Virtual Factory Data Model
Walter TERKAJa, Giulia PEDRIELLIb, Marco SACCOa
a Istituto Tecnologie Industriali e Automazione (ITIA), Consiglio Nazionale delle
b Dipartimento di Ingegneria Meccanica, Politecnico di Milano
Abstract. Manufacturing companies have to manage a large amount of data and
information. This is due to both the presence of advanced sensors systems at the
shop floor level and to the spreading of simulation and decision support software
tools. The exchange of simulated and real data can be effective only if
interoperability is guaranteed. However, most of the software tools are based on
their own data models and proprietary file formats, thus jeopardizing the
interoperability. This paper presents the outcomes of a European research project,
named Virtual Factory Framework (VFF), that aims at developing an integrated
virtual environment to enable the interoperability between software tools
supporting the factory processes along all the phases of its lifecycle. The
cornerstone of the proposed VFF consists in a common Virtual Factory Data
Model (VFDM) that can be considered as the shared meta-language providing a
common definition of the data that are shared among the software tools connected
to the framework. Semantic web technologies have been adopted to develop the
VFDM because of their ability in representing formal semantics, and efficiently
modeling and manage distributed data.
Keywords: Virtual Factory, Data Model, Ontology, Manufacturing Systems
Manufacturing has to cope with a more and more complex and evolving market,
therefore factories and their production systems are required to continuously evolve so
that the changes in the market demand and in the technologies can be effectively faced
and exploited . The digital and virtual factory paradigm   can support the
design and management of a production environment by addressing various key issues
like: (1) reduction of production times and material waste thanks to the analysis of
virtual mock-ups, (2) development of a knowledge repository where people can find
stored information in different versions, with both advisory role and support to the
generation of new knowledge, (3) improvement of workers efficiency and safety
through training and learning on virtual production systems, (4) creation of a
collaboration network among people concurrently working on the same project in
different places .
The complexity of the factory design and management problem calls for support
tools to effectively address all the phases of the factory lifecycle. Indeed, the
Information and Communication Technology (ICT) players in the market (e.g. Siemens
PLM, PTC and Dassault Systèmes) already offer all-comprehensive Product Lifecycle
Management (PLM) suites containing software tools that have been developed or
acquired in the recent years. Although these tools deal with most of the factory
planning and design phases, the current approaches still do not meet the demands of the
industry and fail to provide all the required functionalities, mainly because of the lack
of interoperability. Moreover, Small and Medium Enterprises (SME) cannot afford the
present expensive PLM software suites.
The preliminary analyses highlighted the need of a novel framework for the
Virtual Factory (VF) enabling a step forward in the state of the art, by describing the
factory as a whole consisting of resources, processes, dependencies and interrelations,
data and material flows . The European project “Virtual Factory Framework” (VFF)
is currently carrying out the development of a framework for the VF . The Virtual
Factory Framework (VFF) can be defined as “An integrated collaborative virtual
environment aimed at facilitating the sharing of resources, manufacturing information
and knowledge, while supporting the design and management of all the factory entities,
from a single product to networks of companies, along all the phases of the their
lifecycles” . The VFF architecture (Figure 1) is based on three main pillars: (1)
semantic Virtual Factory Data Model, (2) semantic Virtual Factory Manager, (3)
decoupled Virtual Factory modules.
Figure 1: Virtual Factory Framework architecture
The Virtual Factory Data Model (VFDM) establishes a coherent, standard, extensible,
and common data model for the representation of factory objects related to production
systems, resources, processes and products. The common data model can be considered
as the shared meta-language providing a common definition of the data and knowledge
stored in the shared repository that is governed by the Virtual Factory Manager (VFM)
 . The VFM is the core of the VFF and orchestrates the decoupled Virtual Factory
modules (VF modules) by providing a controlled access to the different virtual factory
projects. The VF modules are the software tools that implement the various methods
and services to support the activities related to factory design, performance evaluation,
management, production monitoring, etc. The VF modules can be commercial or non-
commercial applications located on a remote workstation or on the server where the
VFM resides. The integration of VF modules, endowed with different functionalities
and level of detail, but insisting on the same factory representation, will offer the
possibility to reach a wide range of users.
