Human factor in industry of the future: Knowledge acquisition and motivation

Abstract

Industry of the future bases on people knowledge, creativeness and motivations. Although, the number of workers needed in factories of the future decreases, the requirements concerning employees skills have been increasing. The knowledge of employees determines the factory system quality and efficiency. The motivation of people determines continuous improvement and development realized by problems identification and elimination. Hence, adequate learning methods are required to be implemented to achieve the following goals: empower and motivate people. This paper presents chosen methods such as learning by doing, computer simulations and virtual reality which support knowledge acquisition by people being prepared for work in factories of the future. The presented methods also increase employee awareness concerning possibilities of improvements.

Full text

© Faculty of Mechanical Engineering, Belgrade. All rights reserved FME Transactions (2019) 47, 823-830 823
Received: November 2018, Accepted: June 2019
Correspondence to: Dorota Stadnicka, PhD DSc Eng.
Rzeszow University of Technology Faculty of
Mechanical Engineering and Aeronautics, Poland
dorota.stadnicka@prz.edu.pl
doi:10.5937/fmet1904823S
Dorota Stadnicka
Associate Professor
Rzeszow University of Technology
Faculty of Mechanical Engineering
and Aeronautics
Poland
Paweł Litwin
Assistant Professor
Rzeszow University of Technology
Faculty of Mechanical Engineering
and Aeronautics
Poland
Dario Antonelli
Professor
Politecnico di Torino,
Department of Production
Systems and Economics
Italy
Human Factor in Industry of the Future
– Knowledge Acquisition and
Motivation
Industry of the future bases on people knowledge, creativeness and
motivations. Although, the number of workers needed in factories of the
future decreases, the requiremenets concerning employees skills have been
increasing. The knowledge of employees determines the factory system
quality and efficiency. The motivation of people determines continuous
improvement and development realized by problems identification and
elimination. Hence, adequate learning methods are required to be
implemented to achieve the following goals: empower and motivate people.
This paper presents chosen methods such as learning by doing, computer
simulations and virtual reality which support knowledge acquisition by
people being prepared for work in factories of the future. The presented
methods also increase employee awareness concerning possibilities of
improvements.
Keywords: Industry of the future, Human factor, Learning by doing,
Simulations, Virtual reality.
1. INTRODUCTION
Industry of the future (IoF) concerns factories organized
in line with Industry 4.0 concept, called also "fourth
Industrial Revolution". It is connected with Intelligent
Manufacturing Systems (IMSs) in which humans and
production machines interacts each other. The goal is to
create a smart factory that is characterized by high level
of processes digitalization and Cyber Physical Systems
(CPSs) implementation. Information and communica-
tion technologies (ICT) support processes realized in
smart factories. IMS requires application of innovative
technologies that allow to create smart interconnected
systems between smart objects [1].
IMS can be built with the support of such techno-
logies as Clouds, Big Data, additive manufacturing,
collaborative robotics. They are already implemented in
different industrial contexts but still require further rese-
arch to be on such stage of development that ensures
full exploitation of potential which these technologies
can bring.
One of the most important issues in IMS is human
factor. Human resources have to be prepared to under-
take their role in IMS. In IMS a high level of knowledge
as well as fast knowledge adjustment to new circum-
stances is required from people. However, the proper
training of employees is neglected. That causes prob-
lems in IMS implementation, since IMS requires close
interactions between human operators and machines. If
employees are not adequately prepared to work in the
new production environment the technological innova-
tions cannot be implemented with success and cannot be
fully exploit. Hence, there is a risk that costs incurred
for innovative solutions will never be refunded. The
reason is that the costs of solving problems will exceed
the possible benefits. The whole generation of people
has to switch to new solutions in a short time, since
nowadays the innovations development is very fast.
Therefore, also employees have to be prepared for new
tasks in a short time. It is because the manufacturing
systems are changing radically in IMS. These are not
any more small additive innovations but huge radical
improvements that change the way of management of
the whole factory. In these circumstances new rules
have to be implemented and new skills are required
from employees. Therefore, it is apparent that currently
used learning methods are not sufficient. Manuals and
classroom lessons are effective to ensure knowledge
acquisition but are not equally effective in teaching how
to use the new technologies in everyday factory’s work.
