Conference: IEEE 16th International Conference on Industrial Informatics (INDIN2018). Porto, Portugal – July 18-20, 2018 Title of the paper: An ISA-95 based Ontology for Manufacturing Systems Knowledge Description Extended with Semantic Rules Authors: Seyedamir Ahmadi, Borja Ramis Ferrer, Jose L. Martinez Lastra

1. An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
Date: July, 2018
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: IEEE 16th International Conference on
Industrial Informatics (INDIN2018).
Porto, Portugal – July 18-20, 2018
Title of the paper: An ISA-95 based Ontology for
Manufacturing Systems Knowledge Description
Extended with Semantic Rules
Authors: Seyedamir Ahmadi, Borja Ramis Ferrer,
Jose L. Martinez Lastra
if you would like to recieve a reprint of the
original paper, please contact us.
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
1

2. An ISA-95 based Ontology for Manufacturing
Systems Knowledge Description Extended
with Semantic Rules
Seyedamir Ahmadi1, Borja Ramis Ferrer1, Jose L. Martinez Lastra1
seyedamir.ahmadi@student.tut.fi, borja.ramisferrer@tut.fi, jose.lastra@tut.fi
1 Tampere University of Technology, 33720 Tampere, Finland
16th IEEE Conference (INDIN2018)
Porto, Portugal – July 18-20, 2018

3. Outline
• Introduction
• Motivation
• Objectives
• ISA-95
• Approach
• ECO Model
• The use case
• Semantic rules
• Results
• Conclusion and future work
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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4. Introduction
• Modern systems are comprised of both heterogeneous
software and hardware systems that exchange data on
various levels of the enterprise.
• Industry 4.0 in manufacturing
• Capability of manufacturing systems to adopt to dynamic
changes
• Manufacturing domain has been described as the sum of
product, process, and resource concepts.
• Knowledge Representation and Reasoning (KR&R)
models
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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5. Motivation
• In the modelling of a manufacturing system, clear
understanding of the domain of discourse is significant to
avoid multiple and redundant architecture proposals.
• Hence, the need for well established standards becomes
more significant
• Depending on the level of standard adoption and
application specific extensions, a possible consequence
could be inconsistencies within their implementation
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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6. Objectives
• Identifying standard conformant generic concepts,
taxonomies, and relationships in domain of
manufacturing systems
• Modelling a manufacturing systems ontology based on:
–Core elements of the functional hierarchy, i.e., interface
of enterprise-control integration
–Modular modelling approach
• Addition of standard and use case specific semantic rules
to enhance inferencing capabilities of the model
• Proof of concept in industrial use case
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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7. ISA-95
• ISA-95 was developed to facilitate the integration of enterprise and
control systems in order to decrease the cost, risk, and errors
associated with integration
• It consists of seven parts, identifying
– the main elements in the interface, their corresponding activities, and their
communications (object models and attributes)
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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8. Functional Hierarchy model
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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9. Role-based and Physical asset hierarchy
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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10. Manufacturing Operations Management (MOM)
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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11. Approach
• The Enterprise Control Ontology (ECO) is based on three
sub-ontologies
–Hierarchy ontology
• Role-based hierarchy model
• Physical asset equipment model
–OperationType ontology
• Production, Maintenance, Quality, Inventory
–Resource ontology
• Equipment, Material, Personnel, ProcessSegment
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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12. ECO Model
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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13. The use case: FASTory
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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14. Semantic rules
• The semantic rules added are constructed using the
Semantic Web Rule Language (SWRL)
– Assigning individuals to classes
– Finding the necessary resources for process segments
– Assigning job orders to equipment
– Assigning statuses to properties
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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15. Results (1/5)
S1) WorkCenter(?x)^WorkUnit(?y)->contains(?x,?y)
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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16. Results (2/5)
S4) JobOrder(?x) ^ requiresPhysicalAsset(?x,?y) ^ hasPhysicalAssetCapabilityType(?y,"Available")
^Pallet(?m)^hasPhysicalAssetCapabilityType(?m,"Available")-> assignedTo(?x,?y)^assignedTo(?x,?m)
S5) JobOrder(?x) ^ assignedTo(?x,?y) ^ assignedTo(?x,?m) ^ hasPriority(?x,?p) ^
swrlb:greaterThan(?p,2) -> hasJobOrderStatus(?x,"Ready")
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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17. Results (3/5)
S6) JobOrder(?x) ^ referencesWorkMaster(?x,?y) ^ WorkDirective(?p) ^ hasReferenceTo(?p,?y) ->
assignedToJobOrder(?p,?x)
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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18. Results (4/5)
S2) Equipment(?x) ^ hasPhysicalAssetID(?x,?y) -> AssetHierarchy(?x)
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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19. Results (5/5)
S3) Discrete(?x) ^ Robot(?y) ^ hasFunction(?y,?x) ->
requiresPhysicalAsset(?x,?y)
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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20. Conclusion and future work
• Standardized ontology describing manufacturing systems
• The generic nature of the ECO model ensures reusability and
extendibility in the domain of manufacturing systems
• Addition of semantic rules that permit the automatic inference
of implicit information from the explicit statements
• The rules enable the identification of the capabilities and
needs of industrial equipment for manufacturing product
variants
• Industrial use case
• Further, the ontology will be extended for the scheduling of
machine operations.
An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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21. THANK YOU!
Any questions?
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An ISA-95 based Ontology for Manufacturing Systems
Knowledge Description Extended with Semantic Rules
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