56 resultados para cluster hardening


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The effect of varying both the aspect ratio and the coefficient of friction of contacts with elliptical geometry on their elastic shakedown performance has been examined theoretically for surfaces with two types of subsurface hardness or strength profiles. In stepwise hardening the hard layer is of uniform strength while in linear hardening its strength reduces from a maximum at the surface to that of the core at the base of the hardened layer. The shakedown load is expressed as the ratio of the maximum Hertzian pressure to the strength of the core material. As the depth of hardening, expressed as a multiple of the elliptical semi-axis, is increased so the potential shakedown load increases from a level that is appropriate to a uniform half-space of unhardened material to a value reflecting the hardness of the surface and near-surface material. In a step-hardened material, the shakedown limit for a surface 'pummelled' by the passage of a sequence of such loads reaches a cut-off or plateau value, which cannot be exceeded by further increases in hardening depth irrespective of the value of the friction coefficient. For a linear-hardened material the corresponding plateau is approached asymptotically. The work confirms earlier results on the upper bounds on shakedown of both point and line contacts and provides numerical values of shakedown loads for intermediate geometries. In general, the case depth required to achieve a given shakedown limit reduces in moving from a transversely moving nominal line load to an axisymmetric point load.

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Nanostructured carbon thin films have been grown by deposition of cluster beams produced by a supersonic expansion. Due to separation effects typical of supersonic beams, films with different nanostructures can be grown by the simple intercepting of different regions of the cluster beam with a substrate. Films show a low-density porous structure, which has been characterized by Raman and Brillouin spectroscopy. Film morphology suggests that growth processes are similar to those occurring in a ballistic deposition regime.

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Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic model. A maximum entropy approach avoids hidden assumptions about missing rank positions. Parameter estimators and an efficient EM algorithm for unsupervised inference are derived for the ranking mixture model. Experiments on both synthetic data and real-world data demonstrate significantly improved parameter estimates on heterogeneous data when the incomplete rankings are included in the inference process.