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Apps - COVERSATILE silicone mask injection moulding

CAELIA COVERSATILE silicone mask injection moulding

App related to Manufacturing Simulation in the context of Manufacturing

Description

This application is a decision support tool to adjust moulding parameters of the COVERSATILE silicone mask manufacturing process. It consists of a user interface that allows to perform virtual moulding trials, getting results instantaneously. This is achieved evaluating different Reduced Order Models, one for each result of interest, that have been previously generated. These ROMs are the core of the application and have been developed based on results from a design of experiments of detailed injection moulding simulations in COVERSATILE project with a physically based simulation software, which account for the complex material behaviour and the different transient flow and thermo-chemical-mechanical mechanisms that happen during the moulding of the silicone into the COVERSATILE mould developed by ORP Stampi, ML Engraving and SKM (MS123).

Use Case

In one hand, companies adopting the COVERSATILE silicone mask solution can use it to get insight of the sensitivity of the moulding relevant variables to different process parameters that the machine operator can change. This allows to the operator to self-train in a virtual environment before the mould is available. Afterwards, the app can be used for the quick setting up, troubleshooting and to help optimising the process parameters. The results are representative for the process injecting a commercial platinum curing silicone from MESGO.

On the other hand, it can be seen as a show case of ITAINNOVA CAELIA solution for manufacturing processes, that is a service consisting of creating physically based digital twins of any manufacturing process based on simulations and reduced order modelling (ROM). A company having a manufacturing process with high focus in material transformations (such as polymer transformation processes) may be interested in checking the app.

 

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016070

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