The pillars of VFF were conceived to obtain openness, scalability and easiness by
plugging in the decoupled VF modules, thus reducing the investment costs compared to
“all-in-one” software. Moreover, the VFF aims at promoting major time and operating
This paper focuses on the VFDM of VFF and is organized as follows. Section 2
gives an overview of the present state of the art on data models for virtual enterprises in
manufacturing domain. Section 3 describes how the VFDM was developed, whereas
Section 4 presents a test case. Finally, Section 5 draws the conclusions and suggests
2. Data Models for virtual enterprises in manufacturing domain
The problem of developing a comprehensive data model for the manufacturing domain
has already been addressed in the literature and the main contributions can be traced
back to two main approaches: standard data models and reference frameworks for
information representation based on ontologies.
A lot of effort has been devoted by the data modeling research community towards
the standardization of the information related to the manufacturing domain. For
instance, the Standard for the Exchange of Product Model Data (STEP)  supports
the exchange of information by aiming to create an interlingua for exchanging
manufacturing product data. The Process Specification Language (PSL)  standard
proposes a general ontology for representing manufacturing processes for the exchange
of process information and knowledge. In particular, a neutral, standard language for
process specification is defined to integrate multiple process-related applications
throughout the manufacturing life cycle.
The Industry Foundation classes (IFC) standard by buildingSMART , partially
based on STEP, represents an open specification for Building Information Modeling
(BIM) data that is exchanged and shared among the various participants in a building
construction or facility management project. The IFC model is structured as a set of
schemas that are grouped into four layers: Resource layer (i.e. general purpose or low
level concepts/objects), Core Layer (where the most abstract concepts of the model are
defined), Interoperability Layer defining concepts or objects common to two or more
domains, and the Domains/Application Layer. Even if the standard was mainly
conceived for Architectural Engineering Construction (AEC) industry domains (e.g.
Building Controls, Structural elements, Structural Analysis, etc.), its data structures can
be specialized for other industrial domains, such as the manufacturing domain. The IFC
standard is available as an EXPRESS schema specification .
Most of the aforementioned standards are focused on particular areas of the factory
domain and therefore it is necessary to integrate various contributions to cover all the
knowledge domains required by the VFDM, as already highlighted by Colledani et al.
  and Valente et al.  in previous related works.
Ontologies represent a possible way to generate a more flexible data model
integrating different knowledge domains. Indeed, ontologies  have been developed
and investigated in the field of artificial intelligence and natural language processing to
facilitate knowledge sharing and reuse . Ontologies are an enabler for knowledge
sharing between several applications and, at the same time, they enable a fluent flow of
data between different entities . Beyond traditional data models like UML class
diagrams or entity relationship diagrams, ontologies provide methods for integrating
fragmented data models into a unique model without losing the notation and style of
the individual ones . In addition, the first order logic underlying ontologies ensures
data consistency on a semantic level and reasoning can be performed. Ontologies are
well suited to model logical relationships between different variables in axioms which
can then be used to derive assertions based on available data (e.g. data coming from the
Various ontologies have been developed to support virtual enterprise (e.g.
CIMOSA , FDM , MOSES  and MISSION ) and then extended
focusing on the operational scope. For instance, the TOVE project  and Enterprise
Project  proposed taxonomies along with an explicit virtual enterprise modeling.
However, these projects did not address the manufacturing domain.
Lin et al.  designed a Manufacturing System Engineering (MSE) ontology to
provide a common understanding of manufacturing-related terms and to enhance the
semantic and reuse of knowledge resources within global extended manufacturing
teams. The MSE ontology is based on seven key classes that the authors determined
based on Manufacturing System (MS) information models    : Project,
Flow, Process, Enterprise, Extended_Enterprise, Resource and Strategy.
