B16
SDM 7

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10:40
conference time (CEST, Berlin)
Enabling Simulation Data Management for Custom CAE Processes
28/10/2021 10:40 conference time (CEST, Berlin)
Room: B
A. Mahl (PDTec AG, DEU)
A. Mahl (PDTec AG, DEU)
The acceptance and success of a SDM deployment depends on the seamless integration of existing, customer-specific CAE processes. This includes all aspects of each simulation discipline and shall support the overall CAE process as well as specific engineering tasks. The flexibility and openness of the SDM system is a critical success factor to achieve a high level of integration in early phases of the deployment to promote SDM for all CAE users (professional and casual). Especially the CAE tools used by a larger user community on a day-to-day basis shall have a deep integration into SDM with relevant comfort functions to minimize required interactions. This will elevate the efficiency and increase the quality of simulations. The CAE data integrity along the complete simulation tool chain is a key objective of simulation data management. However, to achieve this goal, critical integration steps have to be implemented. The paper will outline the various options of a modern SDM system to integrate external tools for customized CAE process chains. Examples will demonstrate the higher productivity, faster ROI and greater acceptance of SDM.
SPDM, SDM, Customizing, CAE process, Automation, Tool integration
11:00
conference time (CEST, Berlin)
Successful Use Cases and Application Examples for the Integration of Artificial Intelligence into SPDM
28/10/2021 11:00 conference time (CEST, Berlin)
Room: B
A. Kuhn, N. Stalanich (Andata Entwicklungstechnologie GmbH & Co KG, AUT)
A. Kuhn, N. Stalanich (Andata Entwicklungstechnologie GmbH & Co KG, AUT)
In automotive development the Finite Element Method (FEM) is integrated throughout the virtual product development processes, industrialized, and immanently integrated in the series development. This broad use is supported by simulation process and data management systems (SPDM), which allows extensive automation in modeling, simulation and evaluation of simulation results (post-processing). Regarding post-processing, according to the current state of the art, fixed evaluation scripts are usually executed, and the resulting result values are stored in the databases and reports of the SDMS. Despite the increasing complexity of the simulation models and pure number of daily executed simulations, an appropriate in-depth analysis of individual simulations takes place less and less often and requires the dedicated extraction of the simulations from the SDMS and manual evaluation by experts. Such experts usually require years of training and experience and are therefore increasingly difficult to access. In addition, daily project live seldomly allows time for the necessary depths of the analysis, further resulting in a decrease in expertise and results quality and validity. Hence the development and implementation of suitable expert systems as a special form of Artificial Intelligence promises to be a solution to resolve the discrepancy between analysis experts availability and the immense amount of simulation result being processed in simulation data management. The authors have developed and implemented such expert systems for an AI based, adaptive and “intelligent”, automated evaluation of simulation data results, establishing a new level of simulation data analysis within SPDM. The proposed presentation/paper aims to introduce and illustrate basic concepts, workflows, solution architectures and implementations of automated in-depth, result-dependent, AI-based analyses of Finite Element simulations within SPDM and successful use cases with validated added values, like • automated anomalies detection for result’s plausibilization as simulation data quality management instrument, • automated identification of causal chains in complex structural responses and damage, • decision support system and extended analysis capabilities within in-depth results analysis, • “smart” access to the data in the SPDM databases as fundament sophisticated data mining across different simulations. Certain examples of such implementations will be shown for illustration.
automation, smart post-processing, artificial intelligence, simulation data quality management, decision support, SPDM
11:20
conference time (CEST, Berlin)
Data analysis and exploration with SDM Systems
28/10/2021 11:20 conference time (CEST, Berlin)
Room: B
M. Liebscher, M. Thiele (SCALE GmbH, DEU)
M. Liebscher, M. Thiele (SCALE GmbH, DEU)
During the last years simulation data management has been adopted by more and more automotive OEMs and other industries. However only in few cases the gathered data has been used in order to automatically acquire knowledge beyond individual simulations. Running DOEs, parameter studies and optimizations has always been in the domain of specialized optimization and stochastic tools. These tools are rather complicated in operation for the average user and their integration into the processes within a simulation data management system is usually not an easy task. Furthermore, the application of the methods provided by such software on the analysis of unstructured data, which is generated in large quantities by simulation data management systems, has been approached very rarely. In this paper we shall outlay the possibility given by a simulation data management system to handle such tasks. It will also be demonstrated how the analysis methodologies known from standard optimization tools can be applied to the simulation results directly integrated in the workflows of a simulation data management environment. On the side of model preparation , where all the input data is Administrated within the SDM system it will be shown how to manage also parameters which are directly linked to the input data. This gives great opportunities to integrate with standard optimization tools for setting up DOEs, parameter studies and so on. Furthermore, this can be extended by integrating some of the functionality of these tools directly with the SDM system such that a DOE can be completely set up from within the SDM System. This simplifies the process significantly and helps users who are not so familiar with expert tools. On the other hand a SDM system is also used to manage large amounts of result and even physical test data. It will be shown how the integration of a data analysis framework with additional visualizations options can aid the engineer to discover trends and relationships in their simulation data. For instance, relationships between input and output variables of a DOE can be visualized with correlation plots and response surfaces. In addition, different outlier detection algorithms of scalar values are implemented, so that larger data sets can be examined. In cooperation with partners, investigations in outlier analysis at simulation level has been performed. In this paper the conceptual idea and the results, with focus on the integration in a SDM system, will be presented.
SDM, data analysis, robustness, optimization, outlier analysis, machine learning, time series classification
11:40
conference time (CEST, Berlin)
How Schmitz Cargobull uses SPDM as a Strategic Approach in Virtual Product Development Cycles
28/10/2021 11:40 conference time (CEST, Berlin)
Room: B
J. Lamann (Schmitz Cargobull AG, DEU); Z. Petrovic (Siemens Digital Industries Software, DEU)
J. Lamann (Schmitz Cargobull AG, DEU); Z. Petrovic (Siemens Digital Industries Software, DEU)
The industry today is facing an increased demand of simulation over the entire product development cycle. More than ever, the pace of innovation in technology and business models is pushing companies to areas out of their experience. Increasing development time and cost must be mitigated by using simulation to check the product requirements as early as possible. As a matter of fact, the impact of simulation results on decision-making is significantly increasing throughout the whole product development starting from the first concept. But increasing simulation tasks lead to a point where it is necessary to also improve the overall process on a strategic level. How can the amount of simulation data be handled? How can it be kept up to date in concurrent engineering processes? Simulation Process and Data Management (SPDM) is the appropriate tool to seamlessly embed simulation in product development cycles in a closed loop from requirements to manufacturing. Schmitz Cargobull is a German manufacturer of semi-trailers, trailers and truck bodies and uses various Siemens tools throughout the product development. In this paper, the authors present how Schmitz Cargobull partnered with Siemens Industry Software to address the described challenge and used SPDM as a key strategy to significantly reduce product development time and cost. SPDM allowed to use simulation earlier and more frequently in the design to continuously check the fulfillment of requirements. Above faster execution, SPDM also leads to full traceability of data flow during development which allows more parallel working and increases the overall quality of simulation and how results are used for decision-making within the company. A strategic implementation of an SPLM process comprises different elements. The basic idea is to seamlessly capture all simulation data plus their relations to design revisions, requirements and depending decisions. Managed workflows are used to ensure the proper execution of engineering processes. We will address the different elements of the present SPDM implementation and connect them to how the contribute to the overall goal.
Simulation Process and Data Management,SPDM,Digital Twin,Product Development,PLM,Simulation,Workflows,Simulation Data,Workflow Automation
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