@incollection{KrauseOttoKrebs, author = {Krause, Chris and Otto, Dierk and Krebs, Irene}, title = {Requirements for Simulation Process Data Management in Product Lifecycle Management Systems}, series = {Metody i narzędzia w inżynierii produkcji}, booktitle = {Metody i narzędzia w inżynierii produkcji}, isbn = {978-83-65200-03-7}, pages = {99 -- 108}, abstract = {Undoubtedly, simulation data management gets more and more important for mechanical engineering enterprises. The reasons are manifold. Steadily improved CAD- and manufacturing tools allow more and more complex geometries which are used in different surroundings and fulfil various requirements. The geometries will not be physically tested for these more complex intended purposes since it is not economical and would lead to extended development processes. Consequently, simulations allow designing for these missions. The Commercial of the shelf (COTS) software tools like Abaqus, LS Dyna, Nastran etc. supports simulation to solve complex models. Geometries can be created within automated workflows or manually by designers. The first method reduces distinctly the development process time and leads to a design which is driven by simulations (design driven by simulation instead of simulation driven by design). In the past, simulations were used to check if a nearly final geometry can fulfil all requirements. Obviously, this can lead to time-intensive loops in the development process. This emphasizes the need to check the business processes because improved data storage cannot lead to the full benefits as long as the business processes are not adapted and include waste. A typical example for mechanical engineering companies are aerospace enterprises. This industry has many additional requirements which complicate simulation data management. The major one might be the long product lifecycle. Aerospace engine manufacturers like Rolls-Royce develop and support an engine for a long time, possibly for one of the longest compared to other industries. An engine can be build and supported in service up to 50 or more years, e.g. the Rolls-Royce Tyne (used among others for the Transall C-160) is in service since 1955. Furthermore, the missions for an engine are very complex and so are the simulations and all input parameters. This is the reason to support the analysts with a PLM-tool which is adapted not only to the design world, but also to their needs. The work encloses the analysis of the business processes for the development of engine parts within Rolls-Royce Deutschland as well as an adapted storage method for simulation data in the PLM tool Siemens Teamcenter. However, this paper focuses entirely on the found gaps and requirements in the business processes to adjust the PLM tool. The first and most important requirement is the traceability of the simulation back to all input parameters. Therefore, the data must be well connected. It is not enough to see which geometry was the basis for a simulation (this is an obligation and nothing new in industry or science). Instead, all small files which are created in the development process and serve as input for a simulation must be found and connected. These small files embrace for example the flight mission (the boundary conditions for the engine), the specific versions of the software tools, the documents which describe the data etc. Furthermore, the paper describes additional requirements, for example the need to enable the analysts and the importance for safety in aerospace. Teamcenter and other PLM software tools allow various adaptions so that the tools can reflect the needs of the enterprises using them. Nevertheless, in majority these tools were adjusted on the design area. This does not mean that nothing is enabled for the CAE community, but the data model for simulations is a generic one which must be extended to all small input files. Therefore, the business processes and created data must be investigated to collect all requirements for simulation process data management within product lifecycle management systems.}, language = {en} } @inproceedings{KrauseMeinbergKrebsetal., author = {Krause, Chris and Meinberg, Uwe and Krebs, Irene and Schlauer, Christian and Otto, Dierk}, title = {A Contribution To SPDM Strategies In Aerospace}, series = {NAFEMS SPDM World Congress Stockholm 2017, Summary of proceedings}, booktitle = {NAFEMS SPDM World Congress Stockholm 2017, Summary of proceedings}, isbn = {978-1-910643-37-2}, pages = {S. 157}, abstract = {Long product lifecycles are standard in aerospace, thus simulation data must be handled. This data is critical because it represents real value for the enterprise. Furthermore, simulation data is created in lengthy business processes and often ends in large files. This sets challenges for handling it to allow an efficient storage approach but also full traceability. This paper is an interim result of the research projects VITIV (project number: 80164702) and the ProFIT-Programme supported by the federal state of Brandenburg and the European Union. Initially, the status quo must be analysed. Therefore, the current development processes to develop engine parts were investigated. The focus is the data created by analysts but for this task, all input data created prior must be known, handled and stored as well. Additionally, a new modular data structure is developed to fulfil the aforementioned requirements. However, two concepts for storing must be compared. The first would be to store all input files and boundary conditions separately without storing a full executable simulation file. This approach requires the functionality to automatically rebuild the executable file which takes time but provides a lean and modular storage. The other method would be to store the large runnable file and avoid protracted processes to rebuild the file. In some cases such rebuilding could last for several weeks. In this case, traceability must be secured. Currently, not all data regarding CAE is stored in the PLM system. The reasons differ from constraints in terms of configuration of the system, as well as a lack in foresight. This means that some process actors are focussed on a fast way to store their data but do not take into consideration that these objects must be found and used for investigations in the future. The next step will be to work on the process automation. These workflows should reduce the amount of manual user interactions, hence to speed up the processes and avoid sources of error. Furthermore, the developed method for storing the data has to reach the next level: from the secured test environment into a pre-production system.}, language = {en} }