Conference: 13th IEEE International Conference on Industrial Informatics, INDIN 2015. Cambridge, UK – July 22-24 2015 Title of the paper: A knowledge-based solution for automatic mapping in component based automation systems Authors: Borja Ramis Ferrer, Bilal Ahmad, Andrei Lobov, Daniel Vera, José L. Martinez Lastra, Robert Harrison

1. A knowledge-based solution for
automatic mapping in component
based automation systems
Date: July, 2015
Contact information
Tampere University of Technology,
FAST Laboratory,
P.O. Box 600,
FIN-33101 Tampere,
Finland
Email: fast@tut.fi
www.tut.fi/fast
Conference: 13th IEEE International
Conference on Industrial Informatics,
INDIN 2015. Cambridge, UK – July 22-24
2015
Title of the paper: A knowledge-based
solution for automatic mapping in
component based automation systems
Authors: Borja Ramis Ferrer, Bilal
Ahmad, Andrei Lobov, Daniel Vera, José
L. Martinez Lastra, Robert Harrison
If you would like to receive a reprint of
the original paper, please contact us

2. A knowledge-based solution for
automatic mapping in component
based automation systems
Authors: Borja Ramis Ferrer, Andrei Lobov, José L. Martinez Lastra
{borja.ramisferrer, andrei.lobov, jose.lastra}@tut.fi
Bilal Ahmad, Daniel Vera, Robert Harrison
{b.ahmad, d.a.vera, robert.harrison}@warwick.ac.uk
Tampere University of Technology,
Factory Automation Systems and Technology Lab
The University of Warwick,
Automation Systems Group (ASG), WMG
13th IEEE International Conference on Industrial Informatics,
INDIN 2015. Cambridge, UK – July 22-24 2015

3. Outline
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• Introduction
• Motivation
• 3Deployment Code Generation Approach
• Ontologies in <5 minutes…
• Approach: mapping data within ontologies
• Testing the approach
• Results
• Conclusions and further work

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Introduction
• 3Deployment (Direct Digital Deployment) project
developed an approach for the direct
deployment of PLC code from simulation models
developed in a manufacturing systems modeling
and process planning tool, named as Onevue
• However, the code generation approach still
involves manual tasks, such as I/O mapping and
manufacturing resource components mapping
with Runtime Control Components (RRCs)

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Motivation
• Ontologies can support the existing approach
for automated mapping of resources in
component based automation systems
• Integration of virtual engineering tools and
knowledge representation can be beneficial
for reducing the required engineering
knowledge efforts and time in design phase
• Reduce both facility investment and
operational costs

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3Deployment Code
Generation Approach (1/3)

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3Deployment Code
Generation Approach (2/3)

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3Deployment Code
Generation Approach (1/3)
Represent manual work
Pre-Engineering
Phase
Virtual
Components
Control
Information Create Runtime Data Models
Extract Resource Components
Component Mapping
Parsing Mapping info to Create Main Function
Control Code
Combine with Common Data
Generate Code
CCE Mapper
System Engineering
Phase
PLC-Specific
Common
Data
Virtual System
Modelling
Runtime
Components
I/O
Variables

9. Ontologies in <5 minutes… (1/5)
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• An ontology is “an explicit specification of a
conceptualisation” [Gruber93]
• An ontology is an engineering artefact:
– Constituted by a specific vocabulary used for any
domain description and a set of explicit
assumptions regarding the meaning of the
vocabulary
• Then, an ontology describes a formal specification
of a domain

10. Ontologies in <5 minutes… (2/5)
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• Ontology markup languages…
*
*Jose L. Martinez Lastra, Ivan M. Delamer, Fernando Ubis;
“Domain Ontologies for Reasoning Machines in Factory Automation”;
ISBN: 9781936007011, 2010; 138 pages

11. Ontologies in <5 minutes… (3/5)
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• OWL is one of the clearer syntax, which is human-
readable and machine-understandable
• Ontology editors try to completely abstract away from the
syntax
<owl:Class rdf:about=http://www.tut.fi/FAST/CoSummit2013Demo#Conveyor>
<owl:disjointWith rdf:resource="http://www.tut.fi/FAST/CoSummit2013Demo#Robot"/>
<owl:disjointWith rdf:resource="http://www.tut.fi/FAST/CoSummit2013Demo#ManufacturingCell"/>
<rdfs:subClassOf rdf:resource="http://www.tut.fi/FAST/CoSummit2013Demo#Device"/>
<rdfs:comment>Conveyor device type</rdfs:comment>
<rdfs:subClassOf>
<owl:Restriction>
<owl:cardinality rdf:datatype=http://www.w3.org/2001/XMLSchema#int>1</owl:cardinality>
<owl:onProperty>
<owl:ObjectProperty rdf:about="http://www.tut.fi/FAST/CoSummit2013Demo#hasConveyorStatus"/>
</owl:onProperty>
</owl:Restriction>
</rdfs:subClassOf>
</owl:Class>

12. Ontologies in <5 minutes… (4/5)
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• Editors easies ontology language generation by:
– Reducing the programing and configuration time
– Reducing the complexity (training time)
• Two editor examples:
– Protégé (Standford Medical Informatics Group)
– Olingvo (Tampere University of Technology)
• Includes WS discovery and SPARQL Update queries as
added features of other editors
• Using reasoning engines for inference and model
consistency validation

13. Ontologies in <5 minutes… (5/5)
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14. • Main concepts for the mapping solution:
• SWRL rule:
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Approach: mapping data
within ontologies
*
**if a Component “x” and a Function Block “z” are related to the
same element type “y”, the Component “x” must be mapped to
the Function Block “z”
* A ! Component
B ! FunctionBlock
p1 ! hasElementType
**

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Case study
• FESTO test rig

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Testing the approach (1/3)
HTML + JavaScript RDF Stardog
‘SL’ reasoning option
(It combines all
supported reasoning
types with SWRL rules)

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Testing the approach (2/3)
• Inserting pusher instance to the model:
• Retrieving mappings from the model:
*It will give mappings when reasoning engine is running!
*

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Testing the approach (3/3)

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Results (1/2)

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Results (2/2)

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Conclusions and further work
• The synergy of virtual engineering tools and
knowledge representation can be used to
significantly reduce the required engineering
knowledge and efforts that are necessary during
the design phase of manufacturing systems and
hence reduce the development time and costs
• The approach can be extended to map product
with manufacturing processes and resources for
enabling the dynamic configuration and analysis
of an entire assembly line

22. Acknowledge
• The authors gratefully acknowledge the support of the
UK EPSRC through the Knowledge-Driven Configurable
Manufacturing (KDCM) research project under the
Flexible and Reconfigurable Manufacturing Initiative and
the Doctoral Research Funding of Tampere University of
Technology in carrying out this work.
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THANK YOU!
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