Real-Time Cross-Dataset Quality Production Assessment in Industrial Laser Cutting Machines

In laser cutting processes, cutting failure is one of the most common causes of faulty productions. Monitoring cutting failure events is extremely complex, as failures might be initiated by several factors, the most prominent probably being the high production speeds required by modern standards. The present work aims at creating and deploying a classifier able to assess the status of a production cutting quality in a real-time fashion. To this aim, multiple datasets were collected in different environmental conditions and with different sensors. Model inputs include photo-sensors and production parameters. At first, different algorithms were tested and rated by prediction ability. Second, the selected algorithm was deployed on a GPU embedded system and added to the current machine configuration. The final system can receive the input data from the sensors, perform the inference, and send back the results to the computer numerical control. The data management is based on a client-server architecture. The selected algorithm and hardware showed good performances despite multiple changes in the environmental conditions (domain adaptation ability) both in terms of prediction ability (accuracy) and computational times.