# Analysis of Surface Microgeometry Created by Electric Discharge Machining

^{*}

## Abstract

**:**

^{2}> 0.9) were observed between craters height, diameter, area and curvature using linear and logarithmic regressions. Conventional areal parameter related to heights dispersion were found to correlate stronger using logarithmic regression. Geometric characterization of process-specific topographic formations is considered to be a natural and intuitive way of analyzing the complexity of studied surfaces. The presented approach allows extraction of information directly relating to the shape and size of topographic features of interest. In the tested conditions, the surface finish is mostly affected and potentially controlled by discharge energy at larger scales which is associated with sizes of fabricated craters.

## 1. Introduction

^{2}) between motif and curvature characterizations (i.e., principal, Gaussian or mean curvature) and discharge energies is sought as a function of scale. Motifs are used here to derive geometrical properties of created craters (e.g., area, depth and diameter), whereas curvature allows characterization of their shapes in multiple scales. The proposed approach is feature-based and focuses on the geometric specificity of the topographies created by EDM. As a comparison, additional conventional analysis of surface texture using ISO 25178 standard and its areal characterization parameters are performed. Geometric characterizations of process-specific topographic objects is considered to be a natural and intuitive way of analysis the complexity of EDM surfaces. In contrast to analysis of surface topography through areal texture parameters (as in ISO 25178 2), the presented approach allows extraction of information directly relating to the shape and size of topographic features of interest. The richness of this information is tested via correlations with processing parameter.

## 2. Materials and Methods

#### 2.1. Samples Preparation

_{on}and T

_{off}) as well as face and side gap. Based on the given technological parameters we estimate the discharge energy by applying the formula:

#### 2.2. Measurements of Surface Topography

- Dataset leveling using least squares method;
- Outliers removal;
- Filling in the non-measured points [30];
- Calculation of areal ISO standard parameters, curvature tensor analysis and motif analysis from primary surface;
- Extraction of roughness and waviness surface with gaussian filter;
- Motif analysis and areal parameters extraction from both roughness and waviness surface.

#### 2.3. Analysis of Microgeometry

#### 2.3.1. Conventional Approach with ISO Parameters

- Height, which is a class of parameters, that quantify the information on the z-axis of the surface;
- Functional, derived from the Abbott–Firestone curve, which describes the height cumulative distribution on the surface;
- Spatial, which describe topographic characteristics and quantify the lateral information of the surface;
- Hybrid, a class of surface finish parameters, that consider both the amplitude and spacing between heights;
- Functional (volume), which involves volume parameters calculated from the Abbott-Firestone curve;
- Feature, derived from the segmentation of surface into motifs; and
- Functional (stratified surfaces), which includes parameters designed for automotive industry, considering certain aspects of a surface interactions, such as lubrication and grinding.

#### 2.3.2. Motif Analysis

- Height—distance between the lowest saddle point and pit;
- Area—horizontal area limited by the ridge line;
- Volume—volume of the void below the plane of the lowest saddle point;
- Equivalent diameter—diameter of a disk which area is equal to that of a grain;
- Mean diameter—average diameter of a disc constructed at the center of the gravity of a grain.

- Height—<0.75% Sz (maximum height),
- Area—<0.25% of surface area

#### 2.3.3. Multiscale Curvature Tensor Analysis

_{1}and κ

_{2}represent the principal curvatures, maximal and minimal magnitudes respectively. The sign is used to designate concave surfaces as positive and convex as negative. The eigenvectors

**k**,

_{1}**k**are the corresponding principal directions of maximum and minimum curvature and n is the surface normal unit vector at the location of the calculated curvature. Mean (H) and Gaussian curvature (K) can be calculated from principle curvatures.

