Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design
A Multilevel Modeling Framework for Semantic Representation of Cloud Manufacturing Resources Ning Liu, Xiaoping Li School of Computer Science and Engineering, Southeast University, Nanjing, China
[email protected],
[email protected] unified manufacturing resource model which presents various resource elements for CNC machining systems. Although the model provides support for automation of process planning decision making, there is little consideration on the applications in other aspects in the product development. From the service-oriented computing perspective, Shi et al [6] employed XML schema to encapsulate manufacturing resource information and adopted WSDL to model the accessing operations to manufacturing resources. However, the required resources cannot be automatically discovered by matchmaking algorithms. Semantic Web and Ontology are always adopted to address the above challenges. OWL-S and WSMO are ontology-based approaches which provide a worldwide standard for semantic web services. Capabilities of web services are modeled as service interfaces. Since manufacturing capability has more complex features than web service capability, a more elaborate manufacturing capability model is needed. Lin et al [7] investigated ontology-based approaches for representing information semantics and developed a general manufacturing system engineering ontology model. However, individual partners are required to initially map their vocabularies to the ontology, which is complex and time-consuming. Jang et al [8] extended the UDDI registry specification, which includes OWL-based semantic descriptions on manufacturing services. The descriptions could be utilized for reasoning in service retrieval. Ameri et al [9] proposed a graph-based Manufacturing Service Description Language(MSDL). Although MSDL formalized the domain-specific structural knowledge of manufacturing services, how to manage the complex constraint knowledge between structural knowledge remains a challenge. Cai et al [10] presented a prototype semantic web system to manage distributed manufacturing services. The prototype facilitates the retrieval of required manufacturing services efficiently, accurately, and automatically. From model-driven perspective, Lee et al [11] proposed a multilayered ontology architecture for collaborative enterprises, in which the representation layer and the domain modeling layer are separated. Kim et al [12] presented an efficient model-driven approach for generating OWL-S documents from UML models. However, the proposed method generates OWL-S documents with no service grounding. Lee et al [13] proposed a multilevel product modeling framework. Both the constructed semantic-based product meta-models and a developed editor interface allow engineers to describe product models using familiar methods and terminology. However, the proposed framework only focused on product structural and behavior evaluation within a product lifecycle.
Abstract—Cloud manufacturing aims to perform large-scale collaboration for complex manufacturing by sharing distributed manufacturing resources. Resource representation is a prerequisite for achieving resource optimal allocation and it determines the robustness of cloud manufacturing systems. In this paper, a three-level modeling framework is constructed for semantically representing resource-related information and knowledge. Heterogeneous resource information is represented by the resource model level, functional features that support manufacturing resources to resolve manufacturing problems is described by the capability model level, and semantics of capability models are elaborated and exhibited from multigranularity perspectives by the semantic-based meta-model level. A practical multi-spindle lathe case is adopted to demonstrate how the constructed framework represents cloud manufacturing resources from the semantic view, which is the foundation of resource discovery in cloud manufacturing systems. Keywords—cloud granularity
manufacturing;
I.
resource
modeling;
INTRODUCTION
Cloud manufacturing is characterized by integration of distributed resources and distribution of integrated resources to solve complex manufacturing tasks [1]. “Manufacturing resource” is a general term of various resources. They are provided by heterogeneous enterprises. Majority of enterprises are small and medium-sized enterprises(SMEs). With limited knowledge and experiences, few of them are competitive on the global market [2-3]. By migrating from traditional productcentric business to product-based service-oriented cloud manufacturing systems, SMEs have chances to easily and fast collaborate with a large number of other partners. This can maximize the benefit of participating enterprises. Therefore, cloud manufacturing becomes an attractive manufacturing model that follows the cloud computing paradigm. There are many issues to be solved for realizing the cloud manufacturing. Resource representation is a foundational problem, which determines the robustness of cloud manufacturing systems [1]. Resource representation needs an effective resource modeling framework, which is essential to facilitate cooperation and collaboration among heterogeneous enterprises systems. Many popular techniques, such as complex object representation, SOA, ontology, and MDA, are used for resource modeling from different perspectives. From the software engineering perspective, Steele et al [4] modeled the manufacturing system by integrating functions with an object-oriented resource model that links information from different knowledge domains. Vichare et al [5] proposed a
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descriptions. The framework should provide multigranularity capability profiles to bridge the gap between resource consumers and resource providers.
