Examinando por Autor "Barandiaran, Inigo"
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Ítem Appraisal of open software for finite element simulation of 2D metal sheet laser cut.(Springer-Verlag France, 2016-03-21) Mejia, Daniel; Moreno, Aitor; Ruiz Salguero, Oscar; Barandiaran, Inigo; Universidad EAFIT. Departamento de Ingeniería Mecánica. Grupo de Investigación CAD CAM CAE, Carrera 49 7 Sur-50, Medellín, Colombia.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAEFEA simulation of thermal metal cutting is central to interactive design and manufacturing. It is therefore relevant to assess the applicability of FEA open software to simulate 2D heat transfer in metal sheet laser cuts. Application of open source code (e.g. FreeFem++, FEniCS, MOOSE) makes possible additional scenarios (e.g. parallel, CUDA, etc.), with lower costs. However, a precise assessment is required on the scenarios in which open software can be a sound alternative to a commercial one. This article contributes in this regard, by presenting a comparison of the aforementioned freeware FEM software for the simulation of heat transfer in thin (i.e. 2D) sheets, subject to a gliding laser point source. We use the commercial ABAQUS software as the reference to compare such open software. A convective linear thin sheet heat transfer model, with and without material removal is used. This article does not intend a full design of computer experiments. Our partial assessment shows that the thin sheet approximation turns to be adequate in terms of the relative error for linear alumina sheets. Under mesh resolutions better than 10e−5 m , the open and reference software temperature differ in at most 1 % of the temperature prediction. Ongoing work includes adaptive re-meshing, nonlinearities, sheet stress analysis and Mach (also called ‘relativistic’) effects.Ítem Extending marching cubes with adaptative methods to obtain more accurate iso-surfaces(2010-01-01) Congote, John; Moreno, Aitor; Barandiaran, Inigo; Barandiaran, Javier; Ruiz OE; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAEÍtem Statistical tuning of adaptive-weight depth map algorithm(SPRINGER, 2011-01-01) Hoyos, Alejandro; Congote, John; Barandiaran, Inigo; Acosta, Diego; Ruiz, Oscar; Universidad EAFIT. Departamento de Ingeniería de Procesos; Desarrollo y Diseño de ProcesosIn depth map generation, the settings of the algorithm parameters to yield an accurate disparity estimation are usually chosen empirically or based on unplanned experiments. A systematic statistical approach including classical and exploratory data analyses on over 14000 images to measure the relative influence of the parameters allows their tuning based on the number of bad-pixels. Our approach is systematic in the sense that the heuristics used for parameter tuning are supported by formal statistical methods. The implemented methodology improves the performance of dense depth map algorithms. As a result of the statistical based tuning, the algorithm improves from 16.78% to 14.48% bad-pixels rising 7 spots as per the Middlebury Stereo Evaluation Ranking Table. The performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury. Future work aims to achieve the tuning by using significantly smaller data sets on fractional factorial and surface-response designs of experiments. © 2011 Springer-Verlag.Ítem Tuning of adaptive weight depth map generation algorithms: Exploratory data analysis and design of computer experiments (DOCE)(SPRINGER, 2013-09-01) Acosta, Diego; Barandiaran, Inigo; Congote, John; Ruiz, Oscar; Hoyos, Alejandro; Grana, Manuel; Universidad EAFIT. Departamento de Ingeniería de Procesos; Desarrollo y Diseño de ProcesosIn depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on unplanned experiments. Algorithms' performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury's standards. This work shows a systematic statistical approach including exploratory data analyses on over 14000 images and designs of experiments using 31 depth maps to measure the relative influence of the parameters and to fine-tune them based on the number of bad pixels. The implemented methodology improves the performance of adaptive weight based dense depth map algorithms. As a result, the algorithm improves from 16.78 to 14.48 % bad pixels using a classical exploratory data analysis of over 14000 existing images, while using designs of computer experiments with 31 runs yielded an even better performance by lowering bad pixels from 16.78 to 13 %. © 2012 Springer Science+Business Media, LLC.Ítem Tuning of adaptive weight depth map generation algorithms: Exploratory data analysis and design of computer experiments (DOCE)(SPRINGER, 2013-09-01) Acosta, Diego; Barandiaran, Inigo; Congote, John; Ruiz, Oscar; Hoyos, Alejandro; Grana, Manuel; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAEIn depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on unplanned experiments. Algorithms' performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury's standards. This work shows a systematic statistical approach including exploratory data analyses on over 14000 images and designs of experiments using 31 depth maps to measure the relative influence of the parameters and to fine-tune them based on the number of bad pixels. The implemented methodology improves the performance of adaptive weight based dense depth map algorithms. As a result, the algorithm improves from 16.78 to 14.48 % bad pixels using a classical exploratory data analysis of over 14000 existing images, while using designs of computer experiments with 31 runs yielded an even better performance by lowering bad pixels from 16.78 to 13 %. © 2012 Springer Science+Business Media, LLC.