=Paper=
{{Paper
|id=None
|storemode=property
|title=A Systematic Approach to Developing Ontologies for Manufacturing Service Modeling
|pdfUrl=https://ceur-ws.org/Vol-886/paper_1.pdf
|volume=Vol-886
}}
==A Systematic Approach to Developing Ontologies for Manufacturing Service Modeling==
https://ceur-ws.org/Vol-886/paper_1.pdf
A Systematic Approach to Developing
Ontologies for Manufacturing Service
Modeling
Farhad Ameri 1, Colin Urbanovsky, and Christian McArthur
Texas State University, Department of Engineering Technology
San Marcos, TX, U.S.A
Abstract. As engineering practices are increasingly becoming distributed and
decentralized, formal engineering ontologies are becoming popular solutions for
addressing the semantic interoperability issue in heterogeneous environments and
bridging the gap between the legacy systems. Manufacturing Service Description
Language (MSDL) is an ontology developed for formal representation of
manufacturing services primarily in mechanical machining domain. In this paper, the
metal casting extension to MSDL is introduced. This paper also introduces a
systematic methodology for development of formal manufacturing ontologies that
relies on incremental enhancement of explicit semantics. In particular, the proposed
methodology focuses on the conceptualization phase and demonstrates how Simple
Knowledge Organization System (SKOS) can be used early in the process for creating
a controlled vocabulary, or thesaurus, in the domain of interest. The SKOS-based
thesaurus helps identify the key concepts that will be used in an axiomatic ontology
based on OWL-DL. Also, use of Semantic Web Rule Language (SWRL) for
representation of constraint knowledge is discussed.
Keywords. Ontology, manufacturing supply chains, thesaurus, manufacturing service
1. Introduction
Manufacturing systems are under continuous transformation by the advances of cyber-
enabled technologies such as cloud computing, wireless sensors, and web services.
Automation technologies are transcending the borders of flexible and programmable
automation and entering the intelligent automation area. In next generation automated
manufacturing systems, planning and control are conducted in real-time by distributed
software agents embedded in the hardware devices of manufacturing systems. The control
units of future manufacturing systems have cognitive capabilities, such as learning,
reasoning, and adapting to changes and they are integrated through a cohesive body of
formal knowledge. In this context, formal representation of engineering knowledge is of
utmost importance. In particular, there is an eminent need for development of various
ontological models including product and process models. Ontologies play a key role in
any distributed intelligent system as they provide a shared, machine-understandable
vocabulary for information exchange among dispersed agents. In an environment in
1
Corresponding Author: Assistant Professor, Department of Engineering Technology, 601 University Dr.
San Marcos, TX 78666, E-mail: Ameri@txstate.edu
which agents have no previous knowledge of each other’s type, capabilities, and
interaction models, development of standard communication models with shared
semantics is a necessity. Ideally, the common terminological system of an agent-based
framework should provide the required building blocks for construction of a shared body
of knowledge that can be understood and interpreted by all agents who subscribe to the
terminology.
In the manufacturing domain, ontologies are in their early stage of development.
Several ontologies have been proposed with the objective of facilitating knowledge
management and information exchange across the extended enterprise. Some information
models, such as Process Specification Language (PSL) [1], serve as neutral language for
integrating several process-related applications (including production planning, process
planning, workflow management and project management) throughout the product life
cycle. Some others are aimed at providing a shared vocabulary for communication
between machine control and process planning software applications [2]. Manufacturing
ontologies vary with respect to the level formalism employed in the representation
scheme. Some ontologies are mainly aimed at providing terminological means for
information integration while some others are geared toward enabling advanced reasoning
through providing sophisticated knowledge structures. It should be noted that heavier
ontologies are not always preferred over lightweight ones due to the computational
complexities associated with maintenance and management of heavily axiomatic
ontologies. IEC 62264 standard [3], being developed by ISO TC 184/SC5 technical
committee, is an example of a lightweight ontology that describes its domain through a set
of object models. The purpose of this ontology is to facilitate the integration of business
applications and manufacturing control applications within an enterprise. It mainly
describes the attributes of the various objects in a manufacturing information model.
