A Statistical Approach for Characterizing the Behaviour of Roughness Parameters Measured by a Multi-Physics Instrument on Ground Surface Topographies: Four Novel Indicators
Abstract
:1. Introduction
1.1. State of the Art
1.1.1. Sources of Roughness Parameter Fluctuations
Surface/Instrument Interaction
Measurement Conditions
Environment
1.2. Uncertainty Determination
1.2.1. Classical Methods
1.2.2. Unconventional Methods
(X, Y) Method
Allan Deviation
Coefficient Determination Method
Index Method
1.2.3. Uncertainty Methods Comparison
1.3. Objectives
2. Materials and Methods
2.1. Surface Description
2.2. Measurement Description
2.2.1. Instrument Settings
2.2.2. Measurement Strategy
2.3. Data Post-Processing
2.3.1. Topography Processing
2.3.2. Index Computation
- RP: roughness parameter
- M: instrument mode
- G: grit level
- n: number of QI values
- (Degree of Freedom)
- : number of histogram classes
- : number of Johnson SU model parameters
2.3.3. Roughness Parameter Ranking Method
- Sev: severity rate of a given roughness parameter for a specific measurement mode and grit level;
- Rk: ranking position of the roughness parameter (increasing order);
- Rk−1: ranking position of the roughness parameter (decreasing order);
- Mean_Q: mean value of QI for a given roughness parameter in a specific measurement mode and grit level;
- Homo_Q: homogeneity value of QI for a given roughness parameter in a specific measurement mode and grit level;
- NBmode: number of modes of QI PDF for a given roughness parameter in a specific measurement mode and grit level;
- NP: total number of parameters (in this case, 50).
2.3.4. Summary of the Methodology
3. Results and Discussion
3.1. Novel Indicators Applied on the Sa Parameter
3.2. Example of Cases of QI Regarding the Homogeneity of PDF, the Number of PDF Modes, and the Percentage of Outliers with and Without Outliers
3.3. Ranking of Roughness Parameters
- The Sal parameter (Texture Aspect Ratio of the Surface) represents the autocorrelation length of the map, expressed in wavelength. A higher Sal value indicates a greater presence of long wavelengths within the map. The Sal parameter is a key metric used to evaluate the anisotropy of a surface, meaning the directional properties of the patterns or textures present. It is especially useful for surfaces with regular, oriented textures, such as those produced by grinding or lapping processes, which often result in surfaces with distinct striations. For striated surfaces, such as those produced by grinding or polishing, Sal is directly related to the width and spacing of the striations. A surface with wide, well-aligned striations will have a low Sal, indicating strong directionality. Conversely, if the striations are irregular or vary significantly in spacing, Sal may increase, reflecting a loss of clear directional alignment. A high Mean_Q value for Sal indicates low dispersion of the Sal values across the iteration series, highlighting broad topographic characterization. For the manufactured surfaces (#080 and #120), this suggests that the long wavelengths (wide cutting scratches on the surface) are dependent on the different measured areas, as evidenced by the wide variation in the Sal values. This shows that Sal is a good parameter for distinguishing between different measured areas, particularly regarding the noise observed in the iteration series. However, it is important to note that while variations in wavelength are averaged when calculating Sal, small variations within an iteration series may have minimal impact on the Sal values. Additionally, Homo_Q of Sal is not particularly strong, indicating that the QI ratio, which includes both intra-position and inter-position standard deviations of Sal, may not be stable across different measured areas or throughout the iteration series (i.e., zone-dependent or time-dependent). One of the main limitations of Sal in this context is its sensitivity to the size of the striations. If certain striations are significantly larger or more irregular than the rest, they can disproportionately influence the Sal value, even if they are few in number. This creates a high statistical variability, making it difficult to obtain a representative measure of the surface’s anisotropy. To address this variability, a highly effective approach is to increase the measurement area using a technique called stitching. This involves combining multiple local measurements of a surface into a single, larger image. Stitching allows for the measurement of a greater portion of the surface, thus incorporating more striations into the analysis. By increasing the number of measured striations, the influence of rare, large, or irregular striations is minimized, leading to more reliable and representative Sal measurements.
- The Sz parameter represents the maximum height of the surface, defined as the difference between the highest peak and the deepest valley. The Sz ranking is reliable, characterized by low Homo_Q and NBmode values, which suggest a well-defined histogram and a satisfactory Mean_Q value. Due to differences in topographic features between measured areas, a high dispersion in Sz can be observed. When taking multiple measurements at different locations on a randomly textured surface, such as a ground surface, the Sz parameter can exhibit significant variability, which is closely related to the extreme value theory. Since Sz is based on the highest peaks and lowest valleys of the surface, its value is highly sensitive to local irregularities, such as isolated large peaks or deep pits. On a surface with random striations and irregular features, different measurement locations may capture different extreme values, leading to a wide range of Sz results. This variability arises because each measurement could include a rare or extreme feature that disproportionately impacts the Sz value, despite the overall surface texture being relatively homogeneous. According to extreme value theory, which models the behaviour of maxima or minima in random systems, such extreme events are expected to occur infrequently, but can significantly influence the outcome when they do. This results in Sz being a less reliable indicator of the overall surface roughness, as it is heavily influenced by outliers rather than reflecting the typical texture of the surface. To address this variability, larger measurement areas using stitching techniques can be employed to capture a more representative sample of the surface, averaging out extreme features. Extreme value theory can also be applied to better understand and model the behaviour of these extreme surface features, particularly when they are critical to the application [59]. However, the ratio between topographic representativeness and noise in the iteration series is sufficient to consider Sz as a qualitative parameter, particularly given the robustness of the QI for every measured area and iteration series, as reflected in the well-formed histogram.
