Mechatronic-oriented Engineering of Manufacturing Systems Taking ...

Mechatronic-oriented Engineering of Manufacturing Systems Taking the Example of the Body Shop 1

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Jens Kiefer , Thomas Baer , Helmut Bley DaimlerChrysler Research & Technology, Product and Production Modeling (REI/IP), Ulm, Germany 2 Saarland University, Institute of Production Engineering (LFT), Saarbruecken, Germany

Abstract In order to cope with the various challenges in the automotive industry, new and integrated strategies are required. In this way, the concept of mechatronic-oriented digital production engineering is introduced, taking the example of the body shop. With the focus on the overall lifecycle of a manufacturing system a central planning and data integration platform represented by a mechatronic plant model is established. This cross-domain and 3D-oriented model, which includes mechanical, electrical, and information-technical plant data, assumes different functions in the course of a complete production engineering project: not only does it form the basis for the accomplishment of virtual startups, it is also profitably used for maintenance services in the real factory. Keywords Lifecycle engineering, digital factory, mechatronic plant model, data structures, virtual startup

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CHANGED BASIC CONDITIONS IN THE AUTOMOTIVE INDUSTRY

number of product variants

After years of rising profits, companies in the automotive industry are currently confronted with stagnant or even diminishing markets [1]. Due to the resulting intensified competition for key market shares, car manufacturers are engaged in an innovation race characterized by a soaring number of product variants with numerous product derivates. Furthermore, the innovation and model cycles are constantly decreasing. For example, in 1980 the model cycle in the automotive industry averaged out at 10.6 years; today the time period from one model change to the next amounts to approximately 6 years with a falling tendency for the future [2]. These conditions lead to a higher complexity in the production engineering process, thus resulting commensurately in more complex and highly automated manufacturing systems. This increasing complexity is also related to the rising deployment and the linking of electronics and software in the form of mechatronic plant components [3]. Nowadays, control software engineering is responsible for over half of the functionality of mechatronic manufacturing systems [4]. Yet, today control software engineering still tends to be the last step within the mechanic-oriented and sequential engineering process [5]. In order to develop important cost, quality, and time potentials, the startup and the rampup processes are becoming more and more important ("If we can reach the maximum production capacity in three instead of nine months, it means cash for the company." [6]). Additionally, the number of rampups is constantly rising due to enhanced innovations and increasing market launches of new products and product variants [7]. Apart from the planning, startup, and rampup processes, the production and maintenance services will also have to take up new challenges. In contrast to the model and product cycles, production lifecycles will be increased significantly in the future. In order to avoid long changeover and down-times, highly flexible and agile manufacturing systems are necessary in production. New products and product variants have to be integrated in the running production facilities without delay. These productionoriented challenges have to be taken into account during the production planning process. To summarize, Figure 1 portrays the various changed basic conditions in the automotive industry.

model / product lifecycle complexity of production systems no. of mechatronic components startup / rampup times number of rampups production lifecycle 100% today

tomorrow

trend

Figure 1: Changed basic conditions in the automotive industry. Based on the changed basic conditions set out above and the resulting challenges, the body shop of the automotive industry with its different kinds of data structures will be illustrated in the following. Within the scope of the third chapter a new methodology in the form of an integrated, mechatronic-oriented process solution will be presented. In this context, a cross-domain planning and data integration platform represented by a mechatronic plant model is introduced. Using this 3D-oriented data model, real PLC programs can be validated at a very early stage. Furthermore, the profitable use of this integrated plant model in the real production world as well as the organizational effects of this new, integrated methodology will be pointed out. 2

DATA STRUCTURES IN THE BODY SHOP

In addition to the manufacturing areas powertrain, surface and final assembly, body-in-white planning is the sub-area of production planning that is responsible for the manufacturing of the car body. Hence, the main task of body-in-white planning is the design of all processes and manufacturing systems relevant for the production of the car body. The individual phases of body-in-white planning,

