Surface roughness measuring system

Significant height information of ocean waves, or peaks of rough terrain is obtained by compressing the radar signal over different widths of the available chirp or Doppler bandwidths, and cross-correlating one of these images with each of the others. Upon plotting a fixed (e.g., zero) component of the cross-correlation values as the spacing is increased over some empirically determined range, the system is calibrated. To measure height with the system, a spacing value is selected and a cross-correlation value is determined between two intensity images at a selected frequency spacing. The measured height is the slope of the cross-correlation value used. Both electronic and optical radar signal data compressors and cross-correlations are disclosed for implementation of the system

Surface roughness detector Patent

Roughness detector for recording surface pattern of irregularitie

Surface roughness modeling of CBN hard steel turning

Study in the paper investigate the influence of the cutting conditions parameters on surface roughness parameters during turning of hard steel with cubic boron nitrite cutting tool insert. For the modeling of surface roughness parameters was used central compositional design of experiment and artificial neural network as well. The values of surface roughness parameters Average mean arithmetic surface roughness (Ra) and Maximal surface roughness (Rmax) were predicted by this two-modeling methodology and determined models were then compared. The results showed that the proposed systems can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments modeling technique and artificial neural network can be effectively used for the prediction of the surface roughness parameters of hard steel and determined significantly influential cutting conditions parameters

Calibrating mars orbiter laser altimeter pulse widths at mars science laboratory candidate landing sites

Accurate estimates of surface roughness allow quantitative comparisons between planetary terrains. These comparisons enable us to improve our understanding of commonly occurring surface processes, and develop a more complete analysis of candidate landing and roving sites. A (secondary) science goal of the Mars Orbiter Laser Altimeter was to map surface roughness within the laser footprint using the backscatter pulse-widths of individual pulses, at finer scales than can be derived from the elevation profiles. On arrival at the surface, these pulses are thought to have diverged to between 70 and 170 m, corresponding to surface roughness estimates at 35 and 70 m baselines respectively; however, the true baseline and relationship remains unknown. This work compares the Mars Orbiter Laser Altimeter pulse-widths to surface roughness estimates at various baselines from high-resolution digital terrain models at the final four candidate landing sites of Mars Science Laboratory. The objective was to determine the true baseline at which surface roughness can be estimated, and the relationship between surface roughness and the pulse-widths, to improve the reliability of current global surface roughness estimates from pulse-width maps. The results seem to indicate that pulse-widths from individual shots are an unreliable indicator of surface roughness, and instead, the pulse-widths should be downsampled to indicate regional roughness, with the Slope-Corrected pulse-width dataset performing best. Where Rough Patches are spatially large compared to the footprint of the pulse, pulse-widths can be used as an indicator of surface roughness at baselines of 150 to 300 m; where these patches are spatially small, as observed at Mawrth Vallis, pulse-widths show no correlation to surface roughness. This suggests that a more complex relationship exists, with varying correlations observed, which appear dependent on the distribution of roughness across the sites

Measurement of surface roughness slope

Instrument, consisting of isolator, differentiator, absolute value circuit, and integrator, uses output signal from surface texture analyzer profile-amplifier to calculate surface roughness slope. Calculations provide accurate, instantaneous value of the slope. Instrument is inexpensive and applicable to any commerical surface texture analyzer

Stochastic analysis of surface roughness

For the characterization of surface height profiles we present a new stochastic approach which is based on the theory of Markov processes. With this analysis we achieve a characterization of the complexity of the surface roughness by means of a Fokker-Planck or Langevin equation, providing the complete stochastic information of multiscale joint probabilities. The method was applied to different road surface profiles which were measured with high resolution. Evidence of Markov properties is shown. Estimations for the parameters of the Fokker-Planck equation are based on pure, parameter free data analysis

Model-based observer proposal for surface roughness monitoring

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels

Radar observations of a volcanic terrain: Askja Caldera, Iceland

Surface roughness spectra of nine radar backscatter units in the Askja caldera region of Iceland were predicted from computer-enhanced like- and cross-polarized radar images. A field survey of the caldera was then undertaken to check the accuracy of the preliminary analysis. There was good agreement between predicted surface roughness of backscatter units and surface roughness observed in the field. In some cases, variations in surface roughness could be correlated with previously mapped geologic units

Effects on ellipsometric parameters caused by heat treatment of silicon (111) surface

Heating of a silicon single crystal introduces a surface roughness. Crystals are heated for periods of 45 sec in the temperature range from 560 to 1150°C. Using ellipsometry, Auger electron spectroscopy, mass spectroscopy and micrography it has been shown that the changes in the ellipsometric parameters are caused by surface roughness which in turn is strongly related to the sublimation of silicon during heating. The relation between surface roughness and temperature of the crystal during the heating is not linear