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Life cycle management of production facilities using semantic web technologies
CIRP Annals - Manufacturing Technology 59 (2010) 45–48
Contents lists available at ScienceDirect
CIRP Annals - Manufacturing Technology jou rnal homep age : ht t p: // ees .e lse vi er. com/ci rp/ def a ult . asp
Life cycle management of production facilities using semantic web technologies R. Harms, T. Fleschutz, G. Seliger (1)* Department of Machine Tools and Factory Management (IWF), TU Berlin, Germany
A R T I C L E I N F O
A B S T R A C T
Keywords: Knowledge based system Life cycle Semantic web
High added value along the life cycle stages design, installation, operation, adaptation and disposal of production facilities is achieved by services. Activities like commissioning, maintenance, reuse or training are knowledge intensive and require efficient ways of managing relevant knowledge. Distributed semantic web knowledge bases enable companies or networks to make knowledge explicitly available to all involved agents at the right place and on the right time. This paper presents a semantic web based approach for the life cycle management of production facilities, and verifies it on a reuse planning case study of an automotive body-in-white facility. ß 2010 CIRP.
1. Introduction
1.2. Potentials of semantic web technologies
Services accompanying the life cycle of machinery and equipment in production facilities provide a high added value. The role of activities, such as commissioning, maintenance, reuse or training, is becoming the subject of increased attention [1]. For instance, currently the maintenance costs for a typical machine tool can exceed one third of the total life cycle costs [2]. This trend leads to a variety of new, evolving service oriented business fields such as industrial product-service systems (IPS2) [3]. The efficient planning and conduction of such services is facilitated by a life cycle oriented management approach [4]. This approach must incorporate methods and tools for effective knowledge management throughout the entire life cycle of the machinery or equipment – a trend that is becoming increasingly important throughout the global manufacturing industry [5].
Despite fundamental developments of the world wide web (www), in terms of knowledge dissemination and participative knowledge sharing e.g. Wikipedia, it reaches its limits if applications require automated information processing and reasoning. Homonyms and synonyms, for instance, lead to ambiguous or insufficient information processing that can hardly be handled automatically. In order to accurately process information, software applications require, in addition to the provision of the data itself, its intended meaning. The concept of the semantic web was proposed to overcome these limitations combining the progress in artificial intelligence research, e.g. object oriented systems, graph systems, semantic networks, and description logics, with www technologies, e.g. hypertext transfer protocol (http) or uniform resource identifier (URI). Under this premise, semantic web languages were created e.g. resource description framework (RDF) or web ontology language (OWL). These languages allow existing data, i.e. alphanumerical signs, to be modelled with metadata thus representing its meaning – often referred to as information. The semantic web languages represent such information in a machine readable format forming ontologies [9]. Ontologies are most commonly defined as a formal specification of a shared conceptualization [10], i.e. it defines explicitly concepts, properties, relations and axioms of a specific domain of interest. The main potential of semantic web technologies lies in the formal semantics they are built on, i.e. the ability to represent relevant domains and their interrelations by using first order logic formalisms based on description logics, e.g. SHOIN(D) thus allowing the deduction of new information for practical use, often referred to as knowledge [5]. In addition to the representation of domains, languages like rule interchange format (RIF) or the semantic web rule language (SWRL) have been defined to represent business rules [10]. Thus, the semantic web enables software applications to interpret even very heterogeneous information. It facilitates precise answers to queries using
1.1. Challenges of life cycle management Life cycle management tasks are knowledge intensive. Given information has to be interpreted for its pragmatical use in effective decision making processes. The correct data and information has to be available and its interpretation has to be in accordance with the given context [5,6]. In a production facility this is challenging, because various people and IT-systems possess their own information in context specific terminologies [7]. Also, the information is distributed in time and space and exists in diverse formats and media. In particular, the transfer of information from past activities into present decisions often requires a large effort in making it available and understandable [8]. Additionally, the existing ‘‘knowledge pool’’ in an organization or network is constantly evolving due, in part, to the entry or exit of knowledge carriers as well as experience gained over time.
