Projects
What we have built with clients and partners.
Each project with task, approach and result. We only give numbers with their source.
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 projectClients and project partners