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their most important contents, and the various possibilities of digital body-in-white planning (as a part of the digital factory [8]) are sketched in [9] and [10]. In order to establish a seamless digital process chain in the body shop, all product, process, and resource data have to be structured systematically in accordance with the company-specific boundary conditions [11]. Only in this way are the different body-in-white departments able to work simultaneously and networked on the basis of uniform and consistent data structures. Generally, product structures describe the assignment of product components to each other [12]. Product structures are developed differently in line with the intended purpose, the process time, and the area of responsibility and can represent different contents. In other words, product structures portray different views. In this context, for example, the functional view of an engineering bill of material (EBOM) is distinguished from a process-oriented view of a manufacturing bill of material (MBOM). Apart from different views, product structures are also differentiated according to their representation forms in EDM systems. In this way, the classical product structure represents the functional view of a product in the EDM system. In contrast to this, the developed manufacturing structure with the clamping concept and its processoriented product view forms an integration structure between product development and body-in-white planning ([13], [14]). Parallel to the development of the manufacturing structure, body-in-white planning determines both the processes and the process sequences including the parameters that are responsible for the manufacturing of the product. These process-describing parameters comprise, for example, the process times (e.g., loading times, welding times) as well as profitability characteristics. In the course of digital planning, the process planners utilize graphical aids such as so-called pert and gantt diagrams for process documentation. Normally, the hierarchical structuring, the process types used, and the process-describing parameters are incumbent on company-specific conditions and are handled differently depending on the specific manufacturing area. In parallel with the process planning, the department of tooling design specifies and designs the resources needed to manufacture the respective products. Apart from these technical resources, which are divided into typebound (e.g., clamping fixtures) and type-free operating resources (e.g., robots, workers), a resource structure also contains organizational resources. Examples for organizational resources are production locations and individual buildings. Figure 2 sets out an example of a typical resource structure in the body shop. Similarly to the process structuring, resource structures are also contingent on company- or location-specific boundary conditions. All the different resource types such as fixtures, robots, and welding guns are characterized by specific parameters (e.g., code number, availability, costs). In contrast to product structures, a uniform documentation and archiving form do not exist for resource structuring. For this reason, the department of tooling design structures the resources with the focus on functional criteria [15], while other departments (e.g., robotics, control engineering) favor deviating kinds of resource structuring. As depicted in Figure 2, this paper focuses on the illustration of a new, integrated methodology concerning the engineering process of robot cells and the associated data structures. Thus, in the context of the third chapter, the methodology of mechatronic-oriented digital engineering is presented.

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organizational levels

focus of the paper

manufacturing area 1 manufacturing area 1.1 robot cell 1 fixture

function group 1 … … robots … technical documents robot cell 2 robots technical documents

Figure 2: Resource structure in the body shop. 3

MECHATRONIC-ORIENTED ENGINEERING IN THE BODY SHOP

This chapter illustrates the configuration, structuring, and function of the mechatronic plant model in the course of a production planning project. Based on this integrated data model, the concept of virtual startup together with its potentials and risks are pointed out. On the other hand, the possibilities and advantages of seamless modelbased maintenance services will be indicated. Finally, the effects of this mechatronic-oriented engineering process on the different body-in-white departments are addressed. 3.1 Mechatronic plant model as cross-domain planning and data integration platform Today's planning processes are usually characterized by a product-driven and sequential development procedure. In the course of a body-in-white planning project, this means, for example, that the department of control engineering is first involved in the planning process, when the complete plant mechanics and also the individual process sequences are already present. Because of this sequential planning process, the control engineers are afforded only little time for the generation of the PLC programs due to the strictly fixed project data. This last-minute programming, which frequently takes place only on the construction site, leads inevitably to software solutions with incomplete documentation and suboptimal software quality [16]. Although the control programs developers have very broad and well-founded process knowledge, presently this knowledge is not used for the process design of the plant. In contrast to current planning processes, the concept of mechatronic-oriented digital body-in-white engineering promotes a parallelism of the different planning activities in the sense of simultaneous engineering [17]. Additionally, this concept centers on the overall lifecycle of a manufacturing system. The foundation of this new planning methodology is the integrated view of mechanics, electrics, and information technology in upstream process steps in the sense of a mechatronic development procedure. In this context, a central planning and data integration platform represented by a mechatronic plant model is introduced. Based on this, the different body-inwhite departments are able to work simultaneously and

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networked, using up-to-date, complete, and consistent data sets. Altogether, this integrated plant model consists of several levels in accordance with the structure of a real manufacturing system. With the special focus on the structure of a body-in-white welding fixture, Figure 3 portrays the different structuring levels as well as the resource types of the mechatronic plant model used. C1 MG1