* Corresponding author. 0007-8506/$ – see front matter ß 2010 CIRP. doi:10.1016/j.cirp.2010.03.045
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R. Harms et al. / CIRP Annals - Manufacturing Technology 59 (2010) 45–48
automatic logic-based reasoning rather than a multitude of ambiguous answers, as is the case with current web based queries. A further potential of semantic web lies in the distribution of knowledge over the Internet, creating a vast global knowledge base. Reasoning can be conducted using widespread knowledge sources to obtain and generate new, explicit information for use in knowledge intensive applications, e.g. diagnosis or maintenance planning. 2. Semantic web architecture for knowledge based life cycle management The main objective of the proposed approach is to exploit the potentials of semantic web technologies that provide solutions for the challenges of life cycle management of production facilities, as depicted in Fig. 1. Using semantic web technologies is a promising approach that can foster a more efficient distributed storage and processing of information required for various decision making processes. In order to reach this objective, it is required to create a knowledge model, which includes a formally structured representation of relevant concepts, relations and rules. This was done following the CommonKADs methodology, which provides an extensive framework for describing knowledge intensive business processes and has been successfully applied in various practical cases [11]. This knowledge model will be transferred into an OWLontology. In an initial phase, typical life cycle management application scenarios were analyzed from a top-down perspective. This included an analysis of typical maintenance activities, factory planning activities and reuse planning activities. Process flows, involved agents and applied information objects for these activities were identified. In a second phase, the knowledge identification phase, relevant domain specific concepts and relations were elaborated. Existing norms, guidelines and glossaries were used, e.g. EN3306:2001 ‘‘Maintenance Terminology’’ for a glossary of maintenance relevant terms, DIN8580:2003 ‘‘Manufacturing processes’’ for a taxonomy of production processes and the CIRP Dictionary for a structure of processes and language thesaurus. Also, existing ontology developments were analyzed and relevant concepts were integrated, e.g.: Extended Device Functional Ontology [12], which defines core concepts for the description of technical devices, their functions, and their behavior; OntoStep, which provides a structure of product information based on the STEP standard in OWL [13]; OntoFMEA, which provides, concepts and relations for failure mode and effect analysis [14]. During the third phase, the knowledge specification phase, the ontologies themselves are prepared and mapped in a semantic web based format and distributed over the www. The resulting ontology structure is depicted in Fig. 2. The core ontology contains
Fig. 2. Semantic web-ontology framework.
concepts to describe basic technical artifacts, their behavior and function. Based on this, upper-level ontologies for describing value creation modules as well as product life cycles are defined. The value creation module ontology consists of five sub-ontologies that are used to describe relevant domain concepts. For instance the process-ontology consists of a detailed process taxonomy from top level concepts such as ‘‘joining process’’ to the bottom level concepts such as ‘‘resistance spot welding’’. Each life cycle phase ontology consists of concepts and relations relevant to that particular phase, e.g. the operation-ontology consists of concepts describing operating data, e.g. ‘‘machine hours’’ or ‘‘motor temperature’’. Each ontology can be located on web servers distributed over the web and is accessible via a distinct URI, e.g. http://www.semanticwebsystems.com/ontologies/s_process.owl. The ontologies comprise a framework that can be used as a generic basis to represent any production facility in respect to their function, behavior and current life cycle status. The case study in the following section shows exemplarily elements of the framework. The ontologies provide domain schemas, also called the T-Box, i.e. the terminological schema knowledge. Any software application will be able to re-apply this knowledge and to enhance it by their own concepts, relations, axioms and rules, thus enabling the generation of new explicit information by automated reasoning. Fig. 2 also names exemplary application ontologies, e.g. the maintenance-ontology or the reuse-planning-ontology. Developers can create their own knowledge bases by importing specific classes of the ontology framework, e.g. the class ‘‘IndustrialRobot’’ and add company or application specific entities, e.g. a particular subclass ‘‘CompanyScaraRobot’’ to form the T-Box. After generating the T-Box, the specific ontologies can be enhanced by adding actual existing individuals e.g. ‘‘Welding Robot A’’. This leads to the so called A-Box, i.e. the asserted instance knowledge. The combination of the T-Box and the A-Box constitutes the actual knowledge base on which external application can query or infer new information. The constructed knowledge base is accessible either by web clients, e.g. web portals just showing the information or by specific software applying reasoning machines. 3. Exemplary automotive reuse case study 3.1. Challenges of reuse planning
Fig. 1. Rationale behind the research approach.
