Thresholding Methods for Reduction in Data Processing Errors in the Laser-Textured Surface Topography Measurements
Abstract
:1. Introduction
2. Materials and Methods
2.1. Analysed Details
2.2. Measurement Process
2.3. Applied Methods
3. Results
3.1. Detection of the High-Frequency Errors from the Results of LST Measurement with a Thresholding Approach
3.2. Comparison of Regular Filters for High-Frequency Measurement Noise Removal
3.3. Improving the Procedures for High-Frequency Measurement Noise Suppressions with Analysis of Modelled Data
4. The Outlook
- The effect of size and density of features was not considered for the LST details. In one of the previous studies by the author of this paper, various surface textures were considered with this issue; nevertheless, those with laser texturing were not comprehensively studied. From that issue, the effect of surface topography features sizes and their densities on the process of detection and, respectively, reduction in high-frequency noise should be widely considered;
- The accuracy of the detection process of high-frequency measurement errors can be strongly affected by the amplitude of the noise. Therefore, some improvements to the proposed approaches must be included with different noise amplitude, which was not analysed in the current paper;
- The correlation between the amplitude of the high-frequency noise and the height (amplitude) of the analysed detail were also not studied against their influence on the process of noise detection, and correspondingly, reduction. From that point of view, each of the filters, like Gaussian (regular and with robust performance), spline or fast Fourier, can give different results and their validity can be also discussed;
- Moreover, the influence of amplitude on the high-frequency measurement noise on the results of proposed techniques was not considered in this paper. Furthermore, the effect of the amplitude of high-frequency noise on the results of considered filters application, and also on the results of the calculation of the surface topography parameters (e.g., those from the ISO 25178 standard) must be studied to provide more surface functional advantages.
5. Conclusions
- In the process of detection of the high-frequency measurement noise from the results of laser-textured surface measurements, profile (2D) characteristics may be more convenient than those of areal (3D); however, each of the measured details must be treated individually;
- The application of PSD characteristics may be valuable in high-frequency noise detection; nevertheless, other methods, like ACF or TD, can be required. The most encouraging technique should be based on a few characteristics, using the PSD, ACF and TD approaches simultaneously;
- When detection of the high-frequency measurement noise is hampered by the occurrence of the deep/wide features, like treatment traces in the LST details, the application of the thresholding method can provide positive results. When the surface contains deep or wide features or their density is relatively large, the thresholding technique removes those features from analysed detail (profile or areal data). Application of this method with all of the commonly used algorithms, (e.g., PSD and ACF) can give more accurate responses about the presence of high-frequency noise when a thresholding method is applied;
- Of four general, regular filters available in the commercial software that consider Gaussian methods (regular regression or robust modifications), spline or fast Fourier approaches, this last one can be classified as the most suitable for the reduction in the influence of the high-frequency measurement noise on the results of surface topography measurements. However, suitable application of digital filtering requires careful use so that inappropriately used algorithms can remove necessary data from those raw measurements;
- Generally, the functions available in the commercial software (like PSD, ACF, TD or GF, RGF, SF and FFTF) can be suitable in the process of detection and, correspondingly, reduction in the high-frequency measurement errors from the laser-textures topographies; nevertheless, the minimising of data processing errors must be classified as a required issue.
