Improvement of engineering design and numerical
simulation data exchange based on requirements
deployment: a conceptual framework
Ibrahim Assouroko*
Department of Mechanical Systems Engineering, Université de Technologie de Compiègne, CNRS – UMR6253 – Roberval,
BP 60319, rue du Dr. Schweitzer, 60203 Compiègne, France Fax: +33 (0)3.44.23.52.29
E-mail: [email protected] and
CADeSIS, 142-176,
Avenue de Stalingrad, Parc Technologique des Fossés Jean, 92700 Colombes, France
E-mail: [email protected] *Corresponding author
Guillaume Ducellier
Institut Charles Delaunay – LASMIS, Université de Technologie de Troyes, BP2060, 10010 Troyes Cedex, France E-mail: [email protected]
Farouk Belkadi
and Benoît Eynard
Department of Mechanical Systems Engineering, Université de Technologie de Compiègne, CNRS – UMR6253 – Roberval,
BP 60319, rue du Dr. Schweitzer, 60203 Compiègne, France Fax: +33 (0)3.44.23.52.29
E-mail: [email protected] E-mail: [email protected]
Philippe Boutinaud
CADeSIS, 142-176,
Avenue de Stalingrad, Parc Technologique des Fossés Jean, 92700 Colombes, France
Abstract: Thanks to the latest technological advances both in industries and the academic research centers, leaded by harder to fulfill customer needs and more complex products, innovation and competitiveness remain the major challenges for engineering solutions vendors, service providers companies and for OEMs and the network of their subcontractors. In order to improve quality, time to market and cost, companies and their partners need to collaborate in a basic and global way around product data and information, all along the product lifecycle. This issue is partially addressed in the proposed research work program focusing on the improvement of digital data management between engineering design and numerical simulation. The goal of the paper is first, to provide a literature survey on design and simulation data/information exchange and sharing in a collaborative context. Second, it introduces a conceptual framework dedicated to the management of design and simulation data exchange, based on simulation’s goals deployment and driven by functional requirements.
Keywords: PLM; numerical simulation; data exchange; semantic web; functional requirements; functional requirements deployment; engineering design.
1 Introduction
The deep changes noticed over the last 30 years in the field of product development process led to major changes in design methods evolving from a systematic design to an integrated design. Thus, new design methods such as concurrent engineering (Sohlenius, 1992) and many other concepts such as integrated and collaborative engineering appeared. In addition, these changes had been a major reason for the large development of information and communication technology (ICT) in the field of PLM and collaborative design. Numerous solutions ‘CAx applications and PDM systems’ have then emerged as result of the rapid evolution of digital engineering software, and as supports to engineering activities focused on product-process integration.
Nowadays, PLM systems are considered as being strategic solutions enabling the collaborative management of product data and knowledge within the product development process, and are supposed to allow collaboration between the different experts. In fact, that is not entirely the case for many reasons: PLM systems are too strongly linked to the 3D representation (or not focused enough on a multidisciplinary representation) and PLM systems are only able to manipulate files instead of data (Van Wijk et al., 2008). Moreover, CAx applications and PLM systems appeared to be increasingly heterogeneous and are currently experiencing significant growth in the industrial world. The deployment of such solutions by both medium-sized companies and large groups with worldwide distributed sites underlines a certain complexity that raises many questions related to the need of integration between engineering tools and methods. The heterogeneity of these tools points out the lack of interoperability and raises the issue of communication between digital applications, which appears to be a real barrier to effective information exchange and sharing between experts and tools involved in the product development.
In such a context, the main challenge is to enable the collaboration between different design and engineering applications in order to get a common and coherent product
representation throughout all its lifecycle (Nguyen Van, 2006). The aim is to give more facilities for the data access and sharing between the projects’ doers. In some extent, the development of digital engineering systems, including CAD and CAE systems, has significantly contributed to solve the issues posed by the need of ICT integration in engineering processes. However, communication between engineering tools highlights two important issues:
• build of a common reference in order to satisfy a large set of interoperability needs
• improvement of current data exchange standards to cope with the interoperability between engineering applications, and solve the problem of design entities verification and validation along with the product requirements.
A new way to contribute to the improvement of the integration between CAD and digital analysis tools, is to build a collaborative semantic environment, integrating a relationship manager, to manage various relations between requirements, design and simulation entities, and to allow an effective process of verification and validation of simulation results in accordance with the product requirements.
