- Email: [email protected]
Architecture Information Context in a Design For Manufacturing (DFM) Framework
11th IFAC Workshop on Intelligent Manufacturing Systems The International Federation of Automatic Control May 22-24, 2013. São Paulo, Brazil
WeGT1.4
Architecture Information Context in a Design For Manufacturing (DFM) Framework Andr´ e Luiz Tietb¨ ohl Ramos ∗ Fl´ avio Lorini ∗∗ ∗
Pontif´ıcia Universidade Cat´ olica do Rio Grande do Sul, Porto Alegre, RS 90619-900 BR (e-mail: [email protected]). ∗∗ Universidade Federal do Rio Grande do Sul, Porto Alegre, RS 90040-060 BR (e-mail: [email protected]).
Abstract: This paper presents an analysis of the DFM information demands/requirements and how they could be handled in a framework of this domain. The latest trends as well as both theoretical and technological research advances pose challenges used in the representation of information used by the framework components of this domain. Currently, the main approaches vary from a simple format to an information architecture such as STEP or runtime representation. These aspects, while useful in many cases, are only and mostly focused on data. This limits their flexibility, which is one of the main reasons to use frameworks, especially computer–based ones. Instead, this work proposes the use of a more complete information handling approach based on ontologies. The ontology–based information architecture and its usage in a DFM framework is introduced. Its completeness and usage are important, relevant and complete so it shall be increased in the future when the relatively simple traditional information format becomes obsolete. Along with information architecture and its implementation, the chosen prototype DFM components and architectural aspects requirements are outlined. Keywords: DFM, STEP, Ontologies. 1. INTRODUCTION The growing need for proper handling of the intrinsic complexity in design has placed new challenges for both research and commercial software packages. The complete implementation of such systems is still an open issue leading to relatively extensive research effort. The increasing usage of computers to support the complete set of engineering activities poses new challenges for software capabilities. Among those challenges is the coherent set of information aspects which should more properly support engineering activities. The actual structure, handling and management of manufacturing information aspects as an intrinsic component of the Design for Manufacturing (DFM) is the focus of this paper. In this context, the objective of this work is to describe how to define and implement ontologies in a DFM framework. The Literature Review, section 2, presents a review of some DFM frameworks in order to outline how different architectural solutions of this aspect can be. After that, the different information handling types are shown. It should be noticed that this revision follows the historical information development both theoretically and computer wise. Section 3 describes the specific ontology structure and its usage in this framework as well as explains it along with a direction for the application of this information approach as a more complete information handling solution. Finally, section 3.1 shows our approach for defining and eventually 978-3-902823-33-5/13/$20.00 © 2013 IFAC
using ontologies as an information base of DFM frameworks. 2. LITERATURE REVIEW This section is subdivided into three main concepts relevant to this specific work: design for manufacturing, information handling which reviews the currently available methods, and ontologies which review the available ontology–based approaches in several fields. The complete review in each topic is beyond the scope of this paper but provides adequate consistency to properly understand the main aspects of this work. Therefore, is longer and more detailed. 2.1 Design For Manufacturing Design for manufacturing (DFM) is a relatively extensive approach in the management of the design process specifically in industrial design as described by Boothroyd and Dewhurst (2006) originally. Early examples of DFM usage were implemented by the following authors. Gupta and Nau (1995) specified the fundamental aspects of Design for Manufacturing. Their system approach is based on a feature–based model (FBM) which is integrated in the design loop. Manufacturability analysis is performed after the preliminary design and CAD stages, and the design is returned to CAD if it is not acceptable. Bajaj et al. (2003) described an implementation of a DFM Framework, which consists of four components: a Design Integrator that
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acquires product design information from an ECAD tool and in-house sources and consolidates them into a STEP AP210 model, a Rule-based Expert System that captures the manufacturability constraints as DFM rules and evaluates printed circuit assembly (PCA) designs against them, a Design View Generator that extracts design information from the AP210 model, and the Results Viewer that helps the user browse DFM analysis results and identify design improvement opportunities. Zhao and Shah (2005) developed a domain–independent DFM shell for sheet metal and injection molding. The aspects covered by the shell are both technical and/or economic at different levels of abstraction. The shell considers both qualitative measures and quantitative measures such as manufacturing time and cost. Bajaj et al. (2003) considered the relevance of DFM frameworks in design specifically for Electronic Product Realization (EPR). The framework software modules are integrated via a STEP information model using AP210 as its foundation. Specifically, this framework characteristic is clearly outlined as its advantage in integrating disparate sources such as DFM in this domain. 2.2 Information Handling This subsection reviews information handling techniques. Initially, important concepts are described for better understanding. Data are raw material & unorganized facts which need to be processed. Data are plain facts. When data are processed, organized, structured or presented in a given context so as to make them useful, they are called information. Information consists of facts and data organized to describe a particular situation or condition. Knowledge consists of facts, truths, beliefs, perspectives and/or concepts, judgements and expectations, methodologies and know-how. Knowledge is applied to interpret information about the situation and decide how to handle it. It should be noticed that the meaning of information in this research understands it as the highest level of data+knowledge+semantic content exchange. The first method for modeling information exchange specifications was Integrated DEFinition, or IDEF (Christopher Menzel, 1998). It is systemic–founded and was further expanded into other inherited specifications: IDEF0 (function modeling), IDEF1X (data modeling), and IDEF3 (process modeling). IDEF provides both syntax and semantics for information representation. Eventually, the IDEF5 specification (Christopher P. Menzel, 1992) was developed in order to encompass abstract information capture methods, specifically using ontologies. One of the first standard specifications for file-based exchange was IGES 1.0 which was eventually updated until it became the ANSI standard Y14.26M. It is widely used as an almost standard and adopted by virtually every CAD/CAM system manufacturer but was discontinued due to its legacy-dependent architecture as well as its limits in representing the actual data. The DXF format, developed by Autodesk, Inc., is the defacto data exchange format for PC-class computers. The DXF format is based on a flexible file structure with mandatory as well as optional fields. The optional fields can be used by developers of third978-3-902823-33-5/13/$20.00 © 2013 IFAC