- The Sci parameter is a roughness index that indicates core fluid retention, calculated as the ratio of void volume in the core zone (from 5% to 80%) to the RMS deviation (Sq). Mean_Q of Sci is generally good, outperforming Sz, due to high deviations between different measured areas, which is linked to variations in the surface topographies. NBmode of Sci, while higher than Sz and Sal, is still acceptable. This could be attributed to the presence of high peaks on the surface (groove pile-up) or third core inclusions, as the calculation of Sci is highly sensitive due to its 5% threshold, leading to multiple modes in QI PDF. Nevertheless, Homo_Q remains favourable for this parameter, indicating that the NBmode values are closely aligned or nearly merged.
- The Sdq parameter represents the RMS slope of the surfaces, and appears to be unreliable in terms of ranking. However, it is noteworthy because it indicates that slope is a highly sensitive feature of the surface [49]. Despite this, Sdq performs well in terms of QI, showing that the ratio of deviation between topographical representativeness and noise in the iteration series is generally good. However, the high NBmode and Homo_Q values suggest a local instability in Sdq QI values, both intra- and inter-position. This instability likely stems from the physical limitations of the instruments in measuring slope. When the slope limit is reached, some pixels may be measured inconsistently, leading to variable pixel quality [60]. The Sdq parameter is crucial for assessing surface roughness, but it is highly sensitive to the sampling interval used in measurements. This sensitivity arises because the Sdq calculation involves the derivative of the surface profile, and any noise present in the data can significantly affect the accuracy of this derivative. A larger sampling interval can amplify noise during derivative calculations, as it may exaggerate random fluctuations in the data, leading to less accurate Sdq values. Conversely, a smaller sampling interval can enhance precision by capturing more detailed surface features, but it increases the data volume and may also magnify the effects of measurement noise. To mitigate these issues, several approaches can be employed. Data filtering techniques, such as the Gaussian filter [61], can smooth the data before derivative calculation, reducing noise while preserving essential surface characteristics. Choosing an optimal sampling interval is essential to balance detail and noise effects. Advanced interpolation methods, like B-spline interpolation, can offer more robust performance against noise compared to Lagrange interpolation by providing a smoother approximation of the surface profile. Our sensitivity analysis can help us to understand how variations in the sampling interval impact Sdq calculations, allowing for adjustments in measurement and processing methods, to ensure reliability. By addressing our indexes and implementing appropriate solutions, the accuracy and reliability of Sdq measurements can be significantly improved.
- The S5p parameter belongs to the segmentation-based family. The Wolf pruning algorithm is used to eliminate insignificant motifs by merging smaller ones into larger ones. This parameter is sensitive to the calculation method, representing the average height of the five peaks with the highest global peak height within the defined area. As a result, the area computation is sensitive to small variations in surface iteration series, as evidenced by a poor ranking of Mean_Q for this parameter. Furthermore, the high NBmode and Homo_Q values indicate instability in the inter- and intra-position standard deviations of S5p values. The variability in segmentation caused by noise means that the significant peaks identified may differ from one measurement to another, complicating the consistency and accuracy of the results. To mitigate this issue, several approaches can be employed. Data filtering techniques, such as filters [62], can smooth out the noise before segmentation, helping to preserve essential surface characteristics while reducing random fluctuations. Performing repeated measurements at the same location and obtained a mean map can also help to lessen the impact of noise and provide a more stable assessment of surface quality [50]. Advanced pruning methods, like the Wolf pruning threshold, can be used to refine the results by focusing on the most significant data points and minimizing the influence of noise. By addressing the challenges of noise and segmentation variability through these strategies, it is possible to achieve more reliable and accurate evaluations of surface quality.
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Optical and Geometrical Surface Properties, with Regard to Instrument Performance
Type | Properties | Definition | Instrument Performance | ||
---|---|---|---|---|---|
CSI (VSI) | CM (Field) | FV | |||
Rating | Rating | Rating | |||
Optical | Reflection (mirror, polished surfaces) | Light reflected by the surface | +++ [63,64,65,66,67] | ++ [68] | + |
Absorption (carbon layers, copper oxides, black ceramics, textured silicon) | Ability to absorb certain wavelengths | + | + | + | |
Transmission (optical lens, transparent layer) | Ability of light to pass through the surface | +++ [21,22,60,69] | ++ [70,71] | + [70] | |
Geometrical | Flatness | Degree from which a surface deviates from a perfectly flat plane | +++ [22,72] | + | + |
Form | Degree from which a surface bends or curves | ++ [21,73,74] | ++ [20] | +++ [75] | |
Local slopes | Local gradient of surfaces | ++ [60,71,73,76,77] | +++ [78,79,80] | + [81] | |
Texture (skin, grounding, turning, textile) | Including roughness, waviness, and pattern directionality | ++ [16,81,82,83,84] | ++ [81,85,86] | +++ [18,81,85,87] |
- Optical properties:
- o
- Reflection: the light is either reflected in a single direction, like a mirror, or diffusely reflected in many directions, as seen on matte surfaces. The FV instruments are more sensitive to reflection than the CSI and CM instruments, due to the focusing criteria (maximum contrast gradient between a pixel and its neighbours). It is explained in [65] that the mirror measurement by conventional FV is not possible.
