=Paper=
{{Paper
|id=Vol-2454/paper_10
|storemode=property
|title=FTOnto: A Domain Ontology for a Fischertechnik Simulation Production Factory by Reusing Existing Ontologies
|pdfUrl=https://ceur-ws.org/Vol-2454/paper_10.pdf
|volume=Vol-2454
|authors=Patrick Klein,Lukas Malburg,Ralph Bergmann
|dblpUrl=https://dblp.org/rec/conf/lwa/KleinMB19
}}
==FTOnto: A Domain Ontology for a Fischertechnik Simulation Production Factory by Reusing Existing Ontologies==
https://ceur-ws.org/Vol-2454/paper_10.pdf
FTOnto: A Domain Ontology for a
Fischertechnik Simulation Production Factory
by Reusing Existing Ontologies
Patrick Klein, Lukas Malburg, and Ralph Bergmann
Business Information Systems II, University of Trier, 54286 Trier, Germany
{kleinp,malburgl,bergmann}@uni-trier.de
http://www.wi2.uni-trier.de
Abstract Nowadays, semantic information provided by an ontology is
indispensable in the context of Industry 4.0, especially when using meth-
ods from Artificial Intelligence. The currently available ontologies do not
satisfy the demands of simulation environments used for research pur-
poses. For this reason, we develop an ontology customized to Fischer-
technik simulation factories by reusing existing ontologies. The ontology
has been created according to requirements from two use cases. In our
evaluation, it is determined that the ontology is suitable to represent ma-
chine components and their relationships while satisfying the specified
requirements.
Keywords: Ontology Engineering · Industry 4.0 · Simulation Factory
· Fischertechnik
1 Introduction
In recent years, the industry has been in the process of changing towards the
fourth industrial revolution, also known as Industry 4.0 in the German-speaking
area [10,14]. This transformation is characterized by manufacturing and service
innovations based on Cyber-Physical Systems (CPSs), big data, and the predom-
inant use of Artificial Intelligence (AI) methods [16]. Although a lot of research
work is carried out in this area today, there is still a lack of companies in the
industry that are willing to make sensor or machine data available for research
purposes or allow direct intervention in productive systems. As a consequence,
research data must be generated by using appropriate simulation environments.
In our previous work [11], we use a Fischertechnik (FT) simulation production
factory. Such simulation factories are often used in research, e.g., for augmented
reality [12], to plan, create, and evaluate different factory layouts [13], for re-
search of digital twins by Fraunhofer IESE1 or other related work (e.g., [22,30])
Copyright © 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
1
https://www.iese.fraunhofer.de/de/presse/current_releases/PM_2019_02_25_
Hannover-Messe.html, accessed May 31, 2019.
P. Klein et al.
to name just a few. However, there are no suitable ontologies available to use
them for simulation environments, although ontologies are a part of knowledge
modeling and represent important knowledge. Furthermore, they are also needed
to apply methods from AI such as case-based reasoning [1] or advanced machine
learning. Current ontologies used such as the Manufacturing’s Semantics Ontol-
ogy (MASON) [17] and Manufacturing Service Description Language (MSDL) [2]
do not consider sensor data streams and the CREMA Data Model, Core mod-
ule (CDM-Core) [21] ontology is too comprehensive and detailed with classes
and properties that are not needed in the context of a simulation environment.
This paper addresses this issue and thus the development of a domain ontology
for simulation factories. Therefore, it is investigated which components can be
adopted from existing, related ontologies and which additional components need
to be added. In the following, Sect. 2 introduces foundations for our work and
discusses related work. Section 3 describes the layout of our used Fischertechnik
simulation factory and presents use cases in which the application of semantic
information provided by an ontology could be important. The development pro-
cess and the developed ontology itself is described in detail and evaluated in
Sect. 4. Finally, a conclusion is given and future work is discussed in Sect. 5.
2 Foundations and Related Work
Ontologies are used to describe the knowledge about a domain of interest in a
formal way that can be understood by machines [9]. Therefore, the Web Ontology
Language (OWL) provides classes, properties, individuals, and data values to
express ontologies [29], which means that complex knowledge about individuals,
groups of individuals, and their relationships can be represented [8]. Using an
ontology benefits knowledge sharing between computational entities, knowledge
reuse by using well-defined domain ontologies as well as the application of logical
reasoning [19]. Moreover, an ontology forms the basis for the further use in
knowledge-intensive applications [5].