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Parameters and Abbreviations
ACF | autocorrelation function |
FFTF | Fast Fourier Transform Filter |
GF | Gaussian filter |
HFN | high-frequency noise |
L-surface | long-wavelength surface |
LST | laser surface texturing |
NS | noise surface |
PSD | power spectral density |
RGF | robust Gaussian filter |
S-filter | removes small-scale lateral components |
SF | spline filter |
Sa | arithmetic mean height Sa, µm |
Sal | auto-correlation length, mm |
Sbi | surface bearing index |
Sci | core fluid retention index |
Sdq | root mean square gradient |
Sdr | developed interfacial areal ratio, % |
Sk | core roughness depth, µm |
Sku | kurtosis |
Sp | maximum peak height, µm |
Spc | arithmetic mean peak curvature, 1/mm |
Spd | peak density, 1/mm2 |
Spk | reduced summit height, µm |
Sq | root mean square height, µm |
Ssk | skewness |
Std | texture direction, ° |
Str | texture parameter |
Sv | maximum valley depth, µm |
Svi | valley fluid retention index |
Svk | reduced valley depth, µm |
Sz | the maximum height of surface, µm |
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30° LST Detail Analysis after an HFN Removal by Various Methods | ||||||
---|---|---|---|---|---|---|
Start Data | Noise Data | GF | RGF | SF | FFTF | |
Sq, µm | 9.32 | 9.36 | 9.07 | 8.81 | 9.27 | 9.27 |
Ssk | −2.65 | −2.61 | −2.71 | −2.91 | −2.67 | −2.66 |
Sku | 11.5 | 11.3 | 11.6 | 12.2 | 11.6 | 11.5 |
Sp, µm | 33.5 | 34.2 | 30.4 | 28.4 | 31.8 | 32.1 |
Sv, µm | 51.8 | 54.6 | 51.4 | 51 | 52.6 | 53.1 |
Sz, µm | 85.3 | 88.8 | 81.7 | 79.4 | 84.3 | 85.2 |
Sa, µm | 5.49 | 5.52 | 5.33 | 5.23 | 5.45 | 5.45 |
Sal, mm | 0.0563 | 0.0563 | 0.0585 | 0.0629 | 0.0576 | 0.0563 |
Str | 0.661 | 0.661 | 0.68 | 0.733 | 0.677 | 0.661 |
Std, ° | 90 | 90 | 90 | 90 | 90 | 90 |
Sdq | 0.465 | 0.614 | 0.39 | 0.387 | 0.419 | 0.427 |
Sdr, % | 9.72 | 17.2 | 7 | 6.64 | 8 | 8.29 |
Spd, 1/mm2 | 12.3 | 16.5 | 10.3 | 9.57 | 11.2 | 11.6 |
Spc, 1/mm | 0.211 | 0.306 | 0.0816 | 0.0927 | 0.096 | 0.0895 |
Sk, µm | 2.88 | 3.52 | 2.92 | 3.06 | 3.09 | 3.3 |
Spk, µm | 8.93 | 8.4 | 8.38 | 7.1 | 8.84 | 8.99 |
Svk, µm | 23.4 | 24.6 | 22.8 | 22.5 | 23.5 | 24.4 |
Sbi | 0.354 | 0.346 | 0.384 | 0.398 | 0.375 | 0.37 |
Sci | 0.494 | 0.495 | 0.468 | 0.424 | 0.484 | 0.48 |
Svi | 0.304 | 0.296 | 0.301 | 0.306 | 0.301 | 0.301 |
60° LST Detail Analysis after an HFN Removal by Various Methods | ||||||
---|---|---|---|---|---|---|
Start Data | Noise Data | GF | RGF | SF | FFTF | |
Sq, µm | 9.55 | 9.56 | 9.28 | 9.09 | 9.5 | 9.5 |
Ssk | −2.6 | −2.59 | −2.66 | −2.95 | −2.62 | −2.62 |
Sku | 11.1 | 11 | 11.2 | 12.3 | 11.1 | 11.1 |
Sp, µm | 31.7 | 32.3 | 27.5 | 24.2 | 29.7 | 29.4 |