Léger et al.  presented a Manufacturing's Semantics Ontology (MASON) that
is built upon three main concepts: entities, operations and resources. Similarly, Martin
and D’Acunto  developed an ontology decomposed into product, process and
resource areas. ADACOR (A Collaborative Production Automation and Control
Architecture) could be classified as general-purpose manufacturing ontology .
More recently, Badra et al.  proposed a Semantic Web compliant
representation of the objects, concepts and services related to Renault Product Range
Specification (PRS) that is used to specify the set of all possible car configurations.
Baqar Raza and Harrison  developed an ontology aimed at the formalization of
product data throughout its lifecycle, in particular for the automotive sector. The
research focused on the integration of product, process and resource information from
multiple sources and making the information available via web services.
Moser et al.  presented a tool to support real time decision making, by
providing an integrated view on relevant engineering knowledge in typical design time
and runtime models, which were not originally designed for machine-understandable
Although the domain of the aforementioned ontologies results wider than the one
which is typically handled within the previous technical standards, it can be noticed
that the classes characterizing the ontologies are not imported from any of the available
3. VFDM - Virtual Factory Data Model
The Virtual Factory Data Model (VFDM) aims at formalizing and integrating the
concepts of building, product, process and production resource handled by the digital
tools supporting the factory life-cycle phases. These tools typically deal with
input/output data in the following domains:
Building, i.e. the physical structure of the factory (e.g. walls, columns, etc.).
Product, i.e. the product seen as the production output of the factory.
Process, i.e. the processes that are executed by a system to directly or
indirectly transform a product.
Production Resource, i.e. the resources that are used by a system with the final
goal of transforming the product (or a work in progress). These resources can
be human operators, machines, conveyors, AGV, etc.
Production System, i.e. the transformation system (e.g. manufacturing system,
assembly system) that affects a product by means of physical resources and/or
human resources within a process.
Factory, i.e. the factory seen as whole during its lifecycle.
As highlighted in Sect.2, there is not a unique data model that is already able to cover
all the listed domains. Instead of creating a brand new data model, the development of
the VFDM aimed at exploiting as much as possible the already existing technical
standards for manufacturing, thus trying to favor the interoperability between software
tools. Therefore, the work focused on the integration (managing redundancies and
inconsistencies) of various knowledge domains represented by different technical
standards. This approach requires both the translation of the existing standards into a
common language and the development of the required extensions to represents the
concepts in the scope of the VFF.
At first, it was evaluated the opportunity of implementing the VFDM as a set of
XSD files , thus defining the structure of the XML files that would be stored and
managed by the Virtual Factory Manager (VFM). This solution allowed carrying on a
successful study of the feasibility of the approach . However, the XSD technology
alone is not suitable for knowledge representation and an explicit characterization of
data with their relations on a semantic level is missing. Moreover, intra-document
references are supported but inter-document references (i.e. cross-references) are
poorly modeled, thus endangering referential consistency. Therefore distributed data
can be hardly managed and the integration of different knowledge domains can be
cumbersome. These limitations led to evaluate and finally adopt the Semantic Web
technologies  for developing the VFDM as an ontology . Indeed, the Semantic
Web technologies offer the possibility to the whole VFF to represent a formal
semantics, efficiently model and manage distributed data, ease the interoperability of
different applications, and exploit generic tools that can infer from and reason about an
ontology, thus providing a generic support that is not customized on the specific
domain. The key features of the ontology-based VFDM can be summarized as follows:
the OWL 2.0 vocabulary  of the Web Ontology Language  was
adopted to define all the classes, properties and restrictions that can be used to
create the individuals to be stored in the data repository of the VFM.
after the analysis of the state of the art, the Industry Foundation Classes 
and STEP-NC  were selected to be integrated in the VFDM for modeling
the factory building and the most abstract classes of objects, and for modeling
the manufacturing processes, respectively.
the selected standards were partially translated (based on the scope of the
VFF) into ontologies. Indeed, an official implementation of IFC and
STEP-NC as ontologies does not exist yet. In particular, the IFC standard is
available as EXPRESS schemas . The translation of the schemas into
ontologies took as a reference the suggestions provided by Beetz et al. ,
Krima et al. , and Schevers and Drogemuller .
the technical standards have been extended thus leading to the creation of
novel data structures.
