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In this publication, we use a small convolutional neural network to detect cut interruptions during laser cutting from single images of a high-speed camera. A camera takes images without additional illumination at a resolution of 32 × 64 pixels from cutting steel sheets of varying thicknesses with different laser parameter combinations and classifies them into cuts and cut interruptions. After a short learning period of five epochs on a certain sheet thickness, the images are classified with a low error rate of 0.05%. The use of color images reveals slight advantages with lower error rates over greyscale images, since, during cut interruptions, the image color changes towards blue. A training set on all sheet thicknesses in one network results in tests error rates below 0.1%. This low error rate and the short calculation time of 120 µs on a standard CPU makes the system industrially applicable.

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We report on a photodiode-based sensor system to detect cutting interruptions during laser cutting with a fiber laser. An InGaAs diode records the thermal radiation from the process zone with a ring mirror and optical filter arrangement mounted between a collimation unit and a cutting head. The photodiode current is digitalized with a sample rate of 20 kHz and filtered with a Chebyshev Type I filter. From the measured signal during the piercing, a threshold value is calculated. When the diode signal exceeds this threshold during cutting, a cutting interruption is indicated. This method is applied to sensor signals from cutting mild steel, stainless steel, and aluminum, as well as different material thicknesses and also laser flame cutting, showing the possibility to detect cutting interruptions in a broad variety of applications. In a series of 83 incomplete cuts, every cutting interruption is successfully detected (alpha error of 0%), while no cutting interruption is reported in 266 complete cuts (beta error of 0%). With this remarkable high detection rate and low error rate, the possibility to work with different materials and thicknesses in combination with the easy mounting of the sensor unit also to existing cutting machines highlight the enormous potential for this sensor system in industrial applications.

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.