Three-dimensional topography analysis of electrical discharge textured SS304 surfaces

Journal of Manufacturing Processes 60 (2020) 384–399

Contents lists available at ScienceDirect

Journal of Manufacturing Processes journal homepage: www.elsevier.com/locate/manpro

Technical Paper

Three-dimensional topography analysis of electrical discharge textured SS304 surfaces S. Jithin, Upendra V. Bhandarkar, Suhas S. Joshi * Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India

A R T I C L E I N F O

A B S T R A C T

Keywords: Surface topography Surface texture Areal texture parameters EDT SS304

In the current work, a comprehensive three-dimensional topography characterization of electrical discharge textured SS304 surfaces is performed under the following heads: quantitative representation, microstructures and their distributions, and surface functionality. Topography analysis reveals that peak count distribution tends to become flattened with their mean shifting to higher heights when discharge energy increases. Higher discharge energy generates rougher textured surfaces with taller and sharper, but less densely distributed peaks. These textured surfaces also show improvement in wetting and sealing properties, more running-in period wear volume, more surface area for load carrying, and improved lubricant retention capacity.

MSC: 00-01 99-00

1. Introduction Electrical discharge texturing (EDT), which is evolved from the spark erosion or electrical discharge machining (EDM) process, generates random surface textures. Researchers have employed these random surface textures in a variety of components, such as mill rolls [1], or­ thopedic implants [2], and tool inserts [3] to improve their functional performance. Such random surface generation using EDT is also found to influence specific surface properties, such as wettability, as evident from the hydrophilic-to-hydrophobic transformation of SS304 surfaces on subjecting to EDT [4]. More recently, these textures were also found to be suitable for anti-fouling (in heat transfer) [5], anti-bacterial [6], and self-cleaning [7] applications. Surface characterization of these random surfaces generated using EDT was historically performed in terms of profile roughness parameters, such as Ra, Rq, and Rz, due to limitations in surface measurement methods. Since these parameters are measured along a 2D profile taken on the surface, a majority of surface points are omitted. Therefore, they tend to give only a limited information about the surface. Moreover, in the case of directional surfaces, the measured profile roughness parameters vary drastically with a variation in the direction of the selected section. The recent improvements in surface measurement methods enable scanning three-dimensional topography of the textured surfaces, and thereby, the measurement of areal texture parameters (or 3D roughness parameters), which are evaluated consid­ ering the whole surface topography, as opposed to a single profile or

section. Thus, these parameters are identified as a better representation of surface topography. There are a few attempts on three-dimensional surface topography characterization in the available literature. In one such work, Ram­ asawmy and Blunt [8] characterized the surface topographies generated on tool steel using EDM, in terms of areal texture parameters, such as arithmetic mean height (Sa), root mean square height (Sq), density of summits (Sds), material volume (Sm), core void volume (Sc or Vvc), valley void volume (Sv or Vvv), and core roughness depth (Sk). They also analyzed the influence of operating factors, such as discharge cur­ rent and pulse on-time, on these parameters, and found that current is the most significant factor influencing the surface texture generated during EDT. In a related work [9], they tried to establish a correlation between average white layer thickness (AWLT) of the textured surface, and areal texture parameters. Since the measurement of AWLT usually required destructive methods, the authors aimed to develop regression models to evaluate AWLT in terms of these areal texture parameters, which are comparatively easy to measure and does not require destructive measurement methods. They found that the regression model of AWLT in terms of Sds gave the best fit. Sds was also identified by Deltombe et al. [10] to be the most significant parameter to represent the topography of EDT surfaces. Jithin et al. [11] characterized SS316L surfaces textured using copper, tungsten, and copper-tungsten tool materials, in terms of Sa. They found that the copper electrode induced a larger variation in Sa of these surfaces for varying pulse on-time and gap

* Corresponding author. E-mail address: [email protected] (S.S. Joshi). https://doi.org/10.1016/j.jmapro.2020.10.066 Received 18 July 2020; Received in revised form 20 October 2020; Accepted 23 October 2020 Available online 2 November 2020 1526-6125/© 2020 Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers.

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

voltage compared to that with the other two electrode materials. ´ ´ Swiercz and Swiercz [12] performed EDM on high conductivity tool steel and characterized the resultant surface topographies in terms of areal texture parameters such as Sa, Sds, and arithmetic mean summit curvature (Ssc). They found that an increase in discharge current and pulse duration resulted in higher roughness, larger crater dimensions, and taller and rounder peaks. In another work, they [13] compared surface topographies generated in EDM using pure dielectric and 0.1% reduced graphene oxide (RGO) mixed dielectric, by means of bearing area parameters. They correlated increase in reduced summit height (Spk) with improvement in wear resistance and found that use of RGO improves the texture’s wear resistance. Random or periodic nature of EDT surfaces was analyzed and quantified by Aich and Banerjee [14] in terms of a parameter known as periodicity-to-randomness ratio (PR ratio). They reported that PR ratios for EDT surfaces are very low, which indicates dominance of randomness over periodicity. In another work, Aich [15] reported the dominance of deterministic chaos on surface topographies generated using EDM. Jithin et al. [16] developed two modes of EDT based on the electrode movement for surface texture generation: circular-face EDT (CirEDT) and cylindrical-face EDT (CylEDT). They reported that CirEDT gave lunar-craters-like surface patterns, whereas CylEDT results in sea-waves-like surface patterns. Besides, they performed extensive areal texture parameter analyses on the surface topographies generated. They found that CylEDT surface topographies have more points below mean plane, sharper peaks, more running-in wear volume, and less lubricant retention capacity, as compared to those of CirEDT counterparts. Recently, some interest in the analysis of surface topographies generated using micro-EDM, a micro-scaled version of EDM, could also be found in the literature. Hyde et al. [17] performed micro-EDM of stainless steel and studied the in­ fluence of operating factors on the conventional and fractal texture parameters measured on the textured surface. They also found the discharge current to be the most significant factor. D’Urso et al. [18] utilized surface characterization in terms of areal texture parameters to distinguish between topographies of micro-EDM milled stainless steel and ceramic surfaces. They found that topographies of the former display negative skewness (Ssk), whereas those of the latter exhibited positive skewness. It is understood from their findings that the surface topographies generated on different materials using the EDT process at similar operating conditions show a significant variation among them­ selves. In addition to the experimental surface topography character­ izations, certain researchers such as Izquierdo et al. [19] and Jithin et al. [20,21] have developed three-dimensional mathematical models to predict EDT surface topographies with reasonable accuracy and char­ acterized them. However, these models are unable to simulate surface irregularities such as micro-cracks, micro-globules, blow holes, etc. Hence, experimentally obtained surface topographies gives more infor­ mation about the textured surfaces as compared to that given by their modelled counterparts. From the above study, it could be understood that three-dimensional topography characterizations of EDT surfaces are not extensively carried out in the literature. Those available do not cover a significant number of areal texture parameters. A comprehensive characterization needs to encompass several areal texture parameters to cover the different as­ pects of the surface topography they represent. Moreover, in the avail­ able literature, the majority of areal texture parameters analyzed are areal field parameters, which take into consideration all surface points in the evaluation area. However, areal feature parameters, another class of areal texture parameters, which only considers specific distinguish­ able features of the surface, such as points, lines, or areas, are scarcely analyzed [22]. These parameters are more influential on surface per­ formance [22], and hence, they are essential to be analyzed. Therefore, the current work deals with a comprehensive three-dimensional topography characterization of SS304 surfaces subjected to EDT, in terms of different areal texture parameters. We group these parameters