A survey of upper ontologies regarding their modeling capabilities of a man-
ufacturing system in terms of products, processes, and resources is recently con-
ducted by Cao et al. [4]. One of the surveyed ontologies is the upper OWL-
ontology MASON [17] that conceptualizes the manufacturing domain with three
concepts: entities, operations, and resources. Similarly, the OWL-DL Manufac-
turing Service Description Language (MSDL) ontology [2] focuses on the manu-
facturing process as the core class of manufacturing services. The Manufacturing
System Engineering (MSE) [18] ontology focuses on the inclusion of different tax-
onomies of teams in a manufacturing enterprise to improve collaboration rather
than modeling the manufacturing process itself. These ontologies suffer from
poor modeling of sensor data streams, which is an essential aspect of Industry
4.0. The latest and according to the authors largest publicly available ontology
to model production and maintenance is the OWL2 CREMA Data Model, Core
module (CDM-Core) [21], which is among other things an extension of MASON
and the Semantic Sensor Network (SSN) ontology [7] to describe their data
FTOnto: Domain Ontology for a FT Simulation Production Factory
streams as well as their observed data and to provide an example for condition
monitoring.
3 Fischertechnik Simulation Production Factory Model
Since a lot of information can be extracted from data of a real manufacturing
environment (e.g., production quantity, processing times, failures, etc.), there
are serious confidentiality concerns that make it often impossible for universities
to obtain data for research. In addition to data, knowledge must also be avail-
able in order to semantically model the corresponding relationships of individual
components. For this reason, simulated data is obtained from a model factory
for research purposes. This section describes the layout of the factory and two
exemplary use cases.
3.1 Layout of the Factory
For the simulation of an Industry 4.0 manufacturing environment, we use the
Fischertechnik (FT) factory model shown in Fig. 1.
Figure 1. The FT Factory Simulation Model
It consists of four workstations with five individual modules: a sorting line
with color detection, a multi-processing workstation with an oven and a milling
P. Klein et al.
machine, a high-bay warehouse, and a vacuum gripper robot. Each module is
operated by its own controller based on an ARM Cortex A8 CPU with vari-
ous analog and digital input/output ports running under a LINUX kernel. The
model is equipped with nine light barriers and ten switches for control purposes
of the actuators consisting of ten motors, three compressors, and eight valves.
For condition monitoring purposes, the model is enhanced with dedicated sen-
sors such as four three-axis acceleration sensors that are mounted on motors and
compressors and four differential pressure sensors measuring the pressure gener-
ated from the three compressors. Furthermore, two absolute orientation sensors,
each with a gyroscope, an accelerometer, and a geomagnetic sensor are installed
on the robotic vacuum gripper and the dispensing machine of the high-bay ware-
house. Similar to the continuous transformation of a factory in the context of
Industry 4.0, the model is in a continuous development phase so that in future
more components such as RFID reader/writers as well as additional processing
and transport units will be integrated.
Each module of the factory is steered by its own controller that is connected
via an Ethernet network to communicate via remote procedure calls. For pro-
cessing the data generated by sensors as well as process parameters (e.g., motor
speed), the high throughput distributed messaging system Apache Kafka is used
and Apache Cassandra is installed as database.
The overall manufacturing process is currently designed as a cycle to simulate
a mass production environment. The process starts from the high-bay warehouse
where workpieces are dispensed and transported to the multi-processing station
– the oven and the milling machine. After processing, they are sorted by color,
transported by the vacuum gripper robot and finally stored in the high-bay
warehouse where the process starts again. As can be seen from the transport
routes depicted as dotted lines in Fig. 2, the model also provides the option for
executing manufacturing processes in a more flexible way as typical for Industry
4.0 mass customization [10,14].
3.2 Use Cases
In this subsection, we introduce two typical Industry 4.0 use cases in which we
want to investigate the potential of an ontology. These are: Flexible Production
Processes, Predictive Maintenance (PredM), and their interrelation towards the
direction of the development of a CPS in which the resolution of errors in pro-
duction processes is an important aspect [15].
