Roughness Perception of Haptically Displayed Fractal Surfaces

2000 ◽  
Author(s):  
Michael A. Costa ◽  
Mark R. Cutkosky
Keyword(s):  
Fractal Dimension ◽  
Root Mean Square ◽  
Force Feedback ◽  
Mean Square ◽  
Fractal Surfaces ◽  
Surface Profiles ◽  
Rms Amplitude ◽  
Roughness Perception

Abstract Surface profiles were generated by a fractal algorithm and haptically rendered on a force feedback joystick. Subjects were asked to use the joystick to explore pairs of surfaces and report to the experimenter which of the surfaces they felt was rougher. Surfaces were characterized by their root mean square (RMS) amplitude and their fractal dimension. The most important factor affecting the perceived roughness of the fractal surfaces was the RMS amplitude of the surface. When comparing surfaces of fractal dimension 1.2–1.35 it was found that the fractal dimension was negatively correlated with perceived roughness.

scholarly journals INVESTIGATION ON DATA COLLECTION AND FRACTAL CHARACTERISTICS OF SOIL SURFACE ROUGHNESS

2020 ◽  
Vol 61 (2) ◽  
pp. 135-142
Author(s):  
Yi Qiu ◽  
Zhi Chen ◽  
Zhanfeng Hou ◽  
Haiyang Liu ◽  
Fang Guo ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Root Mean Square ◽  
Soil Surface ◽  
Laser Sensor ◽  
Mean Square ◽  
Roughness Index ◽  
Soil Surface Roughness ◽  
Fractal Characteristics ◽  
Two Parameters

It is of great significance to acquire the soil surface roughness accurately for the study of the interaction between tractors and soil. Based on the laser sensor, this paper proposed the non-contact measuring instrument of the soil surface roughness with the data acquiring system by using Lab-View software. By using W-M theory, three commonly used fractal dimension calculation methods are compared and analyzed.. The result showed that the Root-mean-square method has the highest accuracy and clear physical meaning, which is ideal method to calculate the soil surface roughness characteristics. When the fractal dimension is between 1.4 and 1.6, the acquired data is analysed by the Root-mean-square method to obtain the fractal features of the soil surface roughness. The experiment results indicated that the fractal dimension of the ploughed surface is 1.39, that of disc harrow surface is 1.550, and that of rolled surface is 1.46-1.54. Obviously, the fractal dimension can accurately distinguish the soil surface roughness with the different treatments. However, the fractal dimension selected from different scales showed an obvious instability during calculations. The surface roughness index combined with the two parameters can effectively represent the soil surface roughness, and the larger the surface roughness index is, the greater the surface roughness is.

A surface modeling method for product virtual assembly based on the root mean square of the regional residuals

2019 ◽  
Vol 234 (1-2) ◽  
pp. 229-242 ◽  
Author(s):  
Heng Li ◽  
Lemiao Qiu ◽  
Shuyou Zhang ◽  
Jianrong Tan ◽  
Zili Wang ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Root Mean Square ◽  
Large Deviation ◽  
Virtual Assembly ◽  
Surface Modeling ◽  
Modeling Method ◽  
Surface Model ◽  
Mean Square ◽  
Machining Center ◽  
Part Surface

The micro-topographic quality of a product’s surface directly affects the performance of the virtual assembly. The computational precision of the fractal dimension has a significant effect on the accuracy of the virtual assembly model. To address the imprecision and large deviation of the calculation method under some conditions, a virtual product assembly surface modeling method is proposed in this article based on the root mean square of the area residuals. First, the fractal dimension of the surface is calculated using the regional residual root mean square (3R method). The feasibility of the 3R method is verified by comparing with the existing methods on isotropic and anisotropic surfaces. Second, the fractal dimension of a real part surface is obtained using the 3R method. According to the Weierstrass–Mandelbrot function, a partial surface model of the machined part is established. The surface contours, roughness, waviness, and flatness of the part are extracted by wavelet transform. Then, the digital surface model for virtual assembly with the same profile parameters is generated by reconstructing the surface contours of the actual parts. Finally, the method proposed by taking the virtual assembly of a VTM200/F5 turn-milling composite machining center guideway is verified in this article.