New educational techniques as well as motivating
approaches are indispensable for potential students who
already work in a factory. Factory workers are peculiar
students that can be involved through remote learning,
while staying on the job. Furthermore, they don’t need
the full knowledge about the new technologies, but they
will learn how to introduce the new technologies in their
work and how the technologies will influence the pro-
cess efficiency and problems elimination. Additionally,
survey from [2] finds that the learning process can be
accelerated by applying innovative learning techno-
logies as well as the by tools increase of students’
involvement in the learning process.
Therefore, the goal of the paper is to show how modern
learning tools can be applied to learn to work along with
the enabling technologies in IMS and assess their
application without impacting on the actual industrial
process. Additionally, it can be expected that learning time
as well as cognitive effort will be reduced. However, the
mentioned aspects are out of the scope of this paper.

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This paper presents practical implementation of
chosen educational method that ensure knowledge and
motivation acquisition by students. The second part of the
paper presents motivation behind the presented topic.
Different forms of learning activities are deliberated. The
third part of the paper introduces open educational
platforms, i.e. MOOCs. Then, application of computer
simulations in education is discussed. In the fifth part vir-
tual reality (VR) and augmented reality (AR) in edu-
cational context are deliberated. Next, human-robot
collaboration as an example of learning by doing is
shown. The last learning means is a simulation assembly
line that is used in training program. The summary of the
work presents conclusions and future works.
2. MOTIVATION
Implementation of Industry 4.0 concept is considered as
a long-term objective. Current industry is not ready for
full implementation of IMS and all the enabling tech-
nologies. It is because IMS implementation is connected
with high digitalization level.
Many of enabling technologies support employees’
work and decision-making process even if they do not
realize how many innovations are engaged. For exa-
mple, RFID based system can be used to facilitate com-
petition of goods for shipment in distribution centres
[3]. In CPS operating in a company RFID can be
embedded in products to track them across different
manufacturing departments.
Another example concerns NFC (near-field commu-
nication) which is applied to maintenance processes.
NFC-enabled mobile phones with NFC tags are used to
ensure that all maintenance tasks will be realized in a
predetermined time and documentation will be prepared
in real-time [4].
Designing and then work in CPS can be supported
with the use of simulations and VR or AR. They can be
also used in learning process. Since simulations represent
a real world, many rules can be better understand with the
use of simulations before an employee start working on
own work station. In VR an employee can perform a task
similar to a real one avoiding potential real consequences
in case of mistake.
By modelling the real work and then by performing
simulations an employee can find the best solution to
obtain the expected results of a decision. With the use of
AR a kind of guide can be implemented to lead an
operator through the task to be performed. The AR may
be used only in training process or also in everyday work,
of course with higher costs. In game a real world can be
reflected and a learning by doing methodology can be
implemented. Naturally, the games have limitations.
Therefore, the world represented in the games is sim-
plified. Anyway, the most important issues, that have to
be learned by game participants are incorporated into the
games. This way real problems are analysed in simulated
environment. A positive competition, which can be
provoked by the game leaders, can be an additional
motivator for looking the best way to perform a task. The
huge advantage of games is that employees learn while
having fun [5]. Therefore, gamification is implemented
with enthusiasm in industry as well as in education [6, 7].
Games allow to obtain technical as well as social
skills. It is because in a game a participant performing
predesigned tasks [8] for which he or she is responsible,
might communicate or collaborate with other emplo-
yees. In IoF team work will be the crucial skill because
the interdisciplinary teams are going to work for achi-
eving a common goal. High levels IMS will be main-
tained by a team of highly skilled experts from different
areas. In many cases the collaboration between the
experts is very difficult. That is because they use diffe-
rent technical language. Hence, playing the games with
other people from different disciplines can prepare
better employees for such problems.
In learning process different learning modes can be
applied. Figure 1 presents a classification based on Lean
typology of simulations [8] that shows how learning
modes can be divided based on different criteria.
