Build Quality Surveillance

The tool provides new insights and a better understanding in the physics of the AM process under surveillance.

Input(s):

• Production monitoring data

Main feature(s):

• Train AI model on a set of monitoring data

Output(s):

• Detected anomalies
• Identified anomalies (diagnostic)
• Specific corrective actions

• The method requires full access to monitoring data. Most of the legacy systems are “black box”. Materialise has an open controller (MCP) enabling this
• The solution still requires insight and is intended as a tool for the AM expert rather than the user of AM machines
• Integration with open AI environments is still complex (the interactive notebook of Sirris might help here)

• AM OEMs
• AM experts

OEM Contract, bilateral collaboration.