Given the limited incorporation of explicit semantics in the model, it is placed at the
lower end of the formality spectrum. ADACOR [4], on the other hand, is an example of
heavyweight domain ontology based on a foundational ontology called DOLCE [5].
Foundational, or upper, ontologies are generic ontologies developed with the intention of
formally describing various concepts that have similar interpretation across different
domains. ADACOR is the ontology language of a holonic manufacturing system used for
autonomous manufacturing control and it uses first-order logic as the knowledge
modeling formalism. Most of the existing manufacturing domain ontologies are
descriptive in nature in a sense that they provide the required means for describing
manufacturing transactions and operations within a manufacturing system. However,
there are few ontologies that deal with characterization of a manufacturing system itself
with respect to technological capabilities. Capability characterization is increasingly
becoming important as new manufacturing processes and technologies are being
introduced and supply chains are becoming increasingly distributed. Manufacturing
Service Description Language (MSDL) [6] is a formal domain ontology developed for
representation of capabilities of manufacturing services. MSDL was initially designed to
enable automated supplier discovery in distributed environments with focus on
mechanical machining services. The objective of this paper is to introduce a structured
procedure for developing ontologies for representing manufacturing capability models.
Metal casting is selected as the domain of interest and MSDL is extended to include metal
casting domain knowledge using the devised procedure.
There are several motivations for adapting a methodological approach to engineering
ontology development. First, engineering knowledge models are often complex,
multilayered, and highly interconnected models that need to go through a gradual and
structured process of formalization and enrichment. Second, the knowledge users, who
are typically not experts in knowledge representation and modeling, have to actively
participate in knowledge modeling and validation in order to arrive at viable knowledge
models. Without a well-defined and structured procedure, it is not easy to get all the
ontology stakeholders involved effectively in the social process of knowledge capture and
organization. Third, engineering ontologies that follow the same development path, lend
themselves better to ontology mapping and merging.
This paper is organized as follows. A brief description of the ontology development
methodology adopted in this work is described first. The next section provides an
overview of the manufacturing capability model as conceptualized in MSDL. Various
levels of capability model in MSDL as well as the core concepts are discussed later. The
metal casting thesaurus is introduced afterwards followed by sections related to axiomatic
casting ontology and casting rules.
2. Approach
The proposed methodology for ontology development in this work starts from a light-
weight thesaurus, or controlled vocabulary, and guides the developers through gradual
enrichment of the ontology by augmenting it with further semantics in the form of
concept relationships, axioms, and rules. The proposed methodology uses Simple
Knowledge Organization System (SKOS) [7] as a framework for creating a formal
thesaurus. The created thesaurus helps ontology developers identify the key concepts of
the domain of interest and also build partial taxonomies of the identified concepts and
define some preliminary relationships, such as narrower and broader, between the
concepts in the thesaurus. The identified concepts are further enhanced through
introducing concept properties and imposing necessary and sufficient conditions on the
concepts based on Description Logics
(DL) [8] semantic model and Web
Ontology Language (OWL) syntactic
format. The output of this stage can be
regarded as the structural knowledge of
the domain of interest. The constraint
knowledge is captured and formalized
through introduction of rules modeled in
Semantic Web Rule Language (SWRL),
an extension of OWL that provides the
ability to define complex rules and
perform more advanced reasoning on
the concepts in an ontology. As the
ontology evolves, there is a need for
continuous evaluation of the ontology Figure 1 : The major steps of ontology development
with respect to the level of semantics process
incorporated in the ontology. Therefore,
parallel to semantic evolution of the ontology, there is a need for ontology validation and
verification with respect to accuracy and completeness using quantifiable metrics. Figure
1 demonstrates the major steps of the proposed procedure for engineering ontology
development.
3. What is manufacturing capability model?
Since the proposed procedure is geared toward developing capability ontologies, it is
in order to clearly define manufacturing capability early in this paper. For the purpose of
this work, manufacturing capability is referred to as the limitations and the range of
applicability of a manufacturing facility in transforming raw materials into products of
increased value. More specifically, a capability model characterizes a manufacturing
facility and its constituting elements including devices, machine, cells, operators, and
processes with respect to the range of applicability, speed, cost, quality, and associated
constraints and uncertainties. Based on this definition different dimensions of
manufacturing capability include:
• Technological capabilities such as the resolution, accuracy, feed, speed, power,
and automation level of the manufacturing equipment.