IT-based learning modes
Simul ations
Gaming Modelling
Non IT- based learn ing modes
Learni ng by doing
Playing a role
In virtual
reality
In real world
Educational games
Board games Cards Paper-based In field
Traini ng
With aug mented
reality
Without a ugmented
reality
Figure 1. Classification of learning modes
This paper deals with the group of IT-based learning
modes, which are divided on simulations and learning
by doing. Training simulations can be performed with
the use of simulators or in similar to real environment
when a game participant is supported by augmented
reality while performing a work. They are implemented
in order to ensure that in future work an employee will
have a habit of proper performing of a work. The main
goal of modelling simulations is to support decision-
making process. Learning process can be realized in VR
where a trainee plays a role and have a chance for
observation of own behavior consequences. The
description of the learning modes can be found in [9].
Implementation of digitalization in education means
that a game participant can play in the same time with
other participant connecting to them in a digital world. This
is the paradigm of Web 2.0. Game participants can play in
a designed environment but also they can create or modify
a virtual world. Additionally, they can actively participate
in discussion with the use of forums, blogs or wikis not
only by making a comment but also by creating the
knowledge, based on own experience. This way trainees
shift from passive to active role of knowledge co-creators.
The new knowledge is available for all participants in a
network. This motivates the participants to increase their
ability of critical thinking, because the new knowledge
might be not possible to be applied in all situations.
3. OPEN EDUCATIONAL PLATFORMS (MOOC)
The early beginnings of distance education date back to
the end of the nineteenth century, when the first corres-

FME Transactions VOL. 44, No 4, 2019 ▪ 825
pondence courses appeared. The advent of the digital
age, the dissemination of information and commu-
nication technologies and easy access to the Internet led
to a breakthrough in the development of distance
education and the emergence of the first MOOC (Mas-
sive Open Online Courses) platform in 2008 [10]. The
acronym MOOC stands for the training platform which
is characterized by: unlimited number of users (Ma-
ssive), available for everyone (Open) and accessible
through internet (Online) courses. MOOCs are not an
independent phenomenon, isolated from other develop-
ments in the field of open and distance learning. On the
contrary, MOOCs are strongly associated with other
achievements of education techniques, have the
potential to support lifelong learning, eliminate barriers
in the learning process, ensure equal opportunities in
education, and most importantly enable widespread
access to knowledge [11].
The MOOC platforms can be divided into two cate-
gories: xMOOC and cMOOC, which form two main
pedagogical trends. xMOOC is dedicated to teaching in
accordance with the rules of behavioral pedagogy, i.e.
focused on the content and teacher [12]. The second
category - xMOOC is based on a conceptual connec-
tivist approach and has an innovative character.
Connectivism is based on the assumption that the
learning to think is more important than just acquiring
knowledge. The creator of this theory, George Siemens
[13] presents connectivism as a way of learning in the
digital age. The main idea of the connectivist approach
is that knowledge can be communicated via a network
of links between students [14], usually with the use of
social media platforms, wikis or blogs.
By definition, MOOC offers open courses, which are
free for the learner. However this does not mean, that
such an education platform does not generate costs for
the training content provider. The creation and effective
provision of high-quality training materials for thou-
sands of users requires the involvement of experienced
teachers, technical staff and a robust, scalable IT
infrastructure [15]. According to the class-central report
[16], the number of courses on MOOC platforms is
growing every year, reaching over 11 thousands of
courses in 2018. These courses are provided by over
900 universities around the world. In 2018, the number
of users using MOOC exceeded 101 millions. The
report [16] also provides information about MOOC plat-
forms that support the largest number of users (in
millions of registered students): Coursera (37), edX
(18), XuetangX (14), Udacity (10), FutureLearn (8.7).
The great popularity of MOOC platforms can also be
evidenced by the information on the number of
participants of individual courses. According to the
report [17], the number of students in the three most
popular courses exceeded 3.2 million.
In this context, one should ask the question what
part of the huge number of courses offered on MOOC
platforms is related to the industry of the future? The
largest of the MOOC services - Coursera currently
offers (05/02/2019) 24 courses related to the industry
4.0 keyword. Topics in this set of courses include:
Digital manufacturing and design, CAD and digital
manufacturing, Business models, Business strategy but
also English for Science, Technology, Engineering, and
Mathematics and Dairy production and management.
