1 International Conference on Product Lifecycle Management 1 Improving Interoperability in Mechatronic Product Developement Dr. Alain Biahmou, Dr. Arnulf Fröhlich, Dr. Josip Stjepandic PROSTEP AG Dolivostr. 11, 64293, Darmstadt, Germany Fax: Tel.: Abstract: The development of mechatronic systems involves many disciplines, which utilise their own specific methods, processes as well as software tools in order to create partial models of an overall system. A very strong collaboration of the disciplines is essential since all the partial models are interdependent. However, information between these engineering domains is exchanged only periodically. Furthermore, model services for instance for automatically updating the partial models that represent diverse views of the system are missing. Therefore, the data consistency that is required for an integrated product development is not ensured. PROSTEP AG, as a provider of advanced integration solutions, develops methods, processes as well as software to improve collaborative work. The focus of this paper lies on a sample project in which model services have been developed in order to map and align the models used in key processes of the different disciplines involved in the development of mechatronic products. Suitable and integrative abstraction forms are analysed in order to build mappings between multi-skill engineering domains and views. An approach is developed for the automatic generation of behaviour models out of geometrical models. The developed concepts are applied for implementing the CAMAT (CATIA- MATLAB-Translator) for generating MATLAB / SIMULINK models out of CATIA models as well as an interoperability platform. Keyword: Multidisciplinary simulation, mechatronics, behaviour model generation, collaborative engineering 1 Introduction The product development is characterised by the collaboration of teams, which may be located at different sites, working with different tools according to the development stage as well as their specific domain and therefore to the results that are needed. The different disciplines involved in product development also use different terminologies. Besides each of the engineering domains has its own view (also know as partial model) on the Copyright 200x Inderscience Enterprises Ltd.
2 Dr. Alain Biahmou, Dr. Arnulf Fröhlich, Dr. Josip Stjepandic product and the relationships between the different views are generally kept in the head of some engineers, but are not stored anywhere. Unfortunately these partial models are all available in proprietary formats. Therefore information kept in some models may be accessed only by the tools, which generated the models. This hinders an optimal cross-domain and discipline collaboration. An optimal collaboration is imperative in order to ensure consistent and redundancy-free data, because all the views are representing the same product and logically contain common information related to that product (for instance, the mass of an aircraft is needed not only for aerodynamics, but also for structure and technical design). Due to these common parameters between the partial models, changes that have occurred on one view are to be transmitted to the other views, which are impacted. Furthermore the different views of the product are related either explicitly or implicitly, but the relations are not documented and the models, which are produced, are not linked together. The objectives of the project that is described in this work consist of developing approaches for a model-based collaboration as well as facilitating the search and finding of product information needed, as well as transformation of that information in the suitable form (according to the viewpoint/domain). The project intends: to map and align the partial models used in key processes of the engineering domains involved, to coordinate model-based collaboration at appropriate levels of granularity, to facilitate evaluation of new designs by simulation tools, to manage lifecycle (configuration, version, change) of common information and relations to identify and steer development of tools that are necessary to improve interoperability between the disciplines. Collaborative engineering involves at least five collaboration levels according to methods, models, processes, tools and organisation. This work focuses on collaboration at model and tool level for the disciplines engineering design and system control design. 2 Collaboration at model and tool level between engineering design and behaviour simulation Realising collaboration at model level consists of building, modifying, visualising, accessing and sharing models as well as extracting information out of the models. During the product development process, multiple views (e.g. CAD, FEM, and Behaviour) of the system to be built are developed with the individual engineering skills. The views represent a particular aspect of the entire product. According to most processes, CAD models are reviewed by senior designers and afterwards confirmed by the simulation department. Thus simulation engineers run analyses and make some change requests if necessary. After processing the requested change, designers have to improve the models and run further reviews, until the components are confirmed by the simulation department. Depending on some factors,
3 Improving Interoperability in Mechatronic Product Development such as the complexity of the product to be developed, the size of the company as well as its internal organisation, several loops can be run between designers and simulation engineers. This leads to a long product development time. Therefore, reducing the loops between these departments, contributes to shortening the time to market, through an early efficient interoperability between design and control system engineers. A requirement to the industry, due not only to the need for reducing development time, but also to the high complexity of products which are developed nowadays, is the continual adjustment of the different partial models. This has to be done from the early stages of development onwards. In particular, the development of mechatronics products demands the continual adjustment of mechanical components with the control system. Therefore, multidiscipline simulations that consider both mechanical design and control system design are required. These simulations enable the reduction of useless computations by implementing synchronized workflows between the involved disciplines. Further advantages of this approach are not only the ability to create an association between design and simulation models, but also the consistence of the virtual product model, which is to be insured at all development stages. 2.1 Alignment of partial models - case of the mechanical system with the control system Historically, companies have used physical prototypes to verify that their products match requirements. Thanks to technological improvements, they are relying more and more on simulation to replace physical tests. Indeed, on one hand, modelling, simulation and optimization are strong tools for supporting decision making. On the other hand, their use allows a reduction of costs as well as development time. Whereas simulation fields are traditionally quite disconnected from design (heterogeneous application landscape), one of the main choices for companies is to adopt a multi-disciplinary approach to attain a global optimization of their products. This is particularly appropriate for mechatronics, where mechanics, electronics and software are involved in the development of the final product. During the design of a mechatronics product, it is very important to manage the interaction between the different disciplines, in order to develop suitable solutions. Figure 1: Challenges of mechatronics product development  According to an Aberdeen Group s benchmark report , the challenges for product development in the mechatronics domain rely on the link between disciplines. That study illustrates that three of the top five challenges of mechatronics product development are related to a lack of integration in the development process (see Figure 1).