Cell (C) level

MG2 MGn E/L1 MG1 FG1

Main Group (MG) level

FG2 FGn E/L2 FG1 SG*1

Function Group (FG) level

SG2 SGn E/L3 SG*1 WG1

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WG2 Pn E/L4 WG1 P1

Welding Group (WG) level

P2 Pn P1

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P2 Pn

mechatronic component electrical/logical component

mechanical assembly mechanical part

Figure 3: Configuration of a mechatronic plant model using the example of a welding fixture. In the context of the overall structure of the mechatronic plant model, on the lowest structuring level there is a first component grouping by the creation of so called welding groups. These welding groups bundle all the mechanical parts to be welded to a mechanical assembly in the form of a rigid assembly group. Through the use of rigidly connected sub-groups, mechanical elements that accomplish a common movement are grouped. Normally, these sub-groups consist of welding groups and mechanical parts. In contrast to the typical, mechanical sub-groups, clamping devices assume a special role in two different regards: these special sub-groups are standardized, mechatronic plant components. Thus, apart from the two mechanical welding groups “main body” and “pressure arm”, the modelled and configured clamping device also consists of kinematics, an internal behavior logic, and several I/Os (Inputs/Outputs) controlled by inserted valves of the PLC in the later production process. This mechatronic resource component, which reflects the characteristics of a real clamping device, is made available to the related body-in-white departments in the form of a

standardized mechatronic resource library. Company standards such as certain naming conventions and interfaces in the form of predefined I/Os can be taken into account directly in the course of developing such a library or with the configuration of the mechatronic resource components. In the context of the plant structuring, function groups consisting of several mechanical and mechatronic sub-groups are logical units to fulfill a defined task. Examples for typical function groups are clamping groups (Figure 3), ejectors, and valves. As a next hierarchy level, so-called main groups (complete devices, robots, conveyers, etc.) are introduced: these are the elements that the complete mechatronic plant model finally consists of. The structuring of mechatronic robots, conveyers, etc. is geared as far as possible to the structure of a mechatronic welding fixture. The lasting advantages resulting from the use of standardized, mechatronic plant components or complete mechatronic plant models along the entire production lifecycle are illustrated in chapters 3.2 and 3.3. In addition to the realistic “mechatronization” of resources, a further content of this integrative planning methodology is the control of the resulting data complexity. Only by an intelligent and efficient data management will it be possible to control these large and various data sets in order to be able to guarantee a constant, cross-domain, and consistent data supply. The approach bases on the fact that all body-in-white departments are only responsible for a certain part of the entire plant and that they inevitably do not have all the plant data. Hence, the department of tooling design is, for example, responsible for the complete fixture development whereas the department of robotics is accountable for robot simulation and programming. Furthermore, the departments have to work with data of different degrees of detail, consequently gaining different detailed views of the same robot cell [18]. If it were sufficient for the simulation experts to accomplish collision analyses with simple and grouped assemblies, the focus of the tooling engineer would be on the detailed development of each individual function group with all its individual parts. On the basis of these fundamental considerations, a cross-domain viewing concept is presented. In this context, a so-called master resource structure containing all the resource components and information of a complete robot cell with different degrees of detail is introduced. Using an integrative EDM environment dependent on the respective body-in-white domains, certain views of this master structure are generated. In accordance with Figure 3, Figure 4 illustrates the configuration and contents of such a master structure using the example of the structural and graphic representation of a mechatronic function group (clamping device) for the body-in-white departments tooling design, simulation, and production planning. In parallel with the detailed development of the mechatronic plant model, the cell-specific process planning takes place. Apart from the conventional technical standard processes such as loading, clamping, and welding, the process planners receive an extended selection of process types and process-describing parameters. In reference to the following PLC programming, the graphicoriented process graph (pert diagram) is extended by control-specific aspects such as signal inquiries and further transition conditions. Since this computer-assisted process description forms the basis for the IEC-compliant PLC software generation (IEC: (International Electrotechnical Commission), this integrated process graph is developed collectively by the process planners and the control engineers. Based on this integrated process graph, a first PLC program is generated. Parallel to the

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development of the mechatronic plant model, the PLC program is detailed and completed in close coordination with the activities of the other body-in-white departments. FG – Master View

FG – Design View

FG – Simul. View FG – Planning View

FG - DV

FG - SV

SG*1

SG*1

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FG - PV P1

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Early, 3D-oriented validation and optimization of real PLC programs in interaction with the respective robot cell

