K11
SDM 5

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10:40
conference time (CEST, Berlin)
Global Engineering Teams Implement SPDM to Work Collaboratively at Faurecia Interiors
27/10/2021 10:40 conference time (CEST, Berlin)
Room: K
E. Michau (FAURECIA Interior Systems, FRA)
E. Michau (FAURECIA Interior Systems, FRA)
1 in 3 vehicles in the world is equipped by Faurecia, a top-ten global automotive technology supplier. While this is a proud statement to make, it also introduces worry and stress on the engineering management team, since the engineering development of all the various products that Faurecia delivers happens at multiple locations around the world – specifically, at 39 separate Research & Development centers located in 35 different countries. If this is not complicated enough, there are four (4) unique business groups – Clean Mobility, Seating, Interiors, and Clarion Electronics – which all have different product development cycles, different CAE tools, and customized CAE methods that enable each team to produce their products efficiently, consistently, and at a high level of quality. Use of SPDM is critical to allowing all the engineering teams to collaborate on one platform, making sure the CAE process is managed correctly. SPDM also allows for a standardized data management system to be followed, allowing for management teams to be constantly updated on project maturity and levels of completeness. At Faurecia, a clear focus was put on the need for a collaborative platform that managed all the specific CAE work tasks, including CAE simulation modeling, job submission, and results reporting. Use of SPDM allowed for standardization of these engineering deliverables, but also allowed for work request & delivery management, resulting in a “Kanban Board” so that managers and engineers alike all knew what was completed, in-work, and still to-be-completed. This Kanban Board could be seen by engineering teams at multiple R&D sites, resulting in a clear understanding and “visual snapshot” of what the status was of engineering development of each product at each location. This summer, Faurecia started “phase one” of their SPDM implementation, which included two R&D groups – Faurecia Clean Mobility and Faurecia Interiors – who worked with their engineering IT teams to test the benefits of SPDM on their workflows. The biggest success factor was the overall user experience, which allowed for increased understanding of the overall engineering workflows, and also led to increased adoption of the software across the multiple sites. This “phase one” implementation of SPDM at Faurecia also allowed for quicker end-to-end engineering workflows, and the global CAE management team is now evaluating the feasibility of a “phase two” implementation, which includes collecting end-user feedback, managing (and standardizing) the simulation process, and finally implementing a layer of automation to speed up the workflow time it takes to bring new products to the market.
11:00
conference time (CEST, Berlin)
Eliminating Errors During Model Build Process
27/10/2021 11:00 conference time (CEST, Berlin)
Room: K
A. Fassas, M.Stampouli, S. Karastamatiadis (BETA CAE Systems SA, GRC)
A. Fassas, M.Stampouli, S. Karastamatiadis (BETA CAE Systems SA, GRC)
Over the last decades, simulation plays an increasingly active role in the identification and definition of model properties in the early design stages, to accurately predict model behaviour and assist decision-making. The growing complexity of simulation processes has created new challenges in improving CAE productivity and effectiveness, increasing drastically the size and the amount of simulation data. The loose management of such diverse data leads to error-prone procedures that delay important decisions. In many cases, the information required to downstream CAE processes is unavailable, untraceable, outdated or susceptive to human errors. At the same time, the intellectual property obtained by the accumulating best-practices experience is often not captured as applicable knowledge for future use. Regarding the implementation of simulation processes, a number of concerns frequently arise. Engineers with varying skills, intellect and knowledge, often located in different geographical locations, have to cooperate effectively. During the process, information flowing from numerous sources in a variety of forms requires efficient coordination, processing, verification and management. The deployment of various software and hardware resources, as well as the different data management systems for the storage and retrieval of the simulation data also renders the simulation process error-prone. The aforementioned issues usually lead to duplicate work and, thus, increased costs. This work describes how these challenges can be addressed with KOMVOS, a Simulation Data Management platform that can be integrated in all CAE environments and applied on all disciplines. A typical Model Build workflow scenario is used to demonstrate how CAE data and processes can be used and executed efficiently in a common and comprehensive environment, even for non-expert users. Taking advantage of the interactive browsing, visualization and effective handling of all related data, the effortless triggering of actions with a simple click, the real-time monitoring of the model and process status, the offhand information sharing, as well as the review and comparison of key results and reports offered by KOMVOS, assure a flawless and error-free execution of simulation processes.
