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RFBS: A model for knowledge representation of conceptual design
CIRP Annals - Manufacturing Technology 59 (2010) 155–158
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
RFBS: A model for knowledge representation of conceptual design F. Christophe a,b, A. Bernard (1)a,*, E´. Coatane´a b a b
IRCCyN—Research Institute in Communications & Cybernetic of Nantes, Ecole Centrale de Nantes (ECN), P.O. Box 92101, 1 rue de la Noe¨, FR-44321 Nantes Cedex 3, France Department of Engineering Design and Production, Helsinki University of Technology (TKK), P.O. Box 4100, Otakaari 4, FI-02015 TKK, Finland
A R T I C L E I N F O
A B S T R A C T
Keywords: Design method Modelling Knowledge based system
Conceptual design has been broken down into sub-processes and elementary tasks in methodologies. These methodologies proposed suggest their systematic application. This paper assumes the possible execution of these tasks automatically. Nevertheless, it is necessary for computers to integrate the knowledge required during the conceptual design process. Knowledge models have been proposed, for instance Gero’s Function–Behaviour–Structure (FBS) model for design. This paper presents the integration of methodologies with a model of knowledge for conceptual design in accordance with model-driven engineering. Our proposition extends the FBS model and presents its practical implementation through ontology and language such as SysML. ß 2010 CIRP.
1. Introduction The product development process has to tackle multiple requirements of quality, costs and time-to-market. To that effect and also because it involves several actors providing their vision about the services that the future product should be able to accomplish, engineering design is a complex activity for which processes need to be formalized [1]. Design process models and methodologies have been proposed and used to fulfil these aims [2,3]. These models divide the design activity into mainly three sub-processes: conceptual design, detailed design and production design. As stated by many researchers, conceptual design represents more than 70% of the costs and performance of the product being designed [4]. In fact, due to the iterative nature of the design process [5], a poorly conceived artefact cannot be enhanced during the later phases of the design activity. Guidelines and methodologies for conceptual design were provided by the design community [6]. In fact, in order to understand the conceptual design process, researchers described this process through models and representations of the knowledge involved during that stage of engineering design [7]. Nowadays, with the evolution from document-based engineering into modeldriven engineering, these models and representations could be implemented as digital meta-models and enable a better understanding within the design team thanks to modelling languages such as SysML [8]. This paper presents how knowledge representation (KR) could enable the execution of conceptual design tasks by computers. Firstly, this paper presents an overview of the existing models for knowledge representation of conceptual design. Secondly, this overview enables us to set out the basis of our proposal: a conceptual design model implemented by computer applications. We present the compatibility between our model and the diagrams
* Corresponding author. 0007-8506/$ – see front matter ß 2010 CIRP. doi:10.1016/j.cirp.2010.03.105
provided by the semi-formal language SysML [8]. Thirdly, we consider a particular stage of our model, the synthesis of structural concepts, in consideration with a practical example of a robotic arm. As a conclusion, we discuss the contextual dependency of our model and try to define its fields of application.
2. State-of-the-art of KR in conceptual design Conceptual design is related to creativity. Probably the most relevant work in terms of creativity and inventive design was proposed by Altshuller with TRIZ [9]. The main idea of TRIZ is to generalize the design problem in order to find the essential physical principles which will solve the problem. TRIZ provides principles and rules to find general solutions to the specific problem. Nevertheless, this process of specialization is strongly context dependent and it is not possible to automate it. Therefore, scientists have developed many other conceptual design models adapted to a specific domain of product development, e.g. electronics, mechanics. In this review, we present the knowledge representations provided for computers and expert systems which, in our viewpoint, remain contextually independent. The aim here is not to implement creativity algorithms but to enable the possibility for computers to reuse knowledge. 2.1. Inferential design theory Developed by Arciszewski and Michalski in 1994, the inferential design theory (IDT) proposes a framework for the integration of multiple conceptual design methods [10]. The main idea of IDT is to propose logical operators on the knowledge parts contained in the system’s knowledge base. These operators are called transmutations. They defined 11 pairs of transmutations composed of opposite transmutations. This theory is very interesting due to the combination of knowledge parts that it proposes. To a certain extent, this combination implements a degree of creativity in the system.
