Dynamic Design Intents Capture with Formal Ontology and Perdurants Object Concept for Collaborative Product Design

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1 2016 International Conference on Collaboration Technologies and Systems Dynamic Design Intents Capture with Formal Ontology and Perdurants Object Concept for Collaborative Product Design Md Tarique Hasan Khan Ph.D Candidate, Industrial & Systems Engineering Wayne State University Detroit, USA Frédéric Demoly, Ph.D Associate Professor, IRTES-UTBM Université de Bourgogne Franche-Comté Belfort, France Kyoung Yun Kim, Ph.D Associate Professor, Industrial & Systems Engineering Wayne State University Detroit, USA Abstract Loss of design intents and related information throughout the lifecycle of products are common. Capturing design intents of an assembly, which include a temporal (dynamic) stages, is even harder. This research work aims to enhance the spatiotemporal mereotopology (STM) based ontology in tune with the contemporary efforts in this research domain. The main idea with such STM ontology is to capture dynamic design intents and develop an integrated data translation framework from Computer Aided Design (CAD) system to a visualization system. This integration framework is intended to enhance design sharing in a collaborative environment. In this paper, the framework is demonstrated with a simple mechanical assembly that includes rotational, reciprocating and linear motions, directly related to dynamic design intents. Keywords- Dynamic design intents; perdurants; endurants; ontology; CAD I. INTRODUCTION Product design is a complex process, where human mistakes are often indicated and it may carry negative consequences on downstream processes. Research works have been done to minimize the knowledge and information inconsistencies in the product lifecycle stages. Also, it is assumed that information, intents, and knowledge are prone to loss at every stage of a product lifecycle [26]. For that reason, further research efforts are required to address this issue, especially ensuring knowledge flow consistency at the product lifecycle. Recently, the relationships between engineering objects from a logical or semantic perspective have been investigated so as to overcome these inconsistencies [24]. Ontology is a way to represent and formalize theories in a structured and machine-understandable manner. In PLM systems, ontology ensures the capture, reuse, and consistency of knowledge. For example, Gruhier et al. [14] have proposed a spatiotemporal mereotopology-based theory, called JANUS (Joined AwareNess and Understanding in assembly-oriented design with mereotopology), and an ontology that aims to illustrate the product-process relationships by capturing the relational knowledge between product design and assembly sequence planning. At the same time, PRONOIA and PRONOIA-2 (PROduct relationships description based On mereotopological theory) ontology by Demoly et al. [12], [13], are also considered as spatiotemporal mereotopology-based theories, where ontologies are used to represent the spatial and temporal concepts in engineering and manufacturing domains. Design intents indicate the purpose of the design [16]. Some design can be changed over time (i.e., temporal) and some can remain as it is (i.e., spatial). This phenomenon is illustrated by another branch of philosophy, which is Theories of persistence over time by Thomas et al. [3]. Theories of persistence over time have two branches; the first one is known as Perdurance theory and the second one is known as Endurance theory. According to Perdurance theory, Objects those persist in time in virtue of possessing temporal parts [3] are known as a perdurants object; whereas Objects persists through time in virtue of being wholly present at every time at which it exists at all and it do not have any temporal parts [3] is termed as an endurants object. Design intents are needed to be consistent and preserved for both of the endurants and perdurants perspectives. In this research work, we are interested in capturing the dynamic design intents (e.g., linear, rotational motions) in the perdurants perspective. Many research works are conducted to capture the design intents for solid modeling (using CAD systems). However, limited works were reported in dynamic (spatiotemporal) modeling. According to Jeffrey et al. [21], Design intent is a term commonly defined as a model s anticipated behavior once it undergoes alteration (ex. will a cylindrical hole continue to share concentricity with a boundary arc, should the dimensions be modified?). From the aforementioned definition and example, dynamic behaviors (i.e., dynamic design intents) of products should be perceived, while the model changes. We consider this dynamic (temporal) nature of products as perdurants. This concept was described earlier in this section. These dynamic behaviors are not only restricted as dimensional change; other temporal behaviors of products (e.g., sweep /16 $ IEEE DOI /CTS