party applications for AutoCAD to represent additional information such as material properties, tolerances, etc. Sudarsan et al. (2005) outlined the relevance of proper information modeling in PLM 1 . The International Standards Organization (ISO) heads the development of the Standard for The Exchange of Product model data, or STEP (Wilson, 1993). STEP extends other formats by providing a layered architecture for development of discipline– centered models (ISO, 2005). Eventually, STEP became the ISO 10303 standard (ISO, 2002a,b). 2.3 Contextual Information Definition and Exchange Guerra-Zubiaga and Young (2008) analyzed the inter– relationships between knowledge and manufacture in models of this kind. In addition, they propose an approach for acquiring the knowledge and information interactions in this domain. The research is knowledge–based and modeled via UML 2 . The manufacturing knowledge is subdivided in three types: explicit, implicit, and tacit. Upon this structure, a Manufacturing Knowledge Model (MKM) is developed with three information kinds: manufacturing resources, processes and strategies further subdivided into four system architectural levels: factory, shop, cell, and station. A given model content is directly connected to this information structure. Wand and Weber (1990) expressed that the lack of an adequate formalized information architecture limits computer/information systems. The traditional hypothesis assumes an absolute mapping between input and output states or the objective of the given problem. On the other hand, the ontological view of problem solving focuses in two aspects: internal (how to solve) and external (solve the objective) integrated through a decomposing operator. This approach allows proper functional decomposition, data flows and structure as function of the systemic decomposition, typical in design and manufacturing integration. 2.3.1 Ontologies A review of ontology application concepts, both old and new, is provided in this subsection. Gruber (1993) was one of the first researchers to identify the need to adequately structure knowledge and information representation is better using ontologies. Ferber (1999) provides the foundations of the ontology concept. To summarize this concept, ontologies are agreements about shared concepts (Gruber, 1994b). GomezPerez et al. (2005) structured a defined approach for the usage of ontologies in the engineering domain. Panetto et al. (2012) developed an interoperability– focused PDM architecture for manufacturing. In fact, data are conceptualized, expressed, and used in an abstract view as information. Contextual information use is specified from the abstract information integrated process in a given manufacturing system by using an ISO 10303 3 file through STEP PDM and IEC 62264 4 . Posada et al. (20052006) described how complex CAD model representation can be specifically due to the lack of proper understanding and representation of information context, consequently 1 2 3 4
PLM–Product Life-cycle Management Unified Modeling Language ISO 10303 The STEP standard for product date exchange IEC 62264 Enterprise–Control System Integration
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knowledge via the STEP standard. They propose the use of ontologies to fill this “gap”. Their focus is both on understanding as well as on the revisions of large CAD data sets. Schlenoff et al. (2002) identified that adequate understanding and use of information poses a constraint in manufacturing systems integration. The authors associate this limit with the lack of adequate syntax and semantics in the interfaces among domain applications. They proposed a Product Specification Language (PSL) based on ontologies to solve this problem. PSL uses the Knowledge Interchange Format (KIF) language as its foundation. The language is is based on primary manufacturing process concepts such as activity, object, time point, and relations. Thus, it considers the intrinsic dynamic manufacturing context. Garcia-Crespo et al. (2010) developed a generic approach based on ontologies for representation of industrial processes through their semantics. The basic units of an industrial manufacturing process are composed of a sequence similar to actual dynamic processes with Objects and Attributes. The processes go through distinct states due to different conditions. The conditions cause new actions generating a new state. This ontology model was implemented in a client–server architecture of different parts sizes in variable–batch budget system. Thus, a system was developed by the authors for comparing automatised versus human estimation RFQs 5 . Its results are, in general, positive varying from 3.1% to 9.9%. Barbau et al. (2012) developed the OntoSTEP plunge for the Prot´eg´e environment in order to allow import of STEP files. This plug-in enriches STEP since the EXPRESS language is based on entities and attributes only which do not allow semantics and context representation. By using it, it is possible to represent geometrical and non– geometrical concepts such as functions, behavior, and design form (or structure). Zhao and Liu (2008a,b) developed a way to interpret the STEP information model through ontologies. The main objective is to represent information context by using the available semantics. However, due to the rigid STEP architecture knowledge–wise its operational use is limited. Thus, ontologies are proposed as a means to make inferences about a design model. 3. ONTOLOGY DFM INFORMATION CONTEXT Historically, Zeid (1991) described that any design exchange format must address four main information types in order to be effective: shape, non-shape, design, and manufacturing. Gruber (1994a) adds to the required information types above a knowledge representation level. This representation specification should also allow design procedures themselves to be also exchanged among different systems. Most computer-based systems nowadays need to exchange information with other systems. In the design area, this need is amplified by the nature of design information, which ranges from geometric descriptions of the part itself to manufacturing information such as materials, associated processes, cost, bill–of– materials, and additional requirements of a given company. Typically the approach 5
RFQ = Request For Quote.
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used is a common data format file. Unfortunately, the complete specification of a suitable format is not available yet. This is due to the great complexity of CAD/CAM systems, the distinct requirements for a common design information exchange format by organizations, and the difficulty in translating from proprietary information formats to a common format until the manufacturing interface is achieved. The focus of this work seeks to better explain as well as structure this limitation in a DFM framework, figure 1.