The next section presents a literature survey dealing with the issue of integration between design and engineering systems in a concurrent engineering context. Section 3 extends the previous survey to the interoperability and data exchange topic. Three main interoperability approaches will be discussed in this section. Finally, the Section 4 presents the comprehensive conceptual approach that we proposed for the improvement of data management between designers and analysts in a collaborative design context.
2 Integration between design and engineering tools
Nowadays, engineering design and numerical simulation hold an important place in the product development process. When considered separately, these two expert activities are generally well controlled. However, the respective dependencies between each other are not detailed and formalized enough. Relevant improvements had been achieved regarding the tools supporting these activities, but the main challenge still consists in the full integration of these tools and an effective data management between these heterogeneous tools. According to Troussier et al. (1999), the integration of digital analysis tools and methods into design is achieved to cope with one or more of the following goals:
• automation of the transfer process from the CAD model to another geometric model (CAE model) adapted for numerical simulation tasks
• formalization of digital analysis assumptions in order to reduce simulation errors
• traceability of choices made during simulation process and traceability of simulation results, in order to capitalize the best design decisions for future reuse.
2.1 Use of numerical simulation in a design process
Shepard et al. (2004) proposes a framework for geometry pre-processing in order to improve simulation reliability and promote better management of simulation data by formalizing the simulation process and a simulation based design approach. In a same
point of view, Lee et al. (2005), proposes a common modeling environment for bidirectional CAD-analysis integration. The proposed approach is based on the use of multi-resolution and multi-abstraction modeling techniques, non-manifold topology (NMT) modeling type, which allows the CAD system to automatically generate the simulation model, and allows also the simulation application to automatically change the geometry of a CAD component and restart a new simulation.
2.2 Simulation process formalization and design choices capitalization
In order to reduce numerical simulation errors, Kurowski and Szabo (1997) propose a systematic approach to support the simulation process modeling and to classify the simulation errors in order to make a better interpretation of the results. Turkiyyah and Fenves (1996) illustrated the architecture and the mechanism of Kurowski and Szabo (1997) systematic approach by implementing a knowledge based expert system, describing how to help analysts (with experience or not) to make the required choices in front of new category of problems. Other research works (Baizet et al., 2003) were focused on proposing approaches to support simulation data and knowledge structuring and sharing in engineering tasks in order to integrate these tasks in the design process.
More recently, some interesting approaches concerning the integration of numerical simulation in design process through specific framework for the management of simulation data were developed. In this category of works, Charles et al. (2005) proposed a simulation data management framework using STEP and SDM schema standards to guarantee the management of simulation information throughout the whole product life cycle.
2.3 Synthesis of literature survey
The literature survey highlights several proposal in order to clarify and improve the link between design and simulation tasks in the mechanical engineering field. However, the main remark is the lack of a complete digital approach for an integrated management of engineering design and numerical simulation data.
Requirements are actually managed with no technical support linked to PLM and CAx systems; there exist rather specific methods (VE, QFD…) and dedicated tools (doors, reqtify…) and almost no collaboration with the other phases of the development process.
Furthermore, when talking about the goals of simulation in accordance with design choices, we steel lack a real approach to link the functional requirements of a system to the corresponding results of simulation, as well as an effective verification and validation (V&V) process to make sure that the choices made at each step of the design process meet the primary need and the corresponding functional requirements.
3 Interoperability and data exchange
Software interoperability is one of important scientific topics studied today by different researchers (Augenbroe et al., 2004; Sudarsan et al., 2005). In various scientific communities (NoE INTEROP, STEP, etc.), the term ‘interoperability’ might take different meanings. For example, Institute of Electrical and Electronics Engineers (IEEE)
gives the following definition: ‘The ability of software and hardware on multiple machines from multiple vendors to communicate’. Another definition is given by the European Interoperability Framework for pan-European e-Government services: “Interoperability means the ability of information and communication technology (ICT) systems and of the business processes they support to exchange data and to enable the sharing of information and knowledge”.
Three major categories of interoperability approach are currently used to support the data exchange between CAx applications.