2 volume, rotary motion, and linear motion) are also regarded as the dynamic or temporal behaviors of the products [11]. This paper aims to capture those dynamic design intents, using spatiotemporal mereotopological (STM) ontology. This research work shows a collaborative design environment to capture the dynamic design intents by enabling seamless endurants and perdurants design data and intent transfer. In the next section, a literature review is presented, where contemporary research efforts on spatiotemporal mereotopology are presented in the first part, and in the second, efforts on capturing the design intents are shown. In Section 3, ontology-based dynamic modeling with perdurants is described and it is followed by a system implementation. Finally, a demonstration section illustrates the proposed framework and concluding remarks are given. II. LITERATURE REVIEW This section of the literature review is composed of two different parts: 1) spatiotemporal mereotopological theories and ontologies and 2) contemporary approaches to capture design intents. A. Spatiotemporal mereotopology theories and ontologies Ontological analysis clarifies the structure of knowledge. Given a domain, an ontology forms the heart of any system of knowledge representation for that domain [1]. However, the process of representing knowledge is often case specific and requires more formal approaches. Kim et al. [18] and Demoly et al. [14] used a formal theory (based on mereotopology) to build ontology-based knowledge models. Mereotopological theory is based on mereology and topology. Mereology is a formal theory of parts and associated concepts, which was introduced by Leśniewski [23]. Mereology provides only a part-whole relation, so there is another theory that provides an isconnected-to relation [22]. This theory is known as topology. To express more comprehensively entities that exist in other spaces besides the usual physical one, region-based mereotopology theory is introduced by [10]. Figure 1. Spatiotemporal mereotopological ontology and theories (adapted from Gruhier et al. [11]) In Fig. 1, the horizontal axis represents the types of theories (i.e., spatial, temporal, and spatiotemporal) and the vertical axis represents the stages of a product s lifecycle. Kim et al. [17] - [19] presented mereotopological ontology that provides the assembly information of different mating parts of a mechanical system. Based on the early endeavors of Kim et al. and Demoly et al. [11] - [14] have described product-process relationships at various abstraction levels with mereotopological primitives and has implemented it using ontology. Ontology-web-languagedescription-logic (OWL-DL) and SWRL languages were utilized, to make it machine-interpretable. From Fig. 1, it is seen that core product model (CPM) [26], which is introduced by National Institute of Standard and Technology (NIST), is concerned about developing the knowledge base when the product design is in a conceptual stage. To deal with the assembly issues, CPM was later enhanced and was termed as open assembly model (OAM). CPM-2 was also introduced later to support other stages of a product s lifecycle. Moreover, Matsokis et al. [14] develop semantic object model (SOM) [20], which is based on UML (Unified Modeling Language) diagrammatic representation. Moreover, efforts are made to combine space and time to present the scenario of product evolution. For instance, Welty et al. [4] have developed an ontology, which allows objects to support properties that change over time. Chen and Luo [13] develop a 4D ontology to combine space and time. In addition, a European project called time-determined ontology web language (TOWL) [14] aims to extend OWL language with temporal dimensions. Finally, Hobbs and Pan propose a temporal ontology, called OWL-Time, to describe the temporal properties of web services, using Allen s intervals. Some research efforts were made in 91