Fig. 1. DFM framework. As outlined contextually in the previous section, there is still a “gap” in the information representation field, specifically in the DFM domain. This lack in information representation is due to the fact that most of the frameworks information architecture, when present, are data focused. The objective of this section suggests and describes an information architecture for the DFM domain. Saad and Maher (1996) emphasized previously the importance of achieving a shared common understanding about data in a design system for construction, the need for handling multiple lines of reasoning, and the need for an evolutionary solution. Their statement is clearly shown in figure 1 where information varies from several data types to the context in which they were used, as well as how they are applied. In addition, information usage changes dynamically given the actual set available in a given time. These aspects might become yet more complex if the domain is multifaceted such as DFM. STEP is focused on information rather than data exchange. In this context, information is viewed as the data plus its semantic interpretation. This means that for exchanging information using STEP, systems must be aware of the semantic data content, that is, they must pertain to the same theme/context or knowledge domain. The architectural model in STEP is composed of three layers: the physical layer, the logical layer, and the application layer. The logical layer contains information models of entities of interest for all domains in the STEP model. The implementation of a STEP data exchange model is based on a leveled architecture. The levels allow distinct ways to store and access information using by the STEP product information model. There is a need to include the means by which certain data are obtained. For instance, sometimes it might be more
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3.1 Theoretical Structure Specification As described in the previous section, a proper taxonomy must be developed. This mapping is done using the Prot´eg´e tool. At this stage, the mapping follows the approach suggested by Garcia-Crespo et al. (2010) in a more restricted domain of a complete DFM framework. Our approach to information structure and use seeks to be the closest to the actual domain. In this sense, its hierarchical architecture resembles the domain by similarity with its main components. Note that the focus is on information hierarchical structure describing concepts dependency. Fig. 2. Product semantics indirectly represented using EXPRESS shown in red and plum. important to know how data are obtained than the actual data values. Knowledge bases are fundamental in such situations. Ontologies are a step beyond knowledge bases because they include the domain of discourse of a given field and integrate its semantics indirectly. Ontologies are the most complete information model available. The knowledge formats widen the information context. An ontology does not require a specific exchange format because it relies fundamentally on the ontology software architecture, a similar concept to runtime information exchange. In addition, as just described, the information in frameworks also changes dynamically due data and context availability. This aspect makes it difficult to use static information as described in figure 2. This information characteristic is due to the lack of the representation of semantics and vocabulary domain, in this case the DFM domain. There is a need to be able to represent the intrinsic information relationships intertwined in design components. Therefore, an information standard should have explicit definition allowance of both structure and architecture. The Prot´eg´e tool allows this. Previously, Ramos and Deisenroth (2007) have shown a similar approach because it was more data-targeted consequently more limited. The information architecture emulates design activities and context in a tree. The actual design data are acquired from a STEP file through a plug-in (Barbau et al., 2012; Posada et al., 2005-2006). The aspect shown above is only one regarding the proper information usage. More specifically, the main issues that must be handled are information taxonomy, or concept hierarchy, and vocabulary for the DFM domain. The lack of these characteristics makes the necessary framework information exchange more difficult. Secondly, it should be outlined that the plain usage of a given format, in this case STEP, limits the representation of the actual information in its best way since it is based on the output of a given component. Frameworks are dynamic, in general, and the information context specification with its meaning changes accordingly. Therefore, better information architecture planning requires that the taxonomy, at least, should be done in advance. This lack increases a “gap” between the actual information meaning and its implementation. 978-3-902823-33-5/13/$20.00 © 2013 IFAC
As outlined throughout this paper, along with a given domain taxonomy there must exist a proper vocabulary with its relations. This is the last step before actual data. Only then are data issues embedded in the ontologies’ relations. The ontology approach in the development of this DFM framework ontologies using Prot´eg´e is as follows below and partly shown in figure 3: (1) Specify DFM components’ classes concepts and entities, if any. (2) Specify details and slots, or object and data properties, of the components’ classes concepts. These details can vary in form as annotations, hierarchical relations, and actual data representation. (3) Specify these DFM–related classes: tolerancing, cost, availability of machines and tools, tool accessibility as well as their relations with the general components’ classes. (4) Structure STEP information import through the OntoSTEP plug-in for part 11 EXPRESS (description method) at least, and parts 42, 43, 44 (integrated resources) that deal with product data representation (including geometry). (5) Specify classes’ details, for instance part 47 and 203e2 which are STEP APs ontologies that deal with tolerancing. (6) Specify and/or use properties, which are equivalent to defining how concepts (classes) are related to other classes. Properties specify the rules of information use. This aspect is fundamentally different regarding information usage in typical, or traditional, systems. In this kind of systems it is not clear how information is used for it is an intrinsic part of the solution procedure algorithm. In addition, its lack reduces system integration flexibility to a great extent. (7) Define and detail individuals 6 which are responsible for the specification of components’ information type. Then, the individuals are embedded as intrinsic parts of the classes’ details. (8) Specify the form of information interaction or queries, if needed. (9) Specify interfaces which define the interaction with information from external components. (10) Test application ontologies. (11) Integrate software by using the Prot´eg´e’s code generation feature.
6
An individual is most often the specification of the kind of information. It links the information specification defined by classes to actual data values.
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Fig. 3. Ontology structure, or taxonomy (part of). 4. THEORETICAL STRUCTURE USAGE Ideally, as shown above, the information architecture specification for the chosen DFM domain should generally follow a sequence of steps. Considering this fact and the paper length size limit, an example of one of the frameworks components ontologybased information structure is described below. The component described is Cost. In this example only the part’s intrinsic cost is described, that is no actual system manufacturing or organization costs are used. Thus, a cost design component using this information approach should follow the steps below in order to use a more complete information structure such as ours. It is important to notice that only its structure is its focus. Its objective is to ease the typical burden in the development of a DFM framework components’ interfaces among the several existing ones. Initially, only the associated geometry information is checked in the first two steps described below. (1) Import actual STEP data using its ontology information structure to a data knowledge base. (2) Identify the limits of features geometry for each feature data individual. (3) Verify the access directions of all features. (4) Use the manufacturing availability component associated process to compute two aspects: machine manufacturing capability and tooling accessibility using the information relationships present in the ontology. (5) Compute the cost ontology values using its associated process. 978-3-902823-33-5/13/$20.00 © 2013 IFAC
Fig. 4. Tool accessibility ontology property (part of). (6) Enable cost figures to the information architecture. Manufacturing itself needs several additional aspects to be considered instead of cost only as it is widely known. One of the actual manufacturing issues to be analyzed is the tool access, or tool feasibility. Thus, its information should be structured properly in order to be integrated systemic– wise. Its related property is depicted below in figure 4. Finally, it should be noted that the context description varies in two aspects: (1) The information abstraction interpretation depends on its usage, or focus kind, by a given framework component (or concept). (2) The actual ontology information tree varies according to its intent since it can depend on the designer, that is, it isn’t determining. 5. CONCLUSIONS This work reviewed information models usable in DFM with focus on ontologies. The most recent ontology–based approach for DFM has also been reviewed. The ontology approach is even more flexible than the current standard STEP when the whole information is considered since it incorporates one more step to its information model: information context. Consequently, a higher abstraction level, which is closer to human understanding and use, is obtained. However, STEP is still the most adequate information model because it is formally structured and developed.