- o
- Absorption: this is the ability of a surface to absorb certain or all light wavelengths, converting light energy into heat or other forms of energy. In other words, this type of surface could be assimilated as a light trap. CSI is very sensitive to absorption because the interferogram is based on the succession of fringes and their intensities, while FV takes into account the contrast gradient between one pixel and its neighbours, and CM cannot measure without light projection on the CCD sensor through the pin hole. In this case, Atomic Force Microscopy (AFM) is commonly chosen, as in [88,89], but the measurement size and Z-amplitude are limited.
- o
- Transmission: this refers to the capability of the light to pass through a surface. Transmission is high for transparent materials and almost zero for opaque materials. CSI is not too sensitive if a good scanning range is set [21], because it uses constructive or destructive waviness [22]. As CM uses the principle of laser focalization, it can be disturbed due to the upper and lower interface, as explained in [70]. The FV, based on contrast criteria, cannot be used, due to the lack of contrast gradient on the surface, as explained in [70]. CSI could be slightly better than CM because noise could appear for CM, but CM is able to detect a change in the refractive index.
- Geometrical properties:
- o
- Flatness: this is the degree to which the concerned surface deviates from a perfect plane. The majority of work with measurements on flat surfaces is performed with CSI or AFM instruments. CSI instruments, particularly Phase Shifting Interferometry (PSI) instruments, are preferred for flat surfaces, though Vertical Scanning Interferometry (VSI) can also measure flatness, but not with the same precision.
- o
- Form: this refers to the global shape of the specimen. Measuring form requires a high Z-scanning range, and in most cases, instruments using Focus Variation are predisposed to measure this type of surface. CSI instruments, especially VSI, are also capable of measuring form, but not on as large a scale as FV [21], and the scale for CSI is more relative to low-frequency waviness in this case. The Numerical Aperture (NA) is an important criterion for form measurement with CSI. CM instruments have comparable performance to CSI for form measurement, but like CSI, CM instruments have errors that increase with the specimen slope [20,74].
- o
- Local slope: this refers to the local variation in relief in surface morphology, calculated from heights on measured topographic maps between two pixels, commonly called the surface gradient. According to the literature, the most effective instrument for measuring the local slope is the CM instrument, due to its small lateral resolution. CSI instruments can also be effective, but the numerical aperture of the lenses may limit acquisition and cause measurement errors, due to a lack of signal. FV instruments are generally not capable of measuring the local slope because of the smoothing effect inherent in this technology [81].
- o
- Texture: this refers to the entirety of surface features, representing the global definition of surface characteristics. Surface texture can be seen as a summary of the geometrical properties of the surface. The ISO 25178-2 [58] defines a set of scale-limited features in three class: areal (hill and dale), line (courses line and ridge line) and point (peak, pit and saddle point). This classification is usually used in surface segmentation analysis. Generally, all instruments with sufficient magnification or stitching area can measure surface texture, but with their own abilities. FV instruments are more suited for this task, due to their capability of handling high roughness levels (when surface texture is high), and because a global surface description is often required. CM and CSI instruments can be limited in this application at equivalent magnification because a large field of view (FOV) is required for texture measurement, but they can also provide complementary information when small-scale textures need to be measured. Some textures, such as skin surfaces, can pose challenges due to light traps, especially with CSI instruments. Textures that include step-like features can also be problematic for CM and CSI instruments as they may lead to overestimation around the measured features.
