Int J Flex Manuf Syst (2006) 17:301–314 DOI 10.1007/s10696-006-9030-0
Equipment ontology for modular reconfigurable assembly systems Niels Lohse · Hitendra Hirani · Svetan Ratchev
Springer Science + Business Media, LLC 2006
Abstract Evolvable and Reconfigurable Assembly Systems (RAS) enable enterprises to rapidly respond to changes in today’s increasingly volatile and dynamic global markets. One of the key success factors for the effective use of RAS is methods and tools that can rapidly configure and reconfigure assembly systems driven by changing requirements. The focus of this paper is the development of a suitable equipment model to support the effective design of reconfigurable assembly systems. The work has been motivated by the need to provide solutions for increasing product customisation and volume changes over the product life-cycle that directly impact on the final product assembly. The paper proposes a comprehensive equipment ontology to enable effective decision-making during the design and evaluation of new RAS configurations. The proposed ontology is based on the function-behaviour-structure paradigm, and is formalised to facilitate its application in distributed web-enabled decision-making environments. The equipment configuration and reconfiguration approach and prototype decision-making environment are illustrated using system design examples. Keywords Assembly devices model · Function-behaviour-structure · Knowledge ontology
1. Introduction Increasing product customisation and volume changes over a product’s life-cycle have a direct impact on the final product assembly. It is therefore vital for current and future assembly system solutions to effectively deal with highly dynamic and complex changes, while at the same time remaining cost effective. Evolvable and N. Lohse () · H. Hirani · S. Ratchev Precision Manufacturing Group, School of Manufacturing, Mechanical, and Materials Engineering, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom e-mail: [email protected]
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Reconfigurable Assembly Systems (RAS) enable enterprises to rapidly respond to those changes in today’s increasingly volatile and dynamic global markets, without having to commit substantial investment into excess flexibility (Koren et al., 1999; Onori et al., 2002). One of the main enabling technologies for RAS is the availability of (1) well defined modular equipment solutions and (2) methodologies to rapidly plan and facilitate their initial design and subsequent reconfigurations (Koren and Ulsoy, 2002). The foundation of any such methodology is a suitable model that can capture all the required aspects of the reconfiguration process and cater for the different needs of globally dispersed stakeholders in the reconfiguration process. A significant research and development effort has been directed towards creating suitable system architectures for RAS (Bo¨er et al., 2001; Giusti et al., 1994; Chen, 2001; Hollis and Quaid, 1995; Gaugel et al., 2004; Lastra, 2004). Their focus has mainly been on the physical structure and control aspects that enable reconfiguration, and they highlight modular equipment solutions as one of the fundamental requirements for RAS. The design and decision-making aspects needed for requirement-driven rapid reconfiguration were mostly outside the scope of these works. Several examples of modular assembly systems are already commercially available as reported in Alsterman (2004). They fulfil the basic structural requirements for RAS but still fall short on the control and design side. This shows the current trend towards a reconfiguration based school of thought, but also highlights the necessity for further research into enabling technologies especially on the design side. Several design approaches have been developed that demonstrate the principle feasibility of computer aided and knowledge based assembly systems design (Bley et al., 1994; Bo¨er et al., 2001, Travaini et al., 2002; Lohse et al., 2006). The reported approaches only focus on some decision-making aspects and do not yet address the specific modelling needs for rapid RAS configuration and reconfiguration. The focus of an equipment definition ontology has to be on the functional capabilities of the equipment so that it can be selected and integrated effectively (Vos, 2001). Several approaches have been reported for the definition of device or module capabilities. Zha et al. (2001) use knowledge intensive Petri net for the modelling and analysis of assembly equipment and systems. A language representation of functionbehaviour-structure for mechanical devices has been introduced by Sasajima et al. (1995) based on ontological engineering principles (Mizoguchi and Kitamura 2000). Their main focus is on understanding the functional capability of devices based on their behaviour and structure. Umeda et al. (1996) and Tomiyama et al. (1993) use qualitative physics to define the relation between structure, behaviour and functions. Sasajima et al. (1995), Mizoguchi et al. (2000), Umeda et al. (1996), Tomiyama et al. (1993) all define behaviour based on physical phenomena. A number of domain specific equipment models have also been reported that focus on different aspects and levels of abstraction of the equipment characteristics. Zhang et al. (2000) have developed a model that focuses on the representation of robots and their working environment. Zhang and van der Werff (1993) and Neville and Joskowicz (1992) report models for the specification of mechanisms focused on their mechanical/kinematic aspect. Gausemeier et al. (2001) and Craig et al. (1999) address the need for integrated mechatronic device models. Sch¨afer and L´opez (1999) defined an object oriented model of the control capability in a manufacturing system. Seliger and Bollmann (1990), Meijer et al. (2003), and Zhang et al. (2003) report function Springer