3.1 Standard based approach
The first category uses a standard based mechanism to guarantee the semantic translation between heterogeneous models (Choi et al., 2002). Several standards are proposed. For example, Initial Graphics Exchange Specifications (IGES) and Drawing Exchange Format (DXF) standards are used to manage the geometric data of the product. Other standards support the product models translation such as ISO 10303 – STandard for the Exchange of Product (STEP) model data standards.
3.2 Integration based approach
The second category uses dynamic interfaces, based on Application Programming Interface (API) standards, to guarantee the communication between software (Song et al., 2006). The integration process addresses two complementary aspects:
• First: data integration. In this approach, data from several heterogeneous, distributed and autonomous data sources (data base, data files) are stored in a single structured data source according to a common model (Bellatreche et al., 2006). The major problem with this approach is how to guarantee the updating and the consistency of the data after each data treatment (Sriti et al., 2006).
• Second: software integration through the web services technologies to support the distribution of heterogeneous information between members of a project team (Zhang et al., 2004). In this category, ‘OMG PDM enablers’ based on middleware technologies and ‘PLM services’ can be considered, two web technologies developed to support communication between PLM systems.
Thanks to the recent facilities offered by the World Wide Web technologies, new collaborative concepts, named ‘Web based modeling and simulation’ and ‘collaborative modeling for online simulation’, has been proposed to support the modeling and simulation tasks in a collaborative context. The use of these concepts is facilitated by another concept called model driven engineering (MDE) (Levytskyy et al., 2009) dedicated to the formulation, the formalization and the automation of the development process.
The MDE covers other ‘model oriented’ approaches and initiatives such as the model driven architecture (MDA), the domain specific modeling (DSM), and the model driven software development (MDSD). In this field, Perez et al. (2006) proposed a new model oriented approach for requirements elicitation in the product development process.
3.3 Ontology and semantic web based approaches
The third category uses the ontology and the semantic web concepts to achieve the data mapping between heterogeneous software. The definition of ontologies and their application to design activities has grown significantly. Several studies implementing different approaches to product design have been conducted on ontologies, as standard for data exchange between design and other engineering activities in collaborative tasks (Bellatreche et al., 2006; Sriti et al., 2006; Zhang et al., 2004; Levytskyy et al., 2009; Pérez et al., 2006; Hefke et al., 2005).
Thanks to their implementation facilities, the recent ontological models use the web semantic standards to ensure software interoperability. The most currently used in this category are: XML ‘eXtensible Markup Language’, RDF ‘resource description framework’, RDFS ‘RDF schema’ and OWL ‘Web Ontology Language’. According to Berner-Lee et al. (2001), semantic web aims at integrating a set of technologies and standards in a common infrastructure in order to facilitate ‘man-machine’ interaction. The semantic web approach gives also a set of tools to support an ontological reasoning on conceptual and semantic models (Sriti et al., 2006).
PLM is another interesting application field for ontologies. Based on the core product model developed in the National Institute of Science and Technology (NIST), Fiorentini et al. (2008) proposed a generic approach for the implementation of ontologies in an existing product model. In the same idea, Suh et al. (2008) proposed an ontology based model for interoperability and for the exploitation of the various product lifecycle data as an input for new product development processes.
The ontology approach for systems interoperability seems very promising for the improvement of links between product development’s process phases. No significant work using ontologies for data exchange between CAD and CAE applications has been identified. This underscores the innovative nature of this approach in the field of design-analysis integration, and makes it a potential possible solution in the implementation of the proposed data management approach to improve interoperability between both areas.
4 Proposal of a conceptual framework for collaborative data exchange Regarding the previous sections of the paper, it can be concluded that, despite the many contributions made by the research works on improving interoperability between CAx systems (especially CAD and CAE applications), some limits still exist. Interfaces between different systems are not fully effective and the issue of the monitoring of the links and information flows between lifecycle phases is still applicable.
The research work aims at improving the link between different phases of the product development process. It has being focused on a transverse and comprehensive framework to integrate design and simulation phases, with a better deployment of functional requirements during the product development process. In fact, the proposed framework aims at linking requirements engineering, engineering design and numerical simulation data on one hand, and on the other hand to help experts to check, at each step of the design process that the technical choices are aligned with the requirements, as well as the simulation goals. This section presents the different concepts and principles of the proposed framework.
4.1 Description of the integrated semantic approach
Figure 1 illustrates the main components and entities involved in the proposed idea, as well as the corresponding lifecycle phases covered by the approach. Thus, four complementary modules composed the system:
• The semantic web environment integrating a relationship manager as the kernel of our technical architecture.
