VSURF : un package R pour la sélection de variables à l'aide de forêts aléatoires

National audienceVariable selection is a crucial issue in many applied classication and regression problems. It is of interest for statistical analysis as well as for modelization or prediction purposes to remove irrelevant variables, to select all important ones or to determine a sucient subset for prediction. These main different objectives on a statistical learning perspective involve variable selection to simplify statistical problems, to help diagnosis and interpretation, and to speed up data processing. The authors have proposed a variable selection method based on random forests, and the aim of this presentation is to describe the (recently available on CRAN) associated R package called VSURF and to illustrate its use on real datasets. Introduced by Breiman, random forests (abbreviated RF in the sequel) is an attractive non-parametric statistical method to deal with such problems, since it requires only mild conditions on the model supposed to have generated the observed data. Indeed, since it is based on decision trees and it uses aggregation ideas, RF allow to consider in an elegant and versatile framework dierent models and problems, namely regressions, two-class or multiclass classications. In Genuer et.al. 2010 we have distinguished two variable selection objectives: interpretation and prediction. The first is to find important variables highly related to the response variable in order to select all the important variables, even with high redundancy. The second is to find a small number of variables sucient to a good parsimonious prediction of the response variable. We have proposed the following two-step procedure, the first one is the same for the two situations while the second one depends on the objective

Surface Finish Issues after Direct Metal Deposition

International audienceDerived from laser cladding, the Direct Metal Deposition (DMD) laser process, is based upon a laser beam - projected powder interaction, and allows manufacturing complex 3D shapes much faster than conventional processes. However, the surface finish remains critical, and DMD parts usually necessitate post-machining steps. In this context, the focus of our work was: (1) to understand the physical mechanisms responsible for deleterious surface finishes, (2) to propose different experimental solutions for improving surface finish. Our experimental approach is based upon: (1) adequate modifications of the DMD conditions (gas shielding, laser conditions, coaxial or off-axis nozzles), (2) a characterization of laser-powder-melt-pool interactions using fast camera analysis, (3) a precise check of surface aspects using 3D profilometry, SEM, (4) preliminary thermo-convective simulations to understand melt-pool hydrodynamics. Most of the experimental tests were carried out on a Ti6Al4V titanium alloy, widely investigated already. Results confirm that surface degradation depends on two aspects: the sticking of non-melted or partially melted particles on the free surfaces, and the formation of menisci with more or less pronounced curvature radii. Among other aspects, a reduction of layer thickness and an increase of melt-pool volumes to favor re-melting processes are shown to have a beneficial effect on roughness parameters