Partial Modeling Technique Research Articles

Evaluating the effects of process parameters on maximum extrusion pressure using a new artificial neural network-based (ANN-based) partial-modeling technique

Metal extrusion process accounts for the production of the majority of industrial and domestic aluminum sections. A major limitation to the success of any extrusion operation is the capability of the particular extrusion press to meet the maximum pressure requirements for that operation. In the present work, the effects of industrial extrusion process parameters and their interactions on the resulting maximum extrusion pressure, of an industrially extruded aluminum alloy, have been studied using a newly devised ANN-based partial modeling technique. Two operating parameters (initial billet temperature and ram speed) and three geometrical parameters (extrusion ratio, profile average thickness, and number of die cavities) were investigated. The main objective for developing this modeling technique is to overcome the limitations of presently available statistical modeling tools, as foreseen by the modeling needs for a complex thermo-mechanical process such as extrusion. The main present limitations are accounting for non-linearity in the process behavior, incorporating interaction effects and a meaningful determination of the highly significant process parameters and/or interactions. These three features have been, collectively, incorporated into the present model by means of combining statistical analysis of variance into ANN and by using a partial sum of squares analysis, which we propose to call the “present factor analysis.” Normal linear regression has been also employed for comparison purposes. According to the present model, maximum extrusion pressure has shown various degree of non-linearity in behavior with respect to the different process parameters and their significant interactions. It has been found that variations in the maximum extrusion pressure are mainly a function of initial billet temperature and its interactions with other process parameters, especially the ram speed. The present ANN-based model has shown superior prediction capabilities compared to the linear model with a marginal overall prediction error value of ±2.5 %.

Thermo-Mechanical Behaviors of Ceramic-Coated Structures in Outer Space Using Partial Model Technique

Generally, structures used in outer space are subjected to severe situations, including sublimation, a strong evaporation of lubricants, thermal stresses, high temperature gradients, irradiation, impact from microscopic meteorites, and other factors. Recently, various kinds of coatings have been applied to structural parts under heavy contact stresses to ensure a longer wear-free life and/or reduce the friction coefficients. Accordingly, the current study applied FEM to analyze space parts with a coating layer thermo-mechanically subjected to a contact load. First, a steady state temperature distribution of the space part was obtained, then a quasi-static external load was applied. Thereafter, the total solution was computed and the thermal strain subtracted to obtain the mechanical strains for determining the stresses of the individual part. When using FEM, the model needs to discretize into many sub-domain finite elements. Since the difference, however, in the dimension between the coating layer and the substrate is so large, the analysis needs to be considered carefully. Consequently, the problem was analyzed in two steps. First, the whole model was analyzed using rather coarse meshes, then a small region was cut near the loading point and analyzed using very fine meshes. This method is based on Saint-Venant's principle. Finally, the effect of a thermal load on the stress of a space part with a coating layer was checked with and without cracks in the substrate. Accordingly, the results demonstrate the stresses on a space part under a thermo-mechanical load, along with the effects of various coating materials and a crack in the substrate on the stress distribution.

The Star Formation History and Spatial Distribution of Stellar Populations in the Ursa Minor Dwarf Spheroidal Galaxy

As a part of a project devoted to the study of the Ursa Minor dwarf spheroidal (dSph) galaxy, the star formation history of the galaxy is presented. The analysis uses wide-field photometry, encompassing about 1° × 1° (the total covered area being 0.75 deg2), which samples the galaxy out to its tidal radius. Derivation of the star formation history (SFH) has been performed using the synthetic partial-model technique. The resulting SFH shows that Ursa Minor hosts a predominantly old stellar population, with virtually all the stars having formed earlier than 10 Gyr ago and 90% having formed more than 13 Gyr ago. Nevertheless, the Ursa Minor color-magnitude diagram shows several stars above the main, old turnoff forming a blue plume (BP). If these stars were genuine main-sequence stars, Ursa Minor would have maintained a low star formation rate extending up to ~2 Gyr ago. However, several indications (relative amount and spatial distribution of BP stars and difficulty of retaining processed gas) argue against this possibility. In such a context, the most reliable hypothesis is that BP stars are blue stragglers originating in the old population, with Ursa Minor hence remaining the only Milky Way dSph satellite to host a pure old stellar population. A marginally significant age gradient is detected, in the sense that stars in outer regions are slightly younger on average. The distance of Ursa Minor has been calculated using the magnitude of the horizontal branch and a calibration based on Hipparcos data for main-sequence subdwarfs. We estimate a distance of 76 ± 4 kpc, which is slightly higher than previous estimates. From the red giant branch color, we estimate a metallicity [Fe/H] = -1.9 ± 0.2, in agreement with a previous spectroscopic determination. No metallicity gradients have been detected across the galaxy.

Application of partial modeling techniques for texture segmentation

We describe an approach to texture segmentation that uses robust partial modeling procedures. First, a method for encoding (modeling) amplitude spectra of the texture images by means of sets of bivariate Gaussian functions is described. This procedure involves adaptive determination of a low-pass filter, clustering of the residual high-pass spectrum, and robust parametric encoding of separate spectral segments. Based on this model, a small set of Gabor filters tuned to the channels of high activity in the image Fourier spectrum is selected and applied to generate feature images for texture segmentation. The resulting feature images are segmented by an algorithm that uses the notion that homogeneous texture image regions can be defined as having unimodal texture feature histograms. This algorithm then applies a robust partial modeling technique to encode the feature image histograms as mixtures of univariate Gaussians. The estimated parameters of the univariate Gaussian functions that compose such mixtures are next used for segmenting feature images based on a maximum-likelihood decision rule. Several examples are presented to demonstrate the performance of the proposed approach to texture segmentation.