_{2}- ${\mathsf{\kappa}}_{1}{\mathrm{a}}_{\mathrm{abs}}$—average absolute maximum curvature$${\mathsf{\kappa}}_{1}{\mathrm{a}}_{\mathrm{abs}}=\frac{1}{n}{\displaystyle \sum}_{i=1}^{n}\left|{\kappa}_{1i}\right|,$$
- ${\mathsf{\kappa}}_{1}{\mathrm{q}}_{\mathrm{abs}}$—standard deviation of absolute maximum curvature$${\mathsf{\kappa}}_{1}{\mathrm{q}}_{\mathrm{abs}}=\sqrt{\frac{{{\displaystyle \sum}}_{i=1}^{n}{\left(\left|{\kappa}_{1i}\right|-{\mathsf{\kappa}}_{1}{\mathrm{a}}_{\mathrm{abs}}\right)}^{2}}{n}},$$
- ${\mathsf{\kappa}}_{2}{\mathrm{a}}_{\mathrm{abs}}$—average absolute minimum curvature$${\mathsf{\kappa}}_{2}{\mathrm{a}}_{\mathrm{abs}}=\frac{1}{n}{\displaystyle \sum}_{i=1}^{n}\left|{\mathsf{\kappa}}_{2i}\right|,$$
- ${\mathsf{\kappa}}_{2}{\mathrm{q}}_{\mathrm{abs}}$—standard deviation of absolute minimum curvature$${\mathsf{\kappa}}_{2}{\mathrm{q}}_{\mathrm{abs}}=\sqrt{\frac{{{\displaystyle \sum}}_{i=1}^{n}{\left(\left|{\kappa}_{2i}\right|-{\mathsf{\kappa}}_{2}{\mathrm{a}}_{abs}\right)}^{2}}{n}},$$
- ${\mathrm{Ha}}_{\mathrm{abs}}$—average absolute mean curvature$${\mathrm{Ha}}_{\mathrm{abs}}=\frac{1}{n}{\displaystyle \sum}_{i=1}^{n}\left|{H}_{i}\right|$$
- ${\mathrm{Hq}}_{\mathrm{abs}}$—standard deviation of absolute mean curvature$${\mathrm{Hq}}_{\mathrm{abs}}=\sqrt{\frac{{{\displaystyle \sum}}_{i=1}^{n}{\left(\left|{H}_{i}\right|-{\mathrm{Ha}}_{abs}\right)}^{2}}{n},}$$
- ${\mathrm{Ka}}_{\mathrm{abs}}\text{}$—average absolute Gaussian curvature$${\mathrm{Ka}}_{\mathrm{abs}}=\frac{1}{n}{\displaystyle \sum}_{i=1}^{n}\left|{K}_{i}\right|,$$
- ${\mathrm{Kq}}_{\mathrm{abs}}$—standard deviation of absolute Gaussian curvature$${\mathrm{Kq}}_{\mathrm{abs}}=\sqrt{\frac{{{\displaystyle \sum}}_{i=1}^{n}{\left(\left|{K}_{i}\right|-{\mathrm{Ka}}_{\mathrm{abs}}\right)}^{2}}{n}}.$$

## 3. Results

#### 3.1. Measurements

#### 3.2. ISO Parameters

#### 3.2.1. Height ISO Parameters

^{2}> 0.9) were observed for all parameters except for Ssk and Sku. Skewness exhibit the moderate correlation when applying linear regression for an unfiltered surface. No correlations can be noted in residual roughness and waviness surfaces. Sa, Sq and Sv show strongest correlation when calculated using linear regression. Sp and Sz behave similarly for logarithmic regression. Surface S2 seems to deviate significantly from the trend for Sv, but this effect appears to be incidental. Similar observations were made for most of the others, but not all (Vmp, Spc and Spd), ISO and motif parameters. From visual impressions of captured topographies, it seems not to be radically different than S1 or S3. No changes in measurement conditions were also noted for measurements of S2. In order to fully investigate the reason for which S2 deviates significantly from the trend, other samples machined with same parameters would have to be manufactured and analyzed.