Appendix B. Features of the Raw Surfaces
Appendix C. The Outlier Detection Method
- : Interquartile Range
Appendix D. Overview of Indicator Comparison with and Without Outliers for the Given Instrument Modes and Grits
Appendix D.1. Focus Variation (FV), Grit #080
Appendix D.2. Focus Variation (FV), Grit #120
Appendix D.3. Confocal Microscope (CM), Grit #080
Appendix D.4. Confocal Microscope (CM), Grit #120
Appendix D.5. Coherence Scanning Interferometry (CSI), Grit #080
Appendix D.6. Coherence Scanning Interferometry (CSI), Grit #120
Appendix D.7. Conclusion of Roughness Parameter Ranking
Appendix E. Overview of Roughness Parameters Ranking, Regarding the Severity Rate
References
- Sun, H.; Wu, X.; Wang, D.; Xu, H.; Liang, X.; Shang, Y. Analysis of Deformation Mechanism of Landslide in Complex Geological Conditions. Bull. Eng. Geol. Environ. 2019, 78, 4311–4323. [Google Scholar] [CrossRef]
- Whitehouse, D.J. Surface Geometry, Miniaturization and Metrology. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2012, 370, 4042–4065. [Google Scholar] [CrossRef]
- Zhao, J.; Tang, J.; Ding, H.; Shao, W.; Zhao, X.; Liu, H.; Jiang, T. A Numerical and Experimental Investigation on the Evolution of Three-Dimensional Surface Topography of 12Cr2Ni4A Steel in Shot Peening. J. Manuf. Process. 2021, 70, 259–270. [Google Scholar] [CrossRef]
- Robbins, S.J.; Watters, W.A.; Chappelow, J.E.; Bray, V.J.; Daubar, I.J.; Craddock, R.A.; Beyer, R.A.; Landis, M.; Ostrach, L.R.; Tornabene, L.; et al. Measuring Impact Crater Depth throughout the Solar System. Meteorit. Planet. Sci. 2018, 53, 583–637. [Google Scholar] [CrossRef]
- Jacobs, T.D.B.; Pastewka, L. Guest Editors Surface Topography as a Material Parameter. MRS Bull. 2022, 47, 1205–1210. [Google Scholar] [CrossRef] [PubMed]
- Ardi, D.T.; Li, Y.G.; Chan, K.H.K.; Blunt, L.; Bache, M.R. Surface Topography and the Impact on Fatigue Performance. Surf. Topogr. Metrol. Prop. 2015, 3, 015007. [Google Scholar] [CrossRef]
- Bagno, A.; Di Bello, C. Surface Treatments and Roughness Properties of Ti-Based Biomaterials. J. Mater. Sci. Mater. Med. 2004, 15, 935–949. [Google Scholar] [CrossRef]
- Bruzzone, A.A.G.; Costa, H.L.; Lonardo, P.M.; Lucca, D.A. Advances in Engineered Surfaces for Functional Performance. CIRP Ann. 2008, 57, 750–769. [Google Scholar] [CrossRef]
- Jiang, X.J.; Whitehouse, D.J. Technological Shifts in Surface Metrology. CIRP Ann. 2012, 61, 815–836. [Google Scholar] [CrossRef]
- Brown, C.A. Surface Metrology Principles for Snow and Ice Friction Studies. Front. Mech. Eng. 2021, 7, 753906. [Google Scholar] [CrossRef]
- Larsen-Badse, J. Influence of Grit Diameter and Specimen Size on Wear during Sliding Abrasion. Wear 1968, 12, 35–53. [Google Scholar] [CrossRef]
- Liu, H.; Liu, H.; Zhu, C.; Parker, R.G. Effects of Lubrication on Gear Performance: A Review. Mech. Mach. Theory 2020, 145, 103701. [Google Scholar] [CrossRef]
- Zhang, H.; Goltsberg, R.; Etsion, I. Modeling Adhesive Wear in Asperity and Rough Surface Contacts: A Review. Materials 2022, 15, 6855. [Google Scholar] [CrossRef]
- Stewart, S.; Ahmed, R. Rolling Contact Fatigue of Surface Coatings—A Review. Wear 2002, 253, 1132–1144. [Google Scholar] [CrossRef]
- Wood, R.J.K.; Lu, P. Coatings and Surface Modification of Alloys for Tribo-Corrosion Applications. Coatings 2024, 14, 99. [Google Scholar] [CrossRef]
- Kumar, S.S.; Hiremath, S.S. Effect of Surface Roughness and Surface Topography on Wettability of Machined Biomaterials Using Flexible Viscoelastic Polymer Abrasive Media. Surf. Topogr. Metrol. Prop. 2019, 7, 015004. [Google Scholar] [CrossRef]
- Królczyk, G.; Kacalak, W.; Wieczorowski, M. 3D Parametric and Nonparametric Description of Surface Topography in Manufacturing Processes. Materials 2021, 14, 1987. [Google Scholar] [CrossRef]
- Kapłonek, W.; Nadolny, K.; Królczyk, G.M. The Use of Focus-Variation Microscopy for the Assessment of Active Surfaces of a New Generation of Coated Abrasive Tools. Meas. Sci. Rev. 2016, 16, 42–53. [Google Scholar] [CrossRef]
- Grochalski, K.; Wieczorowski, M.; H’Roura, J.; Le Goic, G. The Optical Aspect of Errors in Measurements of Surface Asperities Using the Optical Profilometry Method. Front. Mech. Eng. 2020, 6, 12. [Google Scholar] [CrossRef]
- Balcon, M.; Carmignato, S.; Savio, E. Performance Verification of a Confocal Microscope for 3D Metrology Tasks. Qual. Access Success 2012, 13, 63–66. [Google Scholar]
- Schmit, J.; Reed, J.; Novak, E.; Gimzewski, J.K. Performance Advances in Interferometric Optical Profilers for Imaging and Testing. J. Opt. A Pure Appl. Opt. 2008, 10, 064001. [Google Scholar] [CrossRef]
- de Groot, P. Principles of Interference Microscopy for the Measurement of Surface Topography. Adv. Opt. Photon. 2015, 7, 1. [Google Scholar] [CrossRef]
- JCGM 200; International Vocabulary of Metrology–Basic and General Concepts and Associated Terms (VIM), 3rd ed. JCGM: Sèvres, France, 2012.
