K10
SDM 4

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08:35
conference time (CEST, Berlin)
What Makes the Integration of an SDM Successful? The Journey and Experiences of an Automotive Supplier
27/10/2021 08:35 conference time (CEST, Berlin)
Room: K
M. Tupy (Brose Fahrzeugteile GmbH & Co. KG, DEU)
M. Tupy (Brose Fahrzeugteile GmbH & Co. KG, DEU)
The successful introduction of a simulation data management system is a complex matter and is subject to a large number of influencing variables and dependencies. Due to the time horizon, some of these variables are in a perpetual state of change and determine the course of the integration. We divided the implementation into four phases. Initially, the needs and potentials of the business units were determined and, based on these, an appropriate SDM tool selection was made in the next step. In phase three, the SDM software was piloted in the working environment and is now being rolled out worldwide. The capability for integration into the existing and future IT landscape, including interfaces to other data management systems, plays a central role in ensuring end-to-end data flows and transparency while reducing manual and administrative efforts to an absolute minimum. This frees up capacities for value-adding activities. In the course of digitalization, automation and connectivity, it is important to question existing CAE processes and adapt them to the new circumstances and align them across teams and business units. Here, internal support and supervision is essential to ensure process understanding and fast maintainable implementations. From the author's point of view, however, the inclusion of users in the change process is the most important building block and the decisive factor for success or failure. While the previously mentioned topics are highly related to the SDM tool, this factor is to be considered absolutely tool-independent. Here, the consultation and active involvement of the users in every phase is decisive. The active involvement of CAE engineers as process designers, pilot users, but also as training providers has a convincing effect and serves as a multiplier for the entire SDM world. The author would like to thank :em engineering methods AG, GNS Systems GmbH and PDTec AG for their support on this journey.
08:55
conference time (CEST, Berlin)
The Standard Based Digital Twin - Making the Foundation for Smarter Manufacturing and Creating Better Products
27/10/2021 08:55 conference time (CEST, Berlin)
Room: K
M. Prado Motta, C. Abel, C. Pelaingre (Cirtes, FRA); R. Lanza, H. Galtung, M. Chaure (Jotne, NOR)
M. Prado Motta, C. Abel, C. Pelaingre (Cirtes, FRA); R. Lanza, H. Galtung, M. Chaure (Jotne, NOR)
Smart manufacturing aims to convert data acquired across the product lifecycle into manufacturing intelligence in order to yield positive impacts on all aspects of manufacturing [TAO18]. Digital Twin (DT) is a powerful concept in smart manufacturing context. Broadly defined as the virtual and computerized counterpart of a physical system [KRI18], DTs basis itself on synchronization between the virtual and real system, thanks to sensed data and connected smart devices, mathematical models and real time data elaboration [NEG17]. In this work a Standard Based Digital Twin of a turning machining process is developed, and a number of domain-specific challenges are surpassed, including diverse communication standards, heterogeneous data structures and interfaces. The real manufacturing process is instrumented with temperature [BAR97] vibration and force sensors strategically positioned closely to the cutting process as well as in the environment [KER12], [LE12], [SAB15]. Sensorial data is collected using a dedicated machining monitoring system that allows real time display of measured variables as well as other important indicators, such as warnings and information about the process state that are derived from these measurements via conditional or artificial intelligence models. With the applications developed in this work users can obtain a fully operational DT in three clicks: (Click1) the user will upload its CAD/STEP/SIMULATION model to automatically create the product breakdown structure, (Click2) mount the appropriate sensors on the product and (Click3) configure the IoT consumer/producer software. Data management of the repository will be done by an ISO-10303 product lifecycle management (PLM) and the Arrowhead Framework. The implementations are achieved by the combination of an ISO-10303 repository [LAN19] and the Open-Source ECLIPSE Arrowhead Framework [PAN21] for IIoT/CPS integrations. The use of the project innovations will, thus, enable optimization of upstream manufacturing processes, like design, engineering analysis and process planning, and sensor data received from the DT. REFERENCES [BAR97] Barlier, C.; Lescalier, C.; Mosian, A. (1997): Continuous Flank Wear Measurement of Turning Tools by Integrated Microthermocouple. In CIRP Annals 46 (1), pp. 35–38. DOI: 10.1016/S0007-8506(07)60770-7. [KER12] Kerrigan, K.; Thil, J.; Hewison, R.; O’Donnell, G. E. (2012): An Integrated Telemetric Thermocouple Sensor for Process Monitoring of CFRP Milling Operations. In Procedia CIRP 1, pp. 449–454. DOI: 10.1016/j.procir.2012.04.080. [KRI18] Kritzinger, Werner; Karner, Matthias; Traar, Georg; Henjes, Jan; Sihn, Wilfried (2018): Digital Twin in manufacturing: A categorical literature review and classification (51). [LAN19] Lanza, R.; Haenisch, J.; Bengtsson, K.; Rølvåg, T. (2019): Relating structural test and FEA data with STEP AP209. In Advances in Engineering Software 127, pp. 96–105. DOI: 10.1016/j.advengsoft.2018.08.005. [LE12] Le Coz, G.; Marinescu, M.; Devillez, A.; Dudzinski, D.; Velnom, L. (2012): Measuring temperature of rotating cutting tools: Application to MQL drilling and dry milling of aerospace alloys. In Applied Thermal Engineering 36, pp. 434–441. DOI: 10.1016/j.applthermaleng.2011.10.060. [NEG17] Negri, Elisa; Fumagalli, Luca; Macchi, Marco (2017): A Review of the Roles of Digital Twin in CPS-based Production Systems. In Procedia Manufacturing 11, pp. 939–948. DOI: 10.1016/j.promfg.2017.07.198. [PAN21] Paniagua, Cristina; Delsing, Jerker (2021): Industrial Frameworks for Internet of Things: A Survey. In IEEE Systems Journal 15 (1), pp. 1149–1159. DOI: 10.1109/JSYST.2020.2993323. [SAB15] Sabkhi, N.; Pelaingre, C.; Barlier, C.; Moufki, A.; Nouari, M. (2015): Characterization of the Cutting Forces Generated During the Gear Hobbing Process: Spur Gear. In Procedia CIRP 31, pp. 411–416. DOI: 10.1016/j.procir.2015.03.041. [TAO18] Tao, Fei; Qi, Qinglin; Liu, Ang; Kusiak, Andrew (2018): Data-driven smart manufacturing. In Journal of Manufacturing Systems 48, pp. 157–169. DOI: 10.1016/j.jmsy.2018.01.006.