#### 3.2.2. Functional ISO Parameters

^{2}= 0.923 and 0.953, respectively) in both primary (unfiltered) and residual roughness surface.

#### 3.2.3. Spatial ISO Parameters

^{2}were achieved for Str (R

^{2}= 0.566, linear regression, unfiltered) and Sal (R

^{2}= 0.815), logarithmic regression, residual roughness).

#### 3.2.4. Hybrid ISO Parameters

^{2}= 0.929 and 0.916, respectively). These values slightly decline (R

^{2}= 0.900 and 0.884), when calculated from residual roughness surface, however both parameters still correlate better for logarithmic regressions.

#### 3.2.5. Functional Volume ISO Parameters

^{2}= 0.973 and 0.977, unfiltered and residual roughness, respectively), both regressed logarithmically. All parameters show good correlation (R

^{2}> 0.9) for both filtered and residual roughness surface.

#### 3.2.6. Feature ISO Parameters

^{2}> 0.9) for unfiltered surfaces. Some of these values slightly decline, when calculated from residual roughness surface. However, for S5p, Sda and Sha they increased. Shv and S5v declined most significantly after filtration to R

^{2}= 0.852 and R

^{2}= 0.888 respectively.

#### 3.2.7. Functional ISO Parameters for Stratified Surfaces

^{2}> 0.92) and residual roughness surface (R

^{2}> 0.92)

#### 3.2.8. General Comments on ISO Parameters

#### 3.3. Motif Analysis

^{2}> 0.96) using linear regression. Logarithmic regression seems to be more appropriate to model the functional relation between number of motifs and the energy (R

^{2}= 0.936). The strongest correlations can be found for mean motif area, for which coefficient of determination is equal to 0.986. Standard deviations of motif geometrical parameters tend to increase with discharge energy. They also show strong correlations when regressed linearly (R

^{2}> 0.95). This might be interpreted as surfaces machined with higher energies exhibit craters whose dimensions and variability is proportional to this energy. This is also supported by the equivalent diameter distributions presented for two extreme and one middle sets of technological parameters as depicted in Figure 7. More information about the regression analysis can be found in Table A4 (Appendix B).

#### 3.4. Multiscale Curvature Analysis

_{1}), calculated for two different scales from representative regions located on three different surfaces (S1, S5 and S10) are shown in Figure 10. Negative curvatures (values on the figure) represent convex surface features or peaks. Different scales of calculation show different features. Grooves and pores are visible on κ

_{1}curvature plot as positive curvature regions. The amplitude of curvatures decreases with scale what can be explained by the fact that for larger scale features of larger size (and radius) can be characterized. This effect can be observed for other research examples using this method of characterization [6,7,42].

_{1}, as a function of scale, for five representative surfaces S1, S3, S5, S8 and S10 are shown in Figure 10

**.**No clear tendency for κ

_{1}a between surfaces can be observed. These values seem to converge with scale to zero but at different rates, what might be associated with the fact that for the larger scale curvature of form is characterized. Since in all analyzed surface, the form is a flat plane, its curvature is null. This parameter can be associated with average shape (convexity or concavity) at certain scale. Considering averages of absolute values maximum, their values decreases with scale for all surfaces. This parameter characterizes mean deviation from zero (flatness), without taking the signs of curvatures into account. It can be used for describing the evolution of curvature magnitude with scale. Fine-scale features are generally characterized with large curvature and the similar observation is made here as a declining trend. Standard deviation of maximum curvature also decreases with scale. This might be explained by the fact that variation of curvatures declines as the scale gets larger. Similar observation can be made for κ

_{1}q

_{abs}. Similar tendencies were noticed for parameters related minimum, mean and Gaussian curvature.

_{1}a, κ

_{2}a, Ha and Ka) demonstrated to be least influenced by different material processing as no clear tendency was observed for them for all analyzed scales. Their absolute values and standard deviations are more useful in finding functional relations between process parameter and resulted curvature. This is noticed for scales greater than 5 µm, and might suggest that those topographies can be discriminated at larger scales and that they do not differ significantly at the finest scales.