The reuse of production machinery and equipment allows the exploitation of unused potentials. The products, for which it was originally designed, often are produced for a shorter time period than the equipment’s potential life time. For operators of the production systems, the repeated deployment of production equipment over several product generations contributes to an increase in the use productivity of the resources, as well as the reduction of investment costs. Even though the industrial trend
R. Harms et al. / CIRP Annals - Manufacturing Technology 59 (2010) 45–48
towards reuse is evident – often more than 50 percent of new facility planning projects are brown field operations including the reuse of equipment – a substantial planning methodology and structured knowledge about how to plan the reuse of production machinery and equipment e.g. in automotive body shop assembly lines hardly exists. Virtually all reuse cases are conducted individually, without using acquired knowledge and experience for successive reuse cases [8]. During the facility planning process the production system operator provides requirements lists and calls for tenders, including potentially reusable equipment. A system manufacturer or internal service provider then conducts a detailed planning offering a quote. If the system integrator has to consider equipment reuse, a series of knowledge intensive assessment, assignment and planning tasks have to be conducted. Examples are the ‘‘assessment of used equipment’’, the ‘‘assignment of used equipment to required future processes’’ and the ‘‘reusability classification of equipment’’. Most tasks require both information about earlier life cycle stages, e.g. ‘‘Welding Gun A has conducted n machine hours’’ and reuse specific information, e.g. ‘‘a welding gun of type X cannot be reused for a certain required welding accuracy Y’’. 3.2. Semantic web knowledge base development The semantic web framework described in section 2 has been adapted for reuse relevant information. Together with a car producer, a prototypical reuse case of a body-in-white door assembly has been analyzed to construct a knowledge base and to test it with the prototypical reuse planning software application. The resourceontology, abbreviated with ‘‘s_resource’’, has been filled with the actual devices of the examined production facility: thirteen industrial robots, six robot grippers, seven welding guns, five stationary welding guns, four framing stations and one conveyer. The assembly product is represented using concepts of the product ontology, abbreviated as ‘‘s_product’’. Processes and parameters were entered according to the door assembly process plan e.g. resistance spot welding. This is conducted using the process-ontology, abbreviated as ‘‘s_process’’. Life cycle data, e.g. operating hours, failures and maintenance actions were simulated for a usage of 10 years and fed into the knowledge base. Fig. 3 shows an excerpt of the knowledge base ontology in a unified modeling language (UML)-based notation. The instances shown are part of the A-Box of the described reuse case. They are linked via URIs to the classes of the ontology in Fig. 2, which give them a semantic from the T-Box. In this example, the ‘‘Welding Gun A’’ is part of a ‘‘Welding Robot A’’, has a failure ‘‘Insufficient Welding Force’’ and has functionality ‘‘Spot Weld Functionality’’. Not shown in this figure is the linkage of a failure mode to potential containment activities, which is used to infer potential adaptation actions and efforts. One of the specific application ontologies required for reuse planning is the maintenance-ontology abbreviated with ‘‘s_maint’’. It includes maintenance measures as well as structured representation for failure modes. A further specific ontology is the reuse-ontology. It holds reuse specific concepts such as ‘‘Process-Resource-Assignment’’ or rules for reusability classification. Those can be used to infer information that is required for conducting the reuse planning processes.
Fig. 3. Excerpt of case study knowledge base.
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Fig. 4. Rule template, specific rule and conjunctive query in SWRL-editor syntax.
A simple and powerful way of representing knowledge, e.g. reuse planning knowledge, acquired by people or automated data mining processes is in representing them in logical implication rules for instance with SWRL. They consist of an antecedence, which is a conjunction of classes or properties and a consequence that is one class or property. Rule templates can be created for similar rules found in an application. One example of a typical rule type occurring in each reuse planning process is the ‘‘ProcessResource-Assignment’’ as shown in Fig. 4(a). It is a rule used for stating requirements a device has to fulfil to be able to conduct a certain process. Besides rule templates, specific rules that include actual values have been added. An example rule, used for categorizing any device X according to existing failures, is shown in Fig. 4(b). An inference machine will infer all components, which have a FMEA risk priority number higher than 80 into the ‘‘ReuseCategoryRed’’, i.e. ‘‘not reusable’’. Another approach to making information explicit is to use logic conjunctive queries. They are used to query information across different ontologies. An example is shown in Fig. 4(c). If a reuse planner requires information about the repair measures Y, typically conducted on industrial robots X, which conducts spot weld processes, the query will provide the answer for all Y and X. With increasing knowledge, such rules and queries can be made more expressive by interlacing and adding new rules. To complete the reuse planning knowledge base the required processes of the future have to be represented in an individual requirement-ontology using the concepts of the process-ontology, e.g. ‘‘moving the frame part’’ becomes an instance of ‘‘s_process: robot handling process’’. 3.3. Implementation of the reuse planning application In Section 3.2 the ontology framework of Section 2 has been applied for the use case, i.e. the A-Box has been filled with reuse specific information. In order to make this instantiated information useable for practical application, a software prototype has been developed. For the implementation of the reuse planning application, a webservice based architecture was constructed. It consists of a knowledge base server administrating the knowledge base, stored in a standardized SQL database. A inference machine [15] is used for reasoning on the knowledge base. Results are provided to other clients using webservices. An external web server communicates with the knowledge base server using these webservices and presents the reuse planning application (CAReP) to external web browsers. This enables platform independency of the reuse planning application. In this implementation, CAReP is integrated into a workplace simulation software. The planner is guided through the reuse planning steps. First, the requirement-ontology is uploaded. For each required process, potential resources are assigned and each assignment is assessed by automated inferences based on the assessment rules. Then the user has the possibility to interact with the application by prioritizing processes in order to avoid double assignment of resources. Finally, the system calculates the result and presents it to the user as seen in Fig. 5. Equipment that is categorized to be reusable (green or yellow) for given processes and associated adaptation processes are shown. Also, a cost
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R. Harms et al. / CIRP Annals - Manufacturing Technology 59 (2010) 45–48
Fig. 5. User interface for reuse planning application CAReP.