Fig. 1. Experimental setup for electrical discharge texturing (EDT) of SS304.

to analyze different aspects of the surface, such as a quantitative rep­ resentation of the textured surface, characterization of surface micro­ structures and their distributions, and the surface functionality in various applications, and to analyze the influence of discharge energy on these surface aspects. 2. Materials and methods The experimental materials and methodologies used to perform characterization of surface topographies generated using EDT are dis­ cussed in this section. These are selected based on the various aspects of the surface topographies to be analyzed. The authors selected stainless steel 304 (SS304) as the work material for this study. SS304 is used in several surface contact applications, such as food processing equipment, chemical containment, and heat ex­ changers. Thus, a comprehensive characterization of random and isotropic surface topographies generated on SS304 by EDT, is of much interest in improving its functionality in current applications and feasibility for new applications. SS304 samples of dimensions 40 mm × 20 mm × 5 mm are precision milled and polished (up to Grade 220 emery paper). The tool material selected was copper, as it gives an extensive range of roughness values on the work surface, in terms of Sa, against variation in discharge current and pulse on-time [11]. We used copper rods of 10 mm diameter with their circular faces polished (up to Grade 220 emery paper). The dielectric material selected for EDT ex­ periments was paraffin oil. The authors conducted the EDT experiments on a CNC EDM, and the experimental setup used is shown in Fig. 1. The milled workpiece is fixed parallel to the machine table using a drill press vice and a magnetic vblock. The circular face of the copper electrode is used for texturing the SS304 samples, which results in a circular textured region. External dielectric flushing is provided to the tool-work gap to prevent debris accumulation and thus reduce surface damages. The study is conducted varying discharge energy (E) to analyze its effect on the different areal texture parameters evaluated. Discharge energy is varied from fine to rough finish levels in the range of 0.5 to 500 mJ. The variation in discharge energy is obtained by varying EDM operating parameters, such as discharge current (I), pulse on-time (ton ), and gap voltage (V) in ranges of 5 to 50 A, 10 to 100 μs, 10 to 100 V, respectively. The main goal of the current work is to perform a comprehensive three-dimensional analysis of EDT SS304 surface topographies. We desire to achieve a quantitative representation of the textured surfaces, characterize the surface microstructures and their distributions, and analyze surface functionality in various applications through this threedimensional topography analysis. Therefore, we performed surface topography analysis with several areal texture parameters, which cater 385

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 2. Classification of parameters used in surface topography characterization.

to each of these aspects. The different aspects of surface topography classification, their sub-focuses/purposes, and the corresponding areal texture parameters used in this analysis are given in Fig. 2. Initially, the authors perform a quantitative representation of the textured surfaces under two heads: roughness estimation and surface height. Secondly, we characterize the microstructures and their random distribution in EDT surface topographies generated under groups such as surface symmetry, isotropy or anisotropy, peak distribution, peak roundedness or sharp­ ness, and surface complexity. Finally, we investigate the applicability of the textured surfaces in various functions under four classes: wetting and sealing properties, wear volume available for the running-in period, surface post-running-in wear, and lubricant retention. We also investi­ gate how these topography aspects vary with a change in discharge energy used for generating the EDT SS304 surfaces. Thus, we arrive at a comprehensive characterization of EDT SS304 surface topography. We employ a 3D optical profilometer known as Alicona Infinite Focus Microscope for scanning the surface topography of EDT SS304 samples. Readings are taken at each quadrant of the textured circular region on SS304 samples, at 10× magnification. Readings are repeated at these quadrants a few times and averaged to reduce errors. We used MountainsMap software to process the scanned data and to evaluate the different areal texture parameters required. Initially, the scanned sur­ face is leveled, and then, the outliers are removed. Form and waviness

components are filtered on the scanned surface to get roughness (S-L) surface, which has only the roughness components. A Gaussian filter with a suitable cut-off wavelength (λc ) needs to be applied to obtain the S-L surface. It is essential to select a proper λc for filtering. The average inter-peak and inter-valley distance on the surface topography was evaluated to be 100 μm, at the lowest discharge energy (0.5 mJ) used for EDT experiments. As per the usual rule of thumb, the cut-off wavelength (λc ) is selected as five times this inter-peak distance. Thus, λc is 500 μm or 0.5 mm. Short-wave (λs ) filtering also needs to be applied on the surface. ISO suggests a bandwidth (λc /λs ) of 300:1 for S-filtering or short-wave filtering [23]. Thus, S-filter is performed with a λs value of 1.67 μm. The topography images and areal texture parameters are measured on this S-L surface obtained after filtering. The areal feature parameters such as ten-point height (S10z), density of peaks (Spd), and arithmetic mean peak curvature (Spc) are evaluated at 5% Wolf pruning. 3. Results and discussion In this section, initially, we analyze the scanned surface topography images to study the surface characteristics. Further, EDT SS304 surfaces are characterized to obtain a quantitative representation of the textured surfaces, to characterize the surface microstructures and their distribu­ tions, and to analyze surface functionality in various applications. 386

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 3. Surface topographies (a–f) obtained at different discharge energies (E).