scholarly journals TESTING, ANALYSIS AND COMPARISON FOR CHARACTERISTICS OF AGRICULTURAL FIELD AND ASPHALT ROAD ROUGHNESS

2020 ◽  
pp. 147-154
Author(s):  
Lijuan Wang ◽  
Jianguo Yan ◽  
Shengshi Xie ◽  
Chunguang Wang
Keyword(s):  
Spectral Density ◽  
Root Mean Square ◽  
Power Spectral Density ◽  
Mean Square ◽  
Agricultural Field ◽  
Power Spectral ◽  
Fitting Method ◽  
Density Fitting ◽  
Asphalt Road ◽  
Surface Profiles

Measuring and analysing the roughness of agricultural field and road have great significance for studying the characteristics of tractor dynamic response. This study was designed to analyse and compare the roughness characteristics of agricultural field and asphalt road profiles. A profiling apparatus was developed to measure field and road surface profiles of parallel tracks. The profile measurements were conducted in a grass field, a corn stubble field, a harvested potato field and on an asphalt road. The root mean square value and two spectrum parameters of surface profiles were calculated and analysed to investigate the roughness characteristics of fields and asphalt road. The results of the study indicate that for the values of the agricultural field and asphalt road surface roughness, waviness and roughness index are both positive associated with the root mean square value. Most of the waviness values of all measured field profiles were less than 2 with the average of 1.8, while the waviness values of all measured asphalt road profiles were greater than 2 with the average of 2.08. The roughness of both field and asphalt road profiles can be distinguished by the power spectral density fitting method. However, it has better performance in characterizing asphalt road profiles than characterizing field profiles with the power spectral density fitting method.

About rms amplitude of zero oscillations of atoms in crystal

2021 ◽  
pp. 48-53
Author(s):  
I. Khidirov ◽  
◽  
S.Dj. Rakhmanov ◽  
Sh.A. Makhmudov ◽  
◽  
...  
Keyword(s):  
Debye Temperature ◽  
Root Mean Square ◽  
Room Temperature ◽  
Materials Science ◽  
Ordinal Number ◽  
Zero Point ◽  
Mean Square ◽  
Root Mean Square Amplitude ◽  
Thermal Vibrations ◽  
Rms Amplitude

It is shown that the values of the energy and amplitude of zero-point vibrations of atoms in a crystal, due to the uncertainty principle, depend on the dynamic characteristics of atoms in the crystal. It was found that the root-mean-square amplitude of thermal and zero-point vibrations of atoms, like other properties, has a periodic dependence on the ordinal number of elements in the Mendeleev's Periodic Table. It is shown that the value of the root-mean-square amplitude of thermal vibrations of atoms in a lattice of elements with a high value of the Debye temperature at room temperature does not differ much from the value of the amplitude of zero-point vibrations of atoms (at T = 0 K). This is explained by the small number of excited vibrations with the maximum frequency in these crystals at room temperature, since the room temperature is much lower than their Debye temperature, at which the entire spectrum of thermal vibrations of atoms in the crystal is excited. The results can be used in materials science and technology to assess the strength and thermo physical characteristics of materials at cryogenic temperatures, without resorting to measuring them directly at absolute zero.

A NEW METHOD FOR CALCULATING THE FRACTAL DIMENSION OF SURFACE TOPOGRAPHY

Fractals ◽  
2015 ◽  
Vol 23 (03) ◽  
pp. 1550022 ◽  
Author(s):  
XUE ZUO ◽  
HUA ZHU ◽  
YUANKAI ZHOU ◽  
YAN LI
Keyword(s):  
Fractal Dimension ◽  
Root Mean Square ◽  
Relative Error ◽  
New Method ◽  
Variation Method ◽  
Triangular Prism ◽  
Mean Square ◽  
Fractal Function ◽  
Fractal Curve ◽  
Machined Surfaces

A new method termed as three-dimensional root-mean-square (3D-RMS) method, is proposed to calculate the fractal dimension (FD) of machined surfaces. The measure of this method is the root-mean-square value of surface data, and the scale is the side length of square in the projection plane. In order to evaluate the calculation accuracy of the proposed method, the isotropic surfaces with deterministic FD are generated based on the fractional Brownian function and Weierstrass–Mandelbrot (WM) fractal function, and two kinds of anisotropic surfaces are generated by stretching or rotating a WM fractal curve. Their FDs are estimated by the proposed method, as well as differential boxing-counting (DBC) method, triangular prism surface area (TPSA) method and variation method (VM). The results show that the 3D-RMS method performs better than the other methods with a lower relative error for both isotropic and anisotropic surfaces, especially for the surfaces with dimensions higher than 2.5, since the relative error between the estimated value and its theoretical value decreases with theoretical FD. Finally, the electrodeposited surface, end-turning surface and grinding surface are chosen as examples to illustrate the application of 3D-RMS method on the real machined surfaces. This method gives a new way to accurately calculate the FD from the surface topographic data.