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Ontologies still require better structuring software wise in addition to its ease of integration. It should also be pointed out as well that the ontology architecture can incorporate a relatively easy and more flexible component integration approach based on software agents. Its use should be increased when both aspects described above become more accepted. ACKNOWLEDGEMENTS The authors thank Vinicius M. Kern ([email protected]), Professor of Information Science from Universidade Federal de Santa Catarina (UFSC), Brazil for his valuable review input as well as expertise in the field. REFERENCES Bajaj, M., Peak, R., Wilson, M., Kim, I., Thurman, T., Benda, M., Jothishankar, M., Ferreira, P., and Stori, J. (2003). Towards next–generation design– for–manufacturability (dfm) frameworks for electronics product realization. Session 210, IEMT, Semicon West 2003. Barbau, R., Krima, S., Rachuri, S., Narayanan, A., and Fiorentini, X. (2012). OntoSTEP: Enriching product model data using ontologies. Computer-Aided Design, 44, 575–590. Boothroyd, G. and Dewhurst, P. (2006). Design for manufacture and assembly. http://www.dfma.com/. Christopher Menzel, R.J.M. (1998). The IDEF Family of Languages. Technical report, Department of Philosophy Texas A&M University Knowledge Based Systems, Inc. Christopher P. Menzel, R.J.M. (1992). Idef5 Ontology Description Capture Method Concepts And Formal Foundations. Technical report, Knowledge Based Systems Lab., Dept. Of Industrial Engineering, Texas A&M University. Ferber, J. (1999). Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley Professional. Garcia-Crespo, A., Ruiz-Mezcua, B., Lopez-Cuadrado, J., and Gomez-Berbiz, J. (2010). Conceptual model for semantic representation of industrial manufacturing processes. Computers in Industry, 61, 595–612. Gomez-Perez, A., Corcho, O., and Fernandez-Lopez, M. (2005). Ontological Engineering. Springer. Gruber, T. (1993). Formal Ontology in Conceptual Analysis and Knowledge Representation, chapter Toward Principles for the Design of Ontologies Used for Knowledge Sharing. Kluwer Academic Press. Gruber, T. (1994a). Toward Principles for the Design of Ontologies Used for Knowledge Sharing. IJHCS, 43(5/6), 907–928. Gruber, T. (1994b). Ontologies defined. Shared Reusable Knowledge Bases (SRKB) Mailing List. Guerra-Zubiaga, D. and Young, R. (2008). Information and knowledge interrelationships within a manufacturing knowledge model. Int. Journal of Advanced Manufacturing Technology, 39, 182–198. Gupta, S.K. and Nau, D.S. (1995). Systematic approach to analysing the manufacturability of machined parts. Computer Aided Design, 27(5), 232–342. ISO (2002a). ISO–10303-21: Industrial automation systems and integration —- Product data representation 978-3-902823-33-5/13/$20.00 © 2013 IFAC
and exchange —- Part 21: Implementation methods: Clear text encoding of the exchange structure. ISO International Standards Organization. ISO (2002b). ISO–10303-22: 1998. Industrial automation systems and integration —- Product data representation and exchange —- Part 22: Implementation methods: Standard data access interface. ISO - International Standards Organization. ISO (2005). ISO–10303-239: Industrial automation systems and integration —- Product data representation and exchange —- Part 239: Application protocol: Product life cycle support. ISO - International Standards Organization. Panetto, H., Assisti, M., and Tursi, A. (2012). ONTO– PDM: Product-driven ONTOlogy for Product Data Management interoperability within manufacturing process environment. Advanced Engineering Informatics, 26, 334–348. Posada, J., Toro, C., Wundrak, S., and Stork, A. (20052006). Using ontologies and STEP standards for the semantic simplification of CAD models in different engineering domains. Applied Ontology, 1, 263–279. Ramos, A.L.T. and Deisenroth, M.P. (2007). Ontologies and STEP Integration in a DFM Framework. ICPR19 19th International Conference on Production Research. Saad, M. and Maher, M. (1996). Shared understanding in computer-supported collaborative design. Computer Aided Design, 28, 183–192. Schlenoff, C., Ivester, R., and Knutilla, A. (2002). A robust process ontology for manufacturing systems integration. In I.C. Society (ed.), DEXA ’02 Proceedings of the 13th International Workshop on Database and Expert Systems Aplications, 597–602. NIST, Washington, DC, USA. Sudarsan, R., Fenves, S., Sriram, R., and Wang, F. (2005). A product information modeling framework for product lifecycle management. Computer–Aided Design, 37, 1399–1411. Wand, Y. and Weber, R. (1990). An Ontological Model of an Information System. IEEE Transactions of an Information System, 16(11). Wilson, P. (1993). A View of STEP, chapter Geometric Modeling for Product Realization. Elsevier Science Publishers. Zeid, I. (1991). CAD/CAM Theory and Practice. McGraw-Hill. Zhao, W. and Liu, J. (2008a). OWL/SWRL representation methodology for EXPRESS–driven product information model. part I: Implementation methodology. Computers in Industry, 59, 580–589. Zhao, W. and Liu, J. (2008b). OWL/SWRL representation methodology for EXPRESS–driven product information model. part II: practice. Computers in Industry, 59, 590– 600. Zhao, Z. and Shah, J. (2005). Domain independent shell for DFM and its application to sheet metal forming and injection molding. Computer-Aided Design, CAD’ 04 Special Issue: Product Design, Integration and Manufacturing(37), 881–898.