F. Christophe et al. / CIRP Annals - Manufacturing Technology 59 (2010) 155–158
156
3. Contribution In this section, we present our vision of conceptual design through the Requirement––Function–Behaviour–Structure model (RFBS model), adapted from Gero’s FBS model in order to fit current model-driven engineering. Then we describe the practical implementation of the RFBS model using SysML diagrams. 3.1. RFBS model
Fig. 1. Gero’s FBS model.
Furthermore, IDT is claimed to be a unified framework due to its multi-strategic aspect. Nevertheless, this theory does not explain the type of necessary knowledge at the conceptual design stage. Next section presents a model of that type of knowledge. 2.2. Function–Behaviour–Structure Gero proposed the FBS model in 1990 [11]. This model was one of the first models of the knowledge required in design for building expert systems. This model is presented in Fig. 1 and each of its processes is described by the grey cells in Table 1. Since then, this model has been the reference for building expert systems based on the triplet of knowledge with modifications, such as Tian’s TFBS [12] or Umeda and Tomiyama who proposed a formal adaptation of Function–Behaviour–State modelling [13]. The original FBS model has evolved with time as Gero and Kannengiesser proposed a version of it situated within the creative environment in 2002 [14]. Researchers have also adapted FBS in order to fit different scopes, such as, for example, Labrousse who provided Function/Behaviour/ Structure/Process/Product/Resource/External Effects modelled for managing company knowledge [15]. This overview has presented the bases of our research on knowledge representation for conceptual design. As presented, the FBS model has evolved according to needs of formalism. Next section, proposes yet another adaptation of the FBS model. This adaptation is relevant with consideration of current designing methods.
As shown previously, Gero’s FBS model contains a few points which needed to be updated according to model-driven engineering. Apart from the fact that this previous model did not include a requirement phase and that the documentation phase has now become secondary compared to the models themselves, a major point needs to be reanalyzed in the synthesis phase of conceptual design. In fact, Gero stated that the only possible link between function and structure was through the expression of behaviour. We argue here that it is possible to create ‘‘embryos’’ of structures out of functions only, thanks to semantics. We call these preliminary structures generic or abstract structures. As abstract classes in object-oriented modelling, their aim is only to encapsulate each atomic function of the system into one or more of the six families of organs from our previous works [16] and in agreement with Hubka’s principles of engineering design [17]. Therefore, we propose the RFBS model presented in Fig. 2, in which the numbers from 1 to 7 represent the sub-processes of conceptual design and each vertex of the graph represents the resulting models of each off these processes. The subprocesses of conceptual design are explained in Table 1. From our viewpoint, conceptual design starts right after the definition of needs discussed between the designers’ team and the customer. The result of the discussion sets the requirements for the future system. Requirement models contain the performance criteria of the product and its service functions. From these models are derived both the technical functions of the system and the expected behaviour of the system. The expected behaviour Be corresponds to the modelling of the physical laws involved in the realisation of the function. Be can be represented, for example, with the use of: differential equations explaining the transfer between inputs and outputs of the technical function a state machine in the case of discrete variables.
Table 1 RFBS processes of conceptual design. Representation stages
Processes of conceptual design
Reformulation processes
R is the set of constraints and performance criteria required by the system
1. Requirement analysis: transforms the design problem, expressed in requirements (R), into functions (F) that the system should provide
70 0 0 . Reformulation type 4: addresses changes in the design state space in terms of requirement variables or their ranges of values (this reformulation involves discussion with the client to find an agreement)
F represents a set of functions, the necessary knowledge in order to be able to explain what the system should do according to requirements, thus F is derived from R
10 . Problem formulation: (Gero’s process 1) transforms the design problem, expressed in function (F) and requirements (R), into behavior (Be) that is expected to enable this function to work with the performance criteria set by the requirements
70 0 . Reformulation type 3: (Gero’s process 8) addresses changes in the design state space in terms of function variables or their ranges of values (this reformulation induces automatic changes in the expected behavior)
Be is the expected behavior of the system, specifically the set of variables showing how the system should work, Be is set according to Requirements and Functions
2. Pre-synthesis: transforms the functional architecture of the system (F) into a generic structure (GS) using abstract organs
GS is the representation of generic structure, specifically abstract classes encapsulating function and their intrinsic attributes, GS is derived from F
3. Synthesis: (Gero’s process 2) specializes GS according to the expected behavior (Be) into a solution structure (S) that is intended to exhibit this desired behavior
70 . Reformulation type 2: addresses changes in the design state space in terms of abstract organs or generic structure variables or their ranges of values