Equipment ontology for modular reconfigurable assembly systems
models for the design of devices and systems. All of these reported models focus only on specific aspects of the equipment characteristics. None of them consider the consequences of modularity in the definition or application of their models. Despite these significant developments, the reported equipment models do not yet fully address the specific knowledge and modelling requirements needed to support effective RAS configuration and reconfiguration decisions. They are either too generic or only focus on specific aspects of equipment representation. They do not provide a holistic view of how assembly equipment can be represented to support the design of modular reconfigurable production systems. The paper reports on a holistic equipment ontology that addresses the specific needs of the RAS design and reengineering process. The ontology defines assembly equipment modules following the functions, behaviour and structure paradigm, and includes separate formalisms for the specification of module capability and interfacing requirements. The specific requirements for a suitable equipment model are outlined through the analysis of their role during the RAS design process. The proposed equipment definition ontology is then introduced including a review of fundamental modelling choices. Finally the intended application of the ontology is demonstrated using an elaborate case study. 2. Assembly system configuration and reconfiguration methodology A knowledge enriched web-enabled assembly system design framework was reported in Lohse et al. (2004). The methodology includes five concurrent steps that correspond
Fig. 1 Assembly system design methodology (Lohse et al., 2004) Springer
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to the agents in the system framework (see Fig. 1): obtaining the product characteristics (project specification during requirements engineering); generation and specification of process plans (process decomposition agent); generation and specification of conceptual design solutions (conceptual design agents); selection and configuration of system alternatives (embodiment design agents); and evaluation of concepts and system alternatives (service agents). Each step is supported by relevant procedural and declarative knowledge. Three main roles of stakeholders in the design process have been identified: customers who potentially require a new system; system integrators who design and build assembly systems; and equipment suppliers who design and manufacture the equipment modules that constitute assembly systems. The customers define the requirements for the assembly system in the form of a product model, project, process, and equipment constraints. The system integrator has the key task of defining assembly systems that fulfil the requirements of the customer, using commercially available equipment modules and solutions from different vendors. This involves the specification of the required assembly process, conceptual design of the assembly system, equipment selection, configuration, and evaluation. The equipment suppliers provide and suggest suitable equipment solutions. The key stages of the design process are implemented as independent application modules (agents) that contain their own decision logic, communication facilities and domain knowledge. The framework deploys a representative agent for each participating stakeholder according to their role. These agents provide basic facilities to manage the design process and the interaction with other stakeholders, including sending and receiving of messages as well as initialisation and coordination of relevant design tasks. Each distinctive design task within the stakeholders’ role is deployed as a separate agent ensuring a high degree of concurrency and a clear separation of the required knowledge during the design process. The agent based design framework is defined in such a manner as to enable the change of any of the input requirements and to allow the resulting change to be propagated through the decision making process. This aspect, combined with a requirements specification methodology that takes into account possible future requirements changes (Hirani, 2004), enables the framework to quickly configure either new systems or reconfigure existing assembly systems according to changed demands.