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WeGT1.4
Architecture Information Context in a Design For Manufacturing (DFM) Framework Andr´ e Luiz Tietb¨ ohl Ramos ∗ Fl´ avio Lorini ∗∗ ∗
Pontif´ıcia Universidade Cat´ olica do Rio Grande do Sul, Porto Alegre, RS 90619-900 BR (e-mail: [email protected]). ∗∗ Universidade Federal do Rio Grande do Sul, Porto Alegre, RS 90040-060 BR (e-mail: [email protected]).
Abstract: This paper presents an analysis of the DFM information demands/requirements and how they could be handled in a framework of this domain. The latest trends as well as both theoretical and technological research advances pose challenges used in the representation of information used by the framework components of this domain. Currently, the main approaches vary from a simple format to an information architecture such as STEP or runtime representation. These aspects, while useful in many cases, are only and mostly focused on data. This limits their flexibility, which is one of the main reasons to use frameworks, especially computer–based ones. Instead, this work proposes the use of a more complete information handling approach based on ontologies. The ontology–based information architecture and its usage in a DFM framework is introduced. Its completeness and usage are important, relevant and complete so it shall be increased in the future when the relatively simple traditional information format becomes obsolete. Along with information architecture and its implementation, the chosen prototype DFM components and architectural aspects requirements are outlined. Keywords: DFM, STEP, Ontologies. 1. INTRODUCTION The growing need for proper handling of the intrinsic complexity in design has placed new challenges for both research and commercial software packages. The complete implementation of such systems is still an open issue leading to relatively extensive research effort. The increasing usage of computers to support the complete set of engineering activities poses new challenges for software capabilities. Among those challenges is the coherent set of information aspects which should more properly support engineering activities. The actual structure, handling and management of manufacturing information aspects as an intrinsic component of the Design for Manufacturing (DFM) is the focus of this paper. In this context, the objective of this work is to describe how to define and implement ontologies in a DFM framework. The Literature Review, section 2, presents a review of some DFM frameworks in order to outline how different architectural solutions of this aspect can be. After that, the different information handling types are shown. It should be noticed that this revision follows the historical information development both theoretically and computer wise. Section 3 describes the specific ontology structure and its usage in this framework as well as explains it along with a direction for the application of this information approach as a more complete information handling solution. Finally, section 3.1 shows our approach for defining and eventually 978-3-902823-33-5/13/$20.00 © 2013 IFAC
using ontologies as an information base of DFM frameworks. 2. LITERATURE REVIEW This section is subdivided into three main concepts relevant to this specific work: design for manufacturing, information handling which reviews the currently available methods, and ontologies which review the available ontology–based approaches in several fields. The complete review in each topic is beyond the scope of this paper but provides adequate consistency to properly understand the main aspects of this work. Therefore, is longer and more detailed. 2.1 Design For Manufacturing Design for manufacturing (DFM) is a relatively extensive approach in the management of the design process specifically in industrial design as described by Boothroyd and Dewhurst (2006) originally. Early examples of DFM usage were implemented by the following authors. Gupta and Nau (1995) specified the fundamental aspects of Design for Manufacturing. Their system approach is based on a feature–based model (FBM) which is integrated in the design loop. Manufacturability analysis is performed after the preliminary design and CAD stages, and the design is returned to CAD if it is not acceptable. Bajaj et al. (2003) described an implementation of a DFM Framework, which consists of four components: a Design Integrator that
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acquires product design information from an ECAD tool and in-house sources and consolidates them into a STEP AP210 model, a Rule-based Expert System that captures the manufacturability constraints as DFM rules and evaluates printed circuit assembly (PCA) designs against them, a Design View Generator that extracts design information from the AP210 model, and the Results Viewer that helps the user browse DFM analysis results and identify design improvement opportunities. Zhao and Shah (2005) developed a domain–independent DFM shell for sheet metal and injection molding. The aspects covered by the shell are both technical and/or economic at different levels of abstraction. The shell considers both qualitative measures and quantitative measures such as manufacturing time and cost. Bajaj et al. (2003) considered the relevance of DFM frameworks in design specifically for Electronic Product Realization (EPR). The framework software modules are integrated via a STEP information model using AP210 as its foundation. Specifically, this framework characteristic is clearly outlined as its advantage in integrating disparate sources such as DFM in this domain. 2.2 Information Handling This subsection reviews information handling techniques. Initially, important concepts are described for better understanding. Data are raw material & unorganized facts which need to be processed. Data are plain facts. When data are processed, organized, structured or presented in a given context so as to make them useful, they are called information. Information consists of facts and data organized to describe a particular situation or condition. Knowledge consists of facts, truths, beliefs, perspectives and/or concepts, judgements and expectations, methodologies and know-how. Knowledge is applied to interpret information about the situation and decide how to handle it. It should be noticed that the meaning of information in this research understands it as the highest level of data+knowledge+semantic content exchange. The first method for modeling information exchange specifications was Integrated DEFinition, or IDEF (Christopher Menzel, 1998). It is systemic–founded and was further expanded into other inherited specifications: IDEF0 (function modeling), IDEF1X (data modeling), and IDEF3 (process modeling). IDEF provides both syntax and semantics for information representation. Eventually, the IDEF5 specification (Christopher P. Menzel, 1992) was developed in order to encompass abstract information capture methods, specifically using ontologies. One of the first standard specifications for file-based exchange was IGES 1.0 which was eventually updated until it became the ANSI standard Y14.26M. It is widely used as an almost standard and adopted by virtually every CAD/CAM system manufacturer but was discontinued due to its legacy-dependent architecture as well as its limits in representing the actual data. The DXF format, developed by Autodesk, Inc., is the defacto data exchange format for PC-class computers. The DXF format is based on a flexible file structure with mandatory as well as optional fields. The optional fields can be used by developers of third978-3-902823-33-5/13/$20.00 © 2013 IFAC
