Mo3Dlling project optimizes and implements expert models based on machine learning techniques for analysing the real-time mould performance based on in-cavity sensor data, encoding and projecting it into a clustered 3D space for fast and easy process control, monitoring and part quality classification.
Moreover, Mo3Dlling covers the entire sensorization value chain by offering recommendations on the best locations within the cavity, driven by a 3D analysis of the uploaded melt flow simulations results.
This information is visualized by operators through an intuitive front-end based on 3D cloud of points and clustering visualization.
This experiment has been supported under the first open call of BEinCPPS project.
Project:
Enterprises:
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Sector
Other manufacturing
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