3. General domain ontology model One of the crucial factors for the success of the proposed RAS design framework is a well defined model that supports the decision-making process. The model needs to be standardised in order to exchange information and knowledge between different stakeholders. At the same time it needs to be extendable to deal with future requirements and provide customisation to cater for the specific needs of the different stakeholders. Furthermore, the model needs to be equally suitable for human as well as machine interpretation since the decision makers could be either one or both. There is also a specific need for the model to cope with incomplete and not fully defined models. Since the purpose of the ontology is to model modular assembly equipment it is important that the resulting model for each equipment module is self contained. Finally the model Springer
Equipment ontology for modular reconfigurable assembly systems Knowledge Representation Level
Domain Ontology Concept Structure:
Classes, Slots, Facets, Instances
Axioms Inference Rules
Ontology Concept Level Generic Domain Ontology Concepts
relationships hi er ar ch y
Assembly Domain Concepts
User Specific Ontology Concepts
Ontology Instance Level
Fig. 2 Equipment ontology structure
needs to provide the means to trace the decision-making process and allow decision makers to reassess critical decisions at a later stage during the design process. An ontology is concerned with the study of being or existence and their basic categories and relationships, to determine what entities and what types of entities exist. It therefore has strong implications for conceptions of reality (Wikipedia, 2006). This is particularly important for the study and definition of equipment models since they have to reflect physical existing entities. An ontology based approach is therefore appropriate for representing the morphology of complex manufacturing systems. To support the decision making, an ontology model is proposed that include three representation levels: the underlying knowledge representation level, the ontology concept level and the instantiation level (see Fig. 2 left side). The knowledge representation level defines how the different concepts, attributes, constraints, and rules are implemented. A frame based knowledge representation has been chosen to define the concepts and their attributes in an object oriented manner. The specific model constraints are expressed as axioms and the design decisions are modelled as inference rules. On the ontology level all the specific domain concepts, attributes, constraints, and rules are defined. The domain ontologies are divided into generic and user specific concepts. This allows all the design decisions to be based on generic concepts while at the same time providing a mechanism to enable different stakeholders to define their own specific terminology and concept interpretation. The basic model is defined along three axes: relationships, hierarchy, and abstraction (see Fig. 2 right side). The relationships define how the concepts within an ontology are related to each other. For example, equipment modules can be connected to each other. The hierarchy aspect deals with the readability of the model. By defining different levels of detail it becomes much easier to define complex structures. The hierarchy is defined by grouping lower level concepts to form higher level concepts. Abstraction is defined using a super/sub-class structure and allows a gradual specification of more and more concrete models. For example at an early design stage it might only be Springer
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known that a feeder will be required but not yet what specific feeder. At a later point the feeder can be specified to be for example a bulk feeder. Both the main concepts like Objects, Functions, Behaviours, etc. as well as their relationships can be abstracted and specified.
4. Assembly equipment domain ontology 4.1. Function-behaviour-structure The assembly equipment domain ontology needs to support three main activities during the design of assembly systems. These are: (1) the selection of suitable equipment; (2) the configuration of the selected equipment to build the system; and (3) the evaluation of alternative configurations to verify that all requirements have been fulfilled and to select the most suitable solution. Before we address how the proposed function-behaviour-structure model fulfils the above requirements it is necessary to clarify the distinctions between the three aspects of the model in general. The functions express the capabilities of a module based on the intention of the designer and are therefore subjective or rather domain specific. Functions are generally defined as an abstraction of behaviour for a specific use or purpose (Umeda et al., 1996). The assumption at this point is that the purpose of each piece of equipment will be to facilitate the assembly of products. The equipment functions will therefore be limited to the assembly specific interpretations. The behaviour of an equipment module defines how it reacts to changes in its environment and in turn how this reaction influences the environment. Behaviour is defined as state transitions from input to output based on physical phenomena. The physical phenomena provide the building blocks for an objective description of assembly equipment modules. The structure defines the physical model of the equipment with objects, attributes and relations. The behaviour model is closely entwined with the structure. Having defined the different aspects of the equipment ontology it can now be seen how the function-behaviour-structure model fulfils the above requirements. The functional scope of an equipment module determines if it has the capabilities for a required aspect of the assembly process. The behaviour together with the structure of the equipment provides the means to assess the performance of the equipment module within a given production situation. The configuration and reconfiguration of equipment modules is a structural process driven by the equipment selection and guided by continuous performance assessment. 4.2. Ontology definition All three aspects of the equipment ontology are defined in separate models that are structured as shown in Section 3. The equipment concept ontology (see Fig. 3) is defining the physical structure of the equipment. The Equipment concept in the middle is a subclass of Object concept which defines that all Equipment concepts are physical objects in the same way as assembly components would be. It also is a subclass of ObjectStructure concept defining that Equipment concepts also have a hierarchy of subcomponents that can either be other Equipment or Components. The Springer