^{2}greater than 0.9.

^{2}) for the curvatures versus the discharge energies are shown as a function of scale in Figure 11. Both average principal curvatures κ

_{1}and κ

_{2}correlate do not correlate well (R

^{2}< 0.8) with the discharge energy for the analyzed range of scales (Figure 11a). When considering absolute values of curvature, strong correlations are observed for κ

_{1}a

_{abs}and logarithmic regression better than linear reflects that relation as R

^{2}is greater than 0.9 for s > 8.089 µm (total of nine analyzed scales) versus the single largest analyzed scale for linear. Similar tendencies can be noticed for average values of κ

_{2}for which only κ

_{2}a

_{abs}correlates strongly when regressed logarithmically for largest scales (>8 µm) (Figure 11c). Standard deviation measures show strong correlations with both models for larger scales (Figure 11b,d). Logarithmic regression correlates for broader range of scales when compared to linear.

^{2}are found using linear regression for Kq (0.977) and Kq

_{abs}(0.981), both at the largest scale equal to 13.716 µm (Figure 11g,h). Ka, as only average non-absolute parameter, corelated strongly but at the middle scale (4.572 µm). Gaussian curvature K appeared to correlate the strongest for the widest range of scales. It is the only parameter for which strong correlations were observed when average values are considered. Taking into account the absolute values of curvatures, it significantly improves the strengths of correlations. These parameters might be attributed with the magnitude of curvature, regardless of its sign.

## 4. Discussion

^{2}> 0.8) for scales starting from between 8 and 9 µm. This corresponds well to the average equivalent radius of detected motif (mean—1 × standard deviation$\approx $10 µm) for the sample created with lowest energy. Starting with those scales the curvature of craters is the most affected by the discharge energy and the fabricated microgeometry is the most adequately characterized as the sizes of features are best discerned at those scales. This follows the concept that scale could be enmeshed with size [3].

## 5. Conclusions

- Strong correlations (R
^{2}> 0.9) were found between discharge energies and ISO parameters that were calculated for original surfaces (prior to S- or L-filtration but after morphological filtration) and S-surfaces (roughness). ISO standard parameters did not correlate well when computed for L-surfaces (waviness). This suggests that the creation of topographic features of larger dimensions is affected by the discharge energy. The dimension limit is constrained by cut-off wavelength of 250 µm. The characteristics of fine-scale surface features do not differ significantly. This is also supported by the outcome of multiscale curvature analysis which indicated that curvature correlated strongly also for larger scales. - In the tested conditions, the surface is mostly affected and potentially controlled by discharge energy at larger scales which is associated with sizes of fabricated craters. For smaller scales, effect of machining with different parameters did not manifest itself.
- Strong correlations (R
^{2}> 0.9) were also observed between motif parameters that characterized height, diameter, area and diameter of the detected motif, which might be associated with craters. This analysis, together with curvature and ISO areal parameters allow comprehensive characterization of surface microgeometry created by EDM. - ISO areal parameters that describe peaks distributions exhibit higher coefficient of determination than others when regressed logarithmically. Correlations using a logarithmic model were also strong for curvature parameters.
- Registration of surface topography using focus variation microscopy leads to the occurrence of surface- and method-specific outliers which are hard to be removed using Gaussian filtration or thresholding. The application of a morphological filter proved to be successful in the outliers removal what was also evident to achieving strong correlations with discharge energy with parameters of three various types.