• Operational capabilities such as production capacity, throughput time, cost per
unit, etc.
• Geometric capabilities such as shape producible, dimensions, wall thickness,
work envelope, etc.
• Quality capabilities such as defect rate, surface finish, and tolerances.
• Relational capabilities that refer to interfaces with other systems and processes
both hardware and software.
• Stochastic capabilities such as reliability, variations, etc.
The challenge in manufacturing capability modeling lies in developing conceptual
capability models that characterize various facets of manufacturing capability in different
levels of abstraction and also formalizing the semantics of the capability model in an
unambiguous fashion.
Two example use cases for formal capability models include autonomous design-to-
fabrication and automated supply chain deployment. Before introducing the metal casting
thesaurus and ontology, a brief overview of MSDL and its core classes is provided next.
4. Manufacturing Service Description Language (MSDL)
As mentioned before, MSDL is a formal ontology since it is contains explicit semantics
coded in a logic-based formalism. OWL-DL 2, a sub-language of OWL, is selected as the
ontology language of MSDL. OWL is recommended by the World Wide Web
Consortium (W3C) as the ontology language of the Semantic Web. OWL uses
RFD/XML as the standard serialization; hence it has enough portability, flexibility, and
extensibility for web-scale applications. Description Logic (DL) is supported by the
Semantic Web meaning that OWL-based ontologies can be shared, parsed, and
manipulated through open-source web-based tools and technologies, including multi-
agent systems. The original purpose of MSDL was to serve as the ontology language of
an agent-based framework for supply chain deployment.
2
http://www.w3.org/TR/owl-guide/
4.1. Capability modem in MSDL
In MSDL, manufacturing capability is decomposed into five levels of abstraction, namely,
and supplier-level, shop-level, machine-level, device-level, and process-level as shown in
Figure 2. These five levels can collectively address the six dimensions of capability
described earlier.
Supplier-level capability model
deals with the capabilities of the
supplier who runs a manufacturing
facility. For example, expertise, skills,
industry focus, product focus, and
certifications are among the features of
supplier-level capabilities. Shop-level
capability describes the system-level
capabilities of a manufacturing system
owned by a supplier and described the
system through its layout and material
handling system and other supporting Figure 2 : Different Levels of the Manufacturing
Capability Model
systems such as production planning
and inventory control. Figure 3 shows
the concept diagram of the Factory class used for describing shop-level capabilities.
Figure 3: Factory class in MSDL is a sub-class of ProductionSystem
Machine-level Capability deals with characterization of the fabrication machines that
are involved in conversion of the raw material into finished goods. Based on the
proposed approach, manufacturing machines are represented through their components.
Description of machines through their components is particularly beneficial in the context
of Reconfigurable Manufacturing Systems (RMS) [9, 10] where conventional naming of
machine tools is no longer applicable (Figure 4).
Figure 4: Ontological description of an RMS machine through its components
Device-level capability deals with characterization of devices, such as feed and
spindle drives in a CNC machine, that are located at the lowest level of the hierarchy of
the physical resources in any manufacturing system. In fact, the capabilities of the higher-
level entities such as machine tools, and shop floor, can be inferred through aggregation
of device-level capabilities. Therefore, the ontology should also cover the capabilities of
the devices that form the basic building blocks of the physical factory. Process-level
capability describes and characterizes manufacturing processes. Process is the most
abstract entity in the capability model. The fundamental question in modeling process-
level capability is how to describe the semantics of different manufacturing process such
as mass change (either additive or subtractive), phase change, structure change,
deformation, and assembly in a formal way. Different manufacturing processes call for
different abstraction and conceptualization approaches.
