Laboratorio CAD/CAM/CAE

URI permanente para esta comunidad

https://hdl.handle.net/10784/9117

Está en la capacidad de prestar servicios y entrenar asistentes para el mercado internacional en investigación y desarrollo de herramientas para diseño, manufactura y mecánica asistidos por computador (CAD/CAM/CAE).

Líneas de investigación: Applied Computational Geometry; Computational Mechanics; Computer Aided Geometric Design; Computer Aided Manufacturing; Geometric Modeling of Cultural Heritage; Geometric Modeling of Materials; Geometric Modeling of Terrain and Coastal Areas; Medical Images; Medical Kinematics; Robot Kinematics.
Código Minciencias: COL0013067.
Categoría 2019: A1.
Escuela: Ingeniería.
Departamento académico: Ingeniería Mecánica.
Coordinador: Juan Manuel Rodríguez Prieto.
Correo electrónico:jmrodrigup@eafit.edu.co

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Mostrando 1 - 6 de 6

Fitting of Analytic Surfaces to Noisy Point Clouds
(Scientific Research Publishing, 2013-04) Ruíz, Óscar; Arroyave, Santiago; Acosta, Diego; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
Fitting -continuous or superior surfaces to a set of points sampled on a 2-manifold is central to reverse engi- neering, computer aided geometric modeling, entertaining, modeling of art heritage, etc -- This article addresses the fit- ting of analytic (ellipsoid, cones, cylinders) surfaces in general position in -- Currently, the state of the art presents limitations in 1) automatically finding an initial guess for the analytic surface F sought, and 2) economically estimat- ing the geometric distance between a point of and the analytic surface SF -- These issues are central in estimating an analytic surface which minimizes its accumulated distances to the point set -- In response to this situation, this article presents and tests novel user-independent strategies for addressing aspects 1) and 2) above, for cylinders, cones and ellipsoids -- A conjecture for the calculation of the distance point-ellipsoid is also proposed -- Our strategies produce good initial guesses for F and fast fitting error estimation for F, leading to an agile and robust optimization algorithm -- Ongoing work addresses the fitting of free-form parametric surfaces to S
HexFlex Mechanism Modeling by Design of Computer Experiments
(Springer London, 2010-04) Acosta, Diego; Restrepo, David; Ruíz, Oscar; Durango, Sebastián; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
Compliant mechanisms are an instance of mechanical devices designed to transfer or transmit motion, force, or energy from specified input ports to output ports by elastic deformation of at least one of its members -- The main advantage of compliant mechanisms with respect to traditional rigid-link mechanism is that fewer parts, fewer assembly process and no lubrication are required -- The HexFlex is a parallel compliant mechanism for nano-manipulating that allows six degrees of freedom of its moving stage -- This mechanism was designed for high precisión an repeatability -- This article presents a methodology to model compliant mechanisms behavior under quasi-static conditions using computer experiments, reducing costs of experimentation of product development -- The methodology is used to establish a mathematical model that relates the actuator forces at the input ports with the position and orientation the end-effector stage of the Hexflex -- This mathematical model has direct application in model-based control as an advantage with respect to other models, e.g. Finite Element Method -- The mathematical model of the HexFlex is achieved using metamodels -- The term methamodel is used to represent a simplified and efficient mathematical model of unknown phenomenon or computer codes – The metamodel of the HexFlex is performed from virtual analyses made using the Finite Element Method (FEM) -- Simulations of the metamodel were made founding good accuracy with respect to the virtual experiments
Sensitivity analysis of optimized curve fitting to uniform-noise point samples
(2012-05) Ruíz, Óscar; Cortes, Camilo; Acosta, Diego; Aristizábal, Mauricio; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
Curve reconstruction from noisy point samples is needed for surface reconstruction in many applications (e.g. medical imaging, reverse engineering,etc.) -- Because of the sampling noise, curve reconstruction is conducted by minimizing the fitting error (f), for several degrees of continuity (usually C0, C1 and C2) -- Previous works involving smooth curves lack the formal assessment of the effect on optimized curve reconstruction of several inputs such as number of control points (m), degree of the parametric curve (p), composition of the knot vector (U), and degree of the norm (k) to calculate the penalty function (f) -- In response to these voids, this article presents a sensitivity analysis of the effect of mand k on f -- We found that the geometric goodness of the fitting (f) is much more sensitive to m than to k -- Likewise, the topological faithfulness on the curve fit is strongly dependent on m -- When an exaggerate number of control points is used, the resulting curve presents spurious loops, curls and peaks, not present in the input data -- We introduce in this article the spectral (frequency) analysis of the derivative of the curve fit as a means to reject fitted curves with spurious curls and peaks -- Large spikes in the derivative signal resemble Kronecker or Dirac Delta functions, which flatten the frequency content adinfinitum -- Ongoing work includes the assessment of the effect of curve degree p on f for non-Nyquist point samples
Statistical tuning of Adaptive-Weight Depth Map Algorithm
(Springer Berlin Heidelberg, 2011) Hoyos, Alejandro; Congote, John; Barandiaran, Iñigo; Acosta, Diego; Ruíz, Óscar; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
In 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 signicantly smaller data sets on fractional factorial and surface-response designs of experiments
Tuning of Adaptive Weight Depth Map Generation Algorithms Exploratory Data Analysis and Design of Computer Experiments (DOCE)
(Springer Verlag, 2013-09) Acosta, Diego; Congote, John; Barandiaran, Iñigo; Ruíz, Óscar; Hoyos, Alejandro; Graña, Manuel; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
In depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on un planned 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 inf uence 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%
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/CAE
In 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.

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Examinando Laboratorio CAD/CAM/CAE por Autor "Acosta, Diego"

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