Learning based on 3D photogrammetry models to evaluate the competences in visual testing of welds

The present work describes a new learning methodology based on the latest scientific research aimed at the three-dimensional macro-photogrammetric reconstruction of welds, which allows the generation of teaching materials aimed at the acquisition and evaluation of competencies in the non-destructive testing laboratory activities without the need for a physical displacement to the physical installation. This methodology, which can be cataloged within those based on virtual laboratories, is applicable in e-learning courses or can also be used as support material for face-to-face programs, mainly in the bachelor’s and master’s related to mechanical, naval and aeronautical engineering. The distribution of the packages is easy to load in learning management system in order to work with the models with open software, easily and without the need for additional cost

Suitability of Automatic Photogrammetric Reconstruction Configurations for Small Archaeological Remains

19 p.Three-dimensional (3D) reconstruction is a useful technique for the documentation, characterization, and evaluation of small archeological objects. In this research, a comparison among different photogrammetric setups that use different lenses (macro and standard zoom) and dense point cloud generation calibration processes for real specific objects of archaeological interest with different textures, geometries, and materials is raised using an automated data collection. The data acquisition protocol is carried out from a platform with control points referenced with a metrology absolute arm to accurately define a common spatial reference system. The photogrammetric reconstruction is performed considering a camera pre-calibration as well as a self-calibration. The latter is common for most data acquisition situations in archaeology. The results for the different lenses and calibration processes are compared based on a robust statistical analysis, which entails the estimation of both standard Gaussian and non-parametric estimators, to assess the accuracy potential of different configurations. As a result, 95% of the reconstructed points show geometric discrepancies lower than 0.85 mm for the most unfavorable case and less than 0.35 mm for the other casesS

Weld bead detection based on 3D geometric features and machine learning approaches

14 p.Weld bead detection is essential for automated welding inspection processes. The non-invasive passive techniques, such as photogrammetry, are quickly evolving to provide a 3D point cloud with submillimeter precision and spatial resolution. However, its application in weld visual inspection has not been extensively studied. The derived 3D point clouds, despite the lack of topological information, store significant information for the weld-plaque segmentation. Although the weld bead detection is being carried out over images or based on laser profiles, its characterization by means of 3D geometrical features has not been assessed. Moreover, it is possible to combine machine learning approaches and the 3D features in order to realize the full potential of the weld bead segmentation of 3D submillimeter point clouds. In this paper, the novelty is focused on the study of 3D features on real cases to identify the most relevant ones for weld bead detection on the basis of the information gain. For this novel contribution, the influence of neighborhood size for covariance matrix computation, decision tree algorithms, and split criteria are analyzed to assess the optimal results. The classification accuracy is evaluated by the degree of agreement of the classified data by the kappa index and the area under the receiver operating characteristic (ROC) curve. The experimental results show that the proposed novel methodology performs better than 0.85 for the kappa index and better than 0.95 for ROC area.S

Interpretation of cavitation using CFD simulation as a low-cost learning activity compatible with e-learning

7 p.In this conference paper, an activity based on computational fluid dynamics that allows STEM students to know the phenomenon of cavitation is proposed. Cavitation is a dangerous phenomenon that occurs in pressurized fluid when they rapidly change of pressure in a liquid and a small vapor-filled cavity is generated in regions where the pressure is low. These cavities (or bubbles) are highly damaging to machines and fluid systems. Due to its dangerousness, it is necessary for the engineer to know the consequences of the cavitation and the factors involved in the phenomenon, in order to design solutions that avoid or minimize the damage caused. The design process of the activity starts with a critical analysis to cover the requirements that allow the activity to be carried out in the simplest way possible, using a model and a simplified computational pipeline that allows maximum adaptation to the real phenomenon without the need for laboratory equipment so that it can be integrated into subjects in the field of fluid-mechanics and maintenance engineeringS

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin

Feature Papers of Drones - Volume II

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 24–41 are focused on drone applications, but emphasize two types: firstly, those related to agriculture and forestry (articles 24–35) where the number of applications of drones dominates all other possible applications. These articles review the latest research and future directions for precision agriculture, vegetation monitoring, change monitoring, forestry management, and forest fires. Secondly, articles 36–41 addresses the water and marine application of drones for ecological and conservation-related applications with emphasis on the monitoring of water resources and habitat monitoring. Finally, articles 42–54 looks at just a few of the huge variety of potential applications of civil drones from different points of view, including the following: the social acceptance of drone operations in urban areas or their influential factors; 3D reconstruction applications; sensor technologies to either improve the performance of existing applications or to open up new working areas; and machine and deep learning development

Point cloud optimization based on 3D geometric features for architectural heritage modelling

[EN] The present article shows a novel methodology to classify 3D point clouds related to architectural heritage elements based on dimensional features, and using open software. The 3D point cloud is the key element for the extraction of semantic and/or vector information, as well as the meshing step for architectural heritage modelling. A point cloud classification that optimizes the point cloud while preserving the relevant information will improve the subsequent operations. The present methodology is based on the extraction of the geometric properties of the 3D point clouds on the basis of the 3D covariance matrix. Among all the possible dimensional features, the omnivariance (Ω) is considered the most suitable for the variety of situations of the architectural heritage elements. For a study case of the Niculoso Pisano Portal of the Monastery of Santa Paula of Seville (Spain), three clusters are defined according to the different level of details. As a result, and in comparison, to a standard spatial sampling of 1 cm, the proposed clustering allowed a weight spatial sampling within the interval 20 – 1 cm, achieving an 85%-point reduction, keeping 3D points in the complex areas, whereas the low detail areas, like planes, were considerably reduced in size for the next steps of parametric modelling. The error of the optimized point cloud, by the comparison with the original point cloud has a mean value of 0.3 mm and a standard deviation of ± 4.6 mm.S