Equipment ontology for modular reconfigurable assembly systems Object Interface
ObjectStructure System isA
Fig. 3 Equipment module ontology concept overview
relationships between the subcomponents of an Equipment concept are defined either by ComponentConnections or by InterfaceConnections. The difference between the two types of relationships is that InterfaceConnections define the connection between two InterfacePorts which have to adhere to an Interface definition while ComponentConnections define the connection between two Objects without any specific interfacing constraints. This approach has been chosen to cover not only modular Equipment but also non-modular equipment that could be used inside an equipment module. Equipment concepts have also been hierarchically ordered into more specific subtypes: system, cell, workstation, unit, device, and element. The order is in decreasing functional complexity. For the configuration and reconfiguration of equipment modules it is important to understand the connection constraints between different equipment modules. The proposed mechanism for the realisation of these constraints is two-fold. First is the hierarchical definition of assembly equipment and constraints placed on the attribute associated to the different levels. For example a Workstation can only have Units, Devices, and Elements as subcomponents. Second is a mechanism to provide the standard definition of the module framework chosen for the implementation of the RAS. The modular structure is defined through the definition of its modules and interfaces (Bi and Zhang 2001; Koren et al., 1999; Ulrich and Tung 1991; Pahl and Beitz 1996). The Module and Interface concepts provide definitions of how equipment modules need to be defined in order to participate within a modular framework. A Module concept defines the Functions and InterfacePorts that need to be implemented by a specific type of module. The Interface concept defines the connectivity constraints between InterfacePorts of the same interface type. Each Equipment concept is directly linked to its Functions and Behaviours via its and attributes. This enables the modelling of each piece of equipment as a self-contained instance. As mentioned above, there is a close relationship between the physical structure and the behaviour of the equipment model. For instance, an InterfacePort is a behaviour that at the same time enables the definition of the physical relationships between equipment modules. The relationship between Springer
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Fig. 4 Assembly activity function classification structure
hasFunctions >> 1
hasFunctions > 1
hasFunctions >= 1
hasFunctions >= 1
hasFunctions >= 1
hasFunctions < 1
hasFunctions < 1
hasFunctions < 1
Fig. 5 Relation between function concepts and equipment concepts
Functions and Behaviour defines how each function is realised through the behaviour of the Equipment. Functions as well as Behaviours are like Equipment concepts structured hierarchically through their and attributes respectively. They also have subclasses that define more specialised functions and behaviours. It has been assumed that the intended purpose of the modelled equipment is the facilitation of assembly activities. Consequently, the classification of the equipment functions has been closely modelled to follow our process classification structure as introduced in (Ratchev and Lohse, 2003). The main sub-function concepts are the execution of the main activity types: task, operation, and action (see Fig. 4). Tasks constitute the highest level of activities by defining the sequence in which the components are being assembled to form the final product. Operations define on an intermediate level the steps required to put the components together including feeding, handling, assembly, etc. Actions on the lowest level define the individual motions and other hardware and control related activities. The relation between the sub-function concepts and the different classes of equipment are shown in Fig. 5. For more detailed definition of the Function and Behaviour concepts see also (Lohse et al., 2004). Springer
Equipment ontology for modular reconfigurable assembly systems
Fig. 6 Equipment module instance definition
5. Illustrative example The proposed equipment ontology has been applied in several industrial and virtual use cases. To illustrate its application a simple assembly scenario has been chosen that includes the definition of a modular device, its integration into a wider functional system, and its replacement with another module of similar functional capability. Figure 1 shows the principle definition of a standard SCARA-type robot. The robot has two primary ports to connect with other equipment modules: a table or base port (P1 ) and a tool port (P2 ). Its main function (F1 ) can be defined as moving something connected to port (P2 ). This function can be specialised for this robot to point to point (F1.1 ), rotary (F1.2 ), linear (F1.3 ), or circular (F1.4 ) motions. The functional definition of the robot is completed with a temporal constraint that specifies that the motion functions can only occur sequentially (Fig. 6). The functional definition is directly linked to the kinematic behaviour of the robot. The behaviour can generally be defined as SCARA type kinematic structure (B1 ) which always breaks down into three rotary (B R1 , B R2 , and B R3 ) and a translatory joint behaviour (BT 1 ). Joint behaviours normally transform electric energy into kinematic energy. The kinematic motion space is defined through the accumulated kinematic energies of the joint behaviours based on their links (Fig. 1). The defined structure could theoretically be interpreted in such a way that the base of the robot would move relative to the tool port, should the tool port be held in place. However, this is not really practical since the drives of a robot are optimised for this specific kinematic structure and can be restricted through the definition of port roles. Finally, the structure of the robot can be defined through four components (C1 to C4 ) that are joined with three connections (L1 to L3 ). The different joint behaviours are linked to the connections they drive. Springer
