DI-Artículos

URI permanente para esta colecciónhttps://hdl.handle.net/10953/218

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A GPU-Based Framework for Generating Implicit Datasets of Voxelized Polygonal Models for the Training of 3D Convolutional Neural Networks
(IEEE, 2020-01-10) Ogáyar-Anguita, Carlos-Javier; Rueda-Ruiz, Antonio-Jesús; Segura-Sánchez, Rafael-Jesús; Díaz-Medina, Miguel; García-Fernández, Ángel-Luis
In this paper we present an efficient GPU-based framework to dynamically perform the voxelization of polygonal models for training 3D convolutional neural networks. It is designed to manage the dataset augmentation by using efficient geometric transformations and random vertex displacements in GPU. With the proposed system, every voxelization is carried out on-the-fly for directly feeding the network. The computing performance with this approach is much better than with the standard method, that carries out every voxelization of each model separately and has much higher data processing overhead. The core voxelization algorithm works by using the standard rendering pipeline of the GPU. It generates binary voxels for both the interior and the surface of the models. Moreover, it is simple, concise and very compatible with commodity hardware, as it neither uses complex data structures nor needs vendor-specific hardware or additional dependencies. This framework dramatically reduces the input/output operations, and completely eliminates the storage footprint of the voxelization dataset, managing it as an implicit dataset.
Modeling material microstructure using the Perlin noise function
(Wiley, 2021-02-24) Conde-Rodríguez, Francisco; García-Fernández, Ángel-Luis; Torres-Cantero, Juan-Carlos
This paper introduces a precise and easy to use method for defining the microstructure of heterogeneous solids. This method is based on the concept of Heterogeneous Composite Bézier Hyperpatch, and allows to accurately represent the primary material proportions, as well as the size and shape of the material phases. The solid microstructure is modeled using two functions: a material distribution function (to compute the portion of the solid volume occupied by each primary material), and a modified Perlin noise function that determines the shape and size of each primary material phase. With this method, the position and orientation of the solid in the modeling space R3 does not affect the portion of its volume that is occupied by each primary material, nor the shape and size of the phases. However, the solid microstructure is coherently and automatically modified when the shape of the solid is edited. Regarding continuity, this method allows to define to which extent continuity (both in shape and material distribution) has to be preserved at the junction of the cells that compose the solid. This makes modeling geometrically complex figures very easy.
Modeling the Internal Architecture of Composites
(Elsevier, 2020-08-24) Conde-Rodríguez, Francisco; García-Fernández, Ángel-Luis; Torres-Cantero, Juan-Carlos
This paper introduces a flexible and easy to use method for designing complex composite heterogeneous materials. These materials feature two distinct phases called core and matrix that remain separate and distinct. Moreover, composite materials have an internal microarchitecture that have to be precisely modeled. All the microarchitecture examples that are shown in this paper have been modeled in the same way, without any particular case nor having to use different implementation strategies or changing the source code depending on the microarchitecture. The microarchitecture is modeled with a function that combines a material distribution function, which models the proportion of each phase at each point of the solid and determines the thickness of the core phase, and a cellular noise function based on distance fields that determines the shape, size, and distribution of the microarchitecture. Modeling the microarchitecture using two components gives our model great flexibility. In addition, it also allows to vary the size or thickness of the microarchitecture continuously inside the solid. With this method, it is possible to model complex composite materials in which the phases (core and matrix) are in turn other composites with two distinct phases. Another important advantage of this method is that a complex object consisting of several different parts made of different materials can be modeled as a single computational object, which is very suitable for editing or computing simulations

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Examinando DI-Artículos por Materia "004.92 - Computer graphics"