If the model is nearly the same, it can be used as basis for further analysis. For the recommendation engine, a similarity criterion is defined, and the user is shown a list of “similar” simulations. The resulting graph contains all the simulation knowledge from the database, which can be queried to provide the desired information. Regularly new simulations are processed, metadata extracted and fed to the knowledge graph. An ontology has been defined to connect the metadata in a meaningful way. More concretely, all the simulations in the database are processed and simulation metadata is extracted automatically. Was this mind reading?ĭefinitely not! The technology powering this recommendation engine? A knowledge graph. She still wonders how the system was able to recommend her the right simulations. Maybe she will manage to deliver the results before the weekend. From this file she can probably learn how to define a new material for her task. Still puzzled, she scrolls down the list and even finds a different model in which the user has changed the material properties. “Can that be real?”, and she starts to understand that a large part of the work does not need to be redone. When she clicks there, she is presented with a list of simulation files related to the job she has to do, many of them even using the same CAD file. The simulation request is finally set up when Sam notices a tab called Recommended Simulations. She really can’t remember how that was done exactly. And then there is also the question of defining a new material. Now it is time to defeature the geometry, generate a good mesh, specify the boundary conditions, and loads before finally running the new simulation. Following the best-practices of her new company, she starts to prepare a new simulation request within the SPDM system, which includes the information provided by her manager about the materials, CAD design, and boundary conditions. Still, she is determined to do as much as she can. There is no way that I can finish that work before the weekend.” Full of self-doubt she plods back to her desk. She feels small and incompetent: “I will have to do the analysis from scratch, and it will become obvious how slow I am. She is alone (except her manager), and no one there to ask. She quickly stands up, walks around the office, but no one is there. Her new colleagues are fun but clearly do not invest much effort in properly documenting and annotating their work. She hastily connects to the database, but what she sees does not help at all: the documentation is scarce at best, and the file names are hieroglyphs to her. Yes, the company manages a simulation model database and every team member is supposed to store the models there together with detailed documentation. Reuse? Rerun? “What is he talking about?” She has no idea how to find the right model. Right after her manager leaves, Sam feels a rush of blood to her head, a hint of panic she hasn’t felt in a while. Simply reuse one of the existing models, change the parameters, and rerun the simulation”. Her manager expects results before the weekend: „Running the analysis does not take long. The design of a product, the team has been working on, has a slight, last-minute change (new supplier, slightly different material properties), and needs a new analysis. One Friday afternoon, her manager comes into her office and has an urgent request. There are many perks: bigger team, friendlier people, and the coffee – equally important – is excellent. Sam has just started her new job as a simulation engineer at a large engineering company. Daniel Berger Arianna Bosco, Siemens Digital Industries Software Joining a brand new environment
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