The second largest service edX currently offers 19
courses related to industry 4.0, including 12 in the field
of engineering. The third largest English-language
service Udacity after typping industry 4.0 in the search
field, shows an information, that it does not currently
provide courses related to this subject. It is possible that
after the modification of the phrase entered into the
search engine more courses related to industry 4.0
would be found, but it would be difficult to give the
reason for the lack of industry 4.0 in the set of keywords
of such courses.
Bearing in mind the summary of the overall number
of courses offered on MOOC platforms with the number
of courses related to industry 4.0 and, on the other hand
the growing popularity of the industry 4.0 concept
clearly shows the need for increasing the presence of
industry 4.0 in the MOOC space.
4. COMPUTER SIMULATIONS IN EDUCATION
Simulation can be defined as the operating model of the
selected system [18] that enables the presentation of
selected aspects of the reality [19]. The most important
simulation features include: representation of real
systems; the ability to change simulation scenarios; low
costs of simulation experiments and protection against
the consequences of serious errors.
By computer scientists, simulation is seen as the
third (next to theory and experiment) way of practicing
science [20, 21]. Simulation methods in science allow
for suggesting the theory, mapping of real processes,
data analysis or apparatus control. After the successful
implementation of simulation methods in science, at the
end of the twentieth century, it was noticed that simu-
lations may be useful also in education. This conviction
is expressed in the statement [22]: "If video games can
be transformed so that their users learn, a great many
people may come to understand and control dynamic
systems". Today, after more than two decades, this
vision has become a reality, and learning based on
computer simulation is an important tool in education.
Education using simulation methods is based on the
assumption that students instead of experimenting on a
real system or device, build a model and examine its
behavior in a simulation environment. It is worth noting
that the use of simulation methods in education may
contain two main tasks:
• Learning to create simulation models in order to
know the techniques and possibilities of modeling;
• Analysis of systems, devices or materials on the base
of simulation experiments.
In studies conducted among teachers using the
nanoHUB.org portal [23], the main learning objectives
were identified using simulation methods. These are:
learning the role of simulation in science and engineering
practice, creating characteristics of real systems and
materials based on data from a simulation experiment,
understanding causal relationships in a simulation model,
estimating the accuracy of a simulation model, validating
the results of a design task, applying computational
techniques in modeling, predicting the results of the

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simulation experiment, matching the appropriate model
to simulate the physical phenomenon.
The research results presented above clearly show
the huge potential of simulation methods in educational
applications. The presented characteristics of computer
simulation make them applicable to explaining non-
obvious phenomena or understanding the way complex
systems work. Such complex systems appear in part-
icular where people play an important role. Individual
abilities and talents, tiredness, emotions and stress can
have a significant impact on the actions and decisions of
a person cooperating with the technical system. Lear-
ning using simulations allows students to build models
and practice the systems and devices they will use in
their future work. The use of simulation that way can
avoid many mistakes with serious economic conse-
quences and often also a threat to health and life.
5. VIRTUAL REALITY AND AUGMENTED REALITY
IN EDUCATION
VR is everything that is not real. It allows you to
experience a world that does not have a physical form.
Head Mounted Display (HMD) is the current form of a
hardware delivering VR experiences to users, and one
of the most common VR terms you'll hear about today.
An HMD is typically a pair of goggles or a helmet of
some type, with which you view the VR experience.
AR is a technology that enhances the real world with
virtual elements (computer-generated). It has three basic
properties [24]: combination of real and virtual objects
in the real environment; realignment of real and virtual
objects; interaction working in real time.
The shift from VR to AR paradigm is seen as a
continuum by [25] and not as a sequence of rigid steps
(Figure 2). Milgram gives a general and technology
independent definition of AR. In a broad sense, AR
refers to “augmenting natural feedback to the operator
with simulated cues”
With a few exceptions, the focus in AR applications
is more oriented on the technology employed to insert
visually credible artificial objects in a real environment
than on the content visualized. This way of working is
justified by the relative novelty of the technology and
by the possibility to continually improve the user
experience with the evolution of the AR devices. Thus it
is only an answer to the question ‘how AR’. The
advancement in the AR supporting technology, both
hardware and software, has made possible to employ
commercial solutions to build AR applications.
Figure 2. The Virtuality Continuum according to Milgram
There are still two open questions: 'what' to display
and 'why'? In the application of AR to learning, the
questions should be answered in reverse sequence:
decide on the kind of assistance required by a student
during the learning activity, select the information to
display, chose the most appropriate AR device.