3 other domains, like Khan and Kim [22] utilize mereotopological primitives to explain assembled additive manufacturing folding process, Randell et al. [8] developed discrete mereotopology to support discretized objects. B. Contemporary approaches to capture design intents: Capturing design intents for solid modeling become an issue when interoperability is needed [28]. Because information loss for CAD-to-CAD interoperability can turn an intelligent model to a dumb model according to [9]. Many research works are done to overcome this problem. STEP, STL, and IGES are filebased approaches [27] that capture and reuse the design intents through the downstream processes of the product lifecycle. Macro parametric approach (MPA), which was introduced by Mun et al. [9], is an interface-based approach and it is used for CAD-to-CAD interoperability. The way of conveying the dynamic design intents throughout the lifecycle of a product can be developed by using simulation tools (e.g., CAE system); however, interoperability between CAE systems is still an issue, when a simulated/dynamic model needs to be transferred from one CAE system to another in a collaborative environment [15]. In order to capture the dynamic behaviors of a product, some research efforts are done by developing integrated framework between CAD to CAE systems. Gujarathi and Ma [15] develop common data model (CDM) to integrate CAD and CAE. The intermediate CDM system is used as an integrator between the CAD and CAE, which contains all parametric and analysis information. Here, CDM captures the dynamic design intents between the two systems. Wan et al. [5] showed a research effort to develop a common geometric module (CGM) between the CAD and CAE system to capture spatial and some variable mesh (temporal) information. Zhang et al. [25] develop a web-based platform to interact between CAD and CAE systems. The intermediate XML-based platform is built to capture the parametric (spatial) CAD information and temporal information for the CAE system. Lei et al. [29] develop a bridge platform to establish the link between CAD and CAE systems; however, the bridge platform contains only the parametric information. III. ONTOLOGY-BASED DYNAMIC MODELING WITH PERDURANTS Previously, there are some research efforts to represent the spatiotemporal mereotopological concepts in different forms of ontologies, which are highlighted in the literature review section. However, to represent the concept of endurants and perdurants, the existing ontological representations are not sufficient. Hence, this paper unifies and enhances the existing concepts into a single ontology, so that it can capture the dynamic design intents of an assembly model built in a CAD system. Fig. 2 represents the concept map of the STM ontology, which contains the objects and their relationships in two different dimensions: endurants and perdurants. Endurants and perdurants are two ontological terms, which refer to two different concepts. Endurants refers to the discrete time-snap of moments for any object during the time that it exists, where the object is wholly present at all the time. For example, a static 2D/3D view of an object, which is completely (with all its parts) present at all the time, is termed as endurants. Perdurants is also termed as occurrents or happenings and it refers to the process or procedures, where an object is not wholly present all the time. A perdurants object may change shape (e.g., swept volume) or deformed with time. For example, a 4D view, where time is an added dimension to the regular 3D view can track the change of a disassembly process. Hence, when the object is not wholly present (as it changes over time) during the time that it exists, it is termed as a perdurants object. Classes, subclasses, properties, and corresponding individuals are developed in the Protégé software to construct the knowledge base, which can later capture the design intents from a designer system (e.g., CAD). Classes and subclasses are linked with properties and individuals, which are semantically linked to facilitate the reuse of existing knowledge [7]. Reusing the captured knowledge in the ontology facilitates someone to convey the knowledge for the next stages of the product s lifecycle. In our case, reusing is exercised to convey the spatial and temporal knowledge for the visualization using VRML (Virtual Reality Modeling Language). Fig. 3 represents the part of the implemented STM ontology. It shows the classes, subclasses, and corresponding individuals to represent the perdurants and endurants concept as well as relationships between them. As shown in Fig. 2, STM ontology consists of PRONOIA-1, PRONOIA-2, Kim et al. s [18] AsD ontology and the newly presented concept. The concept of spatial, temporal, and spatiotemporal primitives are cited from the PRONOIA-1 and PRONOIA-2 ontology by Demoly et al. [13], [14]. Classification of the geometric entity as the spatial region is cited from AsD ontology by Kim et al. [18]. The spatiotemporal region, which is a combined form of the spatial and temporal region is the concept to represent the spatiotemporal behavior of the parts or products. The spatiotemporality is accompanied by spatial region, temporal region, motion type, motion direction, and motion measurement. Hence, motion type, direction, and measurement are the subclasses of the spatiotemporal region. Six different types of motions are considered here to develop the STM ontology and those are linear, twist, reciprocation, oscillation, rotation, and irregular motions. Motion measurement is classified based on the nature of these motions. In this STM ontology, it is considered that motion can be measured in two different approaches; firstly, by counting the numbers of movements (for example, the number of twists, the number of rotations, the number of reciprocation); secondly, by measuring the unit time for movement (e.g., unit time for each cycle of rotation or reciprocation). Thus, the STM ontology is developed based on the literature study of spatiotemporality and further enhancement is done, so that it can capture the dynamic design intents. 92

4 New concept Kim et al. AsD [18] PRONOIA-1 [14] PRONOIA-2 [13] Figure 2. Concept map of the STM ontology Figure 3. Part of STM ontology representing perdurants and endurants 93