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So far the specification was only focused on the general description of the robot. In order for the robot to become a module its definition needs to fulfil the requirements of at least one Module definition of the domain it is intended to be used in. The module definitions impose constraints on the functional and connection capability of the equipment. In the case of the robot the most common constraint is placed on its interface ports. The functional capability is normally quite generic for this type of equipment. The main purpose of the proposed equipment ontology is to make it easier to integrate equipment into higher level functional systems. The following example illustrates the advantage of the definition. For the example the robot module should be used to assemble two components. Its move function does not suffice for the task on its own. It needs to be combined with two other modules that can hold both components during the assembly. Additionally one of the holding devices needs to be connected to one port of the robot and the other to the other port, because the two components need to move relative to each other. Figure 2 shows how the robot could be combined with a gripper and a fixture to achieve the overall desired insertion operation function. The recognition of the overall function from a set of lower level functions is based on activity composition patterns (Lohse et al., 2005). From the connectivity constraints it can be recognised that some additional structural element will be required to connect all three modules. This general approach can be used for assembly process driven equipment configuration as well as for the assessment of functional capabilities that result from the connection of equipment modules. Each module takes responsibility for a part of the overall assembly operation.
6. Prototype implementation The frame based assembly equipment domain ontology has been defined using Prot´eg´e 2.0.1 (Prot´eg´e 2004). Prot´eg´e is a domain ontology definition tool, which defines classes, slots, relationships, facets, and instances based on the Open Knowledge Base Connectivity (OKBC) protocol (Chaudhri et al., 1998). The constraints are expressed as axioms defined in the Prot´eg´e Axiom Language (PAL) plug-in (Grosso 2004). The syntax of PAL is a variant of the Knowledge Interchange Format (KIF) (Genesereth and Fikes 1992). PAL constraints are defined by a variable range and a logical statement defining the condition that needs to hold for the defined variable range. The inference rules have been defined using the definitions of the Java Expert System Shell (JESS) (Friedman-Hill, 2004). Each rule has the form of if-then statements that match existing facts to new facts to be asserted or actions to be executed. The proposed assembly equipment ontology has been implemented as part of a prototype web-enabled decision-making environment for the distributed design of modular assembly systems (E-Race 2004). The aim of the prototype implementation is to test the completeness of the domain ontologies and the design methodology. All steps of the design process are initially supported by human centred decision-making agents, i.e. agents providing decision-making interfaces and initial advisory support. The human centred agents interact via dynamic web pages with the different users. The agent platform provides all the required data and knowledge storage facilities as well as message transport protocols. Fig. 7 shows the user interface for the embodiment Springer
Equipment ontology for modular reconfigurable assembly systems
Fig. 7 Integration of modules into higher level functional systems
design aspect of an assembly system with the underlying knowledge models defined in Prot´eg´e.
7. Discussion and conclusions The reported research addresses the need for a comprehensive equipment ontology to support effective decision making during the configuration and reconfiguration of reconfigurable assembly systems. A new equipment ontology has been proposed for design of reconfigurable assembly systems based on the function-behaviourstructure paradigm. The structure of the ontology is tailored to support key design activities such as equipment selection, system configuration, and performance evaluation. It also facilitates seamless integration and matching between assembly process requirements and equipment capabilities in developing new assembly system solutions. One of the main advantages of the proposed approach is that it allows all the details of individual equipment modules to be encapsulated into autonomous representations that can be communicated over the Internet. The proposed formalization approach meets the specific requirements to support distributed web-enabled system specification and design, and can be used in both human driven and computer supported decision making. It is expected that the proposed model will help to significantly reduce the design effort and increase the quality of new assembly system designs. The approach is considered especially important for successfully developing and utilising truly modular reconfigurable equipment solutions. It also addresses some of the current limitations that prevent available equipment solutions from being formalised and represented at the required level of detail. Moreover,, the approach can potentially lead to a radical rethinking of the current design and sales practices of reconfigurable manufacturing systems and modules. Our future research focus will be on a closer analysis of the proposed modelling approach in more complex system scenarios. Further research will be conducted to advance the understanding on how to effectively utilize the ontology within computer aided concurrent and distributed design environments. Springer
Fig. 8 Web enabled embodiment design interface
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Acknowledgements The reported work is partially funded by the Department of Trade and Industry in the United Kingdom as part of the EUREKA Factory E!2851 E-RACE project, the support of which is gratefully acknowledged.