3.1. Topography analysis Topography analysis is performed in terms of scanned topography images. The surface topographies obtained at varying values of discharge energies are represented in Fig. 3. The authors observe that surface topographies obtained at all levels of discharge energies display random distribution of peaks and valleys in shape, size, and location. On the other hand, we also observe that the dimensions of peaks and valleys tend to increase with the rise in discharge energy used for texturing. This increase in peak and valley dimensions is indicated in the increase in maximum height (Sz) of the surface from 26.6 to 95.0 μm (257% in­ crease) as discharge energy increases from 0.5 to 500 mJ. As the discharge energy increases, it leads to a corresponding increase in spark eroded cavity dimensions, and these larger cavities lead to taller peaks and deeper valleys. The increase in dimensions of the peaks and valleys result in a general increase in the arithmetic mean height (Sa), which is a common field parameter used to represent 3D roughness. We observe that an increase in discharge energy from 0.5 to 500 mJ results in an increase in Sa from 1.64 to 7.54 μm (360% increase). Thus, varying the discharge energy in the EDT process can help achieve textured surfaces of low to high roughness. On the contrary, the increase in peak and valley dimensions with an increase in discharge energy, results in an overall decrease in the density of peaks (Spd), which is a feature parameter used to quantify peak distribution on the surface. We identify the density of peaks (Spd) as a better representation of peak distribution on the EDT surface as compared to the more commonly used field parameter known as the density of summits (Sds). This is because the former only considers the significant peaks, whereas the latter also in­ cludes smaller and insignificant peaks that do not have a significant role in contact applications. An increase in discharge energy from 0.5 to 500 mJ results in a decrease in Spd from 251.9 to 105.0 /mm2 (58% reduction). Thus, high discharge energies result in textured surfaces with fewer peaks and valleys (as seen in Fig. 3). It is also important to characterize the surface topographies in terms of peak count distribution at various heights. Spd only quantifies the number of significant peaks, and Sz only gives the height of the tallest peak. Therefore, analyzing the peak count distribution will give an idea about the height range for a majority of the peaks. The tallest peak can wear off initially during contact applications, which results in a height

Fig. 4. Peak count distribution histograms for surface topographies at discharge energies (E) of (a) 0.5 mJ, (b) 50 mJ, and (c) 500 mJ.

reduction of the textured surface. Thus, the height range of the majority peaks will have a significantly greater influence on the surface func­ tionality, as compared to that by the tallest peak. Therefore, peak count distribution histograms for surface topographies obtained with discharge energy values of 0.5, 50, and 500 mJ are compared (see Fig. 4). We observe that for all the EDT surface topographies considered in Fig. 4, the peak count distribution for various heights follows a normal distribution. Considering the peak count distribution histogram of topography obtained at 0.5 mJ (see Fig. 4(a)), we observe that even though the highest peak height is near 25 μm, the unit evaluation area of the surface has only two peaks at that height. The majority (≈66 nos) of peaks on this surface are at the height ∼17 μm. This due to the random nature of surfaces generated using the EDT process. We also infer that at low discharge energies of 0.5 mJ, the normal distribution is compara­ tively steep, with the highest peak count of 66 peaks/mm2 at the low height of 17 μm (see Fig. 4(a)). However, as the discharge energy in­ creases, the normal distribution tends to become flattened and shifts its mean to higher surface heights. This change in the peak count histogram of surface topographies as discharge energy increases to a value of 50 mJ 387

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 5. Variation in roughness estimation parameters with discharge energy (E).

Fig. 6. Surface profiles at random locations of textured surfaces generated at different discharge energy (E).

(see Fig. 4(b)), can be observed by a reduction in the highest peak count to 29 peaks/mm2 , which is at a higher surface height of 45.5 μm. As the discharge energy is further increased to 500 mJ (see Fig. 4(c)), we observe that the highest peak count reduces to 16 peaks/mm2 , which is at an even higher surface height of 60 μm.

discharge energy. We understand from the graphs that Sa and Sq show an increase with an increase in discharge energy (see Fig. 5(a) and (b)). Thus, the roughness of the textured surfaces, in terms of common roughness indicators, such as Sa and Sq, increases with an increase in discharge energy. This is due to larger peaks and valleys generated at high discharge energies, as discussed in the previous section. We can observe this increase in heights of peaks and valleys with an increase in discharge energy by making a visual comparison of surface profiles evaluated at random locations on these textured surfaces (see Fig. 6). The curve fit equations for Sa and Sq show a good fit with high R2 values, indicating a consistent trend for these parameters with varying discharge energy. It is also observed that for the initial increase in discharge energy from 0.5 to 5 mJ (10 times increase), Sa and Sq show a significant increase from 1.78 to 3.65 μm (105% increase) and 2.28 to 4.65 μm (104% increase), respectively. Whereas for further increase in discharge energy from 5 to 50 mJ (10 times increase), Sa and Sq show increase from 3.65 to 5.45 μm (49% increase) and 4.65 to 7 μm (51% increase), respectively. At last, the increase in discharge energy from 50 to 500 mJ (10 times increase), Sa and Sq only show an increase from 5.45 to 7.35 μm (35% increase) and 7 to 9.58 μm (37% increase), respectively. This gradual decline in percentage increases of Sa and Sq with increasing discharge energy is because the crater dimensions do not increase linearly with an increase in discharge energy. This trend of

3.2. Quantitative representation of textured surface A quantitative representation of the EDT surfaces is required as an identifier of the topographies generated. The section aims at developing a quantitative representation of the textured surface topographies under two heads: roughness estimation and surface heights. 3.2.1. Roughness estimation Roughness estimation is aimed at forming a representation of the roughness of the textured surface. This enables classification of the textured surface as a fine or a rough finish. The areal texture parameters used in roughness estimation are arithmetic mean height (Sa), and root mean square height (Sq). Their variation with discharge energy is rep­ resented in Fig. 5. It is observed that there are two sets of data at three discharge energy levels of 100, 250, and 375 mJ. Such double data sets were because two different combinations of current, pulse on-time, and voltage used for these experiments, which resulted in the identical

Fig. 7. Variation in surface height parameters with discharge energy (E). 388

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 8. Variation in surface symmetry parameters with discharge energy (E).

crater dimensions is due to a larger volume of material to be eroded (as the volume is the third power of crater dimensions) and an increase in molten material resolidification, as discharge energy increases.

