Towards velocity-based feedback control in laser cutting: benchmarking system capability on an industrial case study

To meet stringent requirements in terms of productivity and efficiency, the industry is shifting towards machine tools with sensors and auto-tuning capabilities enabled by Artificial Intelligence (AI) algorithms. Accordingly, in laser cutting the coaxial monitoring of the molten pool provides relevant information that can be interpreted by means of Machine Learning (ML) approaches to enable real-time velocity-based feedback control. In the present research, a holistic control architecture was thus developed and validated on an industrial case-study to demonstrate its applicability. Targeting iso-quality conditions, experiments on sample geometries on 5 mm thick stainless steel allowed to showcase an increase in productivity whilst avoiding critical defects such as cut dominated by plasma formation or loss of cut. The results obtained may be extended to a wide range of materials and sheet thicknesses demonstrating the generalized applicability of the technological framework.