References Alsterman H (2004) Strategic issues for achieving sustainable automatic assembly - towards evolvable assembly systems, PhD thesis, Royal Institute of Technology, ISSN 1650–1888 Bi ZM, Zhang WJ (2001) Modularity technology in manufacturing: taxonomy and issues. I J Adv Manu Technol. Springer-Verlag, London Ltd. 18:381–390 Bley H, Dietz S, Roth N, Zintl G (1994) Knowledge of selecting assembly cell components and its distribution to CAD and an expert system for processing. Ann CIRP 43(1):5–8 Bo¨er CR, Pedrazzoli P, Sacco M, Rinaldi R, De Pascale G, Avai A (2001) Integrated computer aided design for assembly systems. Ann CIRP 50(1):17–20 Chaudhri VK, Farquhar A, Fikes R, Karp PD, Rice JP (1998) Open knowldge base connectivity 2.0.3. SRI international, California, USA, available at: www.ai.sri.com/∼okbc/ Chen I-M (2001) Rapid response manufacturing through a rapidly reconfigurable robotic workcell. Robotics and computer integrated manufacturing, vol. 17. Elsevier Science Ltd., pp 199–213 Craig K, De Vito M, Mattice M, La Vigna C, Teolis C (19–23 September, 1999) Mechatronic integration modeling. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Atlanta, USA E-Race (2004) E-Space for collaborative manufacture of re-configurable assembly cells. Available at: www.e-race.info, Friedman-Hill E (2004) Java expert system shell. Sandia National Laboratories, Livermore, California, USA, available at: herzberg.ca.sandia.gov/jess/ Gaugel T, Bengel M, Malthan D (2004) Building a mini-assembly system from a technology construction kit. Assembly Automation, Emerald Group Publishing Ltd., 24(1):43–48 Gausemeier J, Flath M, M¨ohringer S (8–12 July, 2001) Conceptual design of mechatronic systems supported by semi-formal specification. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Como, Italy Genesereth MR, Fikes RE (1992) Knowledge interchange format version 3.0 reference manual. Computer Science Department, Stanford University, Stanford, California, USA Giusti F, Santochi M, Dini G, Arioti A (1994) A reconfigurable assembly cell for mechanical products. Ann CIRP 43(1):1–4 Grosso W (2004) PAL Constraints and queries tabs. Available at: protege.stanford.edu/plugins/paltabs/ PAL tabs.html Hirani H (2004) Knowledge enriched requirements specification for reconfigurable assembly systems, PhD Thesis, University of Nottingham Hollis RL, Quaid A (October 1995) An architecture for agile assembly. society of precision engineering, 10th Annual Meeting, Austin, Texas, USA, 15–19 Koren Y, Heisel U, Jovane F, Moriwaki T, Pritchow G, Van Brussel H, Ulsoy AG (1999) Reconfigurable manufacturing systems. CIRP Ann 48(2) Koren Y, Ulsoy AG (2002) Vision, principles and impact of reconfigurable manufacturing systems. Powertrain Int, pp 14–21 Lastra Jos´e LM (2004) Reference mechatronic architecture for actor-based assembly system, PhD thesis, Tampere University of Technology, ISBN 952-15-1210-5 Lohse N, Hirani H, Ratchev S, Turitto M (July 19–21, 2005) An ontology for the definition and validation of assembly processes for evolvable assembly systems. The 6th IEEE international symposium on assembly and task planning, Montr´eal, Canada Lohse N., Ratchev S., Chrisp A. (2004) Function-behaviour-structure model for modular assembly equipment. In: Proceedings of the International Precision Assembly Seminar 2004, 11–13 February, Bad Hofgastein, Austria, pp 167–174 Lohse N, Ratchev S, Valtchanov G (2004) Towards web-enabled design of modular assembly systems. Assembly Automation, Emerald Group Publishing Ltd. 24(3):270–279 Lohse N, Sch¨afer C, Ratchev S (2006) Towards an integrated assembly process decomposition and modular equipment configuration. Precision Assembly Technologies for Mini and Micro Products, S. Ratchev (ed.), Springer, New York, ISBN 0-387-31276-5 Springer