S is the set of classes representing the physical structure of the system, S specializes GS according to Be
4. Analysis: (Gero’s process 3) derives the ‘‘actual’’ behavior (Bs) from the synthesized structure (S)
7. Reformulation type 1: (Gero’s process 6) addresses changes in the design state space in terms of structure variables or their ranges of values
Bs is the set of variables enabling the representation of the effective behavior of the system, e.g. its ‘‘actual’’ behavior
5. Evaluation: (Gero’s process 4) compares the behavior derived from structure (Bs) with the expected behavior to prepare the decision if the design solution is to be accepted
D represents the transfer of the models to the next stage of design: detailed design
6. Detailing: prepares all drawn models for the detailed design phase (from work classes into technology involvement)
F. Christophe et al. / CIRP Annals - Manufacturing Technology 59 (2010) 155–158
157
3.2. Practical implementation of the model The System Modelling Language (SysML), comes from an effort from the International Council on Systems Engineering (InCoSE) and from the Object Management Group (OMG) to integrate languages of different disciplines into one interdisciplinary language. SysML is derived from UML2.0, unified modelling language, which was also initiated to gather the semantics from different object-oriented modelling languages. Most diagrams provided originally with the language are coherent with the RFBS model as shown in Table 1. Nevertheless, a specific profile of the language had to be created in order to enable the representation of functional decompositions and architectures [19]. Additionally, in order to enable the integration of the conceptual design knowledge in computer applications, we developed the mid-level ontology shown in Fig. 3. This ontology is called mid-level because integrates lower level ontologies mostly from Hirtz et al. [20]. The following section presents a case study of this ontology with the computer application OPAS.
Fig. 2. The RFBS model.
4. Example
Fig. 3. Mid-level ontology for the synthesis of conceptual solutions.
Technical functions can be mapped with generic organs with the use of semantics and ontologies, as shown in previous research [18]. This provides the generic structures of the system. The input and output flows of the technical functions and their expected behaviour are used to derive these generic structures into the final structures of the system. The behaviour of the system is derived from its simulation and a model of this ‘actual’ behaviour is compared to the expected behaviour in order to evaluate the structure of the system. Once a decision is made about the concepts to pursue, all the models defined during the conceptual design are to be refined during the embodiment and detailed design phases. The next section presents how the RFBS model is implemented practically.
In this section we present the partial development of the manipulator of a mobile robot. In this example, we describe only the pre-synthesis and synthesis parts of the RFBS model because it is the part involving the newest developments. Fig. 4 represents the expressions of the requirements for the entire system, e.g. the mobile robot contains the expectations in performance as well as the constraints due to the system’s environment. This diagram is combined together with the use-case diagram in Fig. 5, presenting the service functions expected from the mobile robot. We consider here the service for manipulating objects as a subsystem of the mobile robot. The verb ‘manipulate’ does not represent a standard technical function. Therefore, a request is sent on a semantic atlas [21] in order to find standard technical function verbs listed as contexonyms of ‘manipulate’. The list of contexonyms obtained from this request is shown in Fig. 6. The breakdown of the service function ‘manipulate’ into trees of technical functions is shown in Fig. 7. As ‘control’ and ‘guide’ were contexonyms of ‘manipulate’, and as they belong to Hirtz’s taxonomy of standard technical functions, the designing system deduced the functional decomposition shown in Fig. 7. Thus, the manipulating system shall have a controlling part and a guiding part.
Fig. 4. Requirement diagram for a mobile robot.
F. Christophe et al. / CIRP Annals - Manufacturing Technology 59 (2010) 155–158
158
Fig. 5. Use-case diagram of a mobile robot.
Fig. 6. Semantic contexonyms of the verb ‘manipulate’. Fig. 9. Models for an object manipulator.
Fig. 7. Technical function breakdown equivalent to ‘manipulate’.
Fig. 8 represents the mapping between functional verbs and generic organs. This mapping is done with OPAS with the search through the ontology of generic organs. This search results in advising designers to use conversion and calculation organs for the control unit of the manipulator, OPAS also advises the use of distribution organs such as rotational and translation links for the guiding unit of the manipulator. Fig. 9 represents how these generic organs are then specialized into concrete mechanical components in the case of the guiding unit and software components for the control unit. These components become concrete due to the use of an ontology of energies and variables enabling the use of physical attributes such as the torque and the position of each component of the robot manipulator. 5. Conclusion This paper has presented the integration of design methodologies and modelling languages through the use of a meta-model of conceptual design: the RFBS model. This model is in accord with model-driven engineering. Furthermore, this paper presents the possibilities provided by this model. From our viewpoint, its major asset is to enable the synthesis of concepts in an automated manner.