According to the above analysis, we can see that the capability representation is the most important viewpoint in the existing resource modeling approaches. However, existing approaches only concentrate on separate capabilities of resources without taking into account the multi-granularity characteristic. In cloud manufacturing, there are two kinds of manufacturing tasks, single resource service request task and multi-resource service request task [14]. The tasks are generally expressed with high-level and coarse-grained capability, like “manufacture plane as cheap as possible”, while the resources registered in cloud platform are usually described with low-level and fine-grained capability, like “drilling”, “boring” etc. Therefore, it is necessary to design and transform multi-granularity capability in resource modeling process to bridge such gap. In addition, an ontology is needed to provide open semantics [15], by which knowledge from multiple sources can be easily combined and checked for consistency [16].
• Dynamic and flexibility. Cloud manufacturing is a dynamic organizational system. The framework should decouple resources with their capabilities to deal with resource failure or changing manufacturing capability demands. • Reusability and extensibility. The framework should be extensible for appending new information. As well, the existing information and semantics should be accumulated as experiences in a knowledge repository. • Semantic consistency. The framework should keep the semantic consistency when heterogeneous resource information is integrated into the cloud platform. Resource information can be correctly interpreted in different contexts only when the semantic is consistent.
In this paper, a new resource modeling framework is investigated by using a multilevel resource modeling approach and ontologies. There are three levels in the framework. The highest level is the semantic meta-model. The concepts and relationships in the semantic meta-model are used to describe capability models in the middle level. The lowest level describes resource models. Through many-to-many mappings, any resource and its capabilities are loosely coupled with each other. A resource is considered as an assembly of various capabilities with different granularities to perform the desired manufacturing operations. Diverse resources can be federated in a meaningful way and cooperate with each other seamlessly.
B. Proposed framework Taking the above requirements into account, the existing multilevel modeling approach is used to represent resources in cloud manufacturing. The multilevel modeling approach deals well with semantic interoperability problems in dynamic and open environments. Fig.1 shows an overview of the proposed framework, which contains three levels: the resource model level(M0), the capability model level(M1), and the semanticbased meta-model level(M2).
The remainder of the paper is organized as follows. Section 2 introduces a general overview of the multilevel resource modeling framework. Section 3 proposes the semantic-based meta-model and explains the critical components in detail. Section 4 shows the implementation of the framework by a real-world manufacturing company, followed by conclusions in Section 5. II.
THE MULTI-LEVEL RESOURCE MODELING FRAMEWORK
In cloud manufacturing, there are three important roles: resource providers, resource consumers, and the operator. Resource representation should conform to the requirements of the relevant roles. In this section, the purpose of developing a resource modeling framework is identified along with an overview of the proposed modeling framework.
Fig. 1. An overview of the proposed framework
• Comprehensiveness and expressiveness. Information commonly used by all resources as well as that used in a particular context should be described. Semantics of resources should be expressed in a computer-readable format to enable automatic resource applications.
The resource model level(M0) is the foundation of cloud manufacturing, which consists of distributed enterprise-specific resource models. Since each enterprise has its own resource management strategies, little semantics could be demonstrated by heterogeneous formats representing different resources. These factors lead to the complex features of manufacturing resources, such as multi-domain, multi-level, and multigranularity. This produces obstacles to the meaningful resource sharing. Therefore, heterogeneous resource models need to be described in an isomorphic manner.
• Multi-granularity capabilities. Resource discovery and matchmaking are based on manufacturing capability
The capability model level(M1) represents functional features of manufacturing resources. In this paper, functional
A. Requirements specification In order to accurately capture and exchange resource information, the following basic requisites should be satisfied by a resource modeling framework.
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resources. In addition, manufacturing capabilities are integrated based on formal semantics.
features refer to manufacturing capabilities. Whether manufacturing resources can be discovered or not depends on the description of manufacturing capabilities. Capability models are derived from resource models at M0 level. This procedure is performed by system designers of enterprises and domain experts. In various environments, a manufacturing resource provides different manufacturing capabilities. Similarly, a manufacturing capability can be provided by several distributed manufacturing resources. Therefore, the relationships between two levels(M0 and M1) are many-tomany mapping. The mapping component( θ ( X , X ' ) ) in the framework records the mapping relationships. However, different designers describe the same capability with different terms, or the same measurement with different metrics. Therefore, it is necessary to keep the semantic consistent among heterogeneous capability models.