estimate is generated, that can be used to give a realistic quote. 3D models can be easily dragged into the simulation environment for work place and material flow modelling purposes. 3.4. Results: benefits and barriers for industrial application An evaluation of the knowledge representation in the ontology framework and of the implemented CAReP application, in cooperation with the industrial partner, has shown that a major benefit is its ability to make equipment life cycle information available and machine readable. This allows reuse planning processes that are based on automated reasoning. Thus, there is no further need for redundant, end-of-use inspection and condition assessment on shop floor level, e.g. reading and interpreting maintenance protocols or linking existing FMEAanalyses to reusability assessments. Furthermore, the linkage between equipment condition and maintenance or adaptation activities facilitates accurate cost estimations. Knowledge about prior reuse cases can be added over time and reapplied automatically, thus avoiding loss of existing experiences. The major challenge for industrial application is to ensure the existence of sufficient relevant semantic web content. To overcome this barrier, companies must promote acceptance of such systems and provide tools for the convenient transfer of existing data into semantic web format. From the experience with the described case study, an implementation for a similar use case based on the existing framework can be estimated to require an effort of 4–6 person months. 4. Conclusion In this paper, a semantic web-ontology framework was proposed, that can be used to improve the effectiveness and efficiency of life cycle management tasks in the field of production facilities. In a first step upper ontologies for representing value creation modules and their life cycles as well as application and company specific ontologies were developed using the CommonKADS methodology. Secondly, the framework has been adapted to an industrial case study for planning the reuse of production facilities. Thirdly, a software prototype was implemented to guide the planner through the planning process. Upcoming work is dedicated to successively include further tasks, e.g. maintenance,
into the application oriented ontologies. Also, the work will focus on systems for automated learning ontologies and increased usability for industrial workers. Acknowledgement The work is part of the project ‘‘Flexible Assembly Systems through Worplace-Sharing and Time-Sharing Human-Machine Cooperation’’, project no. 026697, funded by the European Commission. References [1] Takata S, Kimura F, van Houten F, Westka¨mper E, Shpitalni M, Ceglarek D, Lee J (2004) Maintenance: Changing Role in Life Cycle Management. CIRP Annals 53/ 2:643–655. [2] Enparantza R, Revilla O, Azkarate A, Zendoia J (2006) A Life Cycle Cost Calculation and Management System for Machine Tools. Proceedings of the 13th CIRP International Conference on Life Cycle Engineering, 717–722. [3] Meier H, Kroll M (2009) From Products to Solutions – IPS2 as a Means for Creating Customer Value. Proceedings of the 16th CIRP International Conference on Life Cycle Engineering, 18–25. [4] Westka¨mper E, Alting L, Arndt M (2000) Life Cycle Management and Assessment: Approaches and Visions Towards Sustainable Manufacturing. CIRP Annals 49/2:501–526. [5] Bernard A, Tichkiewitch S, (Eds.) (2008), Methods and Tools for Effective Knowledge Life-Cycle-Management. Springer, Berlin, Heidelberg. [6] Denkena B, Shpitalni M, Kowalski P, Molcho G, Ziporilou Y (2007) Knowledge Management in Process Planning. CIRP Annals 56/1:175–180. [7] Lu SC-Y, ElMaraghy W, Schuh G, Wilhem R (2007) A Scientific Foundation of Collaborative Engineering. CIRP Annals 56/2:605–634. [8] Harms R, Fleschutz T, Seliger G (2008) Knowledge Based Approach to Assembly System Reuse. Proceedings of the 9th Biennial ASME Conference on Engineering Systems Design and Analysis ESDA, 2008, 295–302. [9] Antoniou G, van Harmelen F (2008) A Semantic Web Primer. 2nd ed. The MIT Press, USA. ¨ zsu MT, (Eds.) Encyclopedia of Database [10] Gruber T (2008) Ontology. in Liu L, O Systems. Springer, USA. [11] Schreiber G, Akkermans H, Anjewierden A, de Hoog R, Shadboldt N, de Velde WV, Wielinga B (2000) Knowledge Engineering and Management. MIT Press, England. [12] Kitamura Y, Mizoguchi R (2004) Ontology-Based Systematization of Functional Knowledge. Journal of Engineering Design 15/4:327–351. [13] Krima S, Barbau R, Fiorentini X, Sudarsan R, Sriram RD (2009) OntoSTEP: OWLDL Ontology for STEP. NIST, USA. [14] Dittmann L, Rademacher T, Zelewski S (2004) Performing FMEA Using Ontologies. Proceedings of the18th International Workshop on Qualitative Reasoning, . [15] Sirin E, Parsia B, Grau B, Kalyanpur A, Katz Y (2007) Pellet: A Practical Owl-dl Reasoner. Web Semantics Science Services and Agents on the World Wide Web 5/ 2:51–53.
