Vision transformer based cut interruption detection and prediction of laser fusion cutting from monitored melt pool images

Incomplete cuts during laser fusion cutting result in a closed kerf, preventing the workpiece from detaching from the sheet and resulting in rework or rejection. We demonstrate the approach of a vision transformer, used for image classification, to detect cut interruption during laser fusion cutting in steel and aluminum. With events impending an incomplete cut in steel, we attempt to predict cut interruption before they even occur. To build a data set for training, cutting experiments are carried out with a 4 kW fiber laser, forcing incomplete cuts by varying the process parameters such as laser power and feed rate. The thermal radiation from the process zone during the cutting process is captured with a size of 256 × 256 px2 at sample rates of 20 × 103 fps. The kerf is recorded with a spectral sensitivity between 400 and 700 nm, without external illumination, which enables the melt to be observed in the range of the visual spectrum. The vision transformer model, which is used for image classification, splits the image into patches, linearly embedded with an added position embedding, and fed to a standard transformer encoder. For training the model, a set of images was labeled for the respective classes of a complete, incomplete, and impending incomplete cut. With the trained model, incomplete cuts in steel and aluminum can then be recognized and impending incomplete cuts in steel can be predicted in advance.