Flexible Production Processes Essential aims of Industry 4.0 are to increase
the flexibility to react on individual customer requirements and thus to produce
customized products or to optimize efficiency in terms of the consumption of raw
materials or energy from manufacturing processes. In order to achieve this, it is
necessary to execute manufacturing processes in a more dynamic fashion [10].
However, this process requires knowledge about which machines can perform
similar tasks and what their utilization rate is, which machines are reachable
FTOnto: Domain Ontology for a FT Simulation Production Factory
Sorting Machine
Conveyor 1
Vacuum
Conveyor 3
Gripper
Robot
Dispensing Machine
High-Bay Warehouse
Color Detection
Workstation Transport
Turntable Oven
Conveyor 2
Milling
Machine
Figure 2. Schematic Illustration of the FT Factory Simulation Model
from the current position of the product and how the production processes can
be carried out in an optimized way.
Condition Monitoring Analysis PredM [27] aims at foreseeing a breakdown
of the system by detecting early signs of an upcoming failure to make main-
tenance work more proactive. Thus, it is possible to fix errors in production
before they are happening and therefore prevent cost-extensive down times. An
ontology can support this process by representing machines with their individ-
ual components and by linking connected sensors and measurements (temporal
and spatial relationships). Failure data from individuals of the same (machine-)
class can be used to apply transfer learning for a more robust prediction model.
Furthermore, ontologies are used to solve the interoperability issue by relating
information from heterogeneous sources to enhance condition monitoring data
with contextual information such as from control software or other related sys-
tems [24].
Integration and Interrelation In the event of an inconsistency, e.g., caused
by a bearing failure detected by unusual vibration patterns, the affected com-
ponent or machine can be determined. Thus, the faulty part can be replaced in
time – not too early, but also not too late. If an unexpected failure occurs in
the production process or scheduled maintenance is performed, some machines
or transport routes can be temporarily unavailable. In this case, currently run-
ning production processes or already planned processes may not be executed as
P. Klein et al.
scheduled. By using an ontology, similar machines or alternative transport routes
can be identified to keep production processes running. Thus, an ontology pro-
vides the foundation for the use of planning techniques (e.g., for flexible process
adaptations of unanticipated exceptions [20]) or generally for the integration
of Internet of Things-based data such as sensor data streams with (business)
process management (e.g., to support employees during work with mobile user
guidance [26]).
4 FTOnto: Domain Ontology for a Fischertechnik
Simulation Production Factory
The structure of this section follows the methodology presented in Sect. 4.1.
In this section, the development process of the ontology and the underlying
methodology are presented. Afterwards, the requirements for the ontology to
be developed are specified and the developed ontology and its constituents are
described in detail2 . Finally, in Sect. 4.4 an evaluation is carried out to determine
the suitability of the developed ontology.
4.1 Development Process
The development process of the ontology follows the well-known methodology
of Sure et al. [28] for ontology development. Figure 3 depicts the ontology de-
velopment process schematically. In the kickoff phase, the requirements for the
Application &
Kickoff Refinement Evaluation
Evolution
Figure 3. The Ontology Development Process by Sure et al. [28]
ontology to be developed have been identified (see Sect. 4.2). Furthermore, the
concepts to be developed and the relationships between them have been deter-
mined and existing ontologies have been investigated for reuse. The second phase
refinement, has been performed in a top-down fashion to expand and to elaborate
the rough concepts and relations. In this process, the existing upper ontology
MASON has been used for refinement. As a result, a prototype of the ontology
has been created (see Sect. 4.3). In the evaluation phase, the ontology has been
checked for conformity and consistency (see Sect. 4.4). For this purpose, it is
demonstrated how an exemplary machine from the FT Factory is represented
in the ontology and whether the specified requirements are satisfied. The last
phase Application & Evolution aims at using the developed ontology in research
and to further improve the ontology. Thus, future work is discussed in Sect. 5.
2
The basic components of the ontology were developed and implemented in a student
research project at Trier University by Christian Badouin and Marcel Mischo.