3.2.2. Surface heights We undertake this analysis for forming an estimate of surface heights. This forms another mode of quantitative representation of the surface topography. This analysis is performed with the help of two areal

Fig. 9. Projected area percentages above and below mean plane for different discharge energy (E). 389

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 10. Variation in surface isotropy/anisotropy parameters with discharge energy (E).

texture parameters: maximum height (Sz) and ten-point height (S10z). The variation of Sz and S10z with discharge energy is shown in Fig. 7. We understand that from Fig. 7(a) that the maximum height (Sz) of the surface topography shows an increasing trend with discharge energy. This increase in Sz can be attributed to the increase in crater dimensions with discharge energy, as explained before. Ten-point height (S10z), which takes into consideration the five topmost and bottom-most surface points, also display an increasing trend with an increase in discharge energy (see Fig. 7(b)). S10z, an areal feature parameter, is a more reliable measure for maximum peak-tovalley height of the surface compared to Sz, as it is not significantly affected by a single surface outlier as that by Sz. Both Sz and S10z curve fit equations display a good fit with high R2 values.

above and below mean plane (see Fig. 9(a)). However, for the topog­ raphy generated at 5 mJ, we observe that the projected area percentage below mean plane (52.11%) is larger than that above mean plane (47.89%) (see Fig. 9(b)). As discharge energy is increased to 50 mJ, the projected area percentage of points below the mean plane increases to 54% (see Fig. 9(c)). And for topography generated at 500 mJ, this pro­ jected area percentage further increases to 55.86% (see Fig. 9(d)). This confirms that as the discharge energy increases, we observe that the surface tends to become asymmetric with more points lying below mean plane as compared to those lying above the mean plane. This shift of surface points could be because the crater dimensions increase with an increase in discharge energy, which leads to larger valley regions (formed by craters) compared to the peak regions (formed between craters). The curve fit model for Ssk in terms of E shows a good fit with a high R2 value of 0.8298. Kurtosis (Sku) is an indicator of the presence of sharp (Sku > 3) or blunt (Sku < 3) peaks on the surface. A perfectly symmetric surface will have a Sku value of 3. It is observed that the Sku values of the textured surfaces are more than 3 at all discharge energy levels (see Fig. 8(b)). This indicates that the textured surfaces display slightly sharper peaks and valleys as compared to those on a perfectly symmetrical surface. However, Sku does not show any significant change with discharge en­ ergy. This could be understood from the curve fit equation of Sku in terms of E, which has a poor R2 value. Sku tends to vary slightly around a value of 4.

3.3. Characterization of microstructures and their distributions The surface topographies generated using EDT have microstructures of irregular shapes and sizes, and are randomly distributed over the surface. Hence, the characterization of microstructures and their dis­ tribution on EDT surface topographies is essential to understand the contours and appearance of the surface. In this section, we undertake this characterization with five purposes: surface symmetry, isotropy or anisotropy, peak distribution, peak roundness or sharpness, and surface complexity. 3.3.1. Surface symmetry The EDT surface topographies needs to be analyzed to understand whether they are symmetric about their mean plane or not. This aspect is analyzed in terms of parameters such as skewness (Ssk) and kurtosis (Sku). Skewness (Ssk) of the EDT surface topographies indicates the degree of symmetricity of the surface. A perfectly symmetric surface has an Ssk value of 0. A positive Ssk indicates that more surface points lie below the mean plane than those above, whereas a negative Ssk in­ dicates that a majority of surface points are above the mean plane. Since the mean Ssk values of the textured surfaces under consideration are positive at all discharge energy levels (see Fig. 8(a)), we infer that a majority of the surface points lie below the mean plane. Ssk shows a large variation for different locations of the same EDT surface topog­ raphy at some discharge energy values. This is because Ssk is very sen­ sitive to surface outliers, which may be present. We observe that the skewness value generally tends to increase with the increase in discharge energy. This indicates that as the EDT process takes place at higher discharge energy, the textured surfaces tend to be more positively skewed with an even higher majority of surface points lying below the mean plane. This aspect is confirmed by slicing the surface topography using its mean plane into two groups of points: those lying above mean plane and those lying below mean plane. The projected area images for points lying above the mean plane (white color) and points lying below mean plane (black color) for surface topographies generated at different discharge energy values are represented in Fig. 9. It is observed that at a low discharge energy value of 0.5 mJ, the topography is almost sym­ metric with comparable projected area percentages for points lying

3.3.2. Isotropy and anisotropy The directionality or non-directionality of surface properties influ­ ence the topography’s functionality in different applications. The isotropic or anisotropic nature of the surface topographies is analyzed in terms of parameters such as texture aspect ratio (Str), and texture di­ rection (Std). The variation of these parameters with discharge energy is represented in Fig. 10. Texture aspect ratio (Str) is a parameter that indicates whether the surface topography is dominantly isotropic or anisotropic. Str can have a value between 0 to 1. An ideal anisotropic surface has an Str value of 0, whereas an ideal isotropic surface has an Str value of 1. We observe that Str displays values near 1 for surface to­ pographies generated at all discharge energy values (see Fig. 10(a)). This indicates that the surface topographies generated at all values of discharge energy have dominant isotropic properties. However, it is observed that as discharge energy increases from 0.5 to 500 mJ, Str shows a decrease from 0.94 to 0.82. From this, we understand that the surface shows a higher isotropic nature at low values of discharge energies. Texture direction (Std) represents the angular direction of the dominant lay of the surface topography. For directional surfaces, polar direction graphs will show a single prominent direction. We observe from Fig. 10(b) that Std shows very large variations for topographies scanned at different locations of the same sample. Such substantial variation is observed because the textured surfaces have dominant isotropic nature, and thus, there is no dominant texture direction for the surface topographies. To verify this, we plot polar texture direction 390

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 11. Polar direction graphs (a–d) obtained for surface topographies obtained at similar parameter settings (50 A, 50 μs, & 100 V).

Fig. 12. Variation in peak distribution parameters with discharge energy (E).

Fig. 13. Surface topographies obtained at E = 100 mJ with different parameter combinations. 391

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 14. Variation in density of peaks (Spd) with operating parameters.

graphs for surface topographies scanned at four different locations of the textured surface at a parameter setting (50 A, 50 μs, & 100 V) are shown in Fig. 11(a–d). From these polar graphs, we observe that there are no individual prominent arms in any direction in the polar direction graphs. Thus, we can conclude that no direction has significant prominence over the other directions, which indicates a high isotropic nature of the EDT surfaces.