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

Properties | Units | Test Standard | Values |
---|---|---|---|

Average grain size | µm | ISO 13320 | 3 |

Bulk density | g/cm^{3} | DIN IEC 60413/204 | 1.88 |

Open porosity | Vol. % | DIN 66133 | 10 |

Medium pore entrance diameter | µm | DIN 66133 | 0.6 |

Ambient temperature | cm^{2}/s | DIN 51935 | 0.01 |

Hardness | HR _{5}/_{100} | DIN IEC 60413/303 | 105 |

Resistivity | µΩm | DIN IEC 60413/402 | 13 |

Flexural strength | MPa | DIN IEC 60413/501 | 85 |

Compressive strength | MPa | DIN 51910 | 170 |

Dynamic modulus of elasticity | MPa | DIN 51915 | 13.5 × 10^{3} |

Thermal expansion (20–200 °C) | K^{−1} | DIN 51909 | 4.7 × 10^{−6} |

Thermal conductivity (20 °C) | Wm^{−1}K^{−1} | DIN 51908 | 105 |

Ash content | ppm | DIN 51903 | 200 |

Properties | Units | Values |
---|---|---|

Machine | ||

Architecture | C-frame/Fixed table/Drop tank | |

X, Y, Z travel | mm | 400 × 300 × 350 |

X, Y axes speed | m/min | 6 |

Z axis speed | m/min | 15 |

Positioning resolution | µm | 0.1 |

Work tank size | Mm | 900 × 630 × 350 |

Work table size | Mm | 600 × 400 |

Max. machining current | A | 80 |

Best surface finish Ra | µm | 0.08 |

Dielectric fluid | ||

Color | Straw yellow | |

Kinematic viscosity (20 °C/40 °C) | mm^{2}/s | 5.0/3.0 |

Density | kg/l | 0.77 |

## Appendix B

^{2}) is given for all analyzed parameters.

**Table A3.**Coefficients of determination (R

^{2}) between discharge energies and ISO 25178 areal parameters calculated using linear and logarithmic regressions for primary surfaces. Please see the standard for additional settings given in the table.

Parameter | R^{2} for Linear Regression | R^{2} for Logarithmic Regression |
---|---|---|

Sq | 0.930 | 0.905 |

Ssk | 0.643 | 0.291 |

Sku | 0.016 | 0.001 |

Sp | 0.760 | 0.961 |

Sv | 0.918 | 0.849 |

Sz | 0.891 | 0.934 |

Sa | 0.930 | 0.899 |

Smr (c = 1 µm under highest peak) | 0.024 | 0.207 |

Smc (p = 10%) | 0.911 | 0.923 |

Sxp (p = 50%, q = 97.5%) | 0.953 | 0.846 |

Sal (s = 0.2) | 0.014 | 0.117 |

Str (s = 0.2) | 0.566 | 0.531 |

Std (reference = 0°) | 0.113 | 0.115 |

Sdq | 0.796 | 0.929 |

Sdr | 0.825 | 0.916 |

Vm (p = 10%) | 0.790 | 0.973 |

Vv (p = 10%) | 0.909 | 0.927 |

Vmp (p = 10%) | 0.790 | 0.973 |

Vmc (p = 10%, q = 80%) | 0.941 | 0.881 |

Vvc (p = 10%, q = 80%) | 0.899 | 0.933 |

Vvv (p = 80%) | 0.952 | 0.838 |

Spd (trimming = 0.75%) | 0.661 | 0.947 |

Spc (trimming = 0.75%) | 0.302 | 0.335 |

S10z (trimming = 0.75%) | 0.853 | 0.942 |

S5p (trimming = 0.75%) | 0.557 | 0.901 |

S5v (trimming = 0.75%) | 0.932 | 0.838 |

Sda (trimming = 0.75%) | 0.932 | 0.904 |

Sha (trimming = 0.75%) | 0.938 | 0.890 |

Sdv (trimming = 0.75%) | 0.963 | 0.716 |

Shv (trimming = 0.75%) | 0.959 | 0.836 |

Sk (unfiltered) | 0.923 | 0.887 |

Spk (unfiltered) | 0.772 | 0.980 |

Svk unfiltered) | 0.953 | 0.832 |

Smr1 (unfiltered) | 0.292 | 0.032 |

Smr2 (unfiltered) | 0.631 | 0.398 |

**Table A4.**Coefficients of determination (R

^{2}) between discharge energies and motif parameters calculated using linear and logarithmic regressions.