III. THE SEMANTIC-BASED META-MODEL There are four basic constructs in the semantic-based metamodel. They are Class, Relation, Attribute, and AttributeValue. Class represents a collection of entities that share a common set of characteristics. CloudManufacturingClass is the top class. Classes are hierarchically organized by means of different relationships. Relation is a set of object properties that relates different classes. Attribute is a set of attributes. There is a has_Attribute relation between CloudManufacturingClass and Attribute. The has_Attribute relation specifies attributes of a class. AttributeValue is used to define the data type of an attribute. Fig.2 shows the conceptual model for the semanticbased meta-model, which contains two parts. The first part includes MfgResource, FunctionalFeature, NonFunctional Feature, and Actor classes, which describe the resource information. The second part contains Multi-granularity Description, Pattern, Context, and Contract classes, which represent the information in the resource context. They are crucial concepts for building resource representation specification.
The semantic-based meta-model level(M2) is proposed for achieving seamless collaboration in cloud manufacturing from a knowledge perspective. In this level, the classes, relationships and axioms are defined in order to comprehensively represent resources and their capabilities. The capability models at the M1 level conform to M2. Objects, processes, and rules involved in capability models are transformed by the transforming component( τ ( X ' , M ) ) in the framework. Knowledge related to manufacturing activities are explicitly described. Semantics of M2 can be specified in more details to describe a particular manufacture domain. Classes, relationships, and axioms are formally represented by ontology describing language(OWLDL), which is expressible enough to represent elements defined in the framework. Semantic consistency can be guaranteed and validated by the axioms. By this way, resource information and knowledge can be effectively exchanged and shared.
A. MfgResource and Actor The MfgResource class represents various resources, which is divided into three subclasses: physical resources, nonphysical resources, and human resources. The Physical class represents tangible manufacturing facilities, such as machine tools, fixture, and materials, etc. They are directly related to their physical locations, states, and workload. Intangible resources are represented by the NonPhysical class. They usually refer to data, documents, software etc. These resources can be accessed anywhere and anytime by means of resource services. The Human class represents all kinds of professional persons involving in manufacturing activities. These resources cannot be encapsulated into resource services. Each resource is described by non-functional features and functional features. The specific contents of non-functional features and functional features are contained in the NonFunctionalFeature and FunctionalFeature classes, respectively. A resource is connected with its non-functional features and functional features by two relationships(has_FunctionalFeature, has_ NonFunctionalFeature).
There are two kinds of relationships in the framework. The transforming relationship exists between M2 and M1 while the mapping relationship exists between M1 and M0. The former means that capability models of different enterprises are transformed from the same semantic-based meta-model, which guarantees the semantic interoperability among the collaborative enterprises. The latter assures the dynamic correlations between manufacturing resources and their corresponding capabilities, which guarantees the flexibility of the cloud manufacturing. The framework facilitates to efficiently and effectively manage distributed manufacturing
Fig. 2. Conceptual model for the semantic-based meta-model
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The Actor class represents two roles in cloud manufacturing, resource provider and resource consumer. They are denoted as ResourceProvider and ResourceConsumer subclasses, respectively. The object relationship has_Owner between the MfgResource class and Actor class indicates that any resource has an owner. If the owner is a resource provider, it means that the resource belongs to the enterprise. If the owner is a resource consumer, it means that the resource is used by the user. In cloud manufacturing, the collaboration is temporarily formed to pursue a market opportunity. The subclasses of Actor class and their relationships with MfgResource class intend to record the collaborative interaction between the resource provider and the resource consumer. This type of information should be accumulated as history experiences so that the cloud platform can optimize enterprise resources for a specific business goal.
perspective. Based on different purposes and logical structures, capabilities can be further divided into two subclasses. The capabilities belong to the AtomicCapability class are the kind of no interaction capabilities, such as financial, warehousing, while the capabilities belong to the SuperCapability class are the kind of capabilities used to execute a series of interactive actions. The Tactical class is the middle level of the capability abstraction. Capabilities in this level are modeled from the manufacturing decision perspective. In general, developers decompose a business goal into a number of sub-tasks. These sub-tasks are mapped onto the specific functions. These functions can be viewed as a refinement of capabilities at the strategic level. Functions with similar characteristics are clustered into the same member in the strategic level. The hierarchical relationship between two levels represents that any capability at tactical level is contained or involved by a capability at strategic level. Both the AtomicCapability and SuperCapability class have the same meaning with the classes in strategic level.