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Early validation of different operating modes with the production-specific IT environment

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Relocating of operator trainings from the factory to the control engineer's office

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Early, 3D-oriented playing of possible operating scenarios on the basis of up-to-date and consistent product, control, and resource data

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Attainment of higher degrees of maturity for the plant and control software for SOP (Start Of Production)

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Accelerated and more efficient startup and rampup process with lower overall costs Apart from these various potentials, however, there are also some risks and additional expenses incurred due to the introduction of the concept of virtual start-ups. Related to the entire engineering process, these additional expenses primarily accrue in connection with the development and maintenance of the mechatronic plant model and of the mechatronic resource libraries as well as due to minor adaptations of the control programs. Yet, from the strategic point of view, there are also some risks, additional expenses, and costs to be critically checked concerning the introduction of this integrative validation methodology:

Figure 4: Domain specific resource structures and data representation using the example of a function group.

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Additional software, licence, training, and support costs

Apart from the function as central planning and data integration platform, the mechatronic plant model takes different roles in accordance with the actual process state. Hence, this integrated data model also serves as test and simulation platform for the 3D-oriented validation of real PLC programs before the real startup takes place.

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Rising complexity of the legacy IT infrastructure and increase of the expenditure for system maintenance

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Assurance of a seamless data flow to other IT systems (e.g., to the CAD system in place)

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Consideration and integration of existing company standards for the effective usage

3.2 Virtual startup as transfer from the digital to the real factory

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The mechatronic plant model forms not only the technical but also the methodical foundation for the execution of virtual startups. Fundamentally, a virtual startup enables faults in the real PLC programs to be detected and eliminated at a very early stage. Thus, the PLC programs can be optimized with respect to an improved behavior of the manufacturing system without the necessity of its being physically available. Because of the necessary data inputs in the form of digital product and resource data as well as real control data, the virtual startup is also frequently referred to a transfer from the digital to the real factory [19]. As a central instrument of the virtual startup, HIL technology (Hardware-in-the-Loop) is established. In this way, through the use of the real PLC hardware, an early 3D-oriented validation of the PLC programs is accomplished by means of the mechatronic plant model. The communication between the control software in the form of PLC programs and/or the control hardware on the one hand and the mechatronic plant model on the other hand is carried out over appropriate interfaces (e.g., COM, OPC). Apart from the validation of the real control software and hardware, further factory information systems such as control panels and super-ordinate control systems can be checked and optimized at a very early process stage [20]. This allows operators to be trained by means of the production-specific IT environment in the office of the control engineer using the mechatronic plant model in parallel to the build-up of the real manufacturing system. Related to the startup and rampup process, the concept of virtual startup yields the following benefits:

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Assurance of user acceptance and high qualification level of the user This ambivalent view concerning the introduction of virtual startups in operations shows the necessity of a companyspecific, structured, and standardized evaluation methodology. In the context of this methodology, the companyrelevant benefits are compared with the respective additional expenses. In these profitability analyses, companyspecific boundary conditions such as present experiences with methods and tools of the digital factory, the extant IT infrastructure, and product-specific conditions also have to be considered. 3.3 Model-based maintenance

Apart from the planning, startup and rampup process, the introduction of the mechatronic plant model yields substantial time, quality, and cost potentials, particularly during the real production process. As mentioned in chapter 1, the operation of a manufacturing system or the supervision of volume production are paramount due to the changed basic conditions. In contrast to today’s production processes, it is not inevitable that new production systems will be developed for the manufacture of new products that have to be integrated expensively into the current production. Rather, new vehicle components are to be produced on existing manufacturing systems in the future. Although this demand causes the development and the use of highly flexible and adaptive manufacturing systems, it must be guaranteed that new products can be generally produced on the existing robot cells without causing time- and cost-intensive rebuilding of these production systems during on-going operations. In this context, accelerated and safe rampups are as important

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as the assurance of high degrees of maturity in the plants and software in favor of an efficient production process. In the course of the mechatronic-oriented digital engineering process, the mechatronic plant model and the concept of virtual startup will make a substantial contribution to assuring these production-specific requirements. Thus, both problems and production-specific investigations are relocated from the current production to the office of the respective maintenance specialists. In contrast to the production processes in use today, this process in favor of a model-based maintenance yields the following benefits:

Dynamic run-through of possible failure scenarios and performance of model-based failure analyses

en an ce ma int

simulation

Figure 5: Departments of the mechatronic-oriented engineering process.

