This paper presents an approach for mapping products, processes, and resources for assembly automation using ontologies. The approach uses ontologies to represent product, process, and resource knowledge and SWRL rules to infer required components and tasks for product assembly. The approach was tested on a Festo test rig case study. The results demonstrated that the ontology mappings enabled dynamic configuration and analysis of the automation system and eased the modeling task.

1. An approach for knowledge-driven
product, process and resource
mappings for assembly automation
Date: August, 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: 11th IEEE International
Conference on Automation Science and
Engineering, CASE 2015. Gothenburg,
Sweden – August 24-28, 2015
Title of the paper: An approach for
knowledge-driven product, process and
resource mappings for assembly
automation
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. An approach for knowledge-driven
product, process and resource
mappings for assembly automation
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
11th IEEE International Conference on Automation Science and
Engineering, CASE 2015. Gothenburg, Sweden – August 24-28, 2015

3. Outline
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An approach for knowledge-driven product, process and
resource mappings for assembly automation
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• Introduction
• Motivation
• Ontologies in <5 minutes…
• Approach: mapping product, process and
resources
• Case study
• Testing the approach
• Results
• Conclusions and further work

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An approach for knowledge-driven product, process and
resource mappings for assembly automation
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Introduction (1/2)
• Over the past decade, a number of modeling and
simulation tools have been developed to assist process
planning and reconfiguration of manufacturing systems.
• These tools provide a number of benefits such as
visualization, verification and optimization of the
manufacturing systems before physical build
• However, manufacturing process and resource modeling
is still a challenging task
• Product, process, and resource information and data
sets exist within a given organization, but they are
typically not effectively coupled

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resource mappings for assembly automation
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Introduction (2/2)
vueOne
Core
Editor
V-­‐Rob
Robot
modelling
V-­‐Lib
Component
library
vueOne
Viewer
Lightweight
Viewer
HMI
–
PLC
Code
Deployment
V-­‐man
Operator
modelling

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resource mappings for assembly automation
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Motivation
• There is a need to bridge the gap between
product design and manufacturing operations by
effectively linking products to manufacturing
resources
• The combination of modeling and an ontology-
based system can potentially provide reusable
knowledge-based product, process and resource
description to interconnect product attributes
with related manufacturing resources

7. Ontologies in <5 minutes… (1/4)
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resource mappings for assembly automation
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• An ontology is “an explicit specification of a
conceptualisation” [T.Gruber, A translation approach to portable ontology specifications]
• 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

8. Ontologies in <5 minutes… (2/4)
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resource mappings for assembly automation
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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

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resource mappings for assembly automation
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• Ontology editors try to completely abstract away
from the syntax
• Editors easies ontology language generation by:
– Reducing the programing and configuration time
– Reducing the complexity (training time)
<owl:Class rdf:about=http://www.tut.fi/FAST/CoSummit2013Demo#Conveyor>
<owl:disjointWith rdf:resource="http://www.tut.fi/FAST/CoSummit2013Demo#Robot"/>
<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#hasConStatus"/>
</owl:onProperty>
</owl:Restriction>
</rdfs:subClassOf>
</owl:Class>
Ontologies in <5 minutes… (3/4)

10. Ontologies in <5 minutes… (4/4)
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An approach for knowledge-driven product, process and
resource mappings for assembly automation
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• A semantic reasoner can work with ontologies
• Semantic reasoners are mainly used for:
– Model consistency validation
– Classification
• Semantic reasoners can also understand rules
defined in the ontology
A semantic reasoner is a piece of software that can infer
new facts from existing ones, which are expressed in an
ontology or knowledge base

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resource mappings for assembly automation
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Approach: mapping product,
process and resources (1/2)
• Main concepts for the mapping solution:
requiresTask
requiresComponent

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An approach for knowledge-driven product, process and
resource mappings for assembly automation
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Approach: mapping product,
process and resources (2/2)
• SWRL rules:
• Permits the inference of required components
and tasks for product assembly
Component(?c) ∧ Task(?t) ∧ Operation(?o) ∧ Station(?s) ∧
Product(?pr) ∧ performsOperation(?s, ?o) ∧
includesTask(?p, ?t) ∧ hasProcess(?o, ?p) ∧
needsAssemblyOperation(?pr, ?o) ∧ hasComponent(?s, ?c) ∧
performsTask(?c, ?t) → requiresComponent(?pr, ?c)
Task(?t) ∧ Process(?p) ∧ Operation(?o) ∧ Product(?pr) ∧
includesTask(?p, ?t) ∧ hasProcess(?o, ?p) ∧
needsAssemblyOperation(?pr, ?o) → requiresTask(?pr, ?t)

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resource mappings for assembly automation
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Case study
• FESTO test rig
Distributing
Buffering
Processing
Handling

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resource mappings for assembly automation
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Testing the approach (1/3)
• Use case model instances:

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resource mappings for assembly automation
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Testing the approach (2/3)
Instances
property
assertions

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resource mappings for assembly automation
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Testing the approach (3/3)
• SPARQL query for monitoring mappings:
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX req: <http://www.tut.fi/en/fast/processOntology.owl#>
SELECT ?Product ?requirementType ?Requirement
WHERE {
{?Product rdf:type req:Product.
?Product ?requirementType ?Requirement.
FILTER (?requirementType = req:requiresComponent).
FILTER (?Product = req:product_1)
} UNION {
?Product rdf:type req:Product.
?Product ?requirementType ?Requirement.
FILTER (?requirementType = req:requiresTask).
FILTER (?Product = req:product_1) }
}

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resource mappings for assembly automation
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Results (1/3)

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resource mappings for assembly automation
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Results (2/3)
• Without mapping:
• With mappings:

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resource mappings for assembly automation
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Results (3/3)
• Product mappings with clamping related process and resources:

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resource mappings for assembly automation
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Conclusions and further work
• The application of knowledge-based PPR mapping can
be employed for enabling dynamic configuration and
analysis of assembly automation systems
• The presented use case scenario shows that the
approach easies the modeling of automation systems
• This will make better use of in house engineering
knowledge and reduce system development and
reconfiguration time
• This approach can be extended to allow automatically
querying the available facilities to manufacture a new
product variant

21. 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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resource mappings for assembly automation
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resource mappings for assembly automation
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