insignificant summits are merged to nearby significant peaks. Spd is a better parameter to represent EDT surfaces, compared to Sds as it takes into consideration only significant peaks. Spd displays a decrease with an increase in discharge energy, as per the curve fit model (see Fig. 12 (b)). However, the model does not give a good fit, according to its low R2 value. It was also observed that Spd of surface topographies obtained for similar discharge energies, but different combinations of operating pa­ rameters, display large differences (see Fig. 13). Hence, the variation of Spd with current, pulse on-time, and voltage is thus of interest and is plotted in Fig. 14. It is understood that a significant variation in Spd is only observed with an increasing pulse on-time (ton ). Spd decreases with an increasing ton . Spd displays a slight decrease with an increasing discharge current (I) and a decreasing gap voltage (V). Hence, we conclude that the peak density of the surface decreases with an increase in pulse duration. Furthermore, this decrease in peak density can lead to an increase in contact stresses of the surface. The microstructures or peaks on the textured surfaces are also studied using SEM images to form a better understanding of their shape, size, and distribution. SEM images of textured surfaces obtained at discharge energy values of 200 mJ and 2000 mJ are compared in Fig. 15. For both textured surfaces, a random distribution of microstructures in location, size, and shape can be observed. This is due to the stochastic nature of spark occurrence during the EDT process. Individual discharge

3.3.3. Peak distribution Peak distribution is essential to be analyzed to get an understanding of the microstructure distribution on the EDT surface topography. The parameters analyzed for this purpose are density of summits (Sds) and density of peaks (Spd). The influence of discharge energy on these pa­ rameters is represented in Fig. 12. Density of summits (Sds) represents the number of summits per unit evaluation area. As Sds decreases, the contact stresses increase, which leads to surface damage. Hence, low Sds is not desirable in contact applications. Sds does not show any identifi­ able trend against a change in discharge energy (see Fig. 12(a)). The curve fit equation for Sds does not give a good fit. Hence, we proceed to analyze the density of peaks (Spd), which is a more reliable parameter to characterize the peak distribution on the surface. Density of peaks (Spd) represents the number of significant peaks in a unit of evaluation area. For the evaluation of Spd, all smaller and

Fig. 15. SEM images of textured surfaces at different energy levels. 392

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 16. Variation in peak roundness or sharpness parameters with discharge energy (E).

craters are distinguishable for textured surface generated at 200 mJ (see Fig. 15(a)). Molten material splashing results in formation of microglobules which are visible on the textured surface. Surface deformities such as micro-cracks, blow holes, and micro-cracks are very few or ab­ sent in surface topographies generated at 200 mJ. Similar was the case for textured surfaces generated at discharge energies lower than 200 mJ. However, for textured surface generated at a very high discharge energy value of 2000 mJ, we observe a significant presence of surface de­ formities such as micro-cracks, blow holes, and micro-pores (see Fig. 15 (b)). These surface damages are caused due to the process being more chaotic at large discharge energies and a large amount of molten ma­ terial resolidification at high pulse on-times. These deformities give the textured surfaces generated at very high discharge energies a porous nature. Due to this porous nature, optical profilometer fails to capture points which come inside these surface damages and gives an incom­ plete or improper reading. Hence, we did not consider surface topog­ raphies generated at very high discharge energies in current surface topography characterization. We also observe that generally the mi­ crostructures or peaks on surfaces textured at high discharge energy are larger in size. Since microstructures are formed mostly at crater in­ tersections, they are placed farther apart in topographies generated at higher discharge energies and thereby, leading to a decrease in peak density.

Fig. 17. Variation in fractal dimension (Sfd) with discharge energy (E).

represents the degree of surface complexity. Higher the Sfd value, the higher will be the surface complexity. As per Kang et al. [24], Sfd offers a better representation of the surface topography, being independent of the evaluation area, compared to the conventional 3D roughness pa­ rameters such as Sa and Sq, which are dependent on the size of evalu­ ation area. The variation of Sfd with discharge energy is given in Fig. 17. Fractal dimension decreases with an increase in discharge energy, indicating that the surface complexities decrease with an increase in discharge energy.

3.3.4. Peak roundness or sharpness The shape of the peaks, whether they are round or sharp, is also important to be analyzed. Round or blunt peaks can handle contact stresses better and are not easily worn off contrary to sharp peaks. The presence of sharp peaks increases the frictional forces encountered during contact applications. The shape of the peaks on EDT surface to­ pographies are analyzed using two parameters: arithmetic mean summit curvature (Ssc) and arithmetic mean peak curvature (Spc). The variation of these parameters with discharge energy is represented in Fig. 16. Arithmetic mean summit curvature (Ssc) indicates whether the summits are sharp or rounded. Surface topographies with sharp peaks have a high Ssc value, whereas those with rounded peaks have a low Ssc value. Ssc is found to increase with an increase in discharge energy (see Fig. 16(a)). Therefore, the summits tend to be sharper as the topographies are generated using higher discharge energies. Arithmetic mean peak curvature (Spc), an areal feature parameter, can be a better representation of peak shapes than arithmetic mean summit curvature (Ssc). Spc represents the average of principal curva­ tures of significant peaks. In contrast, Ssc considers even small and insignificant summits. A low Spc indicates rounded peaks, whereas a high Spc indicates sharp peaks. Spc is observed to increase with an in­ crease in discharge energy for EDT surface topographies (see Fig. 16(b)). Thus, we can infer that the peaks tend to be slightly sharper as discharge energy used for surface generation increases.

3.4. Functionality in various applications Certain topographical aspects of the textured surface enhance its functionality for specific applications. An investigation into these topographical aspects in terms of areal texture parameters helps improve the performance of topographies in respective applications. We perform such an investigation in this section under four heads: wetting and sealing properties, wear volume available for running-in period, surface post running-in wear, and lubricant retention. 3.4.1. Wetting and sealing properties Wetting and sealing properties of the surface topography can be analyzed in terms of root mean square gradient (Sdq) and developed interfacial ratio (Sdr). The influence of discharge energy on these pa­ rameters is represented in Fig. 18. Root mean square gradient (Sdq) is evaluated as the root mean square of slopes at all surface points in the evaluation region. A perfectly planar surface has an Sdq value of 0. Sdq is observed to increase with an increase in discharge energy (see Fig. 18 (a)). The developed interfacial ratio (Sdr) gives a measure of the addi­ tional surface area available as a result of the texture over the planar surface area. Thus, a perfectly planar surface has an Sdr value of 0. Discharge energy is observed to have a positive influence on Sdr (see Fig. 18(b)). Such an increase in Sdr indicates that the additional surface area provided by the texture generated increases with an increase in input discharge energy. The curve fit equations of both Sdq and Sdr show a good fit with high R2 values. An increase in Sdq and Sdr with discharge

3.3.5. Surface complexity Surface complexity can be quantified in terms of an areal texture parameter known as the fractal dimension of the surface (Sfd). Sfd 393

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 18. Variation in wetting and sealing parameters with discharge energy (E).

