ParisTech

About: ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Residual stress. The organization has 1888 authors who have published 1965 publications receiving 55532 citations. The organization is also known as: Paris Institute of Technology & ParisTech Développement.

Comparative studies on Mo-Cr-N and Al-Cr-N coatings obtained by PVD dual magnetron sputtering

Abstract: Recently, several studies have shown that the addition of a secondary element likes Al, Si, etc. to nitride and carbide binary systems such as Ti–N and Cr–N improved their structural and mechanical properties and also their thermal stability. In this study, we realized a comparison between the effects of aluminium or molybdenum addition on the properties of the Cr–N system. The (Cr,Al)N and (Cr,Mo)N films were deposited by RF dual magnetron sputtering. To control the aluminium and molybdenum contents in (Cr,Al)N and in (Cr,Mo)N films, respectively, we modified the Cr, Al and Mo target bias. The structural, morphological and composition analyses of the deposited films were carried out using X-ray diffraction (XRD) and SEM equipped with an energy dispersive spectroscopy (EDS) microanalysis. The variation of the residual stresses with the Al and Mo contents has been studied using the Newton's rings method. The obtained Al contents in (Cr,Al)N deposited films varied between 0 and 51 at.% while the Mo contents in (Cr,Mo)N layers varied between 0 and 42 at.%. A morphological change from amorphous to columnar films has been observed with the addition of Al in the case of (Cr,Al)N coatings, while all the (Cr,Mo)N films presented a columnar structure. The residual stresses of the (Cr,Mo)N coatings are higher than the (Cr,Al)N ones but they exhibited a similar behaviour for both coatings.

On the optimal choice of wavelet function for multiscale honed surface characterization

Abstract: Multiscale surface topography characterization is mostly suited than standard approaches because it is more adapted to the multi-stage process generation. Wavelet transform represents a power tool to perform the multiscale decomposition of the surface topography in a wide range of wavelength. However, characterization results depend closely on the topography data acquisition instrument (resolution, height accuracy, sensitivity...) and also on the wavelet analysis method (discrete or continuous transform). In particular, the choice of wavelet function can have significant effect on the analysis results. In this paper, we present experimental work on a number of popular wavelets functions with the aim of finding wavelets that exhibit optimal description of honed surface features when continuous wavelet transform is used. We demonstrate that the regularity property of wavelet function has a significant influence on the characterization performances. This comparative study shows also that the Morlet wavelet is the more adapted wavelet basis function for multiscale characterization of honed surfaces using continuous wavelet transform.

Creativity and its contexts: the emergence, institutionalisation and professionalisation of science and culture in Luxembourg

Abstract: While an abundant literature on the social history of culture and science in Europe has developed over the past few decades, there is to date hardly any academic work exploring the social history of science and culture in Luxembourg. The aim of this article is to put Luxembourg ‘on the map’ by focusing on its scientific research and museums in order to examine the emergence, institutionalisation and professionalisation of Luxembourg's science and culture. It will be argued that the most radical changes occurred in the 1980s and 1990s: a multiplication of infrastructures, an increase in budgets, a professionalisation of practices, the emergence of a market for artworks, and an appearance of dedicated policies. During this period, both the material architecture and the socioeconomic architecture of science and culture have been significantly (re)configured. Within the political debates accompanying these changes, the discourses stressing the positionality and relationality of science and culture are notewor...

Low-temperature plasma nitriding of martensitic stainless steel

Abstract: This study presents the plasma nitriding of Cr–Mo–V tool steel to protect it from environmental degradation The steel samples after metallographic polishing were nitrided in the glow discharge plasma of N2 and H2 gas mixture (80:20) Nitriding was performed at two different temperatures, a lower temperature of 450 °C and the higher temperature of 500 °C The potentiodynamic polarization tests in 35% NaCl electrolyte had shown the enhancement of corrosion resistance of steels after nitriding X-ray diffraction (XRD) studies and scanning electron microscopic analysis coupled with energy dispersive spectroscopic analysis (SEM/EDS) was performed to understand the modification of surface microstructure XRD analysis of the nitrided steels revealed the presence of γ′- and e-nitrides of iron, which were responsible for the improvement of corrosion resistance

Object recognition and retrieval by context dependent similarity kernels

Abstract: The success of kernel methods including support vector machines (SVMs) strongly depends on the design of appropriate kernels. While initially kernels were designed in order to handle fixed-length data, their extension to unordered, variable-length data became more than necessary for real pattern recognition problems such as object recognition and bioinformatics. We focus in this paper on object recognition using a new type of kernel referred to as ldquocontext-dependentrdquo. Objects, seen as constellations of local features (interest points, regions, etc.), are matched by minimizing an energy function mixing (1) a fidelity term which measures the quality of feature matching, (2) a neighborhood criteria which captures the object geometry and (3) a regularization term. We will show that the fixed-point of this energy is a ldquocontext-dependentrdquo kernel (ldquoCDKrdquo) which also satisfies the Mercer condition. Experiments conducted on object recognition show that when plugging our kernel in SVMs, we clearly outperform SVMs with ldquocontext-freerdquo kernels.