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Mechatronic plant model as seamless documentation medium in the case of production changes Finally, all these benefits of a model-based maintenance are reflected in a significant decrease in expensive changeover and down-times of the real manufacturing systems. Changes and optimizations taking place at the real robot cells are verified and documented using the mechatronic plant model. In this way, the departments of production engineering – or even the product development departments – can directly exploit the productionspecific experiences for further projects in the future. Faster and safer market launches of new vehicle variants targeting key market shares are the consequences of such a model-based maintenance. 3.4 Organizational effects The methodology of mechatronic-oriented digital engineering pointed out in the chapters above inevitably calls for and promotes a closer interaction between all the different body-in-white departments set out in Figure 5. In contrast to today’s body-in-white processes, this integrative approach promotes simultaneous engineering between the departments of production planning, tooling design, robotics, simulation, and electrical/control engineering on the basis of a seamless, mechatronic-oriented data model. Due to the introduction of this new methodology on the one hand and the necessary IT infrastructure on the other hand, the roles of the body-in-white departments are also changed. For example, in engineering projects as undertaken today, the department of tooling design is primarily responsible for the development of the mechanical welding fixtures as well as for the determination of the cell-specific process sequences. However, the departments of robotics and simulation exclusively deal with simulation activities such as collision checks and the programming of the respective robots. To sum up, each department currently tries to reach its local optimum without having a holistic view of the entire process. In order to reach a global optimum with the focus on the entire lifecycle of a manufacturing system, this strict task sharing is no longer feasible. Hence, in the course of the mechatronic-oriented engineering process, the development of the mechatronic plant model and the linked integrated process graph takes place in close interactions between the different body-in-white departments, promoting clearly defined and transparent process workflows.

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Validations of mechanical design and PLC program modifications using the mechatronic plant model

mechatronicoriented engineering process rob

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Model-based studies concerning the integration of products and product variants on existing robot cells

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Constructional plant and PLC program optimizations during current production operations without endangerment of the real manufacturing system

ol ntr co l / ng ica eri ctr ine ele eng

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production planning

Both the success of process and organizational changes and of the introduction of new methods and technologies primarily depends on the acceptance of the users. Thus, the introduction of this new methodology, which focuses on the entire lifecycle of manufacturing systems, has to be accompanied by flanking measures in the field of work psychology [21]. 4

SUMMARY AND OUTLOOK

Based on the illustration of the changed basic conditions in the automotive industry, the concept of mechatronicoriented digital production engineering is introduced in this paper, taking the example of the body shop. The foundation of this new planning methodology is an integrated view of mechanics, electrics, and information technology in upstream process steps in the sense of a mechatronic development procedure. In this context, a cross-domain planning and data integration platform represented by a mechatronic plant model is presented. Based on this, the different body-in-white departments are able to work simultaneously and networked, using up-to-date, complete, and consistent data sets. With the focus on the overall lifecycle of a manufacturing system, the mechatronic plant model assumes different roles in the course of a complete production engineering project: On the one hand, this integrated data model serves as test and simulation platform for the 3D-oriented validation of real PLC programs (virtual startup); on the other hand it is profitably used for maintenance services in the real factory. Apart from a considerable acceleration of the planning, startup, and rampup process, the benefits of this new, integrated methodology are also reflected in higher degrees of maturity and lower overall costs. So far, some aspects of this integrated body-in-white methodology have been realized using some practical scenarios with great success. Currently, the seamless implementation of this concept is being verified and critically evaluated on the basis of a real example from the body shop. Much progress has been made in this topic, but the following questions still remain to be addressed in further research activities: •

How detailed should the mechatronic plant model be developed in regard to profitability aspects or what

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kind of information should be integrated in this 3Doriented data model? •

What is the ideal introduction strategy of this mechatronic-oriented methodology for industrial practice?

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To what extent are the internal task distributions and responsibility areas to be adapted to the new boundary conditions or what kind of effects will the introduction of this integrative methodology have concerning the future interaction between the OEM and its suppliers?

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To what extent is it possible to transfer this cellspecific body-in-white methodology to super-ordinate factory levels (e.g., entire production lines) or what boundary conditions have to be considered to effect this transfer?