- Giusca, C.L.; Leach, R.K. Measurement Good Practice Guide No. 128: Calibration of the Metrological Characteristics of Imaging Confocal Microscopes (ICMs); NPL: Teddington, UK, 2012. [Google Scholar]
- Giusca, C.L.; Leach, R.K. Measurement Good Practice Guide No. 127: Calibration of the Metrological Characteristics of Coherence Scanning Interferometers (CSI) and Phase Shifting Interferometers (PSI); NPL: Teddington, UK, 2013. [Google Scholar]
- Podulka, P. The Effect of Surface Topography Feature Size Density and Distribution on the Results of a Data Processing and Parameters Calculation with a Comparison of Regular Methods. Materials 2021, 14, 4077. [Google Scholar] [CrossRef]
- Aurich, J.C.; Hasse, H. Component Surfaces: Manufacturing-Morphology-Property Relationships; Springer Series in Advanced Manufacturing; Springer International Publishing: Cham, Switzerland, 2024; ISBN 978-3-031-35574-5. [Google Scholar]
- Lemesle, J.; Guibert, R.; Bigerelle, M. A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap. Appl. Sci. 2023, 13, 857. [Google Scholar] [CrossRef]
- Giusca, C.L.; Leach, R.K. Calibration of the Scales of Areal Surface Topography Measuring Instruments: Part 3. Resolution. Meas. Sci. Technol. 2013, 24, 105010. [Google Scholar] [CrossRef]
- De Groot, P.J.; DiSciacca, J. Surface-Height Measurement Noise in Interference Microscopy; North Morris, M.B., Creath, K., Burke, J., Davies, A.D., Eds.; SPIE: Bellingham, WA, USA, 2018; p. 26. [Google Scholar]
- Saraç, Z.; Groß, R.; Richter, C.; Wiesner, B.; Häusler, G. Optimization of White Light Interferometry on Rough Surfaces Based on Error Analysis. Optik 2004, 115, 351–357. [Google Scholar] [CrossRef]
- VDI/VDE 2655; Optical Metrology of Microtopographies—Calibration of Interferometers and Interference Microscopes for form Measurement. VDI Ingenieure: Düsseldorf, Germany, 2020.
- JCGM 100; Evaluation of Measurement Data—Guide to the Expression of Uncertainty in Measurement. JCGM: Sèvres, France, 2008.
- JCGM 101; Guide to the Expression of Uncertainty in Measurement with Supplement 1, Evaluation of Measurement Data. JCGM: Sèvres, France, 2008.
- ISO 25178-600; Geometrical Product Specifications (GPS), Surface Texture: Areal Metrological Characteristics for Areal-Topography Measuring Methods. ISO: Geneva, Switzerland, 2019.
- ISO 25178-700; Geometrical Product Specifications (GPS) Surface Texture: Areal Calibration, Adjustment and Verification of Areal Topography Measuring Instruments. ISO: Geneva, Switzerland, 2022.
- Pavliček, P.; Hýbl, O. White-Light Interferometry on Rough Surfaces—Measurement Uncertainty Caused by Noise. Appl. Opt. 2012, 51, 465. [Google Scholar] [CrossRef] [PubMed]
- Pavliček, P.; Michálek, V. White-Light Interferometry—Envelope Detection by Hilbert Transform and Influence of Noise. Opt. Lasers Eng. 2012, 50, 1063–1068. [Google Scholar] [CrossRef]
- Hering, M.; Körner, K.; Jähne, B. Correlated Speckle Noise in White-Light Interferometry: Theoretical Analysis of Measurement Uncertainty. Appl. Opt. 2009, 48, 525. [Google Scholar] [CrossRef]
- Giusca, C.L.; Leach, R.K.; Helary, F.; Gutauskas, T.; Nimishakavi, L. Calibration of the Scales of Areal Surface Topography-Measuring Instruments: Part 1. Measurement Noise and Residual Flatness. Meas. Sci. Technol. 2012, 23, 035008. [Google Scholar] [CrossRef]
- Giusca, C.L.; Leach, R.K.; Helery, F. Calibration of the Scales of Areal Surface Topography Measuring Instruments: Part 2. Amplification, Linearity and Squareness. Meas. Sci. Technol. 2012, 23, 065005. [Google Scholar] [CrossRef]
- ISO 25178-6; Geometrical Product Specifications (GPS). Surface Texture: Areal Classification of Methods for Measuring Surface Texture. ISO: Geneva, Switzerland, 2010; ISBN 978-0-580-70750-6.
- ISO 25178-601; Geometrical Product Specifications (GPS). Surface Texture: Areal Nominal Characteristics of Contact (Stylus) Instruments. ISO: Geneva, Switzerland, 2010; ISBN 978-0-580-60202-3.
- ISO 25178-602; Geometrical Product Specifications (GPS). Surface Texture: Areal Nominal Characteristics of Non-Contact (Confocal Chromatic Probe) Instruments. ISO: Geneva, Switzerland, 2010; ISBN 978-0-580-60203-0.
- ISO 25178-603; Geometrical Product Specifications (GPS). Surface Texture: Areal Nominal Characteristics of Non-Contact (Phase-Shifting Interferometric Microscopy) Instruments. ISO: Geneva, Switzerland, 2013; ISBN 978-0-580-82360-2.
- ISO 25178-604; Geometrical Product Specifications (GPS). Surface Texture: Areal Nominal Characteristics of Non-Contact (Coherence Scanning Interferometry) Instruments. ISO: Geneva, Switzerland, 2013; ISBN 978-0-580-66355-0.