A mechanical-reflex oscillator hypothesis for parkinsonian hand tremor

1976 ◽  
Vol 40 (6) ◽  
pp. 990-998 ◽  
Author(s):  
R. N. Stiles ◽  
R. S. Pozos
Keyword(s):  
Spectral Analysis ◽  
Steady State ◽  
Root Mean Square ◽  
Fold Increase ◽  
Normal Subjects ◽  
Single Frequency ◽  
Mean Square ◽  
Hand Tremor ◽  
S Period ◽  
Rms Amplitude

Spectral analysis was performed on postural hand tremor records obtained from 22 parkinsonian subjects. Of these 22 subjects, 18 had postural hand tremor that occurred primarily at a single frequency during any one 16-s period. In general, this tremor occurred at different steady-state frequencies (each calculated over 16 s) between about 4 Hz and 8–9 Hz. This frequency decreased approximately 1 Hz for each 10-fold increase in displacement amplitude (root-mean-square, rms, amplitude determined at 16 cm from the wrist), decreasing from 8–9 Hz at about 30 mum to 3.75–4.0 Hz at about 30,000 mum. The major finding was that the frequency of parkinsonian hand tremor was nearly the same as that for hand tremor from normal subjects when these frequenceis were compared at similar rms displacement levels. This comparison, plus a comparison between other aspects of these two kinds of tremor, indicate that the mechanism for parkinsonian hand tremor is similar to that for large-displacement (greater than 100 mum) hand tremor of normal subjects, i.e., a mechanical-reflex oscillator mechanism.

A New Adaptive-Surface Elastic-Plastic Contact Model of Rough Surfaces: Parameter Correlations

2006 ◽  
Vol 532-533 ◽  
pp. 961-964
Author(s):  
Min Song
Keyword(s):  
Rough Surface ◽  
Root Mean Square ◽  
Rough Surfaces ◽  
Computing Time ◽  
Contact Model ◽  
Asperity Contact ◽  
Mean Square ◽  
Elastic Plastic ◽  
Surface Profiles

Based on an presented adaptive-surface elastic-plastic asperity contact model which can greatly decrease contact computing time and keep the precision loss less than 5%, a series of 2-D rough surface profiles with different roughness and correlative length are numerically generated to investigate how to select the threshold used in this model for different adaptive rough surfaces. The results show that well acceptable precision of the elastic-plastic contact calculation would be derived when the ratio of threshold to root mean square curvature, δ 1.0 10 6mm2 − < × .

On Pastewka and Robbins' Criterion for Macroscopic Adhesion of Rough Surfaces

2016 ◽  
Vol 139 (3) ◽  
Author(s):  
M. Ciavarella
Keyword(s):  
Rough Surface ◽  
Root Mean Square ◽  
Rough Surfaces ◽  
Mean Square ◽  
Asperity Model ◽  
Factor Α ◽  
Rms Amplitude ◽  
Simplified Form

Pastewka and Robbins (2014, “Contact Between Rough Surfaces and a Criterion for Macroscopic Adhesion,” Proc. Natl. Acad. Sci., 111(9), pp. 3298–3303) recently have proposed a criterion to distinguish when two surfaces will stick together or not and suggested that it shows quantitative and qualitative large conflicts with asperity theories. However, a comparison with asperity theories is not really attempted, except in pull-off data which show finite pull-off values in cases where both their own criterion and an asperity based one seem to suggest nonstickiness, and the results are in these respects inconclusive. Here, we find that their criterion corresponds very closely to an asperity model one (provided we use their very simplified form of the Derjaguin–Muller–Toporov (DMT) adhesion regime which introduces a dependence on the range of attractive forces) when bandwidth α is small, but otherwise involves a root-mean-square (RMS) amplitude of roughness reduced by a factor α. Therefore, it implies that the stickiness of any rough surface is the same as that of the surface where practically all the wavelength components of roughness are removed except the very fine ones.

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