Materials and processes
Gas mixture, plastic part or heat treatment: whenever formulation or process settings determine the outcome, a model can predict the effect and shorten test series.
- Which composition meets the requirement?
- Which process setting reaches the target?
- How much does the result scatter?
From parameter to property
We train models that predict the resulting properties from composition or process settings, for example thermal conductivity, wall thickness or strength. In reverse, they return the setting for a required property.
These models can be built into databases, design tools or process control.
Related projects
Predicting residual wall thickness in water-assisted injection molding
A neural network predicts the residual wall thickness of a hollow plastic part from its process parameters. Run in reverse, it returns the process setting needed for a target wall thickness. A project with Plastic Innovation.
→ View projectDesigning EV charging cables with phase change material
Fast charging drives high currents through cables and battery systems, and they heat up. An ML model built on FEM and prototype data sizes the conductor for a given maximum temperature, using phase change material as a passive heat buffer.
→ View projectGas mixtures for insulating glass: predicting thermal conductivity
A neural network trained in dAIve Predict predicts the thermal conductivity of gas mixtures used in insulating glass units. A demonstration case based on literature data.
→ View projectDoes this match your question?
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