3.1. VFDM Architecture
The various knowledge domains of the VFDM are organized into macro areas
consisting of one or more ontologies, thus creating a hierarchical structure of
ontologies while decomposing the problem and downsizing its complexity. The VFDM
defines only the so-called meta data (i.e. the classes, properties and restrictions),
whereas the actual instances (i.e. the OWL individuals) will be stored in the data
repositories. Table 1 lists the ontologies contained in each VFDM macro area, thus
representing the VFDM complete architecture that is shown in Figure 2 as well. In
summary, the VFDM consists of 593 classes, 508 object properties and one data
property. The following sub-sections present an overview of the VFDM macro areas,
highlighting their main features.
The following rules were adopted while creating the VFDM:
The ontologies containing only novel definitions of classes have a name with
prefix “Vff” (e.g. VffComons), whereas the ontologies that are created by
importing/transforming third party ontologies or technical standards have a
name with prefix equal to the acronym of the source (e.g. “Ifc” for IFC
standard, “StepNc” for STEP-NC standard).
Novel classes are named with prefix “Vff” (e.g. VffDataType), whereas classes
that are imported from a technical standard are named with a prefix after the
standard itself (e.g. IfcProject is imported from IFC standard).
Table 1: Macro Areas in the VFDM
VFDM – Macro Area Ontologies
VffCommons;IfcActorResource, IfcCostResource, IfcDateTimeResource,
IfcUtilityResource, IfcRepresentationResource, IfcExternalReferenceResource,
IfcGeometricModelResource, IfcGeometryResource, IfcMaterialResource,
IfcMeasureResource, IfcPropertyResource, IfcQuantityResource; IfcKernel,
IfcProductExtension, IfcProcessExtension, IfcControlExtension;
Product StepNcAP10, VffProduct
Production Resource VffProductionResource
Production System VffSystem
Given the large number of ontologies that were imported a special attention has been
dedicated to avoid the generation of redundancies and inconsistencies between classes
derived from different standards. If redundancy could not be avoided, then equivalent
classes have been defined or hierarchical relationships have been established between
classes by defining multiple inheritances.
Figure 2. VFDM Architecture
The Commons area contains the basic definitions exploited by the other ontologies in
the VFDM. The ontology in the Commons area are organized into sub-areas according
to their source and content:
VFF Commons (Sect.3.2.1) and VFF Resource (Sect.3.2.2) containing novel
ontology developed in the scope of VFF.
IFC Resource (Sect.3.3.3) and IFC Core (Sect.3.3.4) containing ontologies
derived from the IFC standard.
3.2.1. VFF Commons
The VFF Commons area consists of the VffCommons ontology and models the
common classes and properties that are imported and specialized by the ontologies
belonging to the other VFDM areas. The VffCommons ontology defines three basic
classes: VffDataType, VffList and VffEnumeration. VffDataType class is specialized by
subclasses VffBoolean, VffInteger, VffLogical, VffNumber, VffReal and VffString.
3.2.2. VFF Resource
The VFF Resource area consists of the novel ontologies VffStochasticResource and
VffFailureResource defining fundamental concepts not included in the IFC standard.
VffStochasticResource ontology models the stochasticity characterizing the objects
represented within the data model (e.g. machine processing times, failures
distributions, etc.). Two superclasses are defined within this ontology:
VffProbabilityDistribution and VffStochasticQuantity. VffProbabilityDistribution
represents a general probability distribution and is specialized by
VffDiscreteProbabilityDistribution and VffContinuousProbabilityDistribution to model
the main discrete (e.g. Bernoulli, Poisson, Geometric) and continuous probability
distributions (e.g. Beta, Exponential, Gamma, Log-normal, Normal, Triangular,
VffStochasticQuantity is a generic class that represents complex or simple
quantities that are sampled from a probability distribution and can be associated with
an actual value and a unit of measurement. The subclass VffStochasticQuantityTime
allows defining a stochastic time quantity (e.g. a stochastic time to failure of a machine
tool). A generic time quantity can be defined as an individual of the superclass
VffTimeValueSelect that is specialized by classes IfcQuantityTime and
VffStochasticQuantityTime for deterministic and stochastic time, respectively.