Fig. 8. Mapping standard functions with generic organs.
Automating parts of the conceptual process is a topic of growing interest in the field of engineering design. These efforts present the same issues as our method: they are contextually related. Even though, the proposed solutions are generic, they are still adapted only to a specific domain. Most ontologies are nowadays compatible and a switch between domain specific ontologies may enable the method to be applied to wider ranges of problems and concepts of solutions. References [1] Deneux D (2002) Me´thodes et mode`les pour la conception concourante. HDR, University of Valenciennes and Hainaut Cambre´sis. [2] Pahl G, Beitz W (1988) Engineering Design: A Systematic Approach. SpringerVerlag, Berlin. [3] VDI-Guideline 2221: Systematic Approach to the Design of Technical Systems and Products, (1987), VDI, Du¨sseldorf. [4] Zimmer L, Zablit P (2001) Global Aircraft Predesign based on Contraint Propagation and Interval Analysis. Proceedings of CEAS Conference on Multidisciplinary Aircraft design and Optimisation, Ko¨ln, Germany, . [5] Ullman DG (2003) The Mechanical Design Process. 3rd ed. McGraw-Hill Higher Education, New York. [6] Tomiyama T, Gu P, Jin Y, Lutters D, Kind Ch, Kimura F (2009) Design Methodologies: Industrial and Educational Applications. Keynote CIRP Annals— Manufacturing Technology 58(2):543–565. [7] Tomiyama T, Meijer BR, Van Der Holst BHA, Van Der Werff K (2003) Knowledge Structuring for Function Design. CIRP Annals STC Design 52(1):89. [8] http://www.sysmlforum.com/docs/specs/OMGSysML-v1.1-08-11-01.pdf, November 2008. [9] Altshuller G (1984) Creativity as an Exact Science. Gordon & Breach, Luxembourg. [10] Arciszewski T, Michalski RS (1994) Inferential Design Theory: A Conceptual Outline, Artificial Intelligence in Design. Kluwer Academic Publishers . pp. 295–308. [11] Gero JS (1990) Design Prototypes: A Knowledge Representation Schema for Design. AI Magazine 11(4):26–36. [12] Tian YL, Zou HJ, Guo WZ (2005) An Integrated Knowledge Representation Model for the Computer-aided Conceptual Design of Mechanisms. International Journal of Advanced Manufacturing Technology 28:435–444. [13] Umeda Y, Tomiyama T (1997) Functional Reasoning in Design, AI in Design. IEEE Expert 42–48. [14] Gero JS, Kannengiesser U (2002) The Situated Function–Behaviour–Structure Framework, AI in Design 0 02. Kluwer. pp. 89–104. [15] Labrousse M, Bernard A (2008) FBS-PPRE, An Enterprise Knowledge Lifecycle Model. Springer, Berlin Heidelberg. pp. 285–305. [16] Coatane´a E (2005). Conceptual Modelling of Life Cycle Design: A Modelling and Evaluation Method Based on Analogies and Dimensionless Numbers. Doctoral Dissertation. [17] Hubka V, Eder WE (2001) Design Science. . http://deseng.ryerson.ca/ DesignScience. [18] Christophe F, Sell R, Bernard A, Coatane´a E (2009) OPAS: Ontology Processing for Assisted Synthesis of Conceptual Design Solutions. Proceedings of IDETC/CIE, n. DETC2009-87776, . [19] Chiron F, Kouiss K (2007) Design of IEC 61131-3 Function Blocks using SysML. Mediterranean Conference on Control and Automation, Athens, Greece, July 27–29, . [20] Hirtz J, Stone RB, McAdams DA, Szykman S, Wood KL (2002) A Functional Basis for Engineering Design: Reconciling and Evolving Previous Efforts. Research in Engineering Design 13:65–82. [21] Ji H, Ploux S, Wehrli E (2003) Lexical Knowledge Representation with Contexonyms. Proceedings of the 9th MT Summit, 194–201.