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Meijer BR, Tomiyama T, van der Holst BHA, van der Werff K (2003) Knowledge structuring for function design. Ann CIRP, CIRP 52(1):89–92 Mizoguchi R, Kitamura Y (2000) Foundation of knowledge systematization: role of ontological engineering. In: Roy R(ed) Industrial knowledge management: a micro-level approach. Springer-Verlag Ltd., London, ISBN 1852333391 Neville D, Joskowicz L, (June 23, 1992) A representation language for conceptual mechanism design. International Qualitative Reasoning Workshop Onori M, Barata de Oliveira JA, Lastra J, Tichem M (2002) European precision assembly roadmap 2012. The Assembly-NET Consortium Pahl G, Beitz W (1996) Translated by: ken wallace, engineering design—a systematic approach, 2nd edn. Springer-Verlag, London, ISBN 3-540-19917-9 Prot´eg´e (2004) Stanford University, Stanford University School of Medicine, Stanford Medical Informatics, California, USA, available at: protege.stanford.edu Ratchev, Svetan, Lohse (2003) Data modelling for web enabled design of modular precision assembly devices. Proceedings of the International Precision Assembly Seminar, 17-19 March 2003, Hofgastein, Austria, pp 149–156 Sasajima M, Kitamura Y, Ikeda M, Mizoguchi R (August 21, 1995) FBRL: A function and behavior representation language. Fourteenth International Joint Conference on Artificial Intelligence (IJCAI’95), Montreal, Canada Sch¨afer C, L´opez O (1999) An object-oriented robot model and its integration into flexible manufacturing systems. In: Imam IF, Kodratoff Y, El-Dessouki A, Ali M (eds) Multiple approaches to intelligent systems: 12th international conference on industrial and engineering applications of artificial intelligence and expert systems. Springer, ISBN 3540660763 Seliger G, Bollmann O (1990) Knowledge-based diagnosis in flexible automated assembly. Ann CIRP, CIRP 39(1):9–14 Tomiyama T, Yoshikawa H, Kiriyama T (1993) Conceptual design of mechanisms: a qualitative physics approach. In: Kusiak A (ed) Concurrent engineering: automation, tools, and techniques, John Wiley and Sons, Inc., ISBN 0-471-55492-8 Travaini E, Pedrazzoli P, Rinaldi R, Bo¨er CR (2002) Methodological approach and reconfiguration tool for assembly systems. Ann CIRP 51(1):9–13 Ulrich K, Tung K (1991) Fundamentals of product modularity. Issues in Design Manufacturing/Integration Umeda Y, Ishii M, Yoshioka M, Shimomura Y, Tetsuo T (1996) Supporting conceptual design based on the function-behavior-state modeler. Artificial intelligence for engineering design, analysis and manufacturing, vol. 10. Cambridge University Press, pp 275–288 Vos Jeroen AWM (2001) Module and system design in flexibly automated assembly, PhD thesis, Technische Universiteit Delft, ISBN 90-407-2785706 Wikipedia (2006) The Free Encyclopaedia, available at: www.wikipedia.org Zha XF, Du H, Lim YE (2001) Knowledge intensive Petri net framework for concurrent intelligent design of automatic assembly systems. Robotics and computer integrated manufacturing, vol. 17. Elsevier Science Ltd., pp 379–398 Zhang M, Fisher W, Webb P, Tarn T-J (14–19 September, 2003) Functional model based object-oriented development framework for mechatronic systems. IEEE international conference on robotics & automation, Taipei, Taiwan Zhang WJ, Liu SN, Li Q (2000) Data/knowledge representation of modular robot and its working environment. Robotics and computer integrated manufacturing, vol. 16. Elsevier Science Ltd., pp 143–159 Zhang W, van der Werff K (1993) A Generic mechanism model for use in a CIM environment for the development of mechanized production machines. Ann CIRP, CIRP 42(1):135–138