Sv, µm | 51 | 52.2 | 49.9 | 50 | 51.3 | 53.7 |
Sz, µm | 82.8 | 84.5 | 77.3 | 74.2 | 81 | 83.1 |
Sa, µm | 5.71 | 5.71 | 5.53 | 5.36 | 5.66 | 5.67 |
Sal, mm | 0.0591 | 0.0591 | 0.0613 | 0.0658 | 0.0591 | 0.0591 |
Str | 0.691 | 0.692 | 0.711 | 0.78 | 0.691 | 0.691 |
Std, ° | 30.7 | 30.7 | 30.5 | 176 | 30.5 | 30.5 |
Sdq | 0.455 | 0.508 | 0.387 | 0.432 | 0.421 | 0.435 |
Sdr, % | 9.44 | 11.9 | 6.99 | 7.96 | 8.2 | 8.67 |
Spd, 1/mm2 | 12.1 | 14.2 | 10.7 | 10.4 | 11.5 | 12.6 |
Spc, 1/mm | 0.196 | 0.238 | 0.0695 | 0.0915 | 0.0873 | 0.09 |
Sk, µm | 2.22 | 2.62 | 2.24 | 2.11 | 2.35 | 2.41 |
Spk, µm | 8.46 | 8.37 | 8.2 | 5.86 | 8.38 | 8.31 |
Svk, µm | 26.3 | 26.6 | 25 | 25 | 26 | 26.4 |
Sbi | 0.394 | 0.385 | 0.455 | 0.512 | 0.427 | 0.43 |
Sci | 0.508 | 0.509 | 0.486 | 0.423 | 0.505 | 0.499 |
Svi | 0.305 | 0.302 | 0.3 | 0.306 | 0.302 | 0.303 |
120° LST Detail Analysis after an HFN Removal by Various Methods | ||||||
---|---|---|---|---|---|---|
Start Data | Noise Data | GF | RGF | SF | FFTF | |
Sq, µm | 9.65 | 9.67 | 9.38 | 9.2 | 9.6 | 9.6 |
Ssk | −2.59 | −2.58 | −2.65 | −2.92 | −2.61 | −2.6 |
Sku | 10.9 | 10.9 | 11 | 12 | 11 | 11 |
Sp, µm | 31.7 | 31.4 | 27.6 | 24.4 | 29.6 | 29.5 |
Sv, µm | 51 | 52.2 | 49.7 | 50 | 51.3 | 53.2 |
Sz, µm | 82.6 | 83.7 | 77.3 | 74.4 | 80.9 | 82.8 |
Sa, µm | 5.79 | 5.8 | 5.62 | 5.46 | 5.75 | 5.76 |
Sal, mm | 0.0601 | 0.0601 | 0.0623 | 0.0662 | 0.0604 | 0.0601 |
Str | 0.689 | 0.689 | 0.71 | 0.765 | 0.692 | 0.689 |
Std, ° | 149 | 149 | 149 | 149 | 149 | 149 |
Sdq | 0.458 | 0.511 | 0.39 | 0.438 | 0.424 | 0.438 |
Sdr, % | 9.61 | 12 | 7.11 | 8.17 | 8.36 | 8.82 |
Spd, 1/mm2 | 12 | 14.1 | 10.2 | 9.91 | 11.5 | 12 |
Spc, 1/mm | 0.189 | 0.234 | 0.0702 | 0.0776 | 0.0882 | 0.0929 |
Sk, µm | 2.23 | 2.79 | 2.26 | 2.2 | 2.36 | 2.57 |
Spk, µm | 8.32 | 8.67 | 7.88 | 6.02 | 8.31 | 8.63 |
Svk, µm | 25.4 | 26.2 | 24.3 | 24.4 | 25.2 | 25.9 |
Sbi | 0.401 | 0.405 | 0.46 | 0.517 | 0.435 | 0.435 |
Sci | 0.506 | 0.506 | 0.486 | 0.425 | 0.503 | 0.497 |
Svi | 0.307 | 0.304 | 0.302 | 0.308 | 0.304 | 0.304 |
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Podulka, P. Thresholding Methods for Reduction in Data Processing Errors in the Laser-Textured Surface Topography Measurements. Materials 2022, 15, 5137. https://doi.org/10.3390/ma15155137
Podulka P. Thresholding Methods for Reduction in Data Processing Errors in the Laser-Textured Surface Topography Measurements. Materials. 2022; 15(15):5137. https://doi.org/10.3390/ma15155137
Chicago/Turabian StylePodulka, Przemysław. 2022. "Thresholding Methods for Reduction in Data Processing Errors in the Laser-Textured Surface Topography Measurements" Materials 15, no. 15: 5137. https://doi.org/10.3390/ma15155137