• The lifecycle phases with a set of entities managed by each of them. The approach is based on the concept ‘domain’ (field of expertise). Thus three domains are
considered: the domain of product requirements, the domain of engineering design and the domain of numerical simulation.
Figure 1 Description of the basic components of the proposed approach
Semantic Web Environment
Towards a Semantic Web conceptual framework for data management
Numerical Simulation
Simulation data: CAE Part, Simulation goals, simulation model, results, analysis model, typology of simulation… Product
Requirements Requirements data: functional and non functional Req, functional
specifications…
Engineering Design Design data: CAD Model, Function, Technical Solution…
One of the major challenges that has to be achieved after identifying the different domains, is the clarification of the main data manipulated by each of the components, and the clarification of the linking objects and typology of simulation processed at the product development milestones. Figure 2 gives details on the various entities and the way they are linked to each other. The main components described in Figure 2 include:
• the requirement component composed of functional requirements {FR}, corresponding functional specifications {FS} and non functional requirements {NFR}; all linked to the requirements repository {RR} identified as the reference for customer needs
• the design component made of a set of functions {F} resulting from the functional specification of the ‘requirement’ world; a set of physical principles {PP} (mechanical laws, theorem…) as solutions to represent functions; and a set of technical choices {TC} (system architecture, mechanical component…) putting together some physical principles to fulfill functions. All these entities are linked to the 3D CAD model {3D CAD} identified as the common reference in the ‘design’ domain
• the ‘simulation’ component, characterized by a set of simplified CAD {SCAD} resulting from the 3D CAD model of the ‘design’ world; a set of simulation goals {SG} describing the objectives that the simulation has to satisfy; a set of analysis model {AM} (built on the basis of the simulation goals) as input to the FEA module (including a solver to process the data); a set of simulation results {SR} and the synthesis {SY} made from the post processing tasks and linked to the simulation goals for verification and validation matters; all of these entities are linked to the simulation model {SM} identified as the reference and the collaborative entity of the ‘simulation’ domain
• the ‘semantic web’ component is considered as the kernel of the architecture and will implement a relationship manager, a persistence engine using URI and URL to store the relationships between the entities managed in our different domains, and a set of basic ontologies to represent the data and allow the updating of the main database, along with the creation and change of the data.
It is important to notice that some modeling concepts and design methods (value engineering, QFD …) will be included in the future works related to the ‘design’ domain.
Figure 2 An integrated comprehensive and collaborative semantic approach for product data management Relationship Manager + Basic Ontologies FR RR NFR FS F PP 3DCAD TC SCAD SG C SM SR AM Requirements Design Simulation
After, the conceptual definition of the framework, the main ongoing work addresses the functional and technical specification study of the relationship manager as well as the study and implementation of feasible scenario within the relationship manager. Figure 3 represents the domain model that will be implemented in the relationship manager, and describes how the identified entities evolve inside each world and from one world to another, during the product development process.
Figure 3 The domain model of the relationship manager
As it can be seen on Figure 3, the relationship manager is composed of three modules: the search module to enable project actors to search for existing entities, for reuse purpose, or to be able to understand the different choices and history related to the entities, the classification module in order to allow navigation inside the ontology model, and the creation module allowing users to create entities, the links between entities…
The results of this works will be a demo that will implement the relationship manager data model and will illustrate scenarios of modification of existing design as well as creation of new design. The implementation issues will be presented in a future paper.
5 Conclusions
In this paper, summary survey of the main research works is carried out. First, a focus is done on the field of digital design and engineering tools and their integration. Second, the field of interoperability and data exchange is considered. The resulting state of the art, allows pointing out some weaknesses of existing approach in order to focus this research work on product data management. On the other hand, the paper proposes an integrated semantic approach to improve the link between different phases of the product development process. Future work will address the detailed functional and technical specification of the framework, as well as the implementation issues. This conceptual framework will then be validated throughout industrial use cases of engineering data management and knowledge reuse.
Acknowledgements
The current research work is supported by CADeSIS (a subsidiary of the VISIATIV group, France) a service provider company in PLM, CAD and CAE fields. This research work involves the Université de Technologie de Compiègne, the Université de Technologie de Troyes, and CADeSIS.
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