party applications for AutoCAD to represent additional information such as material properties, tolerances, etc. Sudarsan et al. (2005) outlined the relevance of proper information modeling in PLM 1 . The International Standards Organization (ISO) heads the development of the Standard for The Exchange of Product model data, or STEP (Wilson, 1993). STEP extends other formats by providing a layered architecture for development of discipline– centered models (ISO, 2005). Eventually, STEP became the ISO 10303 standard (ISO, 2002a,b). 2.3 Contextual Information Definition and Exchange Guerra-Zubiaga and Young (2008) analyzed the inter– relationships between knowledge and manufacture in models of this kind. In addition, they propose an approach for acquiring the knowledge and information interactions in this domain. The research is knowledge–based and modeled via UML 2 . The manufacturing knowledge is subdivided in three types: explicit, implicit, and tacit. Upon this structure, a Manufacturing Knowledge Model (MKM) is developed with three information kinds: manufacturing resources, processes and strategies further subdivided into four system architectural levels: factory, shop, cell, and station. A given model content is directly connected to this information structure. Wand and Weber (1990) expressed that the lack of an adequate formalized information architecture limits computer/information systems. The traditional hypothesis assumes an absolute mapping between input and output states or the objective of the given problem. On the other hand, the ontological view of problem solving focuses in two aspects: internal (how to solve) and external (solve the objective) integrated through a decomposing operator. This approach allows proper functional decomposition, data flows and structure as function of the systemic decomposition, typical in design and manufacturing integration. 2.3.1 Ontologies A review of ontology application concepts, both old and new, is provided in this subsection. Gruber (1993) was one of the first researchers to identify the need to adequately structure knowledge and information representation is better using ontologies. Ferber (1999) provides the foundations of the ontology concept. To summarize this concept, ontologies are agreements about shared concepts (Gruber, 1994b). GomezPerez et al. (2005) structured a defined approach for the usage of ontologies in the engineering domain. Panetto et al. (2012) developed an interoperability– focused PDM architecture for manufacturing. In fact, data are conceptualized, expressed, and used in an abstract view as information. Contextual information use is specified from the abstract information integrated process in a given manufacturing system by using an ISO 10303 3 file through STEP PDM and IEC 62264 4 . Posada et al. (20052006) described how complex CAD model representation can be specifically due to the lack of proper understanding and representation of information context, consequently 1 2 3 4
PLM–Product Life-cycle Management Unified Modeling Language ISO 10303 The STEP standard for product date exchange IEC 62264 Enterprise–Control System Integration
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knowledge via the STEP standard. They propose the use of ontologies to fill this “gap”. Their focus is both on understanding as well as on the revisions of large CAD data sets. Schlenoff et al. (2002) identified that adequate understanding and use of information poses a constraint in manufacturing systems integration. The authors associate this limit with the lack of adequate syntax and semantics in the interfaces among domain applications. They proposed a Product Specification Language (PSL) based on ontologies to solve this problem. PSL uses the Knowledge Interchange Format (KIF) language as its foundation. The language is is based on primary manufacturing process concepts such as activity, object, time point, and relations. Thus, it considers the intrinsic dynamic manufacturing context. Garcia-Crespo et al. (2010) developed a generic approach based on ontologies for representation of industrial processes through their semantics. The basic units of an industrial manufacturing process are composed of a sequence similar to actual dynamic processes with Objects and Attributes. The processes go through distinct states due to different conditions. The conditions cause new actions generating a new state. This ontology model was implemented in a client–server architecture of different parts sizes in variable–batch budget system. Thus, a system was developed by the authors for comparing automatised versus human estimation RFQs 5 . Its results are, in general, positive varying from 3.1% to 9.9%. Barbau et al. (2012) developed the OntoSTEP plunge for the Prot´eg´e environment in order to allow import of STEP files. This plug-in enriches STEP since the EXPRESS language is based on entities and attributes only which do not allow semantics and context representation. By using it, it is possible to represent geometrical and non– geometrical concepts such as functions, behavior, and design form (or structure). Zhao and Liu (2008a,b) developed a way to interpret the STEP information model through ontologies. The main objective is to represent information context by using the available semantics. However, due to the rigid STEP architecture knowledge–wise its operational use is limited. Thus, ontologies are proposed as a means to make inferences about a design model. 3. ONTOLOGY DFM INFORMATION CONTEXT Historically, Zeid (1991) described that any design exchange format must address four main information types in order to be effective: shape, non-shape, design, and manufacturing. Gruber (1994a) adds to the required information types above a knowledge representation level. This representation specification should also allow design procedures themselves to be also exchanged among different systems. Most computer-based systems nowadays need to exchange information with other systems. In the design area, this need is amplified by the nature of design information, which ranges from geometric descriptions of the part itself to manufacturing information such as materials, associated processes, cost, bill–of– materials, and additional requirements of a given company. Typically the approach 5
RFQ = Request For Quote.
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used is a common data format file. Unfortunately, the complete specification of a suitable format is not available yet. This is due to the great complexity of CAD/CAM systems, the distinct requirements for a common design information exchange format by organizations, and the difficulty in translating from proprietary information formats to a common format until the manufacturing interface is achieved. The focus of this work seeks to better explain as well as structure this limitation in a DFM framework, figure 1.