VffFailureResource ontology defines two main classes: VffFailureMode and
VffFailure. VffFailureMode represents a generic failure mode, i.e. the manner in which
a component, system or process could potentially fail to meet or deliver the intended
function(s). VffFailure models the occurrence of a specific failure event.
3.2.3. IFC Resource
The IFC Resource area defines supporting data structures and consists of 14 ontologies
that are created by importing the IFC schemas belonging to the Resource Layer of the
release IFC2x4 RC2 . The ontologies in this area import the ontology from the VFF
Commons area and are imported by all the ontologies contained in the other areas,
included the ontology derived from the standard Step-NC within the Product area.
3.2.4. IFC Core
The IFC Core area defines the basic structure, fundamental relationships and common
concepts of IFC that are then specialized by the other VFDM areas. The IFC Core area
consists of four ontologies (i.e. IfcKernel, IfcProductExtension, IfcProcessExtension,
IfcControlExtension) that are created by transforming with a one-to-one mapping the
IFC schemas belonging to the Core Layer .
The IfcKernel ontology defines the most abstract classes that are based on three
generic concepts like object, property and relationship. The abstract class IfcRoot is
specialized by its subclasses IfcObjectDefinition, IfcRelationship and
IfcPropertyDefinition. IfcObjectDefinition class is the generalization of any
semantically treated thing or process, either being a type (i.e. IfcTypeObject) or an
occurrence (i.e. IfcObject) (Figure 3). IfcRelationship class is the generalization of all
relationships between things that are treated as objectified relationships and is
specialized by subclasses IfcRelAssigns, IfcRelAssociates, IfcRelConnects,
IfcRelDeclares, IfcRelDecomposes, IfcRelDefines. IfcPropertyDefinition is the
generalization of all characteristics that may be assigned to objects.
Figure 3. IfcObjectDefinition class and its subclasses
IfcKernel ontology is imported by IfcProductExtension, IfcProcessExtension, and
IfcControlExtension ontology. IfcProductExtension ontology further specifies the
concepts of a (physical) product, i.e. a component likely to have a shape and a
placement; for instance, IfcElement is a subclass if IfcProduct and represents a generic
component that can be contained in a spatial structure, whereas IfcElementType is a
subclass of IfcTypeProduct and represents a generic type of component.
The Building area consists of the IfcSharedBldgElements ontology that imports the
IfcProductExtension ontology. IfcSharedBldgElements ontology models the shared
building elements (e.g. wall, beam, column, slab, roof, stair, ramp, window, door and
covering) that are the main components of the raw building. Two main classes are
defined: IfcBuildingElement and IfcBuildingElementType. The former is a subclass of
IfcElement, the latter is a subclass of IfcElementType (Sect.3.2.4).
The Product area consists of the StepNcAP10 and VffProduct ontologies and models
the data related to the product that is meant as the production goal of the factory.
StepNcAP10 ontology partially translates the STEP-NC ISO 14649-10 standard
 into an ontology. This ontology is placed in the Product area of the VFDM even if
it models some aspects related to production processes that are exploited by the
Process area as well. The following classes are defined in the StepNcAP10 ontology:
StepNcWorkpiece, StepNcManufacturingFeature, StepNcOperation,
StepNcWorkingstep. According to ISO 14649-10, StepNcWorkpiece contains the entire
description of the workpiece, including material, surface condition and geometric data.
StepNcManufacturingFeature is the superclass of all manufacturing features
characterizing a workpiece. StepNcOperation is the superclass defining the generic data
required by all operations (e.g. machining, rapid movement, touch probing).