There is a rationale behind the emergence of these
applications: the attention span is reduced in new
generations, nevertheless the majority of them is able to
engage for extended periods of time when gaming or
using simulations. Researches have shown that we
remember less what we hear, or what we see, while we
are able to recall nearly all of what we do. AR assists
the creation of a credible operating scenario for the
learning of industrial tasks, with guiding instructions
that overlay on the actual reality.
Constructivist learning is fitted for VR and AR as it
allows students to actively practice what they are
learning. Participative learning requires the simulta-
neous presence of a group of learners in the same place.
If the place is virtual the students could be everywhere
(but simultaneously the time constraint is still valid).
VR has both the assets of real world classrooms and of
online (distance) learning. There are significant
drawbacks that could hinder a systematic application of
AR to complex study subjects [26].
• AR requires more work to be implemented if com-
pared with traditional teaching material.
• It must be acknowledged that realistic AR is not cheap
and headsets are not comfortable for a long time use.
• Moreover, the different devices employed must be
continuously updated and maintained without losing
the integration of sensors and displays that is crucial
for the realism of user experience.
• There is a cognitive effort in communicating through
VR that is more than the effort required to talk face
to face.
6. LEARNING BY DOING: HUMAN-ROBOT COLLA-
BORATION
An example of learning by doing experimented in the
laboratory of Politecnico di Torino, Italy, is the
collaborative human-robot assembly of flanges on a
base, as represented in Figure 3. The components are
easy to find in every hardware store.
Figure 3. The sample assembly flange
The students are required to analyse the assembly
task, subdivide it into several elementary operations,
assign each operation respectively to a robot or to a
human operator and, finally, to program the robot to
execute the collaborative assembly tasks (Figure 4).

FME Transactions VOL. 44, No 4, 2019 ▪ 827
The first task is assigned to a human operator. It
consists of placing a base and flanges on the reference
position. The human starts collaborative task execution
by sending direction commands to perform the
assembly in the prescribed horizontal or vertical
direction. In the following task the robot grabs the
flange, moves it to the assembly position and keeps
maintaining the flange in the correct orientation and at
the correct distance from the flange reference points.
The task requires force and accuracy and it is best suited
to the robot. The human can join the flange to the base
by screwing it.
Figure 4. The collaborative robot during the assembly
The task requires high dexterity and, this time, the
human appears more proficient in it. The task
assignment and the robot programming are not defined a
priori, leaving the student wide space of discretion. The
programmed trajectories are shown as a simulated
process on a monitor in front of the student that can
verify the robot program before executing it in the real
workspace.
What is more important, from the viewpoint of
present paper, is that students were able to program the
robot in a fraction of the time usually required as there
is no more need to learn and memorize commands in
robot language but just to learn how to program by
manual guidance.
7. COMPUTER SIMULATIONS: SIMULATION OF
PROJECT RISK
This chapter presents the simulation experiment carried
out in the course of "Risk management in IT projects".
This subject is carried out in the field of "Production
management and engineering" and as a result of the
course students acquire, among others, the ability to:
identify risk factors, model and simulate risk, assess the
impact of risk on the project. Modeling and simulation
are methods often used to estimate the impact of risk on
a project due to the lack of experience with certain risk
factors in previously implemented projects.
A change in macroeconomic conditions (for exam-
ple a change in the rate of inflation) during the project
implementation is a risk factor that can have a
significant impact on the project's result. The main goal
of the task carried out in the course on "Risk mana-
gement in IT projects" is to estimate the impact of the
change in the rate of inflation on the implementation of
the project budget, and in particular on the possibility of
implementing all the activities planned in the project
within the assumed budget.
The students' task is to analyze the project carried
out in the Imple Masters company. The company is
running a project in which 20 people are employed. The
project implementation time is 24 months. The budget
of the project is 10 million euros. The budget should
pay employees' wages (an average of EUR 5,000 per
month), pay for the operation of premises (EUR
120,000 per month) and deliveries of components (1
million in 6th month, 1.5 million in 12 th month and 2
million in 18 th month). The costs indicated were set at
the project start date – they may change as a result of
inflation. Wages in the project change at a rate 1%
lower than inflation rate, operating costs of premises
increase by 2% above inflation rate, and delivery costs
grow 1% above the inflation rate. Funds for project are
stored on an interest-free bank account. The result of the
simulation should be data on the budget resources used,
with inflation amounting from 1% to 6%.
