The document details an ontology-based approach to context-sensitive computing aimed at optimizing flexible manufacturing systems (FMS), showcasing methodologies in context model development and management. It highlights the practical implementation of this approach in an FMS use case for producing assembly parts, including real-time monitoring and data extraction. Future work will focus on enhanced context processing and the development of optimization algorithms to improve decision-making in dynamic manufacturing environments.

1. Ontology-based Context-sensitive
Computing for FMS Optimization
Date: March, 2012 Journal: Assembly Automation, Vol. 32, Issue 2,
Linked to: RTD research and pp.163-174, ISSN: 0144-5154
Self-learning project at FAST
Title of the Paper: Ontology-based Context
-sensitive Computing for FMS Optimization
Authors: M. Kamal Uddin, J. Puttonen, S.Scholze,
Contact Information A. Dvoryanchikova, J.L. Martinez Lastra
Tampere University of Technology,
FAST Laboratory,
P.O Box 600,
FIN 33101, Tampere,
Finlnad
Email: fast@tut.fi
Web: www.tut.fi/fast If you would like to receive a reprint of the
original paper, please contact us

2. 2
Outline
Introduction and Background: Context-Sensitive Computing and
Ontologies in Manufacturing
Methodology: Context-Sensitive Computing for FMS Optimization
• Context model Development
• Context management
• A Framework: Context-Sensitive computing for FMS optimization
FMS Use case implementation
Conclusions and Future work

3. 3
Introduction
FMS Plants are associated with
• Chaotic job processing orders
• Unscheduled events at run time
• Lack of transperency of complex machines/processes
Plants states are isolated and cannot be fully understood since there is a
lack of infrastructure providing explicit manufacturing knowledge
Modern FMS plant utilizes complex control architectures, promoting
integration of various decision support applications
Context-sensitive support applications are emerging in different areas of
manufacturing for providing runtime decision support and,
Ontology-based context-sensitive computing is the top candidate paradigm
to provide optimization support for modern FMS plants

4. 4
Background: Ontology-based Context
Modeling and Applications in Manufacturing
Ontologies allow context modeling at a semantic level, establishing a common
understanding of terms and enabling context sharing, logic inference, reasoning
and reuse in a distributed environment
Recent advancement of context-aware computing and Ontologies in
manufacturing enables a common language for sharing manufacturing
product, process and system knowledge among designers and software
applications
Domain ontologies to capture the manufacturing knowledge to define their
structure and relations in a hierarchical manner
Formally represented domain knowledge facilitate knowledge sharing/ reuse
and infer new knowledge utilizing relations and axioms built in ontologies
With the advent of Web-based software applications in manufacturing and
especially SWSs, research on context-awareness and ontologies are emerging

5. 5
Methodology: Ontology-based Context
Model Development (1/3)
Device
Domain
Process
Domain
Product
Domain
Resource
Domain

6. 6
Methodology: Context Management
Process (2/3)
Conceptual context management process
Context Reasoning
Ontological
reasoning
Identified
Context Domain
Specific
Rule-Based Refined
Context identification Process Reasoning Context
Statistical
Reasoning

7. 7
Methodology: A Famework, Context-
Sensitive Optimization for FMS (3/3)

8. 8
FMS Use Case Implementation: Overview
(1/4)
•A FMS use case, producing assembly parts
(e.g. industrial robots, hydraulic
components, aircraft parts) for automotive
industries
•Control system architecture is based on
SOA principles, where all the production
relevant entities offer web services (WS) to a
Microsoft.Net-based control platform
•A control application software runs the FMS
in real time invoking data from available
services (WSDL files)
•The application contains a set of master
data for product manufacturing and
simulated process devices to run the
operations in a simulated environment
•Pallets are utilized as the job carrying entity
to the loading stations and machining cells

9. 9
FMS Use Case Implementation: Approach
(2/4)
• Context-sensitive information model: An Ontology-based context model is
developed for context extraction from SOA platform
• The interfacing to the SOA control platform is done by creating Java applications
that invoke the web services of the control application to monitor the status of
the production system
• WSs invocation is implemented through dedicated client applications applying
client stub code from the WSDL description
• Invoked services are: manufacturing cell service, loading station
service, machine pallet service, manufacturing process service, manufacturing
template repository service and NC program library service to extract contextual
entities
• To extract the contextual entities from the running services in SOA
platform, different Java classes are implemented to invoke services and create
object model for storing the monitoring data, to analyse obtained monitoring data
and create corresponding OWL individuals to populate the context model

10. 10
FMS Use Case Implementation: Context-
sensitive information model (3/4)

11. FMS Use Case Implementation: Context 11
Extraction and Populating the Context
Model (4/4)

12. 12
Conclusions and Future Work
• The paper presents a modular approach of context-sensitive computing to
achieve optimization to the dynamic operating environment of FMS. The
context model development and the context management approach provides a
common interface for context acquisition, reuse and updates, which are utilized
by desperate client applications for runtime optimal decision making
• The core functional requirements of context-sensitive computing as the context
modelling using OWL ontologies, WS-based interfacing for SOA control
platform, runtime extraction of contextual entities, populating and updating of
the dynamic entities to the context model are implemented within a practical
FMS use case
• The next steps of this work will address higher level implementation of the
presented framework for ontology-based context-sensitive computing for FMS
optimization. High level context processing through context management will be
implemented. An optimization algorithm will be developed, which will utilize the
runtime KPI relevant contexts to provide proactive optimal suggestions to a
separate UI