Simulation Data Management, Simulation Process, Simulation Workflow Management, Process Management, CAE Workflow, Process Monitoring, KOMVOS, Workflow Automation, Process Automation
11:20
conference time (CEST, Berlin)
How to Supplement the Latest Generations of PLM Platforms with an Agnostic and Fine-grained Data Management System
27/10/2021 11:20 conference time (CEST, Berlin)
Room: K
G. Neveu, P. Grimberg, X. Dugros (Digital Product Simulation, FRA)
G. Neveu, P. Grimberg, X. Dugros (Digital Product Simulation, FRA)
“All models are wrong but some are useful”. To set the context of this paper, we may introduce “modeling” as a methodological approach or an activity seeking to provide a usable, effective, efficient and satisfactory representation of real world objects for end-users wanting to leverage those models to analyze or make critical decisions. And today, as Digitalization and Model-Based Processes are rapidly growing in the Systems and Product engineering domains, the location, the number, the heterogeneity as well as the abstraction level of those models are exploding. Thus, not only we may question their “rightness and validity" domain, but we face as well the challenges of traceability, interoperability and efficiency of model-based collaboration between disciplines in a concurrent and integrated engineering approach. PLM-centric platforms are offering a unified and interconnected framework and engineering DMU-oriented modules to support digital continuity and model traceability. However, third party or in-house applications, processes and models authored outside those platforms can still be difficult – but necessary - to handle and the transition to a definitive “single source of truth” will not be achieved in the near future without important efforts. By bridging the gap between applications, disciplines and models with an agnostic perspective, KARREN software provides a very straightforward answer with out-of-the-box capabilities and features to leverage heterogeneous processes and models content in the context of a collaborative and concurrent process dealing with fine-grained artefacts like parameters or general attributes: the ID-card of the system or product. This presentation will show how KARREN can supplement a PLM platform such as the 3D EXPERIENCE to share key product information and metadata from and to other application in order to embrace the global complexity and heterogeneity of the engineering ecosytem landscape. The principles of an agnostic and fine-grained data management system supplementing PLM-oriented software will then be introduced and we will insist on why and how models could be “more useful” and reliable as soon as we put more control on the data that directly feed them.
Model-Based Engineering, Data Management System, PLM, Collaborative Engineering, Multidisciplinary Analysis
11:40
conference time (CEST, Berlin)
Automatic Outlier Detection for Crash Simulation Results
27/10/2021 11:40 conference time (CEST, Berlin)
Room: K
D. Borsotto, L. Jansen, V. Krishnapp, S. Mertler, C-A. Thole (Sidact GmbH, DEU)
D. Borsotto, L. Jansen, V. Krishnapp, S. Mertler, C-A. Thole (Sidact GmbH, DEU)
To cope up with the ever growing amount of simulation runs being performed, tools and techniques are needed to make use of the huge amount of simulation data being stored. While current Simulation Data Management systems and modern IT infrastructure already allows to store and access huge datasets and would facilitate putting this into action for analysis, the user usually only has tools and the time to make rather straight forward model to model comparisons between current model versions and their immediate predecessors. To take analysis capabilities and model development a leap forward it is necessary to also make use of whole model development branches to learn from the gathered simulation information. Having developed a database which allows querying simulation information of thousands of simulations, we will on the one hand give some insights about challenges like the management of changing designs during the development phase and how to still being able to compare geometrically altered parts. On the other hand we will point out how to make use of the simulations of the entire model development branch to speed up and improve the engineer's daily work. The approach currently being developed illustrates how it is possible to automatically detect anomalies / outliers within the crash deformation behaviour, to point the engineers attention exactly to the location in space and time where the model is showing unknown or unwanted deformation patterns. While in daily work the engineer often only has time to compare single simulations with each other, this approach shows how to compare the current simulation with hundreds of predecessors at a time. As an additional benefit this approach covers analysis of the behaviour of all parts over all states, such that even areas which are not in the immediate focus of the engineer are being evaluated and highlighted if conspicuous.
Outlier detection, artificial intelligence, simulation database
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