- ISO 25178-605; Geometrical Product Specifications (GPS). Surface Texture: Areal Nominal Characteristics of Non-Contact (Point Autofocus Probe) Instruments. ISO: Geneva, Switzerland, 2014; ISBN 978-0-580-68207-0.
- ISO 25178-606; Geometrical Product Specification (GPS). Surface Texture: Areal Nominal Characteristics of Non-Contact (Focus Variation) Instruments. ISO: Geneva, Switzerland, 2015; ISBN 978-0-580-76436-3.
- Lemesle, J.; Moreau, C.; Deltombe, R.; Martin, J.; Blateyron, F.; Bigerelle, M.; Brown, C.A. Height Fluctuations and Surface Gradients in Topographic Measurements. Materials 2023, 16, 5408. [Google Scholar] [CrossRef] [PubMed]
- Lemesle, J.; Moreau, C.; Deltombe, R.; Blateyron, F.; Martin, J.; Bigerelle, M.; Brown, C.A. Top-down Determination of Fluctuations in Topographic Measurements. Materials 2023, 16, 473. [Google Scholar] [CrossRef]
- Vanrusselt, M.; Haitjema, H. Characterization of Measurement and Instrument Noise in Areal Surface Topography Measurements by the Allan Deviation. CIRP Ann. 2023, 72, 485–488. [Google Scholar] [CrossRef]
- Brown, C.A. Uncertainty and Quality in the Measurement and Characterization of the Texture of Abrasive Media. In Proceedings of the ISAAT 2007/SME International Grinding Conference, Dearborn, MI, USA, 26–28 September 2007; Society of Manufacturing Engineers: Dearborn, MI, USA, 2007. [Google Scholar]
- Peta, K.; Love, G.; Brown, C.A. Comparing Repeatability and Reproducibility of Topographic Measurement Types Directly Using Linear Regression Analyses of Measured Heights. Precis. Eng. 2024, 88, 192–203. [Google Scholar] [CrossRef]
- Moreau, C.; Bigerelle, M.; Marteau, J.; Lemesle, J.; Paez, D.; Guibert, R.; Blateyron, F.; Brown, C.A. A Novel Methodology to Assess Optical Profilometer Stability to Discriminate Surface Roughness. Surf. Topogr. Metrol. Prop. 2024, 12, 025018. [Google Scholar] [CrossRef]
- Moreau, C.; Lemesle, J.; Páez Margarit, D.; Blateyron, F.; Bigerelle, M. Statistical Analysis of Measurement Processes Using Multi-Physic Instruments: Insights from Stitched Maps. Metrology 2024, 4, 141–163. [Google Scholar] [CrossRef]
- De Groot, P. The Meaning and Measure of Vertical Resolution in Optical Surface Topography Measurement. Appl. Sci. 2017, 7, 54. [Google Scholar] [CrossRef]
- Johnson, N.L. Systems of Frequency Curves Generated by Methods of Translation. Biometrika 1949, 36, 149–176. [Google Scholar] [CrossRef]
- ISO 25178-1; Geometrical Product Specifications (GPS)—Surface Texture: Areal—Part 2: Terms, Definitions and Surface Texture Parameters. ISO: Geneva, Switzerland, 2012.
- Najjar, D.; Bigerelle, M.; Lefebvre, C.; Iost, A. A New Approach to Predict the Pit Depth Extreme Value of a Localized Corrosion Process. ISIJ Int. 2003, 43, 720–725. [Google Scholar] [CrossRef]
- Liu, M.; Cheung, C.F.; Ren, M.; Cheng, C.-H. Estimation of Measurement Uncertainty Caused by Surface Gradient for a White Light Interferometer. Appl. Opt. 2015, 54, 8670. [Google Scholar] [CrossRef] [PubMed]
- Berkmans, F.; Lemesle, J.; Guibert, R.; Wieczorowski, M.; Brown, C.; Bigerelle, M. Two 3D Fractal-Based Approaches for Topographical Characterization: Richardson Patchwork versus Sdr. Materials 2024, 17, 2386. [Google Scholar] [CrossRef] [PubMed]
- Guibert, R.; Hanafi, S.; Deltombe, R.; Bigerelle, M.; Brown, C.A. Comparison of Three Multiscale Methods for Topographic Analyses. Surf. Topogr. Metrol. Prop. 2020, 8, 024002. [Google Scholar] [CrossRef]
- Fu, S.; Cheng, F.; Tjahjowidodo, T. Surface Topography Measurement of Mirror-Finished Surfaces Using Fringe-Patterned Illumination. Metals 2020, 10, 69. [Google Scholar] [CrossRef]
- Jeon, S.H.; Gil, S.-K. Measurement of a Mirror Surface Topography Using 2-Frame Phase-Shifting Digital Interferometry. J. Opt. Soc. Korea 2009, 13, 245–250. [Google Scholar] [CrossRef]
- Leach, R. Advances in Optical Surface Texture Metrology; IOP Publishing Ltd.: Bristol, UK, 2020; ISBN 978-0-7503-2528-8. [Google Scholar]
