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Laser Powder Bed Fusion (LPBF) is especially interesting for applications in industries with high quality requirements. There are different expensive and time-consuming strategies for quality assurance. A cheaper and faster approach is to analyze the data acquired during fabrication. In this work Convolutional Neural Networks (CNN) are investigated as a tool for data analysis of meltpool monitoring data. The goal is to automatically distinguish between porous and non-porous part regions. Therefore, the training data is categorized based on CT-scans of the test specimens. For increased interpretability of the results, Gradient-Weighted Class Activation Maps (Grad-CAM) are used.

In this note we determine the unique solution to the functional equation \(f(x + y) ( x- y) = \left( f(x) -f (y) \right) (x + y)\). We require no additional assumptions on the function \(f\). Moreover we solve this functional equation if \(f\) is only defined on a finite interval. The interest in this type of functional equation is motivated by the study of symmetrizing measures for (the generator of) a Levy-driven Ornstein–Uhlenbeck process.

This paper presents model-based methods and algorithms for describing and analyzing complex technological interactions in production process chains for the manufacturing of composite materials and components. The presented holistic approach includes the modeling of the process chain, the construction of suitable designs of experiments, the logging and the collection of experimental and production data, the data analysis, as well as the visualization of analysis results and the deduction of the room of maneuver for production settings. Supported by these methods, the development and the putting into operation of complex process chains will become much more efficient.