Surface roughness and high density of cubic twins and hexagonal inclusions in cubic GaN epilayers

Surface roughness and its correlation with the polarity of internal hexagonal inclusions and cubic twins have been investigated by atomic force microscopy (AFM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD). The surface roughness resulted from large amount of strips, which prolonged in [1 (1) over bar0] direction with small size in [110] or [110] direction. The sidestep of each strip is just the top of high density of hexagonal inclusions or cubic microtwins. Moreover, XRD shows that the amount of hexagonal inclusions and cubic microtwins measured in [110] direction are twice or more as much as in [110] direction. Therefore, it is hexagonal inclusions, cubic twins and their distributive polarity that is responsible to the surface characteristics of cubic GaN epilayers.

DETERMINATION OF SURFACE-ROUGHNESS OF INP (001) WAFERS BY X-RAY-SCATTERING

The surface roughness of polished InP (001) wafers were examined by x-ray reflectivity and crystal truncation rod (CTR) measurements. The root-mean-square roughness and the lateral correlation scale were obtained by both methods. The scattering intensities in the scans transverse to the specular reflection rod were found to contain two components. A simple surface model of surface faceting is proposed to explain the experimental data. The sensitivities of the two methods to the surface structure and the role of the resolution functions in the CTR measurements are discussed.

Fabrication of Iron Oxide Core/Gold Shell Submicrometer Spheres with Nanoscale Surface Roughness for Efficient Surface-Enhanced Raman Scattering

A method to synthesize Fe3O4 core/Au shell submicrometer structures with very rough surfaces on the nanoscale is reported. The Fe3O4 particles were first modified with uniform polymers through the layer-by-layer technique and then adsorbed a lot of gold nanoseeds for further Au shell formation. The shell was composed of a large number of irregular nanoscale An particles arranged randomly, and there were well-defined boundaries between these Au nanoparticles. The Fe3O4 core/Au shell particles showed strong plasmon resonance absorption in the near-infrared range, and can be separated quickly from solution by an external magnet.

Surface roughness characterization of soft x-ray multilayer films on the nanometer scale

The soft x-ray reflectivity of multilayer films is affected by the surface roughness on the transverse nanometer scale. Scanning tunneling microscopy (STM) is an ideal instrument for providing high-lateral-resolution roughness measurements for soft x-ray multilayer films that cannot be obtained with other types of instruments on the transverse nanometer scale. The surface roughnesses of Mo/Si, Mo/C, and W/Si soft x-ray multilayer films prepared by an ion-beam-sputtering technique were measured with a STM on the vertical and transverse attributes. The film roughnesses and average spatial wavelengths added to the substrates depend on the multilayer film fabrication conditions, i.e., material combinations, number of layers, and individual layer thickness. These were estimated to lead to a loss of specular reflectivity and variations of the soft x-ray scattering angle distribution. This method points the way to further studies of soft x-ray multilayer film functional properties and can be used as basic guidance for selecting the best coating conditions in the fabrications of soft x-ray multilayer films. (C) 1996 American Vacuum Society.

Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC)

In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models - Multiple regression, Random Forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply Random Forest or Quantile regression techniques to the machining domain. The performance of these models was compared to each other to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).

Fluence dependence of the surface roughness of InP after N2 + bombardment

InP(1 0 0) surfaces were sputtered under ultrahigh vacuum conditions by 5 keV N2+ ions at an angle of incidence of 41° to the sample normal. The fluence, φ, used in this study, varied from 1 × 10¹⁴ to 5 × 10¹⁸ N2+ cm^-2. The surface topography was investigated using field emission scanning electron microscopy (FE-SEM) and atomic force microscopy (AFM). At the lower fluences (φ ≤ 5 × 10¹⁶ N2+ cm^-2) only conelike features appeared, similar in shape as was found for noble gas ion bombardment of InP. At the higher fluences, ripples also appeared on the surface. The bombardment-induced topography was quantified using the rms roughness. This parameter showed a linear relationship with the logarithm of the fluence. A model is presented to explain this relationship. The ripple wavelength was also determined using a Fourier transform method. These measurements as a function of fluence do not agree with the predictions of the Bradley-Harper theory. © 2004 Elsevier B.V. All rights reserved.

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components

Stability lobes diagram identification and surface roughness monitoring in milling processes

La millora de la productivitat i la qualitat són indubtablement dues de les principals exigències del sector productiu modern i factors clau per la competitivitat i la supervivència. Dins aquest sector,la fabricació per arrancada de material juga encara avui en dia un paper protagonista tot i l'aparició de noves tècniques de conformat per addició.Indústries com l'aeronàutica, l'automobilística,la del motlle o l'energètica, depenen en bona part de les prestacions de les màquines-eina. Aquesta Tesi aborda dos aspectes rellevants quan es tracta de millorar de la productivitat i la qualitat del sector productiu: el problema del fimbrament, més conegut per la denominació anglosaxona chatter,i la monitorització de la rugositat superficial en el mecanitzat a alta velocitat.

Sensitivity of radar backscatter to desert surface roughness

Synthetic aperture radar (SAR) data have proved useful in remote sensing studies of deserts, enabling different surfaces to be discriminated by differences in roughness properties. Roughness is characterized in SAR backscatter models using the standard deviation of surface heights (sigma), correlation length (L) and autocorrelation function (rho(xi)). Previous research has suggested that these parameters are of limited use for characterizing surface roughness, and are often unreliable due to the collection of too few roughness profiles, or under-sampling in terms of resolution or profile length (L-p). This paper reports on work aimed at establishing the effects of L-p and sampling resolution on SAR backscatter estimations and site discrimination. Results indicate significant relationships between the average roughness parameters and L-p, but large variability in roughness parameters prevents any clear understanding of these relationships. Integral equation model simulations demonstrate limited change with L-p and under-estimate backscatter relative to SAR observations. However, modelled and observed backscatter conform in pattern and magnitude for C-band systems but not for L-band data. Variation in surface roughness alone does not explain variability in site discrimination. Other factors (possibly sub-surface scattering) appear to play a significant role in controlling backscatter characteristics at lower frequencies.

Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland

Surface roughness is an important geomorphological variable which has been used in the Earth and planetary sciences to infer material properties, current/past processes, and the time elapsed since formation. No single definition exists; however, within the context of geomorphometry, we use surface roughness as an expression of the variability of a topographic surface at a given scale, where the scale of analysis is determined by the size of the landforms or geomorphic features of interest. Six techniques for the calculation of surface roughness were selected for an assessment of the parameter`s behavior at different spatial scales and data-set resolutions. Area ratio operated independently of scale, providing consistent results across spatial resolutions. Vector dispersion produced results with increasing roughness and homogenization of terrain at coarser resolutions and larger window sizes. Standard deviation of residual topography highlighted local features and did not detect regional relief. Standard deviation of elevation correctly identified breaks of slope and was good at detecting regional relief. Standard deviation of slope (SD(slope)) also correctly identified smooth sloping areas and breaks of slope, providing the best results for geomorphological analysis. Standard deviation of profile curvature identified the breaks of slope, although not as strongly as SD(slope), and it is sensitive to noise and spurious data. In general, SD(slope) offered good performance at a variety of scales, while the simplicity of calculation is perhaps its single greatest benefit.

A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened Steel turning

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