- Xue, S.; Chen, S.; Fan, Z.; Zhai, D. Adaptive Wavefront Interferometry for Unknown Free-Form Surfaces. Opt. Express 2018, 26, 21910. [Google Scholar] [CrossRef]
- Mejía-Barbosa, Y.; Malacara-Hernández, D. A Review of Methods for Measuring Corneal Topography. Optom. Vis. Sci. 2001, 78, 240–253. [Google Scholar] [CrossRef]
- Udupa, G.; Singaperumal, M.; Sirohi, R.S.; Kothiyal, M.P. Characterization of Surface Topography by Confocal Microscopy: I. Principles and the Measurement System. Meas. Sci. Technol. 2000, 11, 305–314. [Google Scholar] [CrossRef]
- Feng, X.; Senin, N.; Su, R.; Ramasamy, S.; Leach, R. Optical Measurement of Surface Topographies with Transparent Coatings. Opt. Lasers Eng. 2019, 121, 261–270. [Google Scholar] [CrossRef]
- Leach, R. (Ed.) Optical Measurement of Surface Topography; Springer: Berlin/Heidelberg, Germany, 2011; ISBN 978-3-642-12011-4. [Google Scholar]
- Bakhtazad, A.; Chowdhury, S. An Evaluation of Optical Profilometry Techniques for CMUT Characterization. Microsyst. Technol. 2019, 25, 3627–3642. [Google Scholar] [CrossRef]
- Jansen, M.; Schellekens, P.; Haitjema, H. Development of a Double Sided Stitching Interferometer for Wafer Characterization. CIRP Ann. 2006, 55, 555–558. [Google Scholar] [CrossRef]
- Gao, F.; Leach, R.K.; Petzing, J.; Coupland, J.M. Surface Measurement Errors Using Commercial Scanning White Light Interferometers. Meas. Sci. Technol. 2008, 19, 015303. [Google Scholar] [CrossRef]
- Su, R.; Wang, Y.; Coupland, J.; Leach, R. On Tilt and Curvature Dependent Errors and the Calibration of Coherence Scanning Interferometry. Opt. Express 2017, 25, 3297. [Google Scholar] [CrossRef] [PubMed]
- Yuan, L.; Guo, T.; Qiu, Z.; Fu, X.; Hu, X. An Analysis of the Focus Variation Microscope and Its Application in the Measurement of Tool Parameter. Int. J. Precis. Eng. Manuf. 2020, 21, 2249–2261. [Google Scholar] [CrossRef]
- Abdel-Aal, H.A.; El Mansori, M.; Mezghani, S. Multi-Scale Investigation of Surface Topography of Ball Python (Python Regius) Shed Skin in Comparison to Human Skin. Tribol. Lett. 2010, 37, 517–527. [Google Scholar] [CrossRef]
- De Groot, P.J. The Instrument Transfer Function for Optical Measurements of Surface Topography. J. Phys. Photonics 2021, 3, 024004. [Google Scholar] [CrossRef]
- Aguilar, J.F.; Méndez, E.R. On the Limitations of the Confocal Scanning Optical Microscope as a Profilometer. J. Mod. Opt. 1995, 42, 1785–1794. [Google Scholar] [CrossRef]
- Mauch, F.; Osten, W. Model-Based Approach for Planning and Evaluation of Confocal Measurements of Rough Surfaces. Meas. Sci. Technol. 2014, 25, 105002. [Google Scholar] [CrossRef]
- Mueller, T.; Jordan, M.; Schneider, T.; Poesch, A.; Reithmeier, E. Measurement of Steep Edges and Undercuts in Confocal Microscopy. Micron 2016, 84, 79–95. [Google Scholar] [CrossRef]
- Senin, N.; Thompson, A.; Leach, R.K. Characterisation of the Topography of Metal Additive Surface Features with Different Measurement Technologies. Meas. Sci. Technol. 2017, 28, 095003. [Google Scholar] [CrossRef]
- Kovalev, A.E.; Dening, K.; Persson, B.N.J.; Gorb, S.N. Surface Topography and Contact Mechanics of Dry and Wet Human Skin. Beilstein J. Nanotechnol. 2014, 5, 1341–1348. [Google Scholar] [CrossRef] [PubMed]
- Marteau, J.; Bigerelle, M.; Mazeran, P.-E.; Bouvier, S. Relation between Roughness and Processing Conditions of AISI 316L Stainless Steel Treated by Ultrasonic Shot Peening. Tribol. Int. 2015, 82, 319–329. [Google Scholar] [CrossRef]
- Pawlus, P.; Reizer, R.; Wieczorowski, M.; Krolczyk, G.M. Study of Surface Texture Measurement Errors. Measurement 2023, 210, 112568. [Google Scholar] [CrossRef]
- Leksycki, K.; Królczyk, J.B. Comparative Assessment of the Surface Topography for Different Optical Profilometry Techniques after Dry Turning of Ti6Al4V Titanium Alloy. Measurement 2021, 169, 108378. [Google Scholar] [CrossRef]
- Masuda, Y.; Oguri, M.; Morinaga, T.; Hirao, T. Three-Dimensional Morphological Characterization of the Skin Surface Micro-Topography Using a Skin Replica and Changes with Age. Ski. Res. Technol. 2014, 20, 299–306. [Google Scholar] [CrossRef] [PubMed]