4.2. Core Classes of MSDL
One of the core classes of MSDL is the Service class. Suppliers are the providers of
manufacturing services and customers are the consumer of manufacturing services. In
MSDL, supply and demand are represented by the SupplierProfile and RFQ (Request for
Quote) classes respectively. As can be seen in Figure 5, a Supplier Profile has two major
components, namely, the Supplier and the Manufacturing Services that the supplier
provides. Services are further described through their associated processes, materials,
resources, and supporting services. There are two primary methods for encoding further
semantics (beyond concepts and properties) in MSDL. The first method is building
taxonomies (i.e., explicit parent-child relationships) and the second method is axiomatic
definition of classes. For example, the semantics of the Industry class are encoded in the
form of an explicit taxonomy based on the North American Industry Classification
System 3 (NAICS). Concepts such as Process and Material, on the other hand, are
formally defined through necessary and sufficient conditions. Further constraints are
applied on concepts using rules modeled in Semantic Web Rule Language (SWRL).
SWRL rules are used by automated reasoners such as Pellet [11] and Hermit [12] to
interpret the rules. For example, in a supply chain deployment scenario, supplier and
customer agents can locally store instances of the MSDL concepts that pertain to their
particular capabilities and needs.
3
http://www.census.gov/eos/www/naics/
Figure 5: Concept diagram for the Supplier Profile class
Figure 6 shows the subclasses of the Process class in MSDL. As can be seen in this
figure, the main subcategories of Process class in MSDL are addition processes,
subtraction processes, consolidation processes, solidification processes, deformation
processes, and property enhancing processes. The first revision of MSDL was limited to
subtraction processes (i.e., conventional machining processes such as drilling, turning,
and milling). This paper reports the metal casting extension of MSDL which is regarded
as a solidification process. The metal casting ontology is developed based on a new
methodology that starts with a semi-structured thesaurus. The casting thesaurus is
discussed next.
Figure 6: Manufacturing Process categorization in MSDL
5. Metal Casting Thesaurus
From a linguistic perspective, a thesaurus is a collection of terms connected through
lexical relationships such as synonym, antonym, and metonym. International Standards
Organization (ISO) defines thesaurus as “ the vocabulary of a controlled indexing
language, formally organized with the aim of stating explicitly the relationships between
the concepts” [13]. WordNet [7] is an example of a linguistic thesaurus developed for
English terms. The process of integrating thesauri with information retrieval systems
started in early 1990’s and they gradually evolved from mere lexical resources towards
powerful instruments for conceptual representation and knowledge organization [14].
A thesaurus improves the performance of electronic information retrieval systems
through indexing documents by a controlled vocabulary in which terms and concepts are
linked together through hierarchical relationships, associative relationships, and
equivalence relationships. There exist several formal thesauri such as NAL Agricultural
Thesaurus [15], Medical Subject Heading [16], and GEMET [17] (GEneral Multilingual
Environmental Thesaurus) developed to support automated information retrieval in
different application domains. However, in engineering domain, there are few thesauri
that are specifically designed for information retrieval and knowledge organization. A
lack of adaptation of controlled vocabulary in engineering can be attributed to the isolated
nature of engineering activities, both in design and manufacturing, which has traditionally
dominated the engineering realm. This has spawned a plethora of proprietary engineering
information constructs that typically do not interoperate. Nevertheless, as engineering
practices are increasingly becoming collaborative, interdisciplinary, and distributed, there
is an eminent need for unifying frameworks, such as engineering thesauri and ontologies
that can semantically connect apparently heterogeneous and disparate information models.
Although the need for developing comprehensive engineering thesauri endorsed by
various stakeholders form government, industry, and academia, is a very real need that
should be addressed eventually, this work is intended to explore how thesauri can be used
for knowledge management in engineering domain. In other words, through developing a
prototype thesaurus with a limited number of concepts, the authors investigate a
systematic approach to engineering ontology development based on incremental
enhancement of formal semantics embedded in the model. In a sense, a thesaurus can be
regarded as a lightweight ontology that connects various concepts through elementary
semantic relations. Since terms are regarded as the basic semantic units conveying
abstract concepts, a thesaurus can be used for indentifying the core concepts and classes
of a more complex ontology. The prototype thesaurus that is developed in this work helps
in identification of the key concepts of the casting extension of the MSDL ontology. Since
MSDL is an OWL-based ontology, SKOS (Simple Knowledge Organization System)
modeling is used for thesaurus development. Similar to OWL, SKOS is based on
Resource Description Framework (RDF), which allows concepts to be composed and
published on the World Wide Web, linked with data on the Web and integrated into other
concept schemes. SKOS provides a structured framework for creating different types of
controlled vocabulary such as thesauri, concept schemes, and taxonomies. SKOS thesauri
are concept-based, as opposed to term-based, in nature. In a term-based thesaurus, terms
are directly connected together by semantic relationships whereas, in a concept-based
thesaurus, semantic connection is at a concept level and terms are the lexical labels for the
concepts, or units of thought, and may or may not have lexical relationships established
among themselves. A SKOS thesaurus, like any other concept-based thesaurus, has a
three-level structure (a) conceptual level, where concepts are identified and their
interrelationships established; (b) terminological correspondence level, where terms are
associated (preferred or alternative) to their respective concepts and (c) lexical level
where lexical relationships are defined to interconnect the terms. The conceptual nature of
SKOS is particularly useful in ontology development as it urges the developers to draw a
distinction between terms and concepts and build a sound conceptual understanding of the
domain of discourse.