In the first stage of the task implementation, students
should develop a model of financial flows in the project.
The project's funds are sent from the “Project funds”
account to the “Payrol costs”, “Operating costs” and
“Components” accounts. Cash flows depend on fixed
values (wages, costs of component purchases, costs of
the operation of premises) and on the time and rate of
inflation. The cash flow model in the project is
illustrated in Figure 5.
Figure 5. Cash flow model in the project
Formula (1) shows the method of calculation of
monthly salaries (in thousands of euros).
Salaries = (5*20)*(1+Time*(Inflation-0.01)/12) (1)
In the simulation experiment the rate of inflation is
set (from 1% to 6% with a step of 1%) and then after the
simulation, the account balance of the project is checked
at the end of the project. The simulation results are
presented in Table 1. The results presented in Table 1
indicate that the implementation of the project within
the planned budget is possible only with 1% inflation
rate.
With higher inflation rates (2% or more) it will be
necessary to involve additional funds. The simulation
model also allows to specify the time in which the
project's account balance will go down to zero. This
example is shown in Figure 6 - budget funds for 2%

828 ▪ VOL. 44, No 4, 2019 FME Transactions
inflation rate have been used 2 months before the end
of the project.
Table 1. Simulation results; PAB – Project account balance
Inflation rate PAB Inflation rate PAB
1% 37.2 4% -264.6
2% -63.4 5% -365.2
3% -164.0 6% -465.8
The example shown is not sophisticated, and at the
same time allows for the implementation of several
educational goals. Students learn the basics of modeling
in the System Dynamics method (SDM), identify and
describe mathematically causal relationships, conduct
a simulation experiment and interpret its results.
Fifteen students developed a model and performed
simulations. Then, they have been asked to assess the
level of difficulty and workload. The task completion
time given by students was 2.5 to 4 hours (median is 3.5
hours). The difficulty was assessed on the Likert scale
(1-very easy, 2-rather easy, 3-I have no opinion, 4-
rather difficult, 5-very difficult). Students almost unani-
mously (86%) assessed the difficulty of the task as 4 -
rather difficult; highlighting in comments that the diffi-
culty comes from the first contact with the System
Dynamics method and Vensim software.
Figure 6. The project account balance on 2% inflation rate
It should be noticed that the students had an alter-
native to solve the task in an Excel spreadsheet.
However, Excel was used only for some students to
present the simulation results obtained in Vensim. This
is probably due to the necessity of using in Excel
financial mathematics functions (e.g. future value of
capital), which are usually not familiar to engineers.
8. VIRTUAL REALITY: MANUFACTURING SYS-
TEMS OPERATIONS IN THE VIRTUAL WORLD
VR can be created to be a copy of the real world. An
example, presented in this papers, concerns an assembly
line existing in Rzeszow University of Technology,
Poland [27]. The line is dedicated for education in the
field of mechatronic systems [28]. The physical
assembly line has own digital twin in which operations
can be tested before they are applied in the reality.
Additionally, the work can be realized simultaneously in
VR and in the real world based on the same working
program. Any discrepancies in behaviour of both
systems can be easily notified. The students program the
system, observe its behaviour, implement changes and
assess performance looking for the best solution.
The system is composed of different elements that
can play different roles in the learning process. The
work [9] describes the equipment included in the
system. With the use of the system the students can
learn about robots collaboration problems. They can
also deal with the quality issues. The recognition system
(Figure 7) can easy identify a wrong part. Taking a
wrong product can also cause collisions what can lead
to safety problems. The problems can also arise when in
a storage (Figure 8) the products are placed in wrong
places or in a wrong order.
Figure 7. A recognition system in reality
Figure 8. A recognition system in VR
The proper process parameters have to be chosen to
ensure the required results of the assembly process.
Transport belts are the elements of a transport system.
The adequate speed has to be chosen to ensure that the
products will be on time ready to be grasped by a robot.