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
RFBS: A model for knowledge representation of conceptual design F. Christophe a,b, A. Bernard (1)a,*, E´. Coatane´a b a b
IRCCyN—Research Institute in Communications & Cybernetic of Nantes, Ecole Centrale de Nantes (ECN), P.O. Box 92101, 1 rue de la Noe¨, FR-44321 Nantes Cedex 3, France Department of Engineering Design and Production, Helsinki University of Technology (TKK), P.O. Box 4100, Otakaari 4, FI-02015 TKK, Finland
A R T I C L E I N F O
A B S T R A C T
Keywords: Design method Modelling Knowledge based system
Conceptual design has been broken down into sub-processes and elementary tasks in methodologies. These methodologies proposed suggest their systematic application. This paper assumes the possible execution of these tasks automatically. Nevertheless, it is necessary for computers to integrate the knowledge required during the conceptual design process. Knowledge models have been proposed, for instance Gero’s Function–Behaviour–Structure (FBS) model for design. This paper presents the integration of methodologies with a model of knowledge for conceptual design in accordance with model-driven engineering. Our proposition extends the FBS model and presents its practical implementation through ontology and language such as SysML. ß 2010 CIRP.
1. Introduction The product development process has to tackle multiple requirements of quality, costs and time-to-market. To that effect and also because it involves several actors providing their vision about the services that the future product should be able to accomplish, engineering design is a complex activity for which processes need to be formalized [1]. Design process models and methodologies have been proposed and used to fulfil these aims [2,3]. These models divide the design activity into mainly three sub-processes: conceptual design, detailed design and production design. As stated by many researchers, conceptual design represents more than 70% of the costs and performance of the product being designed [4]. In fact, due to the iterative nature of the design process [5], a poorly conceived artefact cannot be enhanced during the later phases of the design activity. Guidelines and methodologies for conceptual design were provided by the design community [6]. In fact, in order to understand the conceptual design process, researchers described this process through models and representations of the knowledge involved during that stage of engineering design [7]. Nowadays, with the evolution from document-based engineering into modeldriven engineering, these models and representations could be implemented as digital meta-models and enable a better understanding within the design team thanks to modelling languages such as SysML [8]. This paper presents how knowledge representation (KR) could enable the execution of conceptual design tasks by computers. Firstly, this paper presents an overview of the existing models for knowledge representation of conceptual design. Secondly, this overview enables us to set out the basis of our proposal: a conceptual design model implemented by computer applications. We present the compatibility between our model and the diagrams
* Corresponding author. 0007-8506/$ – see front matter ß 2010 CIRP. doi:10.1016/j.cirp.2010.03.105
provided by the semi-formal language SysML [8]. Thirdly, we consider a particular stage of our model, the synthesis of structural concepts, in consideration with a practical example of a robotic arm. As a conclusion, we discuss the contextual dependency of our model and try to define its fields of application.
2. State-of-the-art of KR in conceptual design Conceptual design is related to creativity. Probably the most relevant work in terms of creativity and inventive design was proposed by Altshuller with TRIZ [9]. The main idea of TRIZ is to generalize the design problem in order to find the essential physical principles which will solve the problem. TRIZ provides principles and rules to find general solutions to the specific problem. Nevertheless, this process of specialization is strongly context dependent and it is not possible to automate it. Therefore, scientists have developed many other conceptual design models adapted to a specific domain of product development, e.g. electronics, mechanics. In this review, we present the knowledge representations provided for computers and expert systems which, in our viewpoint, remain contextually independent. The aim here is not to implement creativity algorithms but to enable the possibility for computers to reuse knowledge. 2.1. Inferential design theory Developed by Arciszewski and Michalski in 1994, the inferential design theory (IDT) proposes a framework for the integration of multiple conceptual design methods [10]. The main idea of IDT is to propose logical operators on the knowledge parts contained in the system’s knowledge base. These operators are called transmutations. They defined 11 pairs of transmutations composed of opposite transmutations. This theory is very interesting due to the combination of knowledge parts that it proposes. To a certain extent, this combination implements a degree of creativity in the system.
F. Christophe et al. / CIRP Annals - Manufacturing Technology 59 (2010) 155–158
156
3. Contribution In this section, we present our vision of conceptual design through the Requirement––Function–Behaviour–Structure model (RFBS model), adapted from Gero’s FBS model in order to fit current model-driven engineering. Then we describe the practical implementation of the RFBS model using SysML diagrams. 3.1. RFBS model
Fig. 1. Gero’s FBS model.