To create the casting thesaurus, three main sources were utilized: 1) the casting
textbooks 2) the web profiles of the providers of casting services and 3) DBpedia, the
structured datasets gleaned from Wikipedia. DBpedia was used extensively to create the
seed thesaurus early in the project by importing the relevant concepts and their associated
sub-trees. Pool Party (PP), a thesaurus management system, was employed for creating
the thesaurus. Figure 7 shows the concept diagram for the molding sand based on the
SKOS terminology. Each concept in SKOS has exactly one preferred label (prefLabel)
and can have multiple alternative labels (altLabel). For example, the sand that is used in
casting is typically referred to as molding sand but foundry sand and casting sand are also
used interchangeably to point to the same concept. In other terms, molding sand, casting
sand, and foundry sand are synonyms in the casting thesaurus. The broader concept of the
molding sand is sand. Silica sand and chromite sand are the narrower concepts; meaning
that they are more specialized forms of the molding sand. Molding sand is also related to
mold for example. Technically, all terms in the casting thesaurus can be related to one
another. Therefore, broader, narrower, and related are the semantic relations used in any
SKOS thesaurus. Also, each SKOS concept can have a definition provided in plain
English or any other natural language.
Figure 7: The concept diagram of the molding sand based on SKOS terminology.
One advantage of using SKOS is that any SKOS-based thesaurus can be connected to
the Linked Open Data (LOD) 4 in order to reuse the existing datasets available on the LOD
cloud. In fact, DBpedia, which was used for the purpose of creating the seed thesaurus in
this work, is part of the LOD cloud currently containing more than 3.4 million concepts
described by one billion relationships. A SKOS thesaurus can also be published and
linked to the LOD cloud as RDF triples, thus allowing a larger community of users to
validate and expand it. It should be noted that a SKOS-based thesaurus can serve as a self-
sufficient ontology in many cases and adequately address the semantic needs of many
knowledge organization and information retrieval systems. However, to enable more
advanced reasoning capabilities, such as creating inferred taxonomies, the semantic
4
http://linkeddata.org/
content of the thesaurus needs to be enriched by further constraining the identified
concepts via logic-based restrictions.
6. Formal Ontology for Metal Casting
To further enhance the semantics of the created thesaurus and develop a formal axiomatic
ontology, an OWL-based modeling is adapted in this work. A thesaurus can be evolved
into an ontology by going through several formalization steps. In the first step of
formalization, core concepts of the domain of interest, already identified in the thesaurus,
are represented through formal classes with known properties. There isn’t always a one-
to-one mapping between the concepts in the thesaurus and the concepts in the ontology.
Instead, a cluster of concepts in the thesaurus may define a single concept in the ontology.
The concepts in the casting thesaurus have no properties assigned to them but in the
ontology, it is necessary to provide more details about each concept through introducing
some attributes that describe each concept. For example, as can be seen in Figure 8, the
weight and dimensions of the die casting machine are regarded as the properties of the
machine with numeric values. The properties sometime take Boolean or literal values at
their range. For instance, isHotchamber is a Boolean property used to determine if a die
cast machine is hot chamber or cold chamber. At the next level of formalization, concepts
are connected to one another through object properties. For example, the Die Casting
Machine is related to the Die Casting Process through hasProcess relation or Sand
Casting process is connected to Mold through hasMold property. The concepts, once
connected, create a semantic network that defines the main structure of the ontology.