Digital Twin, Product lifecycle management, ISO 10303, Machining Monitoring, Cutting tool instrumentation
09:15
conference time (CEST, Berlin)
Management of CAE Model Variants Within an SDM Environment
27/10/2021 09:15 conference time (CEST, Berlin)
Room: K
S. Tzamtzis, I. Makropoulou (BETA CAE Systems SA, GRC)
S. Tzamtzis, I. Makropoulou (BETA CAE Systems SA, GRC)
In recent years, SDM systems have been established as the reference point for CAE, allowing model data and simulation results to be stored in an organized manner and data relationships to be traced. During the evolution of the design, various base modules are updated and stored in the data repository by different teams. This inevitably leads to the need for a unified environment, where individual users can work on different modules of the same CAE model variant simultaneously, in a simple, yet effective manner. Eventually, these updates need to be incorporated in the CAE model so that new simulation iterations are derived by specifying which modules of the base variant should be changed and which should remain unchanged. Having an SDM solution in place, some key questions arise for engineering teams: How can you best make sense and good use of your data? How can you identify the modifications between the different base module versions and the model variants prepared by the various teams? How can you understand their impact on simulation results and make informed decisions at a timely manner? BETA CAE systems, with its advanced CAE pre-processing software for complete model build and its unique data management tools, responds to these questions providing solutions for the effective management of CAE model variants. A workspace that facilitates the modular organization and derivation of CAE model variants is in place, incorporating key model checks in the default variant creation process, to ensure the validity and integrity of the models. The evolution of CAE data and their dependencies can be identified through a Lifecycle Graph, while the modifications between the different versions are tracked in detail by a changeset management mechanism. Powerful comparison tools are available, facilitating the fast and accurate comparison and matching of different model variants, both in terms of their metadata, as well as their contents. Finally, an overview of the model variant is offered, through functionality that generates detailed reports that capture the evolution of the model, the modifications that were introduced and their impact on simulation results.
simulation data management, CAE model variant, model build, data lifecycle, model comparison, model report
09:35
conference time (CEST, Berlin)
Mastering Incompatibility: Intelligent Integration of a Blackbox CAE Tool with a SPDM System
27/10/2021 09:35 conference time (CEST, Berlin)
Room: K
A. Nicklaß, A. Benden (GNS Systems GmbH, DEU)
A. Nicklaß, A. Benden (GNS Systems GmbH, DEU)
A growing number of CAE tools manage complexity through their own data and process management. This stands in the way of a higher-level simulation data and process management (SPDM) because the tool-internal relationships and metadata are not revealed. The coupling of CAE tools with general SPDM systems is thus considered one of the greatest challenges for simulation data management. GNS Systems realized the integration of such hermetic applica-tions into an SPDM system for an international automotive supplier. In combi-nation with the complete automation of the product development process, the company now benefits from increased efficiency, better data quality and even generates new capacities for the development of innovative products. At the start of the project, the picture was as follows: The customer uses a software solution such as PDTec's SimData Manager to couple the CAE simu-lations with the task management of an ERP system and a design database (PDM). The analysis engineers work with a simulation environment such as Ansys Workbench, which hinders efficient data management with its internal data and process management. GNS Systems therefore designed and imple-mented a workflow which, with the software solution as the hub, creates, ex-ports and imports Workbench bundles. The required CAD data is provided during the export procedure. In addition, a concept was developed for mapping the internal structures of the simulation platform to data elements of the CAE data management solution. The new implementation closed the crucial gap that previously prevented end-to-end data management for users. This resulted in an efficient workflow: it links the analysis tasks with the bundles of the simulation environment and the reports. In addition, the implementation enables the efficient collaboration of computational staff from different locations through central storage and man-agement. Mapping the internal workbench structures in a central CAE data management solution, such as SimData Manager, opens perspectives for com-parisons between simulations of different Workbench bundles.
Simulation Data Management, CAE, Process Automation, Product Data Management, CAD, Ansys Workbench, Product Development, Data Consistency, Automation, Data Quality, Workflow, SimData Manager
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