Evaluation of SLS 3D-Printed Filter Structures Based on Bionic Manta Structures.

We report on additively manufactured filter systems based on bionic manta ray structures and evaluate their filter performance. The filters are periodic lamella structures produced by selective laser sintering using PA12 polyamide powder. Two different lamella types are investigated, which are derived from two manta ray genera, namely, and .

Function Integration in Additive Manufacturing: Design and Realization of an LPBF Built Compressed Air Motor.

We present a compressed air motor, completely built by laser powder bed fusion. To highlight the fully functional integration by additive manufacturing, the rotor, stator, bearings, turbine, gas inlet and outlet were all built in a single print job. The material used was Inconel 718, and the motor was 44 mm tall and 12 mm in diameter.

Image Processing Algorithm for In Situ Monitoring Fiber Laser Remote Cutting by a High-Speed Camera.

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.

Simultaneous Burr and Cut Interruption Detection during Laser Cutting with Neural Networks.

In this contribution, we compare basic neural networks with convolutional neural networks for cut failure classification during fiber laser cutting. The experiments are performed by cutting thin electrical sheets with a 500 W single-mode fiber laser while taking coaxial camera images for the classification. The quality is grouped in the categories good cut, cuts with burr formation and cut interruptions.

Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network.

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.