play a pivotal role [42] in
resolving common challenges of marker-based AR by visual recognition of dynamically
changing patterns. This will lead to a new quality of AR interfaces and create more realistic
experiences with novel applications. However, this is contingent upon considerable
improvement of user interfaces, as AR interface efficiency depends on the recognition of
certain images and sounds, which can be easily compromised in production environments.
3. Proposed methodology
The effective integration of AR technology into smart manufacturing environments
necessitates a meticulous design of the software platform architecture, with particular
emphasis on the velocity and variety of data to be harvested and processed, as well as the
resource constraints of the IIoT components embedded within the production equipment.
Furthermore, the architectural considerations must account for the need to seamlessly
integrate high-resource and high-load AR interface devices, while simultaneously
addressing the potential cybersecurity risks arising from the processing of sensitive
information within such a diverse and interconnected system. The synergistic convergence
of AR technology and smart manufacturing practices demands a holistic approach that
harmonizes the data processing capabilities, resource optimization, and security measures,
thereby enabling the realization of an efficient industrial ecosystem.
Metrological assessments should be conducted at various stages of the production
process to ensure adherence to quality standards and technological specifications. The
initial phase (00) involves shape and size verification of the workpieces through optical
inspection utilizing camera systems and laser measurement sensor upon completion of
metal processing operations. Subsequently, in the internal production silo (phase 01), the
quantity and assortment of the workpieces are accounted for based on their weight and
geometrical parameters. Welding procedures performed by a Kuka robotic system (phase
03) necessitate the examination of weld seams through multispectral imaging and
conductivity analyses. After the corrective press operation (phase 04), the workpieces are
evaluated against a predefined template or through laser measurement sensor data. Upon
completion of the painting process (phase 06) the quality assessment is done with use of an
ultrasound meter, the finished products undergo visual inspection via camera imaging, and
ready parts are laser marked (phase 07). Measurements and metrological assessment are
to be performed after procedures 00, 03, 04, 06, however internal (phases 02, 08) and
external logistics (phases 09, 10) may require additional measurements as well.
Figure 1: Process diagram for the modeled production line.
Compliance of the entire complex of measured characteristics with the technological
production map is a prerequisite for achieving high product quality. In instances where a
processed component deviates from the product specification, it is redirected to a heating
stage (phase 05), after which the correction procedure (phase 04) is repeated iteratively
until conformance is attained. Furthermore, the incorporation of laser-imprinted QR codes
onto the products facilitates compliance with the European Digital Product Passport
initiative, enabling access to product-related data essential for ensuring sustainability and
circularity throughout the product lifecycle.
The diagram depicted in Figure 2 illustrates data processing in the digital twin (DT) for
a metal parts manufacturer. It comprises several blocks that group uniform processes or
data processing hubs, with the aim of avoiding data processing centralization, restricting
information propagation within and outside the DT, and ultimately enabling fast distributed
decision-making and implementation of augmented reality (AR) tools for smart
manufacturing. From the manufacturer's perspective, the most fundamental block is the
digitized industrial platform. While the existing infrastructure simplifies further
development, it also introduces complexities due to the necessity of ensuring system
consistency, especially when legacy equipment interacts with novel components. In such
scenarios, the presence of an architectural diagram becomes a critical requirement to
promptly identify bottlenecks and resolve issues.
The enterprise requirement for the DT was to enable on-site personnel training and
enhance simulation capabilities through a personal AR assistant, justifying connection to a
well-structured, expandable knowledge base, which may be an external component to the
business entity but must be a part of the DT. To leverage the capabilities of AR-enabled
human-machine interfaces optimally, a separate infrastructure and resources must be
allocated for AR development. This block will be most effective if deployed on a cloud
environment that can guarantee high computing power for processing AR images, coupled
with elastic and cost-effective storage. While there is a debate regarding whether storing
data in the cloud may compromise sensitive information and comply with national
legislation, it should be emphasized that a hybrid data infrastructure can be designed (and
subsequently improved) to mitigate such risks. Finally, for implementing the AR interface
on the factory floor, a set of physical devices must be in place.
Figure 2: Processing of data in AR-enabled industrial digital twin.