B. NonFunctionalFeature and FunctionalFeature The NonFunctionalFeature class provides non-functional features of a manufacturing resource. It defines two subclasses to separate static non-functional features from dynamic nonfunctional features. The Static class refers to static information that is used to trace a resource. The Dynamic class refers to dynamic information(e.g. resource status, resource workload) that is used to evaluate and allocate a resource.
The Operational class is the low level of the capability abstraction. Capabilities in this level are constructed according to the registered resources. Each capability cannot exist independently. They must be supported by one or more resources. The AtomicCapability class in this level means that the capability is achieved only by a separate resource. The SuperCapability class in this level means that the capability is derived from two or more resources. For example, a robot arm has the capability of moving and a gripper has the capability of grasping. Both the moving and grasping are instances of the AtomicCapability class. Furthermore, the capability of transporting is derived by the robot arm and the gripper. Transporting is an instance of the SuperCapability class.
The FunctionalFeature class characterizes the manufacturing capabilities that a resource contributes to a manufacturing problem. Meanwhile, it reflects the demands of a manufacturing task. However, there is a granularity gap between manufacturing capabilities and manufacturing demands. In order to manage the complexity of the capability granularity, a tree-like hierarchical structure is proposed to support multi-granularity capability representation. As shown in Fig.3, the Multi-granularityDescription class is abstracted from three levels according to different planning horizons. Each capability in an abstraction level may be an atomic capability or a super capability that can be further refined. The atomic capability is described by AtomicCapability class while the super capability is denoted by SuperCapability class. The refinement of a capability at high-level can be achieved by the memberOf relation. This relation is classified into composeOf and involveIn relations. The two relations are oriented two kinds of industries, assembly-oriented and process-oriented. The three abstraction levels are explained as follows.
The three abstraction levels aim to assure a valid support for the accurate interpretation of manufacturing capabilities. In cloud manufacturing, a capability with larger granularity is more complicated to be achieved. It has more powerful features in function and less flexible possibilities in reconfiguration. A capability with smaller granularity gives the system more flexibility. But it makes the system difficult to control and manage. The Multi-granularityDescription class reaches a good balance between them. C. Pattern, DemandPattern and SuppliedPattern The Pattern class defines the orchestration and the relationships of multiple capabilities to complete a manufacturing task. As shown in Fig.4, a pattern structure is either a basic pattern or a user-defined pattern. The former describes generic collaborative schema of multiple capabilities. The latter is defined according to the recursive procedure of task decomposition and the nested capability combination. The sequence interactive pattern means that a sequence pattern needs the information of another sequence pattern. The branch interactive pattern indicates that there exists information exchanging between two sequence patterns. The circular interactive pattern describes the recursive information exchanging between two sequence patterns. Based on a certain pattern, multiple capabilities are aggregated into a group. The
Fig. 3. The tree-like hierarchical multi-granularity capability
The Strategic class is the high level of the capability abstraction. Capabilities at this level are more generic and less specific. They are abstracted from the business logic
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group can be viewed as an independent component to participate the transformations among three abstraction levels. The DemandPattern and SuppliedPattern are subclasses of the Pattern class. They are used to record the interaction information. Such information is accumulated as knowledge to improve resource discovery.
Fig. 5. A section of concepts, properties, and source code
The left side displays a hierarchical concept tree. These concepts can be further classified into an extensive hierarchy. Taking the Physical class for example, it can be divided into subclasses, such as machine tools, cutting tools, tool holding, etc. Besides the inheritance relationship, concepts are also intertwined with object relationships and their inherent data type properties. The important object relationships are shown in the middle of Fig.5. For example, the has_NonFunctional Feature and has_FunctionalFeature relationships connect the MfgResource class with NonFunctionalFeature and Functional Feature classes respectively. The right side lists the source code of the OWL Schema for the proposed semantic-based meta-model.
