GEOMETRICAL BENCHMARKING OF LASER POWDER BED FUSION SYSTEMS BASED ON DESIGNER NEEDS

Commercially available metal Laser Powder Bed Fusion (L-PBF) systems are steadily evolving. Thus, design limitations narrow and the diversity of achievable geometries widens. This progress leads researchers to create innovative benchmarks to understand the new system capabilities. Thereby, designers can update their knowledge base in design for additive manufacturing (DfAM). To date, there are plenty of geometrical benchmarks that seek to develop generic test artefacts. Still, they are often complex to measure, and the information they deliver may not be relevant to some designers. This article proposes a geometrical benchmarking approach for metal L-PBF systems based on the designer needs. Furthermore, Geometric Dimensioning and Tolerancing (GD&T) characteristics enhance the approach. A practical use-case is presented, consisting of developing, manufacturing, and measuring a meaningful and straightforward geometric test artefact. Moreover, optical measuring systems are used to create a tailored uncertainty map for benchmarking two different L-PBF systems.

Beautiful Visualizations Slain by Ugly Facts: Redesigning the National Hurricane Center’s ‘Cone of Uncertainty’ Map

The Track Forecast Cone, commonly known as the “cone of uncertainty”, is the most popular hurricane and tropical storm forecast product that the National Hurricane Center produces. However, it is often misinterpreted by non-experts. In this study we first explored the most common misconceptions about the cone and produced two alternative redesigns that we expected to be more attractive to and easier to understand by non-expert readers. Our results were mixed, but reveal promising paths for future efforts.

Visualising uncertainty in brightfield to fluorescent image inference with BFNet

Predicting fluorescently labelled cellular structures from brightfield images is a recent application of convolutional neural networks and has already proven a valuable tool in biological imaging studies. These methods reduce the need for time-consuming manual annotations for supervised datasets, potentially reduce the costs in high-throughput imaging screens, and open up a number of potentially novel analyses. However as with any prediction there can be sources of error and uncertainty. Here we present BFNet, a method to visualise the uncertainty in predicted images by applying Monte Carlo dropout during inference and calculating per-pixel variance as an uncertainty map. Our method demonstrates the ability to highlight regions of an image where prediction is difficult or impossible due to imaging artefacts such as occlusions or out-of-focus images, as well as more general uncertainty when a trained model is applied to new data from different imaging of experimental settings. We have provided a python implementation of the method which is available at github.com/swarchal/bfnet.

Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation

This paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.

Strategy for NEO follow-up observations

The Near-Earth Objects (NEOs) belong to the most important small bodies in the solar system, having the capability of close approaches to the Earth and even possibility to collide with the Earth. In fact, it is impossible to calculate reliable orbit of an object from a single night observations. Therefore it is necessary to extend astrometry dataset by early follow-up astrometry. Follow-up observations of the newly discovered NEO candidate should be done over an arc of several hours after the discovery and should be repeated over several following nights. The basic service used for planning of the follow-up observations is the NEO Confirmation Page (NEOCP) maintained by the Minor Planet Center of the IAU. This service provides on-line tool for calculating geocentric and topocentic ephemerides and sky-plane uncertainty maps of these objects at the specific date and time. Uncertainty map is one of the most important information used for planning of follow-up observation strategy for given time, indicating also the estimated distance of the newly discovered object and including possibility of the impact. Moreover, observatories dealing with NEO follow-up regularly have prepared their special tools and systems for follow-up work. The system and strategy for the NEO follow-up observation used at the Klet Observatory are described here. Methods and techniques used at the Klet NEO follow-up CCD astrometric programme, using 1.06-m and 0.57-m telescopes, are also discussed.

GIS-based applications of sensitivity analysis for sewer models

Sensitivity analysis (SA) evaluates the impact of changes in model parameters on model predictions. Such an analysis is commonly used when developing or applying environmental models to improve the understanding of underlying system behaviours and the impact and interactions of model parameters. The novelty of this paper is a geo-referenced visualization of sensitivity indices for model parameters in a combined sewer model using geographic information system (GIS) software. The result is a collection of maps for each analysis, where sensitivity indices (calculated for model parameters of interest) are illustrated according to a predefined symbology. In this paper, four types of maps (an uncertainty map, calibration map, vulnerability map, and design map) are created for an example case study. This article highlights the advantages and limitations of GIS-based SA of sewer models. The conclusion shows that for all analyzed applications, GIS-based SA is useful for analyzing, discussing and interpreting the model parameter sensitivity and its spatial dimension. The method can lead to a comprehensive view of the sewer system.