Real-time grid detection in sheet metal fiber laser cutting through coaxial monitoring

Abstract

Real-time defects’ estimation and control of the cut quality through the coaxial camera monitoring of the kerf are amongst the most promising developments for laser cutting. In industrial systems, sheets are positioned on a metallic grid creating discontinuities in the cutting process due to unpredictable thickness, blown and resolidified material and the time varying position of the grid. The interaction between laser radiation escaping from the kerf and the grid, along with the difficult outflow of molten material, causes changes in the process emission images. These changes influence the defect estimation approach, and, consequently, the control action. In this work, a real-time grid identification and classification-based machine learning algorithm was developed and tested during the fusion cutting of 6 mm thick Al5754, exploiting a NIR coaxial monitoring system. Real-time control experiments with dross estimates were performed, demonstrating a correct identification of the grid and highlighting a feasible industrial application.