Fig. 19. Measured contact angles (θm ), Sdq, and Sdr at discharge energy (E) values of (a) 0 mJ (non-textured surface), (b) 0.5 mJ, and (c) 250 mJ.

Fig. 20. Representation of functional (volume) parameters in Abbott-Firestone curve for different discharge energy (E). 394

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 21. Representation of functional (stratified) parameters in Abbott-Firestone curve for different discharge energy (E).

energy indicates an improvement in the wetting and sealing properties of the textured surfaces [25]. Guo et al. [26] analyzed the sealing effect in terms of a parameter known as area ratio, which is similar to Sdr. They found that higher the area ratio of the texture, more efficient is the sealing effect. They have also reported that the preferable area ratio for good sealing is 5 to 20%, which is similar to Sdr range obtained in current work using EDT process. Since surface textures generated using EDT at higher discharge energies have higher Sdr as compared to that of textures generated at lower discharge energies, the former offers more efficient sealing. The improvement in wetting property of textured surfaces generated at higher discharge energies is confirmed by performing a few contact angle measurement experiments. This also enables checking of variation in wetting property with Sdq and Sdr parameters. The contact angle measurements were made using water droplets (of size 1.8 μl) on the textured surfaces, following the sessile droplet method. The measured contact angle (θm ) on non-textured and textured surfaces at different energy levels, and their corresponding Sdq and Sdr values are given in Fig. 19. The contact angle reading on the non-textured surface was observed to be 42.5◦ (see Fig. 19 (a)). This indicates that the nontextured SS304 has a hydrophilic or water-attracting nature, as θm is

less than 90◦ . In contrast, we observe that for both textured surfaces, θm is greater than 90◦ , indicating that they are hydrophobic or waterrepelling in nature. This indicates that the EDT process results in changes in surface chemistry which lead to wetting property change. It is observed that at a low discharge energy value of 0.5 mJ, the textured surface has Sdq and Sdr values of 0.32 and 4.9%, respectively, and correspondingly θm measured has a value of 112◦ (see Fig. 19(b)). As discharge energy increases to 250 mJ, Sdq and Sdr on the textured sur­ face increase to 0.57 and 12%, respectively, and correspondingly θm measured on the textured surface increases to 127.5◦ (see Fig. 19(c)). Therefore, we can conclude that with an increase in discharge energy, Sdq and Sdr parameters increases, which leads to an improvement in the wetting property of textured SS304 surfaces. For the next three studies in this section, we utilize functional pa­ rameters, which fall into two categories: volume (includes Vmp, Vmc, Vvc, and Vvv) and stratified (includes Sk, Spk, and Svk) parameters. These parameters are derived from the Abbott-Firestone curve or bearing area curve of the surface topography. The functional (volume) and functional (stratified) parameters for EDT surface topographies generated at different energy levels are represented in Abbott-Firestone curves, as shown in Figs. 20(a–d) and 21 (a–d), respectively. Their

Fig. 22. Variation in running-in wear volume parameters with discharge energy (E). 395

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 23. Variation in parameters representing surface post running-in wear with discharge energy (E).

variations are discussed in the upcoming subsections.

23 (a), it is understood that Vmc increases with an increase in discharge energy. An increase in discharge energy from 0.5 to 500 mJ, leads to an increase in Vmc from 1.972 × 10− 3 to 8.206 × 10− 3 mm3 /mm2 (316% increase) (see Fig. 20(a–d)). From Fig. 20(a–d), we also understand that for incremental increases from 0.5 to 5 mJ, 5 to 50 mJ, and 50 to 500 mJ, Vmc shows corresponding increases of 117%, 41%, and 36%, respectively. Core void volume (Vvc) represents the core space available on the surface after the running-in period is completed. We understand that Vvc increases with an increase in discharge energy (see Figs. 20 and 23 (b)). An increase in Vvc from 2.695 × 10− 3 to 1.266 × 10− 2 mm3 /mm2 (370% increase) is resulted when discharge energy increases from 0.5 to 500 mJ (see Fig. 20(a–d)). We also observe that incremental increases in discharge energy from 0.5 to 5 mJ, 5 to 50 mJ, and 50 to 500 mJ, results in corresponding increases of 112.5%, 57%, and 41% in Vvc, respec­ tively (see Fig. 20(a–d)). Thus, the core space available on surface post running-in wear increases for topographies generated using higher discharge energies. Core roughness depth (Sk) is the height difference for material ratios 0% and 100% on the equivalent line. Sk is a measure of the peak-tovalley height of the surface with predominant peaks and valleys removed. From Figs. 21 and 23 (c), we understand that Sk increases with discharge energy. We observe that an increase in discharge energy from 0.5 to 500 mJ results in an increase in Sk from 5.683 to 22.41 μm (294% increase) (see Fig. 21(a–d)). We also observe increases in Sk of 115%, 38%, and 33% for corresponding increments in discharge energy from 0.5 to 5 mJ, 5 to 50 mJ, and 50 to 500 mJ, respectively (see Fig. 21 (a–d)). This increase in Sk with an increase in discharge energy indicates that the surface area for load-carrying after the running-in period of the texture is higher when generated at higher discharge energy.

3.4.2. Wear volume available for running-in period Wear volume available for running-in period represents the amount of material removed from the surface during the running-in period of contact applications. This surface aspect is studied in terms of peak material volume (Vmp) and reduced peak height (Spk). Peak material volume (Vmp) represents the volume of peak material eroded from the surface during its running-in period in contact applications. From Figs. 20 and 22 (a), it is understood that Vmp increases with an increase in discharge energy. As the discharge energy increases from 0.5 to 500 mJ, Vmp increases from 1.161 × 10− 4 to 7.153 × 10− 4 mm3 /mm2 (516% increase) (see Fig. 20(a–d)). For various increments of discharge energy from 0.5 to 5 mJ, 5 to 50 mJ, and 50 to 500 mJ, the corre­ sponding increases in Vmp are 123%, 69%, and 63.5%, respectively (see Fig. 20(a–d)). Thus, there is a significant increase in the wear volume available for running-in period, as discharge energy is increased. Reduced peak height (Spk) is the height difference between the Abbott-Firestone curve and the equivalent line at 0% material ratio. It represents the average of peak heights above the core surface. From Figs. 21 and 22 (b), we understand that Spk increases with an increase in discharge energy. For an increase in discharge energy from 0.5 to 500 mJ, Spk increases from 2.325 to 14.64 μm (530% increase) (see Fig. 21(a-d)). For various increments of discharge energy from 0.5 to 5 mJ, 5 to 50 mJ, and 50 to 500 mJ, the corresponding increases in Spk are 122.5%, 75%, and 62%, respectively (see Fig. 21(a–d)). This in­ crease in Spk indicates that the surface topography generated at higher discharge energy comprises taller peaks compared to those on topog­ raphies generated at low discharge energy. The presence of high peaks increases the volume available to be worn-off during the running-in period. Thus, a study of variation of Vmp and Spk with discharge en­ ergy indicates an increase in running-in wear volume.

