An Ontology-based Semantic Foundation for Flexible Manufacturing Systems

Date: November, 2011

Linked to: Self-Learning (FP7 RDT Project)

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: The 37th Annual Conference of the IEEE Industrial Electronics Society

Title of the paper: An Ontology-based Semantic Foundation for Flexible Manufacturing Systems

Authors: M. Kamal Uddin, A. Dvoryanchikova, A. Lobov, J. L. Martinez Lastra

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Outline

Introduction: Background

Ontologies in Manufacturing

Ontology-based Semantic Foundation to FMS

A FMS use case

Summary

Future research

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Introduction: Background

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

Knowledge-based decision support clients are emerging in different areas of manufacturing dealing with formally represented manufacturing semantics (a comprehensive semantic foundation)

Ontology-based semantic foundation is the top candidate to provide the required level of formalism for application support

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Ontologies in Manufacturing

Recent advancement of Ontology-driven knowledge representation in manufacturing:

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 domain KR and ontologies are emerging.

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Ontology-based Semantic Foundation to FMS (1/2)

Ontology-based semantic foundation aims to provide:

Semantic interoperability of

heterogenous systems

Transperency of complex machines

and processes

Knowledge management between

different design tools

Knowledge exchange in an adaptive

operation environment

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Ontology-based Semantic Foundation to FMS (2/2)

Main Requirements:

Seamntics to be defined clearly to represent the meaning of each

structure in the KR and no ambiguity in the terminology

Precise terms and definitions

Represented knowledge must be interpretable by both human and

machines

Reasoning and query processing capability

Represented knowledge must be suitable for use in the dynamic

operating environment of FMS

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Architectural Viewpoint: The information about the

production orders, job processing data, due dates come from enterprise level

PLC unit communicates to the device level using a proprietary protocol

Wireless WS communications for pallet's transportation

Pallets are utilized as the job carrying entity for loading/machining/unloading

ECA algorithm for jobs (pallets) scheduling

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Ontology-based Semantic Foundation: A FMS use case (1/4)

Control Architecture:The control system architecture is

based on SOA principles where all the production relevant entities offer WSs to Microsoft.Net-based control platform

A control application software runs the FMS in real time invoking data from available services (WSDL files)

The application software contains a set of master data for product manufacturing

It also contains simulated process devices to run the operations in a simulated environment

Proposed domain ontology is modeled upon the main concept of ‘Production Order Template’

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Ontology-based Semantic Foundation: A FMS use case (2/4)

Ontology-based Semantic Foundation: A FMS use case (3/4)

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Process Domain

Product Domain

Resource Domain

Device Domain

Runtime process information integration and update to the OWL model

- The announced WSs from the SOA platform are invoked and the relevant concepts of the ontology model is populated with runtime instances

- The service configuration file contains the description of available interfaces and URLs to access them

Application of SWRL rules to increase the expressivity of OWL and makes it possible to model more domain knowledge than OWL alone

Support for query processing via which users and support applications can interact with such semantic foundation (e.g. SPARQL)

The control application of the use case is utilized for cross platform communication enabling different client applications support based on WS interfaces.

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Ontology-based Semantic Foundation: A FMS use case (4/4)

Example of SWRL: Atom, ComplexNCP (Complex NC program having a machining time more than 100 Sec)

A Framework for Knowledge-based Optimization Support System (1/2)

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Ontologies are stored in local computer/remote server

Reasoners – to load OWL ontologies and support queries

Optimization support system provides optimal scheduling based on request/response queries

A Framework for Knowledge-based Optimization Support System (2/2)

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An example of the query process to the WS communication based FMS use case

Optimization Support

Application

Web Services

Ontology Manager

Format Mapper

ReasonersOntology

Stored in Local computer or

Remote Server

query

response

formatted response

requested data

Query data

Query results

Query response and desired format

access ontology

Summary

Semantic description of device, process and product increases the overall transperency of the FMS system

Proposed ontology-based semantic foundation allows to avoid unnecessary overload of centralized software applications processing the raw data

It also provides a common KB where different design tools/client applications can interact to share, re-use and update domain knowledge and runtime process instances

The proposed framework for knowledge-based optimization support system provides the necessary principles for developing such support applications within the dynamic environment of FMS

The lower-level functionalities of the framework, which are responsible for ontology development, extracting process data to populate the ontology model have already functional implementation

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Future Work

Knowledge-based optimization support system, working on top of the semantic foundation addressed in this work

• An algorithm to optimize the main KPIs (e.g. higher machine utilization rate, maintaining the due delivery date of production order)

Higher level implementation of the proposed framework

Acknowledgement

This work is partly supported by the Self-Learning (Reliable Self-Learning Production Systems based on Context Aware Services) project of European Union's 7th Framework Program, under the grant agreement no. NMP-2008-228857. This document does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of its content.

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Thanks for your attention

Questions?

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SPARQL Example