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Schuh, G., Kampker, A., Franzkoch, B., 2005, Anlaufmanagement, wt Werkstattstechnik 95 (2005) H. 5, pp. 405-409. [2] Kalmbach, R., 2003, Von der Technik zum Kunden. Was die Automobilindustrie von anderen Branchen lernen kann und muss, Markenmanagement in der Automobilindustrie. Die Erfolgsstrategien internationaler Top-Manager, pp. 35-60. [3] Westkaemper, E., 2005, Mächtige Hilfsmittel stehen bereit, Intelligenter produzieren - Die Vernetzung der Digitalen Fabrik mit der realen Produktion, 2005/1, pp. 11-13. [4] Glas, J., 1993, Standardisierter Aufbau anwendungsspezifischer Zellenrechnersoftware, iwb report No. 61. [5] Bender, K., Albert, J., 1999, Echtzeitsimulation zum Test von Maschinensteuerungen, Informationstechnik im Maschinenwesen. [6] Reithofer, N., 2002, interview in VDI news, No. 23, p. 11. [7] Budke, W., Lux, K.-L., Waldminghaus, S., 2003, Megatrends in der Automobilindustrie, Zusammenfassung der wichtigsten Richtungsvorgaben des VDA-Technik-Kongresses in Wolfsburg, des Automobilforums in Stuttgart und der IAA in Frankfurt. [8] VDI-Richtlinie 4499, Digitale Fabrik – Grundlagen, page 1, to be published in 2006. [9] Kiefer, J., 2003, Erarbeitung einer digitalen Planungsmethodik für den Bereich Rohbau im Geschäftsbereich Nutzfahrzeuge, diploma thesis, Saarland University. [10] Schmidgall, G., Kiefer, J., Baer, T., 2005, Objectives of integrated digital production engineering in the automotive industry, Proceedings of the 16th IFAC World Congress, Prague, Czech Republic.

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[11] Baer, T., Kiefer, J., Schmidgall, G., Burr, H., 2005, Objectives of a Seamless Digital Process Chain in the Automotive Industry, Proceedings of the CARV 05, Munich, Germany. [12] Eigner, M., Stelzer, R., 2001, Produktdatenmanagement-Systeme - ein Leitfaden für product-development und Life-cycle-Management, Springer Verlag. [13] Mueller, M., 2004, Erarbeitung eines neuen Konzeptes zur Dokumentation von Prozessdaten im Schnittstellenbereich zwischen digitaler Entwicklung und Planung, diploma thesis, Saarland University. [14] Burr, H., Vielhaber, M., Deubel, T., Weber C., Haasis, S., 2005, CAx/engineering data management integration: enabler for methodical benefits in the design process, Journal of Engineering Design, Vol. 16, No. 4, pp. 385-398. [15] Wingerter, N., 2004, Abbildung Ressourcen in Delmia/Catia, 4. CAx user meeting, Wörth, Germany. [16] Zaeh, M. F., Vogl, W., Wuensch, G., Munzert, U., 2004, Virtuelle Inbetriebnahme im Regelkreis des Fabriklebenszyklus’, iwb report No. 74: Virtuelle Produktionssystemplanung, pp. 1.2-1.21. [17] Ehrlenspiel, K., 2002, Integrierte Produktentwicklung, Carl Hanser Verlag. [18] Kiefer, J., Schmidgall, G., Baer, T., Bley, H., 2005, Integrierte digitale Planung mechatronischer Produktionssysteme in der Automobilindustrie, VDI reports 1892.2: Mechatronik 2005 - Innovative Produktentwicklung, pp. 995-1011. [19] Schloegl, W., 2005, Bringing the Digital Factory into Reality – Virtual Manufacturing with Real Automation Data, Proceedings of the CARV 05, Munich, Germany. [20] Diedrich, C., Franz, G., John, K.-H., Krause, J., Poignée, F., 2005, Support of control application design using digital design and planning of manuth facturing cells, Proceedings of the 16 IFAC World Congress, Prague, Czech Republic. [21] Schulze, H., Haasis, S., Brau, H., Weyrich, M., Rhatje, T., 2005, Human-centered Design of Engineering Applications – Success Factors from a Case Study in the Automotive Industry, Published in Human Factors and Ergonomics in Manufacturing, 15, Number 4, pp. 421-444. CONTACT Jens Kiefer DaimlerChrysler Research & Technology, Product and Production Modeling (REI/IP), 89013 Ulm, Germany, [email protected]

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