4 Dr. Alain Biahmou, Dr. Arnulf Fröhlich, Dr. Josip Stjepandic In order to adopt this multi-disciplinary approach, companies need to reconsider their traditional way of working and develop new methods and tools to link development in the different areas (such as design and simulation). Based on this observation, enterprises have to synchronize the increasingly complex multi-discipline development environments. Based on this observation, enterprises have to synchronize the increasingly complex multi-discipline development environments. More and more solutions, linking simulation and mechanical design, have emerged. Integrated solutions are also accessible, for instance Finite Element Analysis. However, many other simulation areas are still not taken into account. Especially for adjusting mechanical design and control system design, there is a need of general concepts that may be applied regardless of the specific systems that are used. Generally, a behaviour model is modelled manually based on geometrical model. Depending on model size and model complexity, this tedious task may be very time consuming. There is a need of methods for automatic generation of behaviour models out of CAD models. Besides, integrated concepts for running a multidiscipline simulation of control system model and geometrical model is needed. 2.2 Previous work Furthermore, many scientific works have analysed the question of reducing or preventing inconsistencies between partial models [2, 3]. However, there is still an optimisation potential regarding the prevention of inconsistencies between models, especially CAD correspondent behaviour models. An approach for a continuous information exchange between both models is needed. Different levels of coupling CAD model with behaviour model have been proposed in the past either as integrative approach or as co-simulation [4, 5]. One of the main approaches currently used consists of extracting information from geometrical data and inserting it into the simulation model. According to this approach, Modelica models have been aligned to CATIA models . Thereby an interface extracts the properties of CATIA models in order to integrate them into a Modelica model. The involved CAD program CATIA does not play any role during simulation of modelica model. A further approach has been presented in order to exchange data between a SolidEdge model and a Modelica model . The principle consists also of extracting information from SolidEdge to put it in Modelica via a database. However, model parameters that have been stored into database may be modified arbitrarily. Therefore there is an existing risk of running simulation on the base of wrong parameters. Another approach has consisted of using parametric links between both models . Component objects are created for containing information related to a CAD model and its corresponding behavioural model. Therefore data relative to behavioural model may be extracted from CAD model. Additionally to the approaches presented above, there are some integrative approaches . All those approaches have the characteristic in common that the functionality of one system (often the CAD system) and therefore its advantages may not be accessed during the simulation. Simulation tools are not focused on managing geometric information and its visualization in 3D environments. Therefore either the visualisation of the product is not possible with these approaches, or 3D models are reduced to simplified analogous models
5 Improving Interoperability in Mechatronic Product Development (see Figure 2). The perception of designers is consequently limited. Moreover, modifications of the CAD models, from which information has been extracted for input into the simulation environment, can lead to inconsistencies. Considering the arguments mentioned above, an interoperability platform that takes advantage of both CAD and control system design software was to be developed. In addition, the system had to enable simulation of the behaviour of a complex product from the early stages of the development. Figure 2: Simulation and visualization of a Stewart platform with SimMechanics  Furthermore, the realistic visualization of the simulation was mandatory. Modifications during multi-disciplinary simulation had to be considered by the simulation loop in order to avoid data inconsistencies. 3 Concept for mapping and aligning geometrical model onto behaviour model This task is part of the sub-project whose main goal is the realisation of a continuous information exchange between partial models. This implies the development of model services which identify the partial models to be updated after a particular view has been modified and then propagate changes. The propagation of information from a partial model to other partial models is only meaningful if the models have equivalent development stages. Therefore, the concept proposes an approach for generating behaviour models from geometrical models in order to get equivalent partial models at each time. In early development phases, there are many alternatives regarding geometrical models. The effort of elaborating behaviour models for each alternative is too high at this time, if tools for semi-automatic or automatic generation of behaviour models are not available. Following parameters have been considered for the alignment of partial models: Investigation of model specification Location of models (for instance into a domain product data management system) Configuration and versioning of models Processes for which the models are involved. Exchange and sharing of models