Fig. 1. DFM framework. As outlined contextually in the previous section, there is still a “gap” in the information representation field, specifically in the DFM domain. This lack in information representation is due to the fact that most of the frameworks information architecture, when present, are data focused. The objective of this section suggests and describes an information architecture for the DFM domain. Saad and Maher (1996) emphasized previously the importance of achieving a shared common understanding about data in a design system for construction, the need for handling multiple lines of reasoning, and the need for an evolutionary solution. Their statement is clearly shown in figure 1 where information varies from several data types to the context in which they were used, as well as how they are applied. In addition, information usage changes dynamically given the actual set available in a given time. These aspects might become yet more complex if the domain is multifaceted such as DFM. STEP is focused on information rather than data exchange. In this context, information is viewed as the data plus its semantic interpretation. This means that for exchanging information using STEP, systems must be aware of the semantic data content, that is, they must pertain to the same theme/context or knowledge domain. The architectural model in STEP is composed of three layers: the physical layer, the logical layer, and the application layer. The logical layer contains information models of entities of interest for all domains in the STEP model. The implementation of a STEP data exchange model is based on a leveled architecture. The levels allow distinct ways to store and access information using by the STEP product information model. There is a need to include the means by which certain data are obtained. For instance, sometimes it might be more
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3.1 Theoretical Structure Specification As described in the previous section, a proper taxonomy must be developed. This mapping is done using the Prot´eg´e tool. At this stage, the mapping follows the approach suggested by Garcia-Crespo et al. (2010) in a more restricted domain of a complete DFM framework. Our approach to information structure and use seeks to be the closest to the actual domain. In this sense, its hierarchical architecture resembles the domain by similarity with its main components. Note that the focus is on information hierarchical structure describing concepts dependency. Fig. 2. Product semantics indirectly represented using EXPRESS shown in red and plum. important to know how data are obtained than the actual data values. Knowledge bases are fundamental in such situations. Ontologies are a step beyond knowledge bases because they include the domain of discourse of a given field and integrate its semantics indirectly. Ontologies are the most complete information model available. The knowledge formats widen the information context. An ontology does not require a specific exchange format because it relies fundamentally on the ontology software architecture, a similar concept to runtime information exchange. In addition, as just described, the information in frameworks also changes dynamically due data and context availability. This aspect makes it difficult to use static information as described in figure 2. This information characteristic is due to the lack of the representation of semantics and vocabulary domain, in this case the DFM domain. There is a need to be able to represent the intrinsic information relationships intertwined in design components. Therefore, an information standard should have explicit definition allowance of both structure and architecture. The Prot´eg´e tool allows this. Previously, Ramos and Deisenroth (2007) have shown a similar approach because it was more data-targeted consequently more limited. The information architecture emulates design activities and context in a tree. The actual design data are acquired from a STEP file through a plug-in (Barbau et al., 2012; Posada et al., 2005-2006). The aspect shown above is only one regarding the proper information usage. More specifically, the main issues that must be handled are information taxonomy, or concept hierarchy, and vocabulary for the DFM domain. The lack of these characteristics makes the necessary framework information exchange more difficult. Secondly, it should be outlined that the plain usage of a given format, in this case STEP, limits the representation of the actual information in its best way since it is based on the output of a given component. Frameworks are dynamic, in general, and the information context specification with its meaning changes accordingly. Therefore, better information architecture planning requires that the taxonomy, at least, should be done in advance. This lack increases a “gap” between the actual information meaning and its implementation. 978-3-902823-33-5/13/$20.00 © 2013 IFAC
As outlined throughout this paper, along with a given domain taxonomy there must exist a proper vocabulary with its relations. This is the last step before actual data. Only then are data issues embedded in the ontologies’ relations. The ontology approach in the development of this DFM framework ontologies using Prot´eg´e is as follows below and partly shown in figure 3: (1) Specify DFM components’ classes concepts and entities, if any. (2) Specify details and slots, or object and data properties, of the components’ classes concepts. These details can vary in form as annotations, hierarchical relations, and actual data representation. (3) Specify these DFM–related classes: tolerancing, cost, availability of machines and tools, tool accessibility as well as their relations with the general components’ classes. (4) Structure STEP information import through the OntoSTEP plug-in for part 11 EXPRESS (description method) at least, and parts 42, 43, 44 (integrated resources) that deal with product data representation (including geometry). (5) Specify classes’ details, for instance part 47 and 203e2 which are STEP APs ontologies that deal with tolerancing. (6) Specify and/or use properties, which are equivalent to defining how concepts (classes) are related to other classes. Properties specify the rules of information use. This aspect is fundamentally different regarding information usage in typical, or traditional, systems. In this kind of systems it is not clear how information is used for it is an intrinsic part of the solution procedure algorithm. In addition, its lack reduces system integration flexibility to a great extent. (7) Define and detail individuals 6 which are responsible for the specification of components’ information type. Then, the individuals are embedded as intrinsic parts of the classes’ details. (8) Specify the form of information interaction or queries, if needed. (9) Specify interfaces which define the interaction with information from external components. (10) Test application ontologies. (11) Integrate software by using the Prot´eg´e’s code generation feature.
6
An individual is most often the specification of the kind of information. It links the information specification defined by classes to actual data values.
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Fig. 3. Ontology structure, or taxonomy (part of). 4. THEORETICAL STRUCTURE USAGE Ideally, as shown above, the information architecture specification for the chosen DFM domain should generally follow a sequence of steps. Considering this fact and the paper length size limit, an example of one of the frameworks components ontologybased information structure is described below. The component described is Cost. In this example only the part’s intrinsic cost is described, that is no actual system manufacturing or organization costs are used. Thus, a cost design component using this information approach should follow the steps below in order to use a more complete information structure such as ours. It is important to notice that only its structure is its focus. Its objective is to ease the typical burden in the development of a DFM framework components’ interfaces among the several existing ones. Initially, only the associated geometry information is checked in the first two steps described below. (1) Import actual STEP data using its ontology information structure to a data knowledge base. (2) Identify the limits of features geometry for each feature data individual. (3) Verify the access directions of all features. (4) Use the manufacturing availability component associated process to compute two aspects: machine manufacturing capability and tooling accessibility using the information relationships present in the ontology. (5) Compute the cost ontology values using its associated process. 978-3-902823-33-5/13/$20.00 © 2013 IFAC
Fig. 4. Tool accessibility ontology property (part of). (6) Enable cost figures to the information architecture. Manufacturing itself needs several additional aspects to be considered instead of cost only as it is widely known. One of the actual manufacturing issues to be analyzed is the tool access, or tool feasibility. Thus, its information should be structured properly in order to be integrated systemic– wise. Its related property is depicted below in figure 4. Finally, it should be noted that the context description varies in two aspects: (1) The information abstraction interpretation depends on its usage, or focus kind, by a given framework component (or concept). (2) The actual ontology information tree varies according to its intent since it can depend on the designer, that is, it isn’t determining. 5. CONCLUSIONS This work reviewed information models usable in DFM with focus on ontologies. The most recent ontology–based approach for DFM has also been reviewed. The ontology approach is even more flexible than the current standard STEP when the whole information is considered since it incorporates one more step to its information model: information context. Consequently, a higher abstraction level, which is closer to human understanding and use, is obtained. However, STEP is still the most adequate information model because it is formally structured and developed.