StepNcWorkingstep represents the essential building block of an ISO 14649 NC
programme and consists in the machining process for a specified area of the workpiece.
VffProduct is a novel ontology defining the basic concepts of a factory product by
importing and merging the definitions from the ontologies IfcProductExtension and
StepNcAP10. Two main classes are defined: VffProduct and VffProductType.
VffProduct is a subclass of IfcProduct and represents the occurrence of a generic
product that is realized by a factory. An individual of VffProduct can be typed only by
an individual of VffProductType class that represents a generic product type.
VffProductType is a subclass of IfcTypeProduct (Sect.3.2.4) and is specialized by class
VffWorkpieceType that is a subclass of StepNcWorkpiece as well, thus defining a
relationship between the two imported technical standards and the novel class.
The Process area consists of the VffProcess ontology and models the data and
information related to the transformation processes that are executed in a factory.
VffProcess ontology imports the IfcProcessExtension and VffProduct ontologies and
defines three main superclasses: VffProcess, VffProcessType, VffProcessProperties.
VffProcess is a subclass of IfcProcess (Sect.3.2.4) and represents the occurrence of
a generic transformation process. VffProcess is specialized by subclasses
VffManufacturingProcess, VffAssemblyProcess, VffMaintenaceProcess, and
VffHandlingProcess. These classes represent the main production processes that can
occur in a factory (i.e. manufacturing, assembly, maintenance and handling,
VffProcessType is a subclass of IfcTypeProcess (Sect.3.2.4) and models generic
transformation processes that can be used to define various process occurrences.
VffProcessType is specialized by subclasses VffManufacturingProcessType,
VffAssemblyProcessType, VffMaintenanceProcessType and VffHandlingProcessType.
VffManufacturingProcessType is further specialized by VffMachiningProcessType that
is a subclass of StepNcMachiningWorkingstep as well.
VffProcessProperties represents a set of predefined properties for a generic
transformation process. The individuals of this class can be associated with individuals
of both VffProcess and VffProcessType.
3.6. Production Resource
The Production Resource area consists of the VffProductionResource ontology and
models the data related to the production resources that are used by a system to
transform a product (or a work in progress).
VffProductionResource ontology extends the IFC standard by modeling both the
physical and logical aspect of a production resource. The former is modeled by classes
VffMachineryElement and VffMachineryElementType, the latter by classes
VffProductionResource and VffProductionResourceType.
VffMachineryElement class is a subclass of IfcElelment (Sect.3.2.4) and models a
generic machinery element that can be typed by individuals of class
VffMachineryElementType. Being a physical element, an individual of
VffMachineryElement may be associated to a shape representation and a geometric
placement thanks to the restrictions inherited from its superclasses.
VffProductionResource class is a subclass of IfcResource (Sect.3.2.4) and the
generalization of a resource occurrence used in a factory (and its production systems),
mainly labor, material, equipment, product resources, and subcontracted resources and
aggregations, such as a crew resource.
VffProductionResource represents the use of something and does not necessarily
correspond to a single item (e.g. person, machine), but represents a pool of items
having limited availability (e.g. raw materials, labor). Physical objects may be assigned
to a production resource thanks to the objectified relationship class
IfcRelAssignsToResource (Sect.3.2.4). For instance, an individual of
VffMachineryElement class may be assigned to an individual of
3.7. Production System
The Production System area consists of the VffSystem ontology and deals with the
description of transformation systems. VffSystem ontology defines
VffTransformationSystem class as a sublclass of IfcGroup (Figure 3) representing a
generic transformation system that is a logical aggregation of parts for a common
purpose or function or to provide a service. VffTransformationSystem is specialized by
subclasses VffManufacturingSystem, VffAssemblySystem, VffTransportationSystem, and
The Factory area consists of the VffFactory ontology and models the information
related to the factory during its whole lifecycle at an high level of description.
VffFactory ontology defines two main classes: VffFactory and VffFactoryLibrary.