6 Dr. Alain Biahmou, Dr. Arnulf Fröhlich, Dr. Josip Stjepandic Modification of models (e.g. parameter values, parameter names) as well as parameter extraction out of models Transformation of models into more mature models Visualization of models Updating of models Etc. In order to translate CAD models into behaviour models, an abstraction of both models has been performed in order to identify similarities and discrepancies. Based on these abstractions, mappings have been developed not only for model translation (translation mapping), but also for information exchange during a multidiscipline simulation (interoperability mapping). One of the differences between both mappings is emphasized by the fact that the translation mapping determines which element of CAD model is to be translated into which element of its corresponding behaviour model. In contrast, the interoperability mapping determines which element of CAD model has to exchange information with a definite element of its corresponding behaviour model. For instance, a geometrical part may not exchange information with a controller of behaviour model. However, an actuator of a behaviour model may communicate with a drive that is contained in its corresponding geometrical model. Therefore, a rule base has been created to control interoperability. Figure 3: Concept for efficient interoperability of CAD and behaviour model 3.1 Models abstraction A CAD model may include parts, assemblies and their sub-assemblies. Besides, parts may contain parameters as well as relationships with other parts. On the other hand, a mechanical behaviour model includes bodies that are comparable to parts of the geometrical model. The bodies are connected with each other using joints that generally are applied to two bodies. After an analysis of both models (CAD, behaviour model), some observations are to be done: CAD model does not contain a controller for regulating target and actual value CAD model represents geometrical parts and behaviour models use modelling blocks Constraints that are included in geometrical models may be modelled in behaviour models Parts that are included in geometrical models are equivalent to bodies of behaviour models
7 Improving Interoperability in Mechatronic Product Development Drives of geometrical models are equivalent to actuators of behaviour models Both modelling paradigms use assemblies and sub assemblies Based on the observations mentioned above, the following translation mapping has been elaborated. 3.2 Generation of behaviour models out of CAD model Based on the described abstraction of CAD and behaviour models as well as the mapping that has been derived from the abstraction, a principle for generating behaviour models out of geometrical models has been elaborated. Figure 4: Generating behaviour model out of CAD model The canonical pattern (compared to STEP) has been applied. Therefore CAD models are translated into a neutral data format that describes the basic behaviour of the relevant product. The neutral format may then be translated into a multitude of formats of diverse vendors. Doing so reduces the number of translations that would have been necessary, if the CAD models were translated directly into a proprietary format (see Figure 4) 3.3 Concept for aligning the behaviour model based on geometrical model In order to run a multidiscipline simulation of a CAD model with its corresponding behaviour model, an interoperability mapping is necessary. The mapping has to consider rules to ensure that parameters are exchanged between the right elements of both models. Further more, the task of mapping elements of both models should be interactive. For this purpose, an interactive system that provides users with high flexibility should ensure user interaction. The user may delete single mappings that have been defined between one geometrical element and its corresponding element contained in a behaviour model. Additionally, user may delete all mappings existing in the mapping table. A multidiscipline simulation that already has been setup may be interrupted and continued later. Therefore the interoperability platform provides users with the last configuration that has been used for running a multi-discipline simulation. The interoperability platform is independent of CAD and simulation software used for validation. Integrating a further system (e.g. a new CAD system) into the platform necessitates the implementation of a reader as well as a writer of its format.