Contents lists available at ScienceDirect
CIRP Annals - Manufacturing Technology jou rnal homep age : ht t p: // ees .e lse vi er. com/ci rp/ def a ult . asp
Life cycle management of production facilities using semantic web technologies R. Harms, T. Fleschutz, G. Seliger (1)* Department of Machine Tools and Factory Management (IWF), TU Berlin, Germany
A R T I C L E I N F O
A B S T R A C T
Keywords: Knowledge based system Life cycle Semantic web
High added value along the life cycle stages design, installation, operation, adaptation and disposal of production facilities is achieved by services. Activities like commissioning, maintenance, reuse or training are knowledge intensive and require efficient ways of managing relevant knowledge. Distributed semantic web knowledge bases enable companies or networks to make knowledge explicitly available to all involved agents at the right place and on the right time. This paper presents a semantic web based approach for the life cycle management of production facilities, and verifies it on a reuse planning case study of an automotive body-in-white facility. ß 2010 CIRP.
1. Introduction
1.2. Potentials of semantic web technologies
Services accompanying the life cycle of machinery and equipment in production facilities provide a high added value. The role of activities, such as commissioning, maintenance, reuse or training, is becoming the subject of increased attention [1]. For instance, currently the maintenance costs for a typical machine tool can exceed one third of the total life cycle costs [2]. This trend leads to a variety of new, evolving service oriented business fields such as industrial product-service systems (IPS2) [3]. The efficient planning and conduction of such services is facilitated by a life cycle oriented management approach [4]. This approach must incorporate methods and tools for effective knowledge management throughout the entire life cycle of the machinery or equipment – a trend that is becoming increasingly important throughout the global manufacturing industry [5].
Despite fundamental developments of the world wide web (www), in terms of knowledge dissemination and participative knowledge sharing e.g. Wikipedia, it reaches its limits if applications require automated information processing and reasoning. Homonyms and synonyms, for instance, lead to ambiguous or insufficient information processing that can hardly be handled automatically. In order to accurately process information, software applications require, in addition to the provision of the data itself, its intended meaning. The concept of the semantic web was proposed to overcome these limitations combining the progress in artificial intelligence research, e.g. object oriented systems, graph systems, semantic networks, and description logics, with www technologies, e.g. hypertext transfer protocol (http) or uniform resource identifier (URI). Under this premise, semantic web languages were created e.g. resource description framework (RDF) or web ontology language (OWL). These languages allow existing data, i.e. alphanumerical signs, to be modelled with metadata thus representing its meaning – often referred to as information. The semantic web languages represent such information in a machine readable format forming ontologies [9]. Ontologies are most commonly defined as a formal specification of a shared conceptualization [10], i.e. it defines explicitly concepts, properties, relations and axioms of a specific domain of interest. The main potential of semantic web technologies lies in the formal semantics they are built on, i.e. the ability to represent relevant domains and their interrelations by using first order logic formalisms based on description logics, e.g. SHOIN(D) thus allowing the deduction of new information for practical use, often referred to as knowledge [5]. In addition to the representation of domains, languages like rule interchange format (RIF) or the semantic web rule language (SWRL) have been defined to represent business rules [10]. Thus, the semantic web enables software applications to interpret even very heterogeneous information. It facilitates precise answers to queries using
1.1. Challenges of life cycle management Life cycle management tasks are knowledge intensive. Given information has to be interpreted for its pragmatical use in effective decision making processes. The correct data and information has to be available and its interpretation has to be in accordance with the given context [5,6]. In a production facility this is challenging, because various people and IT-systems possess their own information in context specific terminologies [7]. Also, the information is distributed in time and space and exists in diverse formats and media. In particular, the transfer of information from past activities into present decisions often requires a large effort in making it available and understandable [8]. Additionally, the existing ‘‘knowledge pool’’ in an organization or network is constantly evolving due, in part, to the entry or exit of knowledge carriers as well as experience gained over time.