Motif Parameter | Statistics | R^{2} for Linear Regression | R^{2} for Logarithmic Regression |
---|---|---|---|

Number of motifs | - | 0.874 | 0.936 |

Height | Mean | 0.979 | 0.766 |

Height | SD | 0.976 | 0.784 |

Area | Mean | 0.986 | 0.826 |

Area | SD | 0.975 | 0.841 |

Volume | Mean | 0.965 | 0.660 |

Volume | SD | 0.961 | 0.665 |

Mean diameter | Mean | 0.971 | 0.861 |

Mean diameter | SD | 0.955 | 0.877 |

Minimal diameter | Mean | 0.963 | 0.867 |

Minimal diameter | SD | 0.953 | 0.846 |

Maximal diameter | Mean | 0.964 | 0.871 |

Maximal diameter | SD | 0.965 | 0.891 |

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**Figure 1.**Materials prepared for the study: (

**a**) 52 HRC workpiece made of 1.2363 steel as machined; (

**b**) graphite electrode with 3 µm grain size.

**Figure 3.**Morphological surface filtration: (

**a**) raw surface; (

**b**) after filtration using 16μm sphere, closing filter; (

**c**) residue surface, all outliers clearly visible.

**Figure 4.**Six out of ten representatives of measured surfaces randomly chosen after dataset preparation step. Renderings of other surfaces are available in Supplementary Materials (Figure S1).

**Figure 5.**Scatter plots of ISO 25178 areal parameters correlated with discharge energies using linear or logarithmic regression. (

**a**) height ; (

**b**) functional ; (

**c**,

**d**) functional volume; (

**e**) and (

**f**) feature parameters.

**Figure 6.**Visualization of effects of the watershed segmentation. Surfaces with watershed boundaries (ridge lines) superimposed on a surface image.

**Figure 7.**Distributions of equivalent diameters calculated for S1, S5 and S10 surfaces using motif analysis.

**Figure 8.**Scatter plots for motif analysis: (

**a**) mean height; (

**b**) equivalent and mean diameter; (

**c**) mean area; (

**d**) mean volume and number of motifs correlated with discharge energies using linear regression.

**Figure 9.**Maximum curvature κ

_{1}for representative measurements of surfaces: S1, S5 and S10, calculated for two different scales: s = 1.407 and s = 14.068 µm. Please note that vertical scale (z-axis) for upper row is fivefold greater than for the lower row.

**Figure 10.**Various statistical parameters related to curvature calculated for surfaces S1, S3, S5, S8 and S10 depicted as a function of scale: (

**a**) average of absolute maximum curvature, (

**b**) average of absolute maximum curvature, (

**c**) standard deviation of maximum curvature, (

**d**) standard deviation of absolute maximum curvature.

**Figure 11.**Coefficient of determination for linear and logarithmic regression calculated for statistical parameters calculated for: (

**a**) average maximum; (

**b**) standard deviation of maximum; (

**c**) minimum and (

**d**) standard deviation of minimum curvature versus discharge energy; (

**e**) average mean curvature; (

**f**) standard deviation of mean curvature; (

**g**) average Gaussian curvature and (

**h**) standard deviation of Gaussian curvature as a function of scale.

C | Si | Mn | P | S | Cr | Mo | V |
---|---|---|---|---|---|---|---|

0.95–1.05 | 0.10–0.40 | 0.40–0.80 | Max 0.030 | Max 0.030 | 4.80–5.50 | 0.90–1.20 | 0.15–0.35 |

**Table 2.**Basic areal height parameters calculated for pre-electric discharge machined (EDM) (as-ground) surfaces according to ISO 25178 2. Data is presented as mean ± standard deviation for roughness and waviness.

