@article{SchleierAdelmannEsenetal.2021, author = {Schleier, Max and Adelmann, Benedikt and Esen, Cemal and Glatzel, Uwe and Hellmann, Ralf}, title = {Development and evaluation of an image processing algorithm for monitoring fiber laser fusion cutting by a high-speed camera}, series = {Journal of Laser Applications}, volume = {2021}, journal = {Journal of Laser Applications}, number = {33}, doi = {10.2351/7.0000391}, pages = {032004 -- 032004}, year = {2021}, subject = {Faserlaser}, language = {en} } @article{SchleierAdelmannEsenetal.2017, author = {Schleier, Max and Adelmann, Benedikt and Esen, Cemal and Hellmann, Ralf}, title = {Cross-correlation-based algorithm for monitoring laser cutting with high-power fiber lasers}, series = {IEEE Sensors Journal}, volume = {18}, journal = {IEEE Sensors Journal}, number = {4}, doi = {10.1109/JSEN.2017.2783761}, pages = {1585 -- 1590}, year = {2017}, subject = {Laserschneiden}, language = {en} } @article{SchleierAdelmannEsenetal.2022, author = {Schleier, Max and Adelmann, Benedikt and Esen, Cemal and Hellmann, Ralf}, title = {Image Processing Algorithm for In Situ Monitoring Fiber Laser Remote Cutting by a High-Speed Camera}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {8}, publisher = {MDPI AG}, issn = {1424-8220}, doi = {10.3390/s22082863}, year = {2022}, abstract = {We present an in situ process monitoring approach for remote fiber laser cutting, which is based on evaluating images from a high-speed camera. A specifically designed image processing algorithm allows the distinction between complete and incomplete cuts by analyzing spectral and geometric information of the melt pool from the captured images of the high-speed camera. The camera-based monitoring system itself is fit to a conventional laser deflection unit for use with high-power fiber lasers, with the optical detection path being coaxially aligned to the incident laser. Without external illumination, the radiation of the melt from the process zone is recorded in the visible spectral range from the top view and spatially and temporally resolved. The melt pool size and emitted sparks are evaluated in dependence of machining parameters such as feed rate, cycles, and focus position during cutting electrical sheets.}, subject = {Laserschneiden}, language = {en} } @article{SchleierEsenHellmann2022, author = {Schleier, Max and Esen, Cemal and Hellmann, Ralf}, title = {High speed melt flow monitoring and development of an image processing algorithm for laser fusion cutting}, series = {Journal of Laser Applications}, volume = {34}, journal = {Journal of Laser Applications}, number = {4}, publisher = {Laser Institute of America}, issn = {1042-346X}, doi = {10.2351/7.0000785}, year = {2022}, abstract = {This contribution presents high-speed camera monitoring of melt pool dynamics for steel during laser fusion cutting and compares the images with recordings in aluminum. The experiments are performed by a 4 kW multimode fiber laser with an emission wavelength of 1070 nm. To visualize the thermal radiation from the process zone during the cutting process, the kerf is captured at sample rates of up to 170 000 frames per second without external illumination with a spectral response between 400 and 700 nm, allowing measurements of the melt flow dynamics from geometric image features. The dependencies of the melt flow dynamics on laser processing parameters, such as feed rate, gas pressure, and laser power, can be evaluated. The monitoring system is placed both off-axis and mounted to a conventional cutting head, with the monitoring path aligned to the processing laser for a coaxial and lateral view of the cut kerf. The measured signal characteristics of the images captured from the melt pool are examined in the visible spectral range of the emitted thermal radiation from the process zone. Moreover, a specifically developed image processing algorithm is developed that process and analyze the captured images and extract geometric information for a measurement of the melt flow.}, subject = {Laserschneiden}, language = {en} } @article{SchleierEsenHellmann2023, author = {Schleier, Max and Esen, Cemal and Hellmann, Ralf}, title = {Evaluation of a Cut Interruption Algorithm for Laser Cutting Steel and Aluminum with a High-Speed Camera}, series = {Applied Sciences}, volume = {13}, journal = {Applied Sciences}, number = {7}, publisher = {MDPI AG}, issn = {2076-3417}, doi = {10.3390/app13074557}, year = {2023}, abstract = {We report on a monitoring system based on a high-speed camera for fiber laser fusion cutting. The monitoring system is used without an external illumination retrofit on a conventional cutting head, with the optical path aligned coaxially to the incident laser, permitting a direct, spatially, and temporally resolved detection of the melt pool area in the cut kerf from the top view. The dependence of the melt pool area on laser processing parameters such as laser power and feed rate are thus evaluated for stainless steel, zinc-coated steel, and aluminum, respectively. The signal characteristics of the images captured from the melt pool are examined in the visible spectral range of the emitted secondary thermal radiation from the process zone. An ad hoc developed image processing algorithm analyzes the spectral and geometric information of the melt pool from high-speed camera images and distinguishes between complete and incomplete cuts.}, subject = {Laserschneiden}, language = {en} } @article{SchleierEsenHellmann2025, author = {Schleier, Max and Esen, Cemal and Hellmann, Ralf}, title = {Vision transformer based cut interruption detection and prediction of laser fusion cutting from monitored melt pool images}, series = {Journal of Laser Applications}, volume = {37}, journal = {Journal of Laser Applications}, number = {1}, publisher = {Laser Institute of America}, issn = {1042-346X}, doi = {10.2351/7.0001611}, year = {2025}, abstract = {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.}, subject = {Laserschneiden}, language = {en} }