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Ontologies still require better structuring software wise in addition to its ease of integration. It should also be pointed out as well that the ontology architecture can incorporate a relatively easy and more flexible component integration approach based on software agents. Its use should be increased when both aspects described above become more accepted. ACKNOWLEDGEMENTS The authors thank Vinicius M. Kern ([email protected]), Professor of Information Science from Universidade Federal de Santa Catarina (UFSC), Brazil for his valuable review input as well as expertise in the field. REFERENCES Bajaj, M., Peak, R., Wilson, M., Kim, I., Thurman, T., Benda, M., Jothishankar, M., Ferreira, P., and Stori, J. (2003). Towards next–generation design– for–manufacturability (dfm) frameworks for electronics product realization. Session 210, IEMT, Semicon West 2003. Barbau, R., Krima, S., Rachuri, S., Narayanan, A., and Fiorentini, X. (2012). OntoSTEP: Enriching product model data using ontologies. Computer-Aided Design, 44, 575–590. Boothroyd, G. and Dewhurst, P. (2006). Design for manufacture and assembly. http://www.dfma.com/. Christopher Menzel, R.J.M. (1998). The IDEF Family of Languages. Technical report, Department of Philosophy Texas A&M University Knowledge Based Systems, Inc. Christopher P. Menzel, R.J.M. (1992). Idef5 Ontology Description Capture Method Concepts And Formal Foundations. Technical report, Knowledge Based Systems Lab., Dept. Of Industrial Engineering, Texas A&M University. Ferber, J. (1999). Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley Professional. Garcia-Crespo, A., Ruiz-Mezcua, B., Lopez-Cuadrado, J., and Gomez-Berbiz, J. (2010). Conceptual model for semantic representation of industrial manufacturing processes. Computers in Industry, 61, 595–612. Gomez-Perez, A., Corcho, O., and Fernandez-Lopez, M. (2005). Ontological Engineering. Springer. Gruber, T. (1993). Formal Ontology in Conceptual Analysis and Knowledge Representation, chapter Toward Principles for the Design of Ontologies Used for Knowledge Sharing. Kluwer Academic Press. Gruber, T. (1994a). Toward Principles for the Design of Ontologies Used for Knowledge Sharing. IJHCS, 43(5/6), 907–928. Gruber, T. (1994b). Ontologies defined. Shared Reusable Knowledge Bases (SRKB) Mailing List. Guerra-Zubiaga, D. and Young, R. (2008). Information and knowledge interrelationships within a manufacturing knowledge model. Int. Journal of Advanced Manufacturing Technology, 39, 182–198. Gupta, S.K. and Nau, D.S. (1995). Systematic approach to analysing the manufacturability of machined parts. Computer Aided Design, 27(5), 232–342. ISO (2002a). ISO–10303-21: Industrial automation systems and integration —- Product data representation 978-3-902823-33-5/13/$20.00 © 2013 IFAC
and exchange —- Part 21: Implementation methods: Clear text encoding of the exchange structure. ISO International Standards Organization. ISO (2002b). ISO–10303-22: 1998. Industrial automation systems and integration —- Product data representation and exchange —- Part 22: Implementation methods: Standard data access interface. ISO - International Standards Organization. ISO (2005). ISO–10303-239: Industrial automation systems and integration —- Product data representation and exchange —- Part 239: Application protocol: Product life cycle support. ISO - International Standards Organization. Panetto, H., Assisti, M., and Tursi, A. (2012). ONTO– PDM: Product-driven ONTOlogy for Product Data Management interoperability within manufacturing process environment. Advanced Engineering Informatics, 26, 334–348. Posada, J., Toro, C., Wundrak, S., and Stork, A. (20052006). Using ontologies and STEP standards for the semantic simplification of CAD models in different engineering domains. Applied Ontology, 1, 263–279. Ramos, A.L.T. and Deisenroth, M.P. (2007). Ontologies and STEP Integration in a DFM Framework. ICPR19 19th International Conference on Production Research. Saad, M. and Maher, M. (1996). Shared understanding in computer-supported collaborative design. Computer Aided Design, 28, 183–192. Schlenoff, C., Ivester, R., and Knutilla, A. (2002). A robust process ontology for manufacturing systems integration. In I.C. Society (ed.), DEXA ’02 Proceedings of the 13th International Workshop on Database and Expert Systems Aplications, 597–602. NIST, Washington, DC, USA. Sudarsan, R., Fenves, S., Sriram, R., and Wang, F. (2005). A product information modeling framework for product lifecycle management. Computer–Aided Design, 37, 1399–1411. Wand, Y. and Weber, R. (1990). An Ontological Model of an Information System. IEEE Transactions of an Information System, 16(11). Wilson, P. (1993). A View of STEP, chapter Geometric Modeling for Product Realization. Elsevier Science Publishers. Zeid, I. (1991). CAD/CAM Theory and Practice. McGraw-Hill. Zhao, W. and Liu, J. (2008a). OWL/SWRL representation methodology for EXPRESS–driven product information model. part I: Implementation methodology. Computers in Industry, 59, 580–589. Zhao, W. and Liu, J. (2008b). OWL/SWRL representation methodology for EXPRESS–driven product information model. part II: practice. Computers in Industry, 59, 590– 600. Zhao, Z. and Shah, J. (2005). Domain independent shell for DFM and its application to sheet metal forming and injection molding. Computer-Aided Design, CAD’ 04 Special Issue: Product Design, Integration and Manufacturing(37), 881–898.
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