8 Dr. Alain Biahmou, Dr. Arnulf Fröhlich, Dr. Josip Stjepandic Interoperability platform The key elements of the interoperability platform are sensors and actuators. They are independent from both CAD and simulation-based applications, and linked with modules that manage the communication with the geometrical and the simulation system that are used, for instance CATIA and MATLAB (see Figure 5). Figure 5: Concept of interoperability platform One important question has been to clarify how to deal with sensors. Which information should be modelled concerning sensors? For example, should the interface transmit (as output of the sensor) the value of an angular speed, or the corresponding value of an electric voltage? How should the behaviour of the sensors be modelled? A further question has been to determine whether the behaviour of the sensor should be implemented in the interoperability platform or in the MATLAB / Simulink environment? The question of interest is in cases were the values that should be measured are of a certain type, such as pressure, temperature, chemical composition, etc. These are parameters that cannot be modelled in the CAD environment. 4 Realisation 4.1 Development of CAMAT (CATIA-MATLAB-Translator) As proof of concept (PoC), a translator for generating MATLAB / Simulink models from CATIA V5 models has been developed. Assuming the generated behaviour model is to be used for simulation purposes, kinematic constraints have to be defined in the CATIA V5 model. Relevant information is extracted from geometrical model for creating an XML-file. The intermediary XML-file may be imported by MATLAB for generating a MATLAB / Simulink model. Elements that are not available in CATIA (e.g. controller) are not created in the MATLAB / Simulink model. Therefore, the user adds them manually. Figure 6: Principle of CAMAT (CATIA-MATLAB-Translator)
9 Improving Interoperability in Mechatronic Product Development 4.2 Results and Integration into CATIA V5 After selecting a CATIA model, the CAMAT is launched and the generated MATLAB model is loaded into MATLAB. Both MATLAB and CATIA V5 have been selected for the proof of concept because they are much widespread in their respective domains (see Figure 7). Figure 7: Automatic generation of MATLAB model out of a CATIA V5 model In order to run the simulation, an arbitrary CATIA model is to be selected using the graphical User Interface (GUI) of the platform. A specification tree (structure) of CATIA model is displayed to the user. It presents the elements (e.g. parts) that are available in the CATIA model. Assuming the equivalent MATLAB model is already available, it is selected for running simulation. In analogy to CAD model, a tree structure is presented to the user. For setting up a simulation, an interoperability mapping has to be performed. For this purpose, the user has to select two elements each time on the specification trees of CATIA and MATLAB. The mapping that is setup by the user is controlled by rules that prevent assigning parts of CAD model to not corresponding elements of the behaviour model. The system stores the configurations established by the user. An important feature of the interoperability platform is the update feature that adjusts the behaviour model when the geometrical model has been modified, making it unnecessary to build a new MATLAB model. Various tests of the developed multidisciplinary platform have been performed with models of aircrafts and industry robots (see Figure 8). Plots present the progression of current values and reference values during the simulation. Coupling that information with the realistic visualisation, designers and control system engineers can improve their digital models. In practice, control system engineers may define preliminary behaviour models (well know as templates) with minimal requirements. Designers would be able to adjust their CAD models to these preliminary behaviour models, which would be stored in a template library. Control system engineers would then focus on more complex studies. Doing so would accelerate the realisation of preliminary studies, reduce the loops between the design world and the simulation world and therefore shorten the time to market of mechatronics products. The results presented are part of services (e.g. data mapping services, model generation services, data extraction services, lifecycle management services) which are necessary for a suitable engineering collaboration.
10 Dr. Alain Biahmou, Dr. Arnulf Fröhlich, Dr. Josip Stjepandic Figure 8: CAD model of industry robot being controlled by a MATLAB model 5 Summary This paper describes a project of PROSTEP AG dealing with the improvement of interoperability in mechatronic product development. An approach has been described for aligning partial models of mechatronics products in order to reduce inconsistencies and improve product quality. Based on models abstraction, a mapping has been elaborated for translating geometrical model into their corresponding behaviour models. The CAMAT (CATIA- MATLAB-Translator) has been developed as proof of concept, for translating CATIA V5 models into MATLAB / Simulink models. Besides, the development of a platform which supports multi-disciplinary work has been described. The tools that have been developed are used for the verification and the validation of control systems. References 1 Jackson, K. C. (2006) The Mechatronics System Design Benchmark Report - Coordinating Engineering Disciplines, Aberdeen Group report. 2 Tudorache, T. (2006) Employing Ontologies for an Improved Development Process in Collaborative Engineering, PhD thesis, Berlin University of Technology, Berlin, Germany. 3 Easterbrook, S. et. al. (1994) Coordinating distributed viewpoints: The anatomy of a consistency check, International Journal on Concurrent Engineering: Research & Applications, Special issue on conflict management, 2(3): N.N. (2006) SimEnterprise-Extending Simulation to the Entreprise, MSC.Software White Paper. 5 Bhattacharya, P. et. al. (2006) Integration of CATIA with Modelica. In Proceedings of the 5th International Modelica Conference, September 4th 5th Vienna, Austria. 6 Adourian, C. (2006) Integrating CAD Models and Multi-disciplinary Simulation Models, URL: Sinha, R., Paredis, C., Khosla, P. (2000) Integration of mechanical CAD and behavioral modeling, Proceedings of the IEEE/ACM Workshop on Behavioral Modeling and Simulation. 8 N.N. (2007) SimMechanics 2 - User s Guide.