5 IV. SYSTEM IMPLEMENTATION To capture the dynamic design intents using an ontology, the first step is to map the schema between the two systems, where data is transported. Fig. 4 shows the schema mapping process between CAD to OWL, and then OWL to VRML. Schema mapping in the first step comprises, mapping the spatial geometric modeling commands (i.e., endurants information) from the macro files [9] of CAD systems to the desired classes, subclasses, or individuals. The parametric information from the CAD system is mapped with the corresponding individuals (of classes or subclasses and properties) of OWL (ontology). Hence, only individuals of classes, subclasses, and corresponding properties can address the numeric values in an ontology. Since STM ontology represents both the spatial and temporal information; therefore, in the second stage, the spatial and temporal information are mapped from the OWL ontology to the corresponding VRML nodes. In this paper, VRML [6] is used to visualize both the spatial and temporal information. which is a subclass of temporal primitive that contains individuals like BxTime. Linear movement time of a box shape can be addressed by this individual. Therefore, the ontology captures the dynamic design intents of the model. After this step, a data processing system is established between CAD and VRML, which facilitates to translate the mapped data from OWL to WRL file format of VRML. WRL file now contains both the endurants and perdurants (i.e., spatial and temporal) information. Thus, any VRML viewer can visualize the dynamic behaviors of a model. Figure 5. Integration framework Figure 4. Schema mapping between CAD to OWL and OWL to VRML After the completion of schema mapping, the next step is to establish the system architecture to capture the dynamic design intents by the ontology. The system architecture is shown in Fig. 5, which illustrates the overall data translation scenario. According to the scenario, CAD models are built first by macro files in a CAD system, which contains the geometric design information (e.g., endurants information). Then, a data processing system establishes a link between CAD to OWL, which facilitates the translation of the mapped information from CAD to OWL. MATLAB is used to implement the data processing system. Now, OWL contains the endurants or spatial information from CAD. The endurants information are sent to OWL as individuals of various classes, subclasses, and corresponding properties. Dynamic design information is now addressed in this phase using the perdurants classes in the STM ontology. For example, in Fig. 3, it is seen that, At-time_T, V. DEMONSTRATION The STM ontology includes six different types of motions. Among them, three of the motions (i.e., rotational, reciprocating, and linear motions) are demonstrated in this paper. The combination of rotational and linear motions is also implemented. Table 1 shows the types of motions, endurants state of the model (designed by CAD) and perdurants state (visualized by VRML). The rotational motion is demonstrated by a simplified coupling test model, which contains two rotating disks attached at the opposite ends of a shaft. In the third column, clockwise (CW) rotating motion of the disk (perdurants state) can be observed in the VRML viewer, whereas the static model (endurants state) is shown in the second column. Accordingly, the reciprocating motion is demonstrated by a piston cylinder model. Furthermore, the linear motion is demonstrated by a simple block motion within a slot. Lastly, a mixed motion (i.e., rotational and linear motions) is demonstrated by the simple coupling test model in the last row (in Table 1). 94

6 TABLE I. EXPERIMENTS ON CAPTURING DYNAMIC DESIGN INTENTS Motion Types CAD (Endurants State) VRML(Perdurants state) Rotary motion (Coupling test apparatus, CW motion) Reciprocating motion (Piston cylinder) Linear motion (within slot) Mixed motion (Rotary and linear motion) VI. CONCLUDING REMARKS This paper presented an ontology-based dynamic design intents capturing framework to support collaborative product design and intent sharing. This framework is based on spatiotemporal mereotopological ontology and endurants and perdurants object concepts. Contemporary mereotopological theories and ontologies were combined and then enhanced to develop the STM ontology. Later this ontology was utilized to capture the dynamic design intents in the collaborative product design process. The demonstration with a simple assembly with three motions and a combined motion showed that the dynamic design intents can be captured with the formal ontology and the presented framework. In the future research, other three types of motions, which are listed as a subclass of class motion types will be implemented. Intelligent ontology-based framework can be designed to capture more complex dynamic design intents from designer systems. REFERENCES [1] B. Chandrasekaran, J. Josephson and V. Benjamins, "What are ontologies, and why do we need them?", IEEE Intell. Syst., vol. 14, no. 1, pp , [2] B. Smith, "Mereotopology: A theory of parts and boundaries", Data & Knowledge Engineering, vol. 20, no. 3, pp , [3] B. Thomas, M. Donnelly, and B. Smith, "Endurants and perdurants in directly depicting ontologies," AI Communications vol. 17, no. 4, pp , [4] C. Welty, J. M. William, P. P. D. Silva, L. Deborah, M. Guinness, D. Ferrucci and R. Fikes. "Tracking information extraction from intelligence documents," In Proceedings of the 2005 International Conference on Intelligence Analysis, pp. 2-6, [5] C. B. wan, J. Chen, Z. Huang, and Y. Zheng, "CAD/CAE integration framework with layered software architecture," In Computer-Aided Design and Computer Graphics, CAD/Graphics' th IEEE International Conference on,pp , [6] C. H. Thomas, C. Ming, J. C. Lin, S. Gottschalk, and D. Manocha, "V- COLLIDE: accelerated collision detection for VRML," In Proceedings of 95

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