Switching supervisory control in fusion laser cutting with vision-based process state detection
CAPRIO Leonardo, GUERRA Sofia, PACHER Matteo, GANDOLFI Davide, SAVARESI Sergio Matteo, TANELLI Mara, PREVITALI Barbara
Abstract. Future unmanned laser cutting machines will require the capability to automatically detect and adapt to process states avoiding critical defects. In the fusion laser cutting of metals with low thickness (1-3 mm) the process can rapidly evolve from successful separation to plasma-dominated or loss of cut due to uncontrolled factors. Thus industrial systems set the cut velocity conservatively to ensure separation while avoiding low quality or incomplete cuts. This work proposes a novel switching supervisory control architecture to increase process productivity by regulating the velocity in real-time accordingly to the detected condition. A coaxial camera-based vision system was employed to develop a classification framework capable of a low latency process state identification during the cutting of 2 mm thick mild steel. The control logic allowed high quality cutting of 2 mm thick mild steel with a productivity increase of 10-33% hence validating the architecture for autonomous machines.
Keywords
Laser Cutting, Machine Learning, Process Control
Published online 9/10/2025, 9 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: CAPRIO Leonardo, GUERRA Sofia, PACHER Matteo, GANDOLFI Davide, SAVARESI Sergio Matteo, TANELLI Mara, PREVITALI Barbara, Switching supervisory control in fusion laser cutting with vision-based process state detection, Materials Research Proceedings, Vol. 57, pp 335-343, 2025
DOI: https://doi.org/10.21741/9781644903735-39
The article was published as article 39 of the book Italian Manufacturing Association Conference
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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