Furthermore, IDT is claimed to be a unified framework due to its multi-strategic aspect. Nevertheless, this theory does not explain the type of necessary knowledge at the conceptual design stage. Next section presents a model of that type of knowledge. 2.2. Function–Behaviour–Structure Gero proposed the FBS model in 1990 [11]. This model was one of the first models of the knowledge required in design for building expert systems. This model is presented in Fig. 1 and each of its processes is described by the grey cells in Table 1. Since then, this model has been the reference for building expert systems based on the triplet of knowledge with modifications, such as Tian’s TFBS [12] or Umeda and Tomiyama who proposed a formal adaptation of Function–Behaviour–State modelling [13]. The original FBS model has evolved with time as Gero and Kannengiesser proposed a version of it situated within the creative environment in 2002 [14]. Researchers have also adapted FBS in order to fit different scopes, such as, for example, Labrousse who provided Function/Behaviour/ Structure/Process/Product/Resource/External Effects modelled for managing company knowledge [15]. This overview has presented the bases of our research on knowledge representation for conceptual design. As presented, the FBS model has evolved according to needs of formalism. Next section, proposes yet another adaptation of the FBS model. This adaptation is relevant with consideration of current designing methods.
As shown previously, Gero’s FBS model contains a few points which needed to be updated according to model-driven engineering. Apart from the fact that this previous model did not include a requirement phase and that the documentation phase has now become secondary compared to the models themselves, a major point needs to be reanalyzed in the synthesis phase of conceptual design. In fact, Gero stated that the only possible link between function and structure was through the expression of behaviour. We argue here that it is possible to create ‘‘embryos’’ of structures out of functions only, thanks to semantics. We call these preliminary structures generic or abstract structures. As abstract classes in object-oriented modelling, their aim is only to encapsulate each atomic function of the system into one or more of the six families of organs from our previous works [16] and in agreement with Hubka’s principles of engineering design [17]. Therefore, we propose the RFBS model presented in Fig. 2, in which the numbers from 1 to 7 represent the sub-processes of conceptual design and each vertex of the graph represents the resulting models of each off these processes. The subprocesses of conceptual design are explained in Table 1. From our viewpoint, conceptual design starts right after the definition of needs discussed between the designers’ team and the customer. The result of the discussion sets the requirements for the future system. Requirement models contain the performance criteria of the product and its service functions. From these models are derived both the technical functions of the system and the expected behaviour of the system. The expected behaviour Be corresponds to the modelling of the physical laws involved in the realisation of the function. Be can be represented, for example, with the use of: differential equations explaining the transfer between inputs and outputs of the technical function a state machine in the case of discrete variables.
Table 1 RFBS processes of conceptual design. Representation stages
Processes of conceptual design
Reformulation processes
R is the set of constraints and performance criteria required by the system
1. Requirement analysis: transforms the design problem, expressed in requirements (R), into functions (F) that the system should provide
70 0 0 . Reformulation type 4: addresses changes in the design state space in terms of requirement variables or their ranges of values (this reformulation involves discussion with the client to find an agreement)
F represents a set of functions, the necessary knowledge in order to be able to explain what the system should do according to requirements, thus F is derived from R
10 . Problem formulation: (Gero’s process 1) transforms the design problem, expressed in function (F) and requirements (R), into behavior (Be) that is expected to enable this function to work with the performance criteria set by the requirements
70 0 . Reformulation type 3: (Gero’s process 8) addresses changes in the design state space in terms of function variables or their ranges of values (this reformulation induces automatic changes in the expected behavior)
Be is the expected behavior of the system, specifically the set of variables showing how the system should work, Be is set according to Requirements and Functions
2. Pre-synthesis: transforms the functional architecture of the system (F) into a generic structure (GS) using abstract organs
GS is the representation of generic structure, specifically abstract classes encapsulating function and their intrinsic attributes, GS is derived from F
3. Synthesis: (Gero’s process 2) specializes GS according to the expected behavior (Be) into a solution structure (S) that is intended to exhibit this desired behavior
70 . Reformulation type 2: addresses changes in the design state space in terms of abstract organs or generic structure variables or their ranges of values
S is the set of classes representing the physical structure of the system, S specializes GS according to Be
4. Analysis: (Gero’s process 3) derives the ‘‘actual’’ behavior (Bs) from the synthesized structure (S)
7. Reformulation type 1: (Gero’s process 6) addresses changes in the design state space in terms of structure variables or their ranges of values
Bs is the set of variables enabling the representation of the effective behavior of the system, e.g. its ‘‘actual’’ behavior
5. Evaluation: (Gero’s process 4) compares the behavior derived from structure (Bs) with the expected behavior to prepare the decision if the design solution is to be accepted
D represents the transfer of the models to the next stage of design: detailed design
6. Detailing: prepares all drawn models for the detailed design phase (from work classes into technology involvement)
F. Christophe et al. / CIRP Annals - Manufacturing Technology 59 (2010) 155–158
157
3.2. Practical implementation of the model The System Modelling Language (SysML), comes from an effort from the International Council on Systems Engineering (InCoSE) and from the Object Management Group (OMG) to integrate languages of different disciplines into one interdisciplinary language. SysML is derived from UML2.0, unified modelling language, which was also initiated to gather the semantics from different object-oriented modelling languages. Most diagrams provided originally with the language are coherent with the RFBS model as shown in Table 1. Nevertheless, a specific profile of the language had to be created in order to enable the representation of functional decompositions and architectures [19]. Additionally, in order to enable the integration of the conceptual design knowledge in computer applications, we developed the mid-level ontology shown in Fig. 3. This ontology is called mid-level because integrates lower level ontologies mostly from Hirtz et al. [20]. The following section presents a case study of this ontology with the computer application OPAS.
