Twin_in_Fog

Description

Challenge:

There is a huge market potential in using advanced data analytics for proactive in-process control and proactive maintenance in laser-based manufacturing, due to the need for learning from data, since the processes are quite complex and vendors are selling machines as “black boxes”. Based on the digital twin models, it is expected to predict machine failures and issue preventive maintenance warnings, in addition provide set points for the machine to yield best quality and minimal resource utilisation.

Solution:

Continuous model learning in the cloud, which starts learning new models from data in short periods (depending on the input data, e.g. every minute). Continuous trend detection (model drift), which operates on the models calculated in 1.

Project:
CloudiFacturing

Enterprises:
Netico GmbH (ISV); PROCESNA OPREMA OPREMA I DELOVI I RAZNE MAÅ INE (end-user); Nissatech Innovation Centre (SW provider); ‘

Sector
Machinery & equipment

Keywords