FTOnto: Domain Ontology for a FT Simulation Production Factory
4.2 Requirements
In Sect. 3.2, we presented use cases in which the usage of an ontology is valuable.
These use cases necessitate requirements (RQs) to be met by the ontology. In
this section, requirements are derived from the use cases and are presented in
the following:
RQ 1 – Machine Similarity: Similar machines and machine components as
well as the similarity between their executable capabilities is required to be
represented.
RQ 2 – Asset Availability: A relationship should exist between a failed ma-
chine and its impact on the manufacturing processes so that the affected
resources could be identified.
RQ 3 – Transport Routes: The transport possibilities between machines for
the handling of workpieces should be represented. It is necessary to ensure
flexibility in the execution of manufacturing processes.
RQ 4 – Machine-Sensor Relationship: The relationships between machines
and sensors need to be modeled so that signs of failures in a sensor signal
can be related to the monitored machine.
4.3 Description of the Ontology
Since CDM-Core contains concepts such as people and geo-locations that can
be useful in a real factory but are not needed in our case, we decided to build
FTOnto from scratch with MASON and the Sensor, Observation, Sample, and
Actuator (SOSA) ontology, which is a more compact version of SSN as the
foundation. Hence, we are using a subset of CDM-Core, which is built on just two
ontologies and consequently results in fewer classes and a more straightforward
structure. FTOnto’s top structure begins with the main classes Manufacturing
Concept from MASON, the main classes of SOSA as well as parts of ontologies
proposed by Cheng et al. [6] for flexible manufacturing based on web services.
In the following, the refinements of FTOnto are explained.
Manufacturing Concept The class Manufacturing Concept contains the three
head concepts of the MASON ontology: Entity, Operation, and Resource. To
model the physical constituents of our FT Factory, most refinements are made
as new subclasses of the class Resource namely Machine resource and are shown
in the class hierarchy of Fig. 4. A subclass Workstation with a further subclass
for each of the four workstations of the FT Factory is added. Since a machine
consists of several actuators and sensors, the class Machine component with the
subclass Directly addressable for modeling actuators and sensors as the lowest
level and the class Indirectly addressable to model intermediate concepts, which
are parts of the high-level concept Workstation.
The class Entity is extended by the class Workpiece for representing the
workpieces used in the cycle of the model factory. Possible transport routes of
P. Klein et al.
workpieces on the conveyor belts, the turntable or with the vacuum gripper as
shown in Fig. 2 are represented as instances of the class Handling.
Besides classes, we added several abstract roles (object properties) to relate
instances with each other and concrete roles (data properties) to connect indi-
viduals with data values. For example, to express that an instance of the class
Conveyor belt is driven by a motor instance, the abstract role is actuated by was
introduced to relate both instances and the concrete role has default speed can
be used to model that the motor is normally driven with a speed value of 512.
Manufacturing
Concept
Entity Operation Material Resource
Machine Resource
Machine
Workstation
Component
Indirectly Multi Processing Vacuum Gripper High-Bay
Directly Adressable Sorting Machine
Adressable Station Robot Warehouse
Furnace Door
Milling Machine Actuator
Turntable Conveyor Belt Sensor
Suction Head
Piston Crane Jib Valve Motor
Oven Transport Pressure Button
Light Barrier
Platform
Bucket
Color Sensor Compressor Lamp
Figure 4. Part of the Class Hierarchy after Refinement of the Class Machine resource
from MASON Ontology
SOSA Classes The SOSA ontology is required to describe the relationships be-
tween sensors and actuators as well as the measured data. We align the ontology
of the manufacturing system with SOSA through the following class equivalence
axioms:
MASON.Sensor ≡ SOSA.Sensor
MASON.Actuator ≡ SOSA.Actuator
MASON.Indirectly Addressable ≡ SOSA.Platform
For example, a light barrier is an instance of the class Sensor and mounted on
some instance from the MASON class Conveyor belt to measure some Feature
of Interest, e.g., the arrival of a workpiece, by obtaining instances of the class
Observations where the class Result contains the measurement value.
Process and Service Ontology We use parts of the approach for web service
integration for a flexible manufacturing system by Cheng et al. [6]. We remodeled
their process ontology with the classes Process and OperationSequence that are
related with the object property hasArray and linked it to MASON by changing
the range of the object property hasOperation to the MASON class Operation.
