Citation: Erdei, T.I.; Krakó, R.; Husi,
G. Design of a Digital Twin Training
Centre for an Industrial Robot Arm.
Appl. Sci. 2022, 12, 8862. https://
doi.org/10.3390/app12178862
Academic Editors: Zhihan Lv, Kai Xu
and Zhigeng Pan
Received: 9 August 2022
Accepted: 31 August 2022
Published: 3 September 2022
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Article
Design of a Digital Twin Training Centre for an Industrial
Robot Arm
Timotei István Erdei
1,
*, Rudolf Krakó
2
and Géza Husi
1
1
Department of Air- & Road Vehicles, Faculty of Engineering, University of Debrecen, Ótemet˝o Str. 2–4,
4028 Debrecen, Hungary
2
Faculty of Engineering, University of Debrecen, Ótemet˝o Str. 2–4, 4028 Debrecen, Hungary
* Correspondence: timoteierdei@eng.unideb.hu; Tel.: +36-52-415-155
Abstract:
The Cyber-Physical and Intelligent Robotics Laboratory has been digitally recreated, and
it includes all the key elements that allow 6-axis industrial robots to perform PTP, LIN, and CIRC
motions. Furthermore, the user can create a program with these motion types. The human–machine
interface is also integrated into our system. It can also assist SMEs in developing their in-house
training. After all, training on an industrial robot unit does not entail installation costs within the
facility. Nor are there any maintenance and servicing costs. Since the lab is digital, additional robot
units can be added or removed. Thus, areas for training or production can be pre-configured within
each facility. Because of the customizability and virtual education format, there is no room capacity
problem, and trainees can participate in the exercises in parallel. Exercises were also conducted to
evaluate the program’s impact on teaching, and the results showed that using machine units can
improve teaching. Even today’s digital labs cannot physically convey the sense of space or the relative
weights of different elements in virtual space. Even with these features, individuals can operate a
machine more effectively than relying solely on traditional, non-interactive demonstration materials.
Keywords: industrial robot; digital twin; hmi; CPS Lab; industry 4.0; Unreal Engine
1. Introduction
The problem posed by our study is whether it is possible to improve the quality of
education by digitalizing our industrial laboratory, which is attended by many students
in other courses and at a different stage of their training (semester). It means that it is
more challenging for the trainers to transfer the same knowledge because of their diverse
backgrounds, the capacity of the lab, and the reduced number of hours in the industrial
lab due to time conflicts. We seek to answer whether this virtual education can bridge the
differences between the disciplines, and whether it will help the participants or be seen as
a hindrance.
Mass production alone will not suffice to increase production capacity. More advanced
mass customization is required, requiring a paradigm shift [
1
]. The modeled production
lines enable rapid redesign. However, this generally requires the use of a framework that
includes virtual copies of various elements. The innovative approach employed during
education in the laboratory gives the opportunity to our students to create additional
learning experiences by solving industrial themed tasks. Moreover, these completed tasks
were followed by compiling the documentation about the work done, which was then
handed over to every following class. This means that development cycles are not limited
to one particular semester, but rather go on continuously. Thanks to this continuous,
factory-like progress, the laboratory has started to function as a template Industry 4.0
laboratory, for both educational and research purposes [
2
]. Among these goals is to offer
a testing environment to industrial partners, where they can carry out various tests in an
isolated environment, be it an industrial controller, or machine units. With respect to the
controller, testing can be as simple as mimicking the behavior of the controller in a virtual
Appl. Sci. 2022, 12, 8862. https://doi.org/10.3390/app12178862 https://www.mdpi.com/journal/applsci