Figure 8: Logic view and property view for the Die Casting Figure 9: Formal definition of the Solidification Process in
Machine in MSDL MSDL
At the third level of formalization, concepts are further annotated by axioms to form
defined concepts. Defined concepts are basically formed through intersecting multiple
conjuncts that collectively serve as a set of necessary and sufficient conditions that
logically characterize the concepts. For example, concepts such as Process and Material
are formally defined through necessary and sufficient conditions. Figure 9 provides the
formal definition of the solidification process in MSDL. As the name implies, a
solidification process is a MfgProcess that changes the state of its input material from
either liquid or powder to solid. Casting, molding, and powder processes are examples of
the solidification process. These processes do not reduce the mass of its input material but
change the density and mechanical properties and typically change the geometry of the
input material as well. Casting is a specific case of the solidification process in which the
input material is a metal. The definitions of Sand Casting and Die Casting, as two sub-
classes of the casting process, are provided in Figure 11 and Figure 10 respectively. The
definition of sand casting implies that it is a casting process in which the mold is
expendable and is made of sand and it is a gravity pouring process and the castable
materials include cast iron, aluminum, bronze, brass, and stainless steel. The definition of
the die casting process describes it as a casting process with a permanent mold made of
steel. This process can be applied to nonferrous materials and does not use gravity for
pouring. In this way, all casting processes can be uniquely defined using logical axioms.
Figure 10: Formal definition of the Die Casting process in Figure 11: Formal definition of the Sand Casting
MSDL process in MSDL
The concepts embedded within each definition may have formal definitions
themselves. For example, Aluminum is not merely a string of characters but it is a subclass
of nonferrous metals with known chemical and physical properties formally defined in the
ontology. Figure 12 shows the formal definitions of aluminum and stainless steel in
MSDL. DL reasoners, such as Racer [18] or Pellet [19] can be used to classify a flat set of
defined classes and arrive at an inferred taxonomy. In other words, with an axiomatic
approach for encoding semantics, there is no need for creating an explicit taxonomy of
concepts from automated information processing standpoint. However, to make
ontologies more readable and comprehensible for human developers, it is recommended
to build explicit taxonomies while developing a formal ontology. Concept classification is
one of the cornerstones of similarity measurements in formal ontologies.
Figure 12: Formal definitions of Aluminum and Stainless Steel in MSDL
7. Metal Casting Rule Modeling
The next step of semantic enhancement of an ontology entails creation of the rules that
convey further information about the concepts and their relations. In fact, the richness of a
formal ontology depends on the level of details incorporated in the axiomatic definition of
the concepts as well as the number and diversity of the rules encoded in the ontology.
Rules are the main enablers of ontological reasoning and inference by machine agents. As
the complexity of queries increases, so does the significance of knowledge-based
reasoning and inference.
Human reasoning and cognition mechanism has been the subject of research in the
Artificial Intelligence (AI) community for several decades now. Expert systems
developed in AI domain are intended to imitate the way a human expert analyzes a
particular situation by using different reasoning techniques such as rule-based, case-based,
fuzzy logic, neural networks, and Bayesian networks [20]. Rule-based techniques, due to
their structured nature, are the most common techniques adopted in expert systems [21].
OWL has the required level of expressivity for representing structural knowledge
through concepts and the relationship between the concepts. Also it is possible to define
concepts using different types of restriction such as quantifier, cardinality, and hasValue.
However, for rule representation, OWL fails in providing the necessary building blocks
especially when it comes to complex rules. To fill this gap, OWL was supplemented by a
rule modeling language referred to as Semantic Web Rule Language (SWRL). SWRL is
an extension of OWL that provides the ability to define complex rules and perform more
advanced deductive reasoning about concepts in an ontology. SWRL rules are used by
automated reasoners such as Pellet [19] and Hermit [22] to interpret the rules. SWRL is
built on OWL DL and shares its formal semantics.
SWRL rules are composed of an antecedent (body) and a consequent (head). Both
body and head are composed of positive conjunction of atoms. A SWRL rule follows an
“if-then” logic. If the antecedent, or premise, holds true, the consequent must be true as
well. For example, the flowing rule states that if a part is made of aluminum and its
minimum wall thickness is greater than or equal to 3 mm, then it can be sand casted.