3.4.4. Lubricant retention EDT surface topographies have previously been employed in the texturing of rake face of tool inserts for lubricant retention leading to a reduction in cutting forces [3]. Thus, an analysis of the lubricant retention property of the EDT surface topography is critical. It is also desired to understand the variation in lubricant retention for surface topographies generated at different discharge energies. Lubricant retention of EDT surface topographies is analyzed in terms of two pa­ rameters: valley void volume (Vvv) and reduced valley height (Svk). Valley void volume (Vvv) represents the volume available for lubricant retention on the surface. We understand that Vvv increases with an

3.4.3. Surface post running-in wear After the completion of running-in wear, the resultant surface is the one that takes part in contact applications for a majority of the com­ ponent’s running life. Thus, it is essential to characterize this aspect of the EDT surface topography. The surface post running-in wear is analyzed in terms of parameters such as core material volume (Vmc), core void volume (Vvc), and core roughness depth (Sk). Core material volume (Vmc) represents the surface volume that does not play any significant role in contact applications or lubrication. From Figs. 20 and 396

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

Fig. 24. Variation in lubricant retention parameters with discharge energy (E).

Fig. 25. Furrows on surface topographies generated at different discharge energy (E).

increase in discharge energy (see Figs. 20 and 24 (a)). An increase in discharge energy from 0.5 to 500 mJ results in a corresponding increase in Vvv from 2.459 × 10− 4 to 7.735 × 10− 4 mm3 /mm2 (214.5% in­ crease) (see Fig. 20(a–d)). For increments of discharge energy from 0.5 to 5 mJ, 5 to 50 mJ, and 50 to 500 mJ, the corresponding percentage increases in Vvv are 118%, 36.5%, and 6%, respectively (see Fig. 20 (a–d)). Thus, the lubricant retention capacity of surface topographies increases when they are generated using higher discharge energies. Reduced valley height (Svk) is the height difference between the equivalent line and the Abbott-Firestone curve at 100% material ratio. It represents the average of valley depths below the core surface. Svk is found to increase with an increase in discharge energy (see Figs. 21 and 24 (b)). We observe that Svk increases from 2.103 to 5.608 μm (167% increase) as discharge energy increases from 0.5 to 500 mJ (see Fig. 21

(a–d)). For incremental increases in discharge energy from 0.5 to 5 mJ, 5 to 50 mJ, and 50 to 500 mJ, there are 127% increase, 37% increase, and a 17% decrease, respectively (see Fig. 21(a–d)). A higher Svk for to­ pographies generated at higher discharge energies indicates a higher depth of the area for liquid accumulation, thereby improving lubrication properties. Thus, an increase in Svk with discharge energy, combined with an increase in Vvv, indicates an improvement in the lubrication retention capacity of the EDT surface topographies. Furrow analysis of topographies is also performed to study the change in lubricant retention capability of textured surfaces generated at different discharge energy values. The furrows on the surface texture act as a lubricant reservoir in contact applications. Hence, as furrow dimensions increases, the lubricant retention capability of the surface texture also increases. The furrow images of topographies generated at 397

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

lying below the mean plane, while accompanied by a slight reduction in the surface’s isotropic nature. Textured surfaces generated at higher discharge energies display a reduction in peak density, but the indi­ vidual peaks tend to become sharper. Higher discharge energy also re­ sults in a reduced surface complexity of the textured surface. The textured surfaces show high wetting and sealing properties as a conse­ quence of being generated at high discharge energy. Similarly, textures generated at high discharge energy tends to provide higher running-in wear volume, more surface area for improved load carrying capacity, and higher lubricant retention capacity.

Table 1 Concluding remarks of surface characterization of EDT SS304 surfaces. Surface topography aspect

Quantitative representation of the textured surface

Specific purpose

Low discharge energy

High discharge energy

Roughness estimation (in terms of Sa and Sq)

Low surface roughness

High surface roughness

Surface heights (in terms of Sz and S10z)

Low peaks and shallow valleys

High peaks and deep valleys

Symmetry (in terms of Ssk and Sku)

Lower surface asymmetry

Higher surface asymmetry

Isotropy (in terms of Str and Std) Characterization of microstructures and their distributions

Functionality in various applications

Peak distribution (in terms of Sds and Spd)

Higher isotropic nature

Lower isotropic nature

Higher peak density

Lower peak density

Peak roundness or sharpness (in terms of Ssc and Spc)

Rounded peaks

Sharper peaks

Surface complexity (in terms of Sfd)

Slightly more complex surface texture

Slightly less complex surface texture

Wetting and sealing properties (in terms of Sdq and Sdr)

Lower wetting and sealing properties

Higher wetting and sealing properties

Lower wear volume for running-in

Higher wear volume for running-in

Surface post running-in wear (in terms of Vmc, Vvc, and Sk)

Lower surface area for load carrying

Higher surface area for load carrying

Lubricant retention (in terms of Vvv and Svk)

Lower lubricant retention capacity

Higher lubricant retention capacity

Running-in wear volume available (in terms of Vmp and Spk)