* Corresponding author. 0007-8506/$ – see front matter ß 2010 CIRP. doi:10.1016/j.cirp.2010.03.045
46
R. Harms et al. / CIRP Annals - Manufacturing Technology 59 (2010) 45–48
automatic logic-based reasoning rather than a multitude of ambiguous answers, as is the case with current web based queries. A further potential of semantic web lies in the distribution of knowledge over the Internet, creating a vast global knowledge base. Reasoning can be conducted using widespread knowledge sources to obtain and generate new, explicit information for use in knowledge intensive applications, e.g. diagnosis or maintenance planning. 2. Semantic web architecture for knowledge based life cycle management The main objective of the proposed approach is to exploit the potentials of semantic web technologies that provide solutions for the challenges of life cycle management of production facilities, as depicted in Fig. 1. Using semantic web technologies is a promising approach that can foster a more efficient distributed storage and processing of information required for various decision making processes. In order to reach this objective, it is required to create a knowledge model, which includes a formally structured representation of relevant concepts, relations and rules. This was done following the CommonKADs methodology, which provides an extensive framework for describing knowledge intensive business processes and has been successfully applied in various practical cases [11]. This knowledge model will be transferred into an OWLontology. In an initial phase, typical life cycle management application scenarios were analyzed from a top-down perspective. This included an analysis of typical maintenance activities, factory planning activities and reuse planning activities. Process flows, involved agents and applied information objects for these activities were identified. In a second phase, the knowledge identification phase, relevant domain specific concepts and relations were elaborated. Existing norms, guidelines and glossaries were used, e.g. EN3306:2001 ‘‘Maintenance Terminology’’ for a glossary of maintenance relevant terms, DIN8580:2003 ‘‘Manufacturing processes’’ for a taxonomy of production processes and the CIRP Dictionary for a structure of processes and language thesaurus. Also, existing ontology developments were analyzed and relevant concepts were integrated, e.g.: Extended Device Functional Ontology [12], which defines core concepts for the description of technical devices, their functions, and their behavior; OntoStep, which provides a structure of product information based on the STEP standard in OWL [13]; OntoFMEA, which provides, concepts and relations for failure mode and effect analysis [14]. During the third phase, the knowledge specification phase, the ontologies themselves are prepared and mapped in a semantic web based format and distributed over the www. The resulting ontology structure is depicted in Fig. 2. The core ontology contains
Fig. 2. Semantic web-ontology framework.
concepts to describe basic technical artifacts, their behavior and function. Based on this, upper-level ontologies for describing value creation modules as well as product life cycles are defined. The value creation module ontology consists of five sub-ontologies that are used to describe relevant domain concepts. For instance the process-ontology consists of a detailed process taxonomy from top level concepts such as ‘‘joining process’’ to the bottom level concepts such as ‘‘resistance spot welding’’. Each life cycle phase ontology consists of concepts and relations relevant to that particular phase, e.g. the operation-ontology consists of concepts describing operating data, e.g. ‘‘machine hours’’ or ‘‘motor temperature’’. Each ontology can be located on web servers distributed over the web and is accessible via a distinct URI, e.g. http://www.semanticwebsystems.com/ontologies/s_process.owl. The ontologies comprise a framework that can be used as a generic basis to represent any production facility in respect to their function, behavior and current life cycle status. The case study in the following section shows exemplarily elements of the framework. The ontologies provide domain schemas, also called the T-Box, i.e. the terminological schema knowledge. Any software application will be able to re-apply this knowledge and to enhance it by their own concepts, relations, axioms and rules, thus enabling the generation of new explicit information by automated reasoning. Fig. 2 also names exemplary application ontologies, e.g. the maintenance-ontology or the reuse-planning-ontology. Developers can create their own knowledge bases by importing specific classes of the ontology framework, e.g. the class ‘‘IndustrialRobot’’ and add company or application specific entities, e.g. a particular subclass ‘‘CompanyScaraRobot’’ to form the T-Box. After generating the T-Box, the specific ontologies can be enhanced by adding actual existing individuals e.g. ‘‘Welding Robot A’’. This leads to the so called A-Box, i.e. the asserted instance knowledge. The combination of the T-Box and the A-Box constitutes the actual knowledge base on which external application can query or infer new information. The constructed knowledge base is accessible either by web clients, e.g. web portals just showing the information or by specific software applying reasoning machines. 3. Exemplary automotive reuse case study 3.1. Challenges of reuse planning
Fig. 1. Rationale behind the research approach.
