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We use a skin color model based on the Mahalanobis metric and a shape analysis based on invariant Fourier-Mellin moments to automatically detect and locate human faces in two-dimensional complex scene images. First, color segmentation of an input image is performed by thresholding in a normalized hue-saturation color space where the effects of the variability of human skin color and the dependency of chrominance on changes in illumination are reduced. We then group regions of the resulting binary image that have been classified as face candidates into clusters of connected pixels. Discarding the smallest clusters in the image ensures that only a small number of clusters will be used for further analysis. Fully translation-, scale-and in-plane rotationinvariant moments are calculated for each remaining cluster. Finally, in order to distinguish faces from distractors, a multilayer perceptron neural network is used with the invariant moments as the input vector. Supervised learning of the network is implemented with the backpropagation algorithm, at first for frontal views of faces. Preliminary results show the efficiency of the combination of color segmentation and of invariant moments in detecting faces with a large variety of poses and against relatively complex backgrounds.

Wire arc additive manufacturing (WAAM) is a direct energy deposition (DED) process with high deposition rates, but deformation and distortion can occur due to the high energy input and resulting strains. Despite great efforts, the prediction of distortion and resulting geometry in additive manufacturing processes using WAAM remains challenging. In this work, an artificial neural network (ANN) is established to predict welding distortion and geometric accuracy for multilayer WAAM structures. For demonstration purposes, the ANN creation process is presented on a smaller scale for multilayer beads on plate welds on a thin substrate sheet. Multiple concepts for the creation of ANNs and the handling of outliers are developed, implemented, and compared. Good results have been achieved by applying an enhanced ANN using deformation and geometry from the previously deposited layer. With further adaptions to this method, a prediction of additive welded structures, geometries, and shapes in defined segments is conceivable, which would enable a multitude of applications for ANNs in the WAAM-Process, especially for applications closer to industrial use cases. It would be feasible to use them as preparatory measures for multi-segmented structures as well as an application during the welding process to continuously adapt parameters for a higher resulting component quality.

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Additive Manufacturing in Construction (AMC) enables new design methods and strategies within the construction industry. In particular, Shotcrete 3D Printing (SC3DP) offers a high degree of design freedom by enabling the deposition of concrete at variable layer orientation based on a wet-mix shotcrete process. However, the mechanical properties and geometry of the printed layers are dependent on the material and process parameters used. In this context, the effects of air and concrete flow rates, path planning parameters, and material parameters have been investigated in previous research. The here presented study investigates the influence of the nozzle geometry on the resulting strand properties, e.g. strand geometry, layer bond strength, and compressive strength, to evaluate nozzle diameter and length as control parameters for the SC3DP process. Experimental investigations were performed with fixed nozzle diameters between 10 and 30 mm and nozzle lengths ranging from 100 to 200 mm. The results show a significant influence of the nozzle diameter on the resulting strand geometry as well as the mechanical properties. Finally, concepts for a nozzle with a controllable outlet diameter were developed and evaluated.