Part (?p) ^ isMadeOf (?p, ? m) ^ Aluminum (?m) ^ hasMinWallThickness (?
th )^ swrlb:greaterThan (?th, 3)
-> SandCastAblePart (?p)
In essence, this rule creates a temporary class called SandCastablePart and any
instance of the class Part that satisfies the conditions given in the body of the rule
becomes the subclass of this temporary class. This classification utility is especially useful
for narrowing down the search space when, for example, the goal is to find the parts that
can be manufacturing using sand casting process. SWRL rules can be attached to the
OWL ontology or they can be applied programmatically on the fly. It is recommended to
apply the rules programmatically especially if the rules are parametric.
Rules can be used for multiple purposes in the casting ontology. For example, design
validation can be conducted automatically using SWRL rules if the design itself is
represented in OWL. Design validation in the context of an ontology can be translated
into a consistency checking process. As another example, a rule-based approach can be
adapted for finding the qualified suppliers for a particular casting service. The following
rule describes a query for a casting service that accepts parts heavier than 100 pounds,
with the tolerance of 0.01 inch or less, surface finish of 64 microinch or less, and
production volume of 500 or more.
Service (?s)
^ hasProcess (?s, ?pr) ^ Casting(?pr)^ hasPart(?s, ?pt)
^ hasWeight (?pt, w?) ^ swrl:greaterThan (?w, 100)
^ hasAccuracy (?s, ?ac) ^ swrl:smallerThan (?ac. 0.01)
^ hasSurfaceFinish (?s, ?sf) ^ swrl:smallerThan (?sf, 64)
^ hasProductionVolume (?s, ?pv) ^ swrl:greaterThan (?pv, 500)
->DesirableService (?s)
This rule creates a temporary class called DesirableService that subsumes all
instances of the Service class that satisfy the requirements. Another rule is required for
identifying the suppliers who provide the described service. This rule is constructed as
follows:
SupplierProfile (?sp) ^ hasService (?sp, ?s) ^ DesirableService (?s)
-> QulifiedProfile (?sp)
It should be noted that rules such as above can be expressed in OWL as class
subsumption (e.g. SupplierProfile and (hasService some DesrirableService) subClassof
QualifiedProfile). However, such expressions require addition of permanent classes such
as QualifiedProfile or DesirableService to the ontology which will make the ontology
more application-dependent and less generic. In general, with the aid of rules, the
dynamic classes that have operational purposes can be kept separate from the conceptual
and generic (static) classes that constitute the main body of the ontology. Although,
SWRL is more expressive that OWL DL alone, this extra expressivity comes at the
expense of risk of undecidability. Therefore, care should be taken when introducing
SWRL rules. Especially one should avoid binding the rules to the individuals that are not
known to the ontology as it renders the ontology undecidable.
8. Conclusions
The objective of this paper was two-fold: First, to report the metal casting extension
of MSDL and second, to propose a systematic approach to developing manufacturing
capability ontologies. The metal casting extension is currently limited to sand casting and
die casting but in the future, it will be extended to all metal casting processes and
equipment. The proposed approach for ontology development suggests breaking down the
capability model into five distinct levels, namely, supplier-level, shop-level, machine-
level, device-level, and process-level. Also, the proposed approach recommends
identifying the concepts within the ontology through creation of a thesaurus early in
development process. Simple Knowledge Organization System (SKOS) was used as the
thesaurus modeling formalism. The adoption of SKOS as a common model to represent
manufacturing thesaurus allows standard representation of conceptual thesauri. With a
standard representation, linking of different manufacturing thesaurus is facilitated and
therefore, multiple thesauri can be merged and combined to arrive at more comprehensive
thesauri with wider scopes. The joint use of SKOS, OWL, and SWRL would offer a high
level of flexibility with respect to arriving at a trade-off between expressivity
requirements and computational complexity constraints. Future work in this area include
enhancement of the developed thesaurus and ontology as well as and creating the
necessary search tools that leverage the semantic structure of the developed knowledge
model for different use cases.
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