The reuse of production machinery and equipment allows the exploitation of unused potentials. The products, for which it was originally designed, often are produced for a shorter time period than the equipment’s potential life time. For operators of the production systems, the repeated deployment of production equipment over several product generations contributes to an increase in the use productivity of the resources, as well as the reduction of investment costs. Even though the industrial trend
R. Harms et al. / CIRP Annals - Manufacturing Technology 59 (2010) 45–48
towards reuse is evident – often more than 50 percent of new facility planning projects are brown field operations including the reuse of equipment – a substantial planning methodology and structured knowledge about how to plan the reuse of production machinery and equipment e.g. in automotive body shop assembly lines hardly exists. Virtually all reuse cases are conducted individually, without using acquired knowledge and experience for successive reuse cases [8]. During the facility planning process the production system operator provides requirements lists and calls for tenders, including potentially reusable equipment. A system manufacturer or internal service provider then conducts a detailed planning offering a quote. If the system integrator has to consider equipment reuse, a series of knowledge intensive assessment, assignment and planning tasks have to be conducted. Examples are the ‘‘assessment of used equipment’’, the ‘‘assignment of used equipment to required future processes’’ and the ‘‘reusability classification of equipment’’. Most tasks require both information about earlier life cycle stages, e.g. ‘‘Welding Gun A has conducted n machine hours’’ and reuse specific information, e.g. ‘‘a welding gun of type X cannot be reused for a certain required welding accuracy Y’’. 3.2. Semantic web knowledge base development The semantic web framework described in section 2 has been adapted for reuse relevant information. Together with a car producer, a prototypical reuse case of a body-in-white door assembly has been analyzed to construct a knowledge base and to test it with the prototypical reuse planning software application. The resourceontology, abbreviated with ‘‘s_resource’’, has been filled with the actual devices of the examined production facility: thirteen industrial robots, six robot grippers, seven welding guns, five stationary welding guns, four framing stations and one conveyer. The assembly product is represented using concepts of the product ontology, abbreviated as ‘‘s_product’’. Processes and parameters were entered according to the door assembly process plan e.g. resistance spot welding. This is conducted using the process-ontology, abbreviated as ‘‘s_process’’. Life cycle data, e.g. operating hours, failures and maintenance actions were simulated for a usage of 10 years and fed into the knowledge base. Fig. 3 shows an excerpt of the knowledge base ontology in a unified modeling language (UML)-based notation. The instances shown are part of the A-Box of the described reuse case. They are linked via URIs to the classes of the ontology in Fig. 2, which give them a semantic from the T-Box. In this example, the ‘‘Welding Gun A’’ is part of a ‘‘Welding Robot A’’, has a failure ‘‘Insufficient Welding Force’’ and has functionality ‘‘Spot Weld Functionality’’. Not shown in this figure is the linkage of a failure mode to potential containment activities, which is used to infer potential adaptation actions and efforts. One of the specific application ontologies required for reuse planning is the maintenance-ontology abbreviated with ‘‘s_maint’’. It includes maintenance measures as well as structured representation for failure modes. A further specific ontology is the reuse-ontology. It holds reuse specific concepts such as ‘‘Process-Resource-Assignment’’ or rules for reusability classification. Those can be used to infer information that is required for conducting the reuse planning processes.
Fig. 3. Excerpt of case study knowledge base.
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Fig. 4. Rule template, specific rule and conjunctive query in SWRL-editor syntax.
A simple and powerful way of representing knowledge, e.g. reuse planning knowledge, acquired by people or automated data mining processes is in representing them in logical implication rules for instance with SWRL. They consist of an antecedence, which is a conjunction of classes or properties and a consequence that is one class or property. Rule templates can be created for similar rules found in an application. One example of a typical rule type occurring in each reuse planning process is the ‘‘ProcessResource-Assignment’’ as shown in Fig. 4(a). It is a rule used for stating requirements a device has to fulfil to be able to conduct a certain process. Besides rule templates, specific rules that include actual values have been added. An example rule, used for categorizing any device X according to existing failures, is shown in Fig. 4(b). An inference machine will infer all components, which have a FMEA risk priority number higher than 80 into the ‘‘ReuseCategoryRed’’, i.e. ‘‘not reusable’’. Another approach to making information explicit is to use logic conjunctive queries. They are used to query information across different ontologies. An example is shown in Fig. 4(c). If a reuse planner requires information about the repair measures Y, typically conducted on industrial robots X, which conducts spot weld processes, the query will provide the answer for all Y and X. With increasing knowledge, such rules and queries can be made more expressive by interlacing and adding new rules. To complete the reuse planning knowledge base the required processes of the future have to be represented in an individual requirement-ontology using the concepts of the process-ontology, e.g. ‘‘moving the frame part’’ becomes an instance of ‘‘s_process: robot handling process’’. 3.3. Implementation of the reuse planning application In Section 3.2 the ontology framework of Section 2 has been applied for the use case, i.e. the A-Box has been filled with reuse specific information. In order to make this instantiated information useable for practical application, a software prototype has been developed. For the implementation of the reuse planning application, a webservice based architecture was constructed. It consists of a knowledge base server administrating the knowledge base, stored in a standardized SQL database. A inference machine [15] is used for reasoning on the knowledge base. Results are provided to other clients using webservices. An external web server communicates with the knowledge base server using these webservices and presents the reuse planning application (CAReP) to external web browsers. This enables platform independency of the reuse planning application. In this implementation, CAReP is integrated into a workplace simulation software. The planner is guided through the reuse planning steps. First, the requirement-ontology is uploaded. For each required process, potential resources are assigned and each assignment is assessed by automated inferences based on the assessment rules. Then the user has the possibility to interact with the application by prioritizing processes in order to avoid double assignment of resources. Finally, the system calculates the result and presents it to the user as seen in Fig. 5. Equipment that is categorized to be reusable (green or yellow) for given processes and associated adaptation processes are shown. Also, a cost
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Fig. 5. User interface for reuse planning application CAReP.