VffFactory is a subclass of IfcProject (Figure 3) and represents the root of every
factory project. Some of the definitions made in a project have meaning only in its
context (e.g. placement of an object), whereas other definitions can be imported from a
library. More than one project can make use of the same libraries.
VffFactoryLibrary is a subclass of IfcProjectLibrary (Figure 3) and represents the
root of every factory library. Both the individuals of VffFactory and VffFactoryLibrary
are associated with the units of measurement to be used in their context. Furthermore,
individuals of VffFactory can be associated with one or more representation contexts.
4. Test Case
This section briefly presents a test case that has been developed to evaluate how the
VFDM can be employed to create factory projects and share them among different
digital tools. The test case consists of six ontologies, thus exploiting the data
distribution empowered by the Semantic Web approach: five factory libraries
(VffLibrary01, VffLibrary02, VffLibrary03, VffLibrary04, VffLibrary05) and one main
factory project (i.e. VffProject01). All these ontologies import the VFDM ontologies.
The whole test case consists of 582 individuals and 1927 corresponding triples.
VffLibrary01 ontology defines a production site (as individual of class IfcSite) and
a building (as individual of IfcBuilding). The building is associated with two possible
shape representations in VRML and 3DS format. VffLibrary02 ontology defines six
types of machinery element (as individuals of class VffMachineryElementType). Each
machinery element is associated with two possible shape representations in VRML and
3DS format. VffLibrary03 ontology imports VffLibrary02 ontology and defines one
occurrence of machinery element (as individual of class VffMachineryElement).
VffLibrary04 ontology defines a part type (as individual of class
VffWorkpieceType). VffLibrary05 ontology imports VffLibrary04 ontology and defines
the process types (as individuals of class VffManufacturingProcessType) that must be
executed to obtain the demanded part type. The process types are characterized by
processing time, operations sequence and needed types of production resource (as
individuals of VffProductionResourceType).
VffProject01 ontology contains the factory project that imports and enriches the
data provided by the libraries. The factory project (as individual of VffFactory) defines
the units of measurement, the representation context and world coordinate system
where the production site and the building imported from VffLibrary01 are placed. One
manufacturing system (as individual of VffManufacturingSystem) is designed and
consists of six occurrences of machinery element (one imported from VffLibrary03 and
five new occurrences). Each machinery element is characterized by a shape
representation and by a placement. Finally, some machinery elements are assigned to
the production resource types needed by the process types.
Figure 4. Test Case opened and modified by GIOVE Virtual Factory
Figure 4 shows a representation of the factory project made by a specific VF module
named GIOVE Virtual Factory (GIOVE-VF) . GIOVE-VF has been developed in
C++ and can import/export ontologies serialized in RDF/XML format thanks to a C++
library named VF Connector C++ Library providing functionalities to parse, create
and modify the ontologies by exploiting an internal map between OWL
classes/restrictions and C++ classes/methods. The instances of C++ classes are used as
handlers of the ontology individuals to support and ease the binding between the
factory project individuals and the internal data structure of GIOVE-VF. The VF
Connector C++ Library is based on the Redland C libraries  and makes use of an
in-memory RDF storage.
Section 4 has shown an example of non-commercial software tool that is already able
to import/export data compliant with the VFDM. However, the potentiality and
applicability of the VFDM need to be further investigated. New test cases will delve
into the relations between processes (e.g. VffProcess), production resources (e.g.
VffProductionResource) and physical elements (e.g. VffMachineryElement). These new
test cases will be particularly useful to check if the VFDM already provides all the data
structures needed to evaluate the performance of manufacturing systems (e.g. via
Discrete Event Simulation).
The VFDM will be further extended by delving into the manufacturing domain, so
that more business processes can be supported. VF Connector Libraries will be
developed for various programming languages (e.g. C++, Java, C#). Finally, as soon as
the size of Factory projects grows, the performance of persistent RDF storage will be
evaluated and compared to in-memory RDF storage.
The research reported in this paper has been funded by the European Union Seventh
Framework Programme (FP7/2007-2013) under grant agreement No: NMP2 2010-
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