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    Slice2mesh: Meshing Sliced Data for the Simulation of AM Processes
    (The Eurographics Association, 2018) Livesu, M.; Cabiddu, D.; Attene, M.; Livesu, Marco and Pintore, Gianni and Signoroni, Alberto
    Accurately simulating Additive Manufacturing (AM) processes is useful to predict printing failures and test 3D printing without wasting precious resources, both in terms of time ad material. In AM the object to be fabricated is first cut into a set of slices aligned with the build direction, and then printed, depositing or solidifying material one layer on top of the other. To guarantee accurate simulations, it is therefore necessary to encode the temporal evolution of the shape to be printed within the simulation domain. We introduce slice2mesh, to the best of our knowledge the first software capable of turning a sliced object directly into a volumetric mesh. Our tool inputs a set of slices and produces a tetrahedral mesh that endows each slice in its connectivity. An accurate representation of the simulation domain at any time during the print can therefore be easily obtained by filtering out the slices yet to be processed. slice2mesh also features a flexible mesh generation system for external supports, and allows the user to trade accuracy for simplicity by producing approximate simulation domains obtained by filtering the object in slice space.
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    Adaptive Environmental Sampling: The Interplay Between Geostatistics and Geometry
    (The Eurographics Association, 2018) Berretta, S.; Cabiddu, D.; Pittaluga, S.; Mortara, M.; Spagnuolo, M.; Zuccolini, M. Vetuschi; Livesu, Marco and Pintore, Gianni and Signoroni, Alberto
    In environmental surveys a large sampling effort is required to produce accurate geostatistical maps representing the distribution of environmental variables, and the analysis of each sample is often expensive. Typically, the sample locations are completely specified in the survey design phase, prior to data-collection. Usually, the sampling points are located on a regular grid, or along directions that are selected with respect to any a-priori knowledge of the expert. No feedback is available during the survey. In this paper, we present a different sampling strategy, namely adaptive sampling. Our approach exploits geostatistics constructs in order to determine on-the fly the next best sample location. After initializing the system with few sampling points, an iterative routine predicts the variable distribution from the data sampled so far, and suggests the next sample to be acquired in order to optimize the uncertainty of the estimates. At every iteration a new sample is acquired, and the variable distribution map is refined, along with the uncertainty map related to that distribution. Our method allows to build a representation of the survey area as precise as the one provided by the traditional methods, but with less samples, thus reducing both time and costs of the survey. We show a preliminary evaluation of the adaptive strategy in the bi-dimensional case based on a synthetic scenario, and describe the generalization of these encouraging results to the full 3D domain in the concrete setting of water quality monitoring. A proper geometric representation of the three dimensional survey area, coupled with a proper visualization of distribution and related uncertainty, will provide real-time feedback during the survey.