With the architecture designed, the actual selection of edge electronic devices and
software development necessitate the specification of a model for the product
manufacturing cycle, which will serve as a tool for process optimization and a data flow
model for cybersecurity purposes. From an operator's perspective, the modules of the
digitized industrial platform and the simulation and training modules will be
indistinguishable, provided that the training scenarios are highly realistic and encompass
all possible operations, malfunctions, and that the AR processing device possesses sufficient
computing power. This condition can be easily fulfilled by modern-day smartphones and
tablet devices.
In contrast to a conventional web-oriented digital twin, our workflow assigns equal value
to physical and virtual assets, rendering them interchangeable for simulation purposes. This
approach may offer enhanced flexibility and faster informed decision-making cycles. The
AR layer will essentially enhance on-site information comprehension by incorporating
scene capture and visual recognition. There exists an opportunity to avoid critical failures
through an AI-assisted digital twin, whose improvement will be a natural extension of the
present work. The reprocessing of AR layers is prompted by state changes, reflecting both
the evolution of parameters and the outcomes of optimization. In this manner, the industrial
digital twin empowers human operators to make well-informed real-time decisions.
4. Results
The present research is grounded in a holistic view of the industrial DT for Industry 4.0,
encompassing models for manufacturing facility equipment, manufacturing processes, the
product and its characteristics, interfaces for harvesting data and providing support for
decision-making, quality assurance, and equipment maintenance [43-47]. The showcased
metal processing enterprise, with its peculiar features and commonalities, allows for the
design of a secure-by-design architecture if the data, energy, and material flows are
considered on equal footing in the DT model [48].
Figure 3: Virtual models of the robotic welding apparatus (left) and the support structure
(right).
The proposed development workflow, as illustrated by Figures 3-4, enables intuitive,
efficient and safe AR-enabled monitoring of the metal processing, allowing to represent all
relevant data from industrial IoT sensors, as well as real-time data processed by DT, to
optimize manufacturing processes and avoid hazards and malfunctions [4, 18, 49, 50]. The
enterprise personnel will be supplied with accurate and relevant data for decision-making
and predictive maintenance [22, 23]. This will allow management and personnel to focus
on choosing and implementing innovative solutions for greater customer satisfaction and
improved production efficiency.
Figure 4: Window of the mobile application with the superimposed AR objects, messages
and alerts.
It is known that when developing an effective interface for a digital twin of a production
line compatible with Industry 4.0 to ensure natural and precise movements, imitating the
functions of human hands, artificial neural networks are used in feedback loops to improve
the coordination of movements, accuracy and speed of production processes [51].
The article [52] presents the results of a qualitative study of a neural network, including
discrete and distributed time delays. A method for calculating the exponential decay rate
for a neural network model based on differential equations with a discrete delay was
developed and applied [53], [54].
When developing an effective interface for a digital twin of a production line compatible
with Industry 4.0, the direction of using sensors [55], [56], in particular for monitoring the
coordination of movements, accuracy and speed of production processes, is promising. An
important characteristic [57] of different types of biosensors is stability [58], [59]. Scientific
studies [60], [61] provide examples of modeling sensor responses. Numerical modeling in
cyberphysical biosensor systems [62], [63] is important at the stage of their design.
5. Conclusions
Digital Twins with augmented reality interfaces can serve as a powerful tool for realizing
human creativity in smart manufacturing, ensuring numerous advantages such as higher
product quality, better energy efficiency, and more efficient predictive maintenance [10,
16]. As an information technology, the implementation of AR interfaces holds great
potential for industrial DTs. AR can provide more realistic visualizations of relevant data,
enabling informed decision-making by human operators and facilitating communication in
spatially distributed teams [64]. Consequently, digital twins for smart manufacturing,
developed in accordance with the Industry 4.0 model, will be greatly enhanced by
incorporating AR interfaces for human-machine interaction. The central challenge faced in
DT development is the adequate implementation of connectivity between the physical
facility and the DTs [4, 65].
For a characteristic example of a metal processing line with a high level of digitization,
industrial data-based modeling allows for the design of an appropriate DT architecture. The
paper emphasizes the importance and practical viability of the following workflow for DT
design for Industry 4.0: industry-specific data analysis, manufacturing process modeling,
human-machine interface design prior to the actual development and deployment of the DT.
While each of these steps individually depends on the peculiarities of the manufacturing
process, the industrial data platform design, specifications of embedded sensors and
actuators, the general approach is quite universal, and none of the stages can be skipped.
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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