Fig. 4. The definition of pattern structure
D. Context and Contract The Context class refers to the situation in which a resource exists or operates. The semantics of a resource is greatly depends on its context. For example, when a drilling machine deals with different raw materials, it has different completion time. Equipped with the contextual representation, the semantics of a resource is explicitly expressed so that the resource can be evaluated in a particular situation. The HistoryExperience and CollaborativeAwareness classes are two manners to represent resource contexts. The History Experience class describes resource contexts through recording information on resource usage. The CollaborativeAwareness class provides resource contexts by the awareness among similar demands. The Context class transforms information into knowledge for cloud platform.
B. An example for semantic description The semantic representation of a manufacturing resource implies describing characteristics of the resource, such as geometrical features, components features, and the context of the resource being used. The main steps of the process are divided into three stages, tangible resource, intangible capability, and resource service. In the first stage, the general information of a tangible resource, such as resource reference code, basic description, resource state, and resource workload are obtained from the resource provider. The second stage refers to extracting manufacturing capabilities for tangible resources. This stage is mainly completed by domain experts. Fig.6 shows the hierarchical structure of multi-granularity capabilities of a multi-spindle lathe.
The Contract class defines business contracts of collaborative transactions. Two main types of contracts are identified by the EnforcedResponsibility and Negotiation classes respectively. The former forces some given conditions to be satisfied by a manufacturing capability when it participates manufacturing activities. The latter allows a negotiation process among partners during transactions until they reach an agreement. Generally, a negotiation is triggered by some special conditions. For example, a resource offers to be paid an extra $100 for a week ahead of delivery. This kind of unexpected information is represented by the Trigger Condition class. IV.
IMPLEMENTATION
This section aims to implement the various elements in the proposed semantic-based meta-model. Then an illustrative example is described to demonstrate how a real-world manufacturing resource is semantically represented by using the proposed modeling framework. Fig. 6. The multi-granularity capabilities of a multi-spindle lathe
A. The implementation of the semantic-based meta-model The semantic-based meta-model is developed by Protege 3.5. All the elements, such as concepts, relationships and axioms are described by formal representation language(OWL DL). Fig. 5 shows the graphical user interface for semanticbased meta-model building and maintenance.
The resource is equipped with multi-granularity capabilities, such as mechanical processing, lathing, and multispindle rotating. The similar capabilities are aggregated into a granular. The third stage is responsible for generating semantic representation for resources. It can be viewed as an instantiation of the proposed modeling framework. Fig.7 shows
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the semantic description document of a multi-spindle lathe. The document contains all critical information related to the resource. The content of semantic description document is more comprehensive than the description based on existing service description standards such as OWL-S, which expresses service capabilities based on IOPRs.
ACKNOWLEDGMENT This work was supported by National Natural Science Foundation of China (Grant 61070160, 61272377). REFERENCES [1]
Multi-spindle machine tool This document introduces a manufacturing resource and its capabilities. It is a multi-spindle drilling latheing machine. It has three functional capabilities. Available The capability at strategic level is performing a conical bearing. The capability at tactical level is lathing. The capability at operational level is feeding, rotating, and roughness. 45 23
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Fig. 7. A segment of semantic representation for a multi-spindle lathe
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V. CONCLUSIONS A multilevel modeling framework is proposed in this paper for semantically representing cloud manufacturing resources. The framework consists of three levels. Heterogeneous resources are converted into machine understandable knowledge through the transformations among three levels. This plays a prelude to the meaningful resource discovery and allocation. Manufacturing resources are loosely coupled with manufacturing capabilities by the mapping relationships between M0 and M1. That facilitates cloud manufacturing to manage distributed resources in a flexible way. Manufacturing capabilities are unified by the semantic-based meta-model in M2. Based on the classes, relations, and axioms defined in the semantic-based meta-model, semantics related to resources, such as multi-granularity capability, collaborative awareness, history experience, and negotiation are explicitly represented. Thus, diverse resources can be federated in a meaningful way and resource discovery can be tackled in a coordinated way. Moreover, the concepts in the semantic-based meta-model are as general as possible, they can be easily specialized for domain-specific applications. This enhances the applicability of the proposed framework.
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