- Krolczyk, G.M.; Nieslony, P.; Krolczyk, J.B.; Samardzic, I.; Legutko, S.; Hloch, S.; Barrans, S.; Maruda, R.W. Influence of Argon Pollution on the Weld Surface Morphology. Measurement 2015, 70, 203–213. [Google Scholar] [CrossRef]
- Du, J.; Zhang, D.; Wang, X.; Jin, H.; Zhang, W.; Tong, B.; Liu, Y.; Burn, P.L.; Cheng, H.-M.; Ren, W. Extremely Efficient Flexible Organic Solar Cells with a Graphene Transparent Anode: Dependence on Number of Layers and Doping of Graphene. Carbon 2021, 171, 350–358. [Google Scholar] [CrossRef]
- Wei, J.; Li, Y.; Dai, D.; Zhang, F.; Zou, H.; Yang, X.; Ji, Y.; Li, B.; Wei, X. Surface Roughness: A Crucial Factor to Robust Electric Double Layer Capacitors. ACS Appl. Mater. Interfaces 2020, 12, 5786–5792. [Google Scholar] [CrossRef]
- Bigerelle, M.; Mathia, T.; Bouvier, S. The Multi-Scale Roughness Analyses and Modeling of Abrasion with the Grit Size Effect on Ground Surfaces. Wear 2012, 286–287, 124–135. [Google Scholar] [CrossRef]
- Bigerelle, M.; Giljean, S.; Mathia, T.G. Multiscale Characteristic Lengths of Abraded Surfaces: Three Stages of the Grit-Size Effect. Tribol. Int. 2011, 44, 63–80. [Google Scholar] [CrossRef]
- Sasada, T.; Oike, M.; Emori, N. The Effect of Abrasive Grain Size on the Transition between Abrasive and Adhesive Wear. Wear 1984, 97, 291–302. [Google Scholar] [CrossRef]
- Yang, F.; Johnson; Vision, J. Univariate Outlier Detection Using SAS. 2 November 2023. Available online: https://www.wuss.org/proceedings/2023/WUSS-2023-Paper-158.pdf (accessed on 26 October 2024).
Uncertainty Method | Strengths | Weaknesses |
---|---|---|
GUM/ISO |
|
|
X, Y mapping |
|
|
Allan deviation (lateral and temporal) |
|
|
Correlation |
|
|
Statistical index |
|
|
Observation | Instrument Mode | Roughness Parameter | Grit | ||
---|---|---|---|---|---|
Indicator | Case | #080 | #120 | ||
Mean_Q | Best | CSI | Sal | 532.99 | 480.48 |
Homo_Q | Worst | 150.13 | 250.72 | ||
Mean_Q | Worst | FV | Sds | 8.80 | 6.71 |
NBmode | Highest | CM | Smr | 2 | 4 |
Homo_Q | Best | FV | Vmc | 1.43 | 0.91 |
%-Out (%) | Lowest | CM | Sal | 0 | 0 |
%-Out (%) | Highest | FV | Sp | 21.37 | 14.33 |
Observation | Instrument Mode | Parameter | Grit | ||
---|---|---|---|---|---|
Indicator | Case | #080 | #120 | ||
Mean_Q | Best | CSI | Sal | 531.26 | 472.80 |
Homo_Q | Worst | 96.29 | 311.17 | ||
Mean_Q | Worst | FV | Sds | 8.87 | 6.73 |
NBmode | Highest | CM | Smr | 2 | 5 |
Homo_Q | Best | FV | Vmc | 2.51 | 1.94 |
%-Out (%) | Lowest | CM | Sal | 0 | 0 |
%-Out (%) | Highest | FV | Sp | 0 | 0 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Moreau, C.; Lemesle, J.; Páez Margarit, D.; Blateyron, F.; Bigerelle, M. A Statistical Approach for Characterizing the Behaviour of Roughness Parameters Measured by a Multi-Physics Instrument on Ground Surface Topographies: Four Novel Indicators. Metrology 2024, 4, 640-672. https://doi.org/10.3390/metrology4040039
Moreau C, Lemesle J, Páez Margarit D, Blateyron F, Bigerelle M. A Statistical Approach for Characterizing the Behaviour of Roughness Parameters Measured by a Multi-Physics Instrument on Ground Surface Topographies: Four Novel Indicators. Metrology. 2024; 4(4):640-672. https://doi.org/10.3390/metrology4040039
Chicago/Turabian StyleMoreau, Clément, Julie Lemesle, David Páez Margarit, François Blateyron, and Maxence Bigerelle. 2024. "A Statistical Approach for Characterizing the Behaviour of Roughness Parameters Measured by a Multi-Physics Instrument on Ground Surface Topographies: Four Novel Indicators" Metrology 4, no. 4: 640-672. https://doi.org/10.3390/metrology4040039
APA StyleMoreau, C., Lemesle, J., Páez Margarit, D., Blateyron, F., & Bigerelle, M. (2024). A Statistical Approach for Characterizing the Behaviour of Roughness Parameters Measured by a Multi-Physics Instrument on Ground Surface Topographies: Four Novel Indicators. Metrology, 4(4), 640-672. https://doi.org/10.3390/metrology4040039