A finished product warehouse can be used to test dif-
ferent arrangement of the ready products taking into
account clients’ orders.
The learning process provides the following inten-
ded learning outcomes (ILO). After training session on
the assembly line a student will:
ILO 1: Plan an assembly process based on a client
requirements, schedule the manufacturing tasks in an
adequate order and control the process.
ILO 2: Program the machine tools incorporated in
the manufacturing line.
ILO 3: Program the cylindrical and Cartesian robots
to work in collaborative environment.

FME Transactions VOL. 44, No 4, 2019 ▪ 829
ILO 4: Program PLC controllers to ensure
undisturbed work.
The expected system operation is realized in the
following steps:
• With the use of a transport belt a component is
transported from the input warehouse to the
recognition system to check the kind of element.
• With the use of a cylindrical robot the element is
transported to a preparation workstation and next to
an assembly workstation.
• With the use of the same robot the second element is
transported to the assembly workstation.
• With the use of a hydraulic press the elements are
assembled.
• With the use of the second robot the assembled
product is transported to the second transport belt.
• The third robot transfers the product to a Cartesian
robot directly.
• The Cartesian robot places the product in a final
warehouse.
In the line such additional elements as turning CNC
machine ST-20 or milling CNC machine VF-2 can be
included. Also, a grabber warehouse can be applied.
Moreover, for workpieces manipulating a manipulation
system which can be connected with the iRVision
system and Fanuc M-10iA robot can be introduced.
Furthermore, a Mitsubishi RV-M2 robotic assembly
station can be included.
The areas in automation technology that can be
incorporated in a teaching program are as follow: task
sequence optimization, set-up operations, design of
logic control, commissioning, automatic operations,
possible problem identification and elimination, and
others. Learning process includes individual and team
projects. Students have to communicate to be sure that
all system elements will collaborate to achieve a goal.
9. CONCLUSIONS
IoF requires well skilled employees who continuously
improve their knowledge and skills. The development of
technologies forces companies to constant changes.
Therefore, the employees should be ready and moti-
vated to understand new situation and to undertake new
tasks. They also should be prepared for unknown prob-
lems and they should know tools to be used in the
problems analyses and solving. Hence, the appropriate
learning methods need to be introduced to provide
people with readiness and motivation for different cha-
llenges. The paper presents classification of learning
modes which can be used in learning process. These
modes which are connected with novel IT tools are
underlined and some examples of their application are
discussed, presenting the usefulness in learning process
of complex problems. As it was discovered in a survey,
the modern tools (e.g. SDM) do not have to be time
consuming and students can be willing to use them
more that well known tools (such as Excel).
The companies have different possibilities and can
choose the most suitable learning tools taking into
account the organization context. The presented
examples of practical application of the chosen methods
can help the companies to make decision. Certainly, the
same methods can be used in higher education to
prepare future employees for upcoming challenges.
In the future work the authors are planning to apply
different methods in a learning process and assess how
students perceive the proposed methods and what
results can be achieved with these methods.
ACKNOWLEDGEMENTS
This work has been partially supported by the “TIPHYS
4.0 – Social Network based doctoral Education on
Industry 4.0”, project No 2017-1-SE01-KA203-03452
funded by ERASMUS+ of the European Commission.
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ЉУДСКИ ФАКТОР У ИНДУСТРИЈИ
БУДУЋНОСТИ - СТИЦАЊЕ ЗНАЊА И
МОТИВАЦИЈА
Д. Стадницка, П. Литвин, Д. Антонели
Индустрија будућности заснива се на знању,
креативности и мотивацији људи. Иако се број
потребних радника у фабрикама у будућности
смањује, захтеви везани за вјештине запослених су у
порасту. Знање запослених одређује квалитет и ефи-
касност фабричког система.
Мотивација људи одређује континуирано
усавршавање и развој који се остварују
идентификацијом и елиминацијом проблема. Због
тога су потребне адекватне методе учења да би се
постигли следећи циљеви: дати људима одговорност
и мотивисати их. Овај рад представља изабране
методе као што су учење кроз рад, рачунарске
симулације и виртуална стварност које подржавају
стицање знања оних који се припремају за рад у
фабрикама будућности. Представљене методе
такође повећавају свест запослених о могућностима
побољшања.