FTOnto: Domain Ontology for a FT Simulation Production Factory
To associate products with their manufacturing process, we used the object
property hasProcess to relate MASON ’s Entity class, which is designed to model
products, to a process of the Process class. To provide a service oriented execution
of manufacturing processes, we also remodeled their service ontology. Thus, each
operation from MASON is connected by the object property isRalizedBy to a
service that has a description and an URI.
4.4 Evaluation
This subsection presents an example of the semantic description for the model
of a milling machine from the previously described Fischertechnik factory simu-
lation (see Sect. 3.1). The simplified model of a milling machine is framed with
green color in Fig. 5 and our semantic description is depicted as a graph in Fig.
6. By describing the graph, we would like to briefly address the requirements
that have been specified in Sect. 4.2. In addition, the ontology was checked with
OntOlogy Pitfall Scanner! (OOPS!)3 for correctness.
Figure 5. The Milling Machine in the FT Factory
Classes are surrounded by an orange circle and instances by a purple rect-
angle. The dashed arrow between both states that the instance is from the type
of this class. For example, MPS_MillingMachine is the instance of the class
MillingMachine. This implementation satisfies RQ 1 that similar machines must
be identifiable, which can be determined by the relationship between instances
and their classes since instances of the same class can be considered to be similar.
3
http://oops.linkeddata.es/
P. Klein et al.
In addition, MPS_MillingMachine is driven by MPS_Motor2, which is mod-
eled through the arrow that represents the property actuates. Moreover, the
motor is controlled by MPS_Machine_Controller as indicated by the arrow
labeled with controls. All three previously mentioned instances are part of the
MPS_MultiProcessingStation, which is modeled by the property has component.
Moreover, the MPS_MillingMachine instance provides a Milling_Service that
enables a Milling_Operation. The presented relations are useful with regard to
RQ 2 so that in the case of a failure, relationships assist to determine which parts
of the factory and corresponding services are affected. For example, if the motor
of the milling machine fails, the milling machine is not working properly and
consequently not its provided Milling_Service. A service enables an operation
that is part of an operation sequence that in turn is part of a process instance
that represents the manufacturing process of a product [6]. Each operation has
a relationship to the subsequent operation (e.g., Transport_From_Milling). By
adding a start and end position to each instance of a transport route, it is pos-
sible to find alternative routes and thus to enhance flexibility (see RQ 3).
Additionally, the motor MPS_Motor2 is related via the property hosts with a
sensor AccSensor_ADXL345 that observes its vibration for condition monitoring
purposes. This SOSA property allows to model the relationship between the
milling machine and the sensor that monitors its condition (see RQ 4).
Figure 6. Part of FTOnto with Focus on the Semantic Annotations of the Milling
Machine as Graph
5 Conclusion and Future Work
This paper investigates the development of an ontology to represent a Fischer-
technik manufacturing simulation model by reusing existing ontologies. Simula-
tion factories are a common method used for research purposes to investigate de-
veloped artifacts under laboratory conditions to examine their suitability and be-
fore they are potentially used in practice. The developed ontology contributes to
support future research with simulation environments. It is intended to provide
the developed ontology available for download under https://iot.uni-trier.de.
FTOnto: Domain Ontology for a FT Simulation Production Factory
In future work, we investigate how the developed ontology can be further
improved for our research purposes, e.g., by adding a context module to cap-
ture situation changes adequately such as process states [3,25]. This is especially
important when controlling the execution of manufacturing processes. Further-
more, we plan to expand the FT Factory model with additional and redundant
machines to provide similar services. Thus, it is possible to facilitate the use
of process adaptation techniques in case of a failure. In addition, we plan to
implement semantic web services to encapsulate the functions of workstations
and thus to execute arbitrary manufacturing processes in our simulation envi-
ronment. In this context, event-based ontology updates should also be examined
(e.g., [6,23]). Finally, the combination of semantics with machine learning for
PredM is investigated.
Acknowledgments. This work is funded by the German Research Foundation
(DFG) under grant No. BE 1373/3-3.
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