Parameter | Sq (µm) | Sp (µm) | Sv (µm) | Sz (µm) | Sa (µm) |
---|---|---|---|---|---|

Roughness | 0.541 ± 0.281 | 9.852 ± 4.866 | 3.752 ± 1.356 | 13.604 ± 5.448 | 0.418 ± 0.218 |

Waviness | 0.232 ± 0.116 | 0.733 ± 0.389 | 0.577 ± 0.254 | 1.310 ± 0.635 | 0.184 ± 0.091 |

Surface | U (V) | I (A) | T_{on} (µs) | T_{off} (µs) | Face Gap (mm) | Side Gap (mm) | Discharge Energy (µJ) | Theoretical VDI Class |
---|---|---|---|---|---|---|---|---|

S1 | 100 | 3.0 | 0.5 | 6.9 | 0.0126 | 0.126 | 150 | 16 |

S2 | 100 | 3.0 | 0.9 | 7.1 | 0.0150 | 0.0150 | 270 | 17 |

S3 | 100 | 3.0 | 1.8 | 7.5 | 0.0155 | 0.0155 | 540 | 18 |

S4 | 100 | 3.0 | 3.0 | 8.0 | 0.0160 | 0.0160 | 900 | 19 |

S5 | 100 | 3.0 | 4.8 | 8.8 | 0.0164 | 0.0164 | 1440 | 20 |

S6 | 100 | 3.0 | 7.3 | 9.9 | 0.0244 | 0.0206 | 2190 | 21 |

S7 | 100 | 3.0 | 10.9 | 11.5 | 0.0333 | 0.0253 | 3270 | 22 |

S8 | 100 | 3.0 | 16.1 | 13.8 | 0.0432 | 0.0304 | 4830 | 23 |

S9 | 100 | 3.2 | 22.8 | 16.6 | 0.0543 | 0.0361 | 7296 | 24 |

S10 | 100 | 3.6 | 26.3 | 19.7 | 0.0567 | 0.0376 | 9468 | 25 |

Parameter | Unit | Value |
---|---|---|

Magnification | - | 50× |

Area Dimensions | μm | 323 × 323 |

Est. Vertical Resolution | μm | 0.016 |

Est. Lateral Resolution | μm | 2.31 |

Sampling intervals in x- and y-directions | μm | 0.176 |

Parameter Group | Parameter Symbol |
---|---|

Height Parameters | Sq, Ssk, Sku, Sp, Sv, Sz, Sa |

Functional Parameters | Smr, Smc, Sxp |

Spatial Parameters | Sal, Str, Std |

Hybrid Parameters | Sdq, Sdr |

Functional Parameters (Volume) | Vm, Vv, Vmp, Vmc, Vvc, Vvv |

Feature Parameters | Spd, Spc, S10z, S5p, S5v, Sda, Sha, Sdv, Shv |

Functional Parameters (Stratified surfaces) | Sk, Spk, Svk, Smr1, Smr2 |

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Bartkowiak, T.; Mendak, M.; Mrozek, K.; Wieczorowski, M.
Analysis of Surface Microgeometry Created by Electric Discharge Machining. *Materials* **2020**, *13*, 3830.
https://doi.org/10.3390/ma13173830

**AMA Style**

Bartkowiak T, Mendak M, Mrozek K, Wieczorowski M.
Analysis of Surface Microgeometry Created by Electric Discharge Machining. *Materials*. 2020; 13(17):3830.
https://doi.org/10.3390/ma13173830

**Chicago/Turabian Style**

Bartkowiak, Tomasz, Michał Mendak, Krzysztof Mrozek, and Michał Wieczorowski.
2020. "Analysis of Surface Microgeometry Created by Electric Discharge Machining" *Materials* 13, no. 17: 3830.
https://doi.org/10.3390/ma13173830