Fig. 2. The RFBS model.
4. Example
Fig. 3. Mid-level ontology for the synthesis of conceptual solutions.
Technical functions can be mapped with generic organs with the use of semantics and ontologies, as shown in previous research [18]. This provides the generic structures of the system. The input and output flows of the technical functions and their expected behaviour are used to derive these generic structures into the final structures of the system. The behaviour of the system is derived from its simulation and a model of this ‘actual’ behaviour is compared to the expected behaviour in order to evaluate the structure of the system. Once a decision is made about the concepts to pursue, all the models defined during the conceptual design are to be refined during the embodiment and detailed design phases. The next section presents how the RFBS model is implemented practically.
In this section we present the partial development of the manipulator of a mobile robot. In this example, we describe only the pre-synthesis and synthesis parts of the RFBS model because it is the part involving the newest developments. Fig. 4 represents the expressions of the requirements for the entire system, e.g. the mobile robot contains the expectations in performance as well as the constraints due to the system’s environment. This diagram is combined together with the use-case diagram in Fig. 5, presenting the service functions expected from the mobile robot. We consider here the service for manipulating objects as a subsystem of the mobile robot. The verb ‘manipulate’ does not represent a standard technical function. Therefore, a request is sent on a semantic atlas [21] in order to find standard technical function verbs listed as contexonyms of ‘manipulate’. The list of contexonyms obtained from this request is shown in Fig. 6. The breakdown of the service function ‘manipulate’ into trees of technical functions is shown in Fig. 7. As ‘control’ and ‘guide’ were contexonyms of ‘manipulate’, and as they belong to Hirtz’s taxonomy of standard technical functions, the designing system deduced the functional decomposition shown in Fig. 7. Thus, the manipulating system shall have a controlling part and a guiding part.
Fig. 4. Requirement diagram for a mobile robot.
F. Christophe et al. / CIRP Annals - Manufacturing Technology 59 (2010) 155–158
158
Fig. 5. Use-case diagram of a mobile robot.
Fig. 6. Semantic contexonyms of the verb ‘manipulate’. Fig. 9. Models for an object manipulator.
Fig. 7. Technical function breakdown equivalent to ‘manipulate’.
Fig. 8 represents the mapping between functional verbs and generic organs. This mapping is done with OPAS with the search through the ontology of generic organs. This search results in advising designers to use conversion and calculation organs for the control unit of the manipulator, OPAS also advises the use of distribution organs such as rotational and translation links for the guiding unit of the manipulator. Fig. 9 represents how these generic organs are then specialized into concrete mechanical components in the case of the guiding unit and software components for the control unit. These components become concrete due to the use of an ontology of energies and variables enabling the use of physical attributes such as the torque and the position of each component of the robot manipulator. 5. Conclusion This paper has presented the integration of design methodologies and modelling languages through the use of a meta-model of conceptual design: the RFBS model. This model is in accord with model-driven engineering. Furthermore, this paper presents the possibilities provided by this model. From our viewpoint, its major asset is to enable the synthesis of concepts in an automated manner.
Fig. 8. Mapping standard functions with generic organs.
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