Production and quality management

Machine learning is used to detect errors in the cutting process at an early stage. Data from log files and service reports enable reliable prediction and minimization of machine failures.

The combination of AI methods and simulation techniques enables the precise monitoring and improvement of assembly processes in order to drive forward automation in enclosure assembly.

The final acceptance requirements for employees were reviewed with the help of UNAs. AI potentials were identified and suggested.

The sensor-based adjustment of welding parameters optimizes the welding process through automatic control, improves seam quality and simplifies programming under varying production conditions.

A sensor-guided TIG welding cobot automates labor-intensive weld seams on containments, reduces costs by 20 % and reduces the ergonomic burden on employees.