estimate is generated, that can be used to give a realistic quote. 3D models can be easily dragged into the simulation environment for work place and material flow modelling purposes. 3.4. Results: benefits and barriers for industrial application An evaluation of the knowledge representation in the ontology framework and of the implemented CAReP application, in cooperation with the industrial partner, has shown that a major benefit is its ability to make equipment life cycle information available and machine readable. This allows reuse planning processes that are based on automated reasoning. Thus, there is no further need for redundant, end-of-use inspection and condition assessment on shop floor level, e.g. reading and interpreting maintenance protocols or linking existing FMEAanalyses to reusability assessments. Furthermore, the linkage between equipment condition and maintenance or adaptation activities facilitates accurate cost estimations. Knowledge about prior reuse cases can be added over time and reapplied automatically, thus avoiding loss of existing experiences. The major challenge for industrial application is to ensure the existence of sufficient relevant semantic web content. To overcome this barrier, companies must promote acceptance of such systems and provide tools for the convenient transfer of existing data into semantic web format. From the experience with the described case study, an implementation for a similar use case based on the existing framework can be estimated to require an effort of 4–6 person months. 4. Conclusion In this paper, a semantic web-ontology framework was proposed, that can be used to improve the effectiveness and efficiency of life cycle management tasks in the field of production facilities. In a first step upper ontologies for representing value creation modules and their life cycles as well as application and company specific ontologies were developed using the CommonKADS methodology. Secondly, the framework has been adapted to an industrial case study for planning the reuse of production facilities. Thirdly, a software prototype was implemented to guide the planner through the planning process. Upcoming work is dedicated to successively include further tasks, e.g. maintenance,
into the application oriented ontologies. Also, the work will focus on systems for automated learning ontologies and increased usability for industrial workers. Acknowledgement The work is part of the project ‘‘Flexible Assembly Systems through Worplace-Sharing and Time-Sharing Human-Machine Cooperation’’, project no. 026697, funded by the European Commission. References [1] Takata S, Kimura F, van Houten F, Westka¨mper E, Shpitalni M, Ceglarek D, Lee J (2004) Maintenance: Changing Role in Life Cycle Management. CIRP Annals 53/ 2:643–655. [2] Enparantza R, Revilla O, Azkarate A, Zendoia J (2006) A Life Cycle Cost Calculation and Management System for Machine Tools. Proceedings of the 13th CIRP International Conference on Life Cycle Engineering, 717–722. [3] Meier H, Kroll M (2009) From Products to Solutions – IPS2 as a Means for Creating Customer Value. Proceedings of the 16th CIRP International Conference on Life Cycle Engineering, 18–25. [4] Westka¨mper E, Alting L, Arndt M (2000) Life Cycle Management and Assessment: Approaches and Visions Towards Sustainable Manufacturing. CIRP Annals 49/2:501–526. [5] Bernard A, Tichkiewitch S, (Eds.) (2008), Methods and Tools for Effective Knowledge Life-Cycle-Management. Springer, Berlin, Heidelberg. [6] Denkena B, Shpitalni M, Kowalski P, Molcho G, Ziporilou Y (2007) Knowledge Management in Process Planning. CIRP Annals 56/1:175–180. [7] Lu SC-Y, ElMaraghy W, Schuh G, Wilhem R (2007) A Scientific Foundation of Collaborative Engineering. CIRP Annals 56/2:605–634. [8] Harms R, Fleschutz T, Seliger G (2008) Knowledge Based Approach to Assembly System Reuse. Proceedings of the 9th Biennial ASME Conference on Engineering Systems Design and Analysis ESDA, 2008, 295–302. [9] Antoniou G, van Harmelen F (2008) A Semantic Web Primer. 2nd ed. The MIT Press, USA. ¨ zsu MT, (Eds.) Encyclopedia of Database [10] Gruber T (2008) Ontology. in Liu L, O Systems. Springer, USA. [11] Schreiber G, Akkermans H, Anjewierden A, de Hoog R, Shadboldt N, de Velde WV, Wielinga B (2000) Knowledge Engineering and Management. MIT Press, England. [12] Kitamura Y, Mizoguchi R (2004) Ontology-Based Systematization of Functional Knowledge. Journal of Engineering Design 15/4:327–351. [13] Krima S, Barbau R, Fiorentini X, Sudarsan R, Sriram RD (2009) OntoSTEP: OWLDL Ontology for STEP. NIST, USA. [14] Dittmann L, Rademacher T, Zelewski S (2004) Performing FMEA Using Ontologies. Proceedings of the18th International Workshop on Qualitative Reasoning, . [15] Sirin E, Parsia B, Grau B, Kalyanpur A, Katz Y (2007) Pellet: A Practical Owl-dl Reasoner. Web Semantics Science Services and Agents on the World Wide Web 5/ 2:51–53.