H
Hugues Favreliere
Researcher at University of Savoy
Publications - 43
Citations - 482
Hugues Favreliere is an academic researcher from University of Savoy. The author has contributed to research in topics: Polynomial texture mapping & Pixel. The author has an hindex of 10, co-authored 43 publications receiving 409 citations. Previous affiliations of Hugues Favreliere include Polytech'Savoie.
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Modeling of 2D and 3D Assemblies Taking Into Account Form Errors of Plane Surfaces
TL;DR: This work has built a geometrical model based on the modal shapes of the ideal surface and compute for the completely deterministic contact points between this pair of shapes according to a given assembly process.
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Multiscale roughness analysis of engineering surfaces: A comparison of methods for the investigation of functional correlations
Gaëtan Le Goïc,Gaëtan Le Goïc,Maxence Bigerelle,Serge Samper,Hugues Favreliere,Maurice Pillet +5 more
TL;DR: In this article, the correlations between the topography of different damaged rough surfaces and process conditions were investigated by using Gaussian Filtering, Wavelet Transform and a more recent approach named Discrete Modal Decomposition.
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Multi Scale Modal Decomposition of Primary Form, Waviness and Roughness of Surfaces
TL;DR: An innovative method for the multi-scale analysis of high value-added surfaces is introduced, which consists of applying a method based on a new parameterization that allows us to characterize the form, waviness and roughness defects of a surface.
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On the dependence of static flat seal efficiency to surface defects
TL;DR: In this paper, the role of surface defects on static flat seal efficiency is investigated on synthetic "turned-like" surfaces generated by combinations of the first 50 vibrational eigen modes determined from modal discrete decomposition.
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Outlier filtering: a new method for improving the quality of surface measurements
TL;DR: In this article, the authors proposed a new filtering technique that aims to improve the quality of measured surface data by removing measurement artefacts, such as spikes and batwings, that impact the data analysis.