Acknowledgement We want to express our gratitude towards Department of Science and Technology, Advance Manufacturing Technology Committee, Govern­ ment of India, for supporting this work under the project titled “Generating Functional Quality Textured Surfaces using Electrical Discharge Machining for Biomedical And Machining Applications” (DST File No: DST/TSG/AMT/2015/239). We also thank Digital Surf, France, for exceptionally extending the free trial of their MountainsMap® soft­ ware for this study. Conflict of interest: The authors declare no conflict of interest. References [1] Aspinwall D, Wise M, Stout K, Goh T, Zhao F, El-Menshawy M. Electrical discharge texturing. Int J Mach Tools Manuf 1992;32(1):183–93. https://doi.org/10.1016/ 0890-6955(92)90077-T. ´kov´ [2] Harcuba P, Baˇc´ akov´ a L, Str´ aský J, Baˇca a M, Novotn´ a K, Janeˇcek M. Surface treatment by electric discharge machining of Ti-6Al-4V alloy for potential application in orthopaedics. J Mech Behav Biomed Mater 2012;7:96–105. https:// doi.org/10.1016/j.jmbbm.2011.07.001. [3] Koshy P, Tovey J. Performance of electrical discharge textured cutting tools. CIRP Ann – Manuf Technol 2011;60(1):153–6. https://doi.org/10.1016/j. cirp.2011.03.104. [4] Jithin S, Bhandarkar UV, Joshi SS. Establishing edm as a method for inducing hydrophobicity on ss 304 surfaces. In: Shunmugam MS, Kanthababu M, editors. Advances in micro and nano manufacturing and surface engineering. Singapore: Springer Singapore; 2019. p. 731–40. [5] He Z, Luo S, Liu C, Jie X, Lian W. Hierarchical micro/nano structure surface fabricated by electrical discharge machining for anti-fouling application. J Mater Res Technol 2019;8(5):3878–90. https://doi.org/10.1016/j.jmrt.2019.06.051. [6] Bui VD, Mwangi JW, Schubert A. Powder mixed electrical discharge machining for antibacterial coating on titanium implant surfaces. J Manuf Process 2019;44 (November 2018):261–70. https://doi.org/10.1016/j.jmapro.2019.05.032. [7] Wang H, Chi G, Jia Y, Yu F, Wang Z, Wang Y. A novel combination of electrical discharge machining and electrodeposition for superamphiphobic metallic surface fabrication. Appl Surf Sci 2020;504(July 2019):144285. https://doi.org/10.1016/ j.apsusc.2019.144285. [8] Ramasawmy H, Blunt L. Effect of EDM process parameters on 3D surface topography. J Mater Process Technol 2004;148(2):155–64. https://doi.org/ 10.1016/S0924-0136(03)00652-6. [9] Ramasawmy H, Blunt L, Rajurkar K. Investigation of the relationship between the white layer thickness and 3D surface texture parameters in the die sinking EDM process. Precis Eng 2005;29(4):479–90. https://doi.org/10.1016/j. precisioneng.2005.02.001. [10] Deltombe R, Kubiak KJ, Bigerelle M. How to select the most relevant 3D roughness parameters of a surface. Scanning 2014;36(1):150–60. https://doi.org/10.1002/ sca.21113. [11] Jithin S, Shetye SS, Rodrigues JJ, Mhetre KS, Mastud SA, Joshi SS. Analysis of electrical discharge texturing using different electrode materials. Adv Mater Process Technol 2018;4(3):466–79. https://doi.org/10.1080/ 2374068X.2018.1457350. ´ ´ [12] Swiercz R, Oniszczuk-Swiercz D. Experimental investigation of surface layer properties of high thermal conductivity tool steel after electrical discharge machining. Metals 2017;7(12). https://doi.org/10.3390/met7120550. ´ ´ [13] Swiercz R, Oniszczuk-Swiercz D. Investigation of the influence of reduced graphene oxide flakes in the dielectric on surface characteristics and material removal rate in EDM. Materials 2019;16(6). https://doi.org/10.3390/ ma12060943. [14] Aich U, Banerjee S. Characterizing topography of EDM generated surface by time series and autocorrelation function. Tribol Int 2017;111:73–90. https://doi.org/ 10.1016/j.triboint.2017.02.016. [15] Aich U. Investigation for the presence of chaos in surface topography generated by EDM. Tribol Int 2018;120(January):411–33. https://doi.org/10.1016/j. triboint.2018.01.013.

discharge energy values of 0.5 mJ, 5 mJ, 50 mJ, and 500 mJ, are shown in Fig. 25. It could be understood that the furrow parameters such as maximum and mean depth of furrows increases as topographies are progressively generated at higher discharge energies. However, the mean density of furrows does not vary much among the textured sur­ faces. As the maximum and mean depth of the furrows increases, the lubricant retention capability of the textured surface increases. Thus, we can conclude from furrow analysis that the surfaces generated at higher discharge energy have higher lubricant retention capacity. 4. Concluding remarks In this work, three-dimensional characterization of surface topog­ raphies generated on SS304 using the EDT process is conducted. An analysis of surface topographies indicates that the microstructure dis­ tribution is random in shape, size, and location. The peak distributions of surface topographies tend to become flattened with their mean shifting to higher heights, as the textures are generated at higher discharge energies. The characterization in terms of areal texture pa­ rameters is performed with three goals: quantitative representation of the textured surface, analysis of surface microstructures and their dis­ tributions, and surface functionality in various applications. The major understandings from the areal texture parameter analysis are summa­ rized in Table 1. It is observed that the EDT process can produce highly rough surface textures with high peaks and deep valleys at higher discharge energies. Also, at high discharge energies, the surface texture tends to be more asymmetric with a higher majority of surface points 398

S. Jithin et al.

Journal of Manufacturing Processes 60 (2020) 384–399

[16] Jithin S, Bhandarkar U, Joshi S. Characterization of surface topographies generated using circular- and cylindrical-face EDT. Surf Topogr: Metrol Prop 2020;(October). https://doi.org/10.1088/2051-672X/abc320. [17] Hyde JM, Cadet L, Montgomery J, Brown CA. Multi-scale areal topographic analysis of surfaces created by micro-EDM and functional correlations with discharge energy. Surf Topogr: Metrol Prop 2014;2(4). https://doi.org/10.1088/ 2051-672X/2/4/045001. [18] D’Urso G, Giardini C, Quarto M. Characterization of surfaces obtained by microEDM milling on steel and ceramic components. Int J Adv Manuf Technol 2018;97 (5–8):2077–85. https://doi.org/10.1007/s00170-018-1962-5. [19] Izquierdo B, Plaza S, S´ anchez JA, Pombo I, Ortega N. Numerical prediction of heat affected layer in the EDM of aeronautical alloys. Appl Surf Sci 2012;259:780–90. https://doi.org/10.1016/j.apsusc.2012.07.124. [20] Jithin S, Bhandarkar U, Joshi SS. Analytical simulation of random textures generated in electrical discharge texturing. J Manuf Sci Eng 2017;139(11):111002. https://doi.org/10.1115/1.4037322. 1–12. [21] Jithin S, Raut A, Bhandarkar UV, Joshi SS. Finite element model for topography prediction of electrical discharge textured surfaces considering multi-discharge

[22] [23] [24] [25]

[26]

399

phenomenon. Int J Mech Sci 2020;177:105604. https://doi.org/10.1016/j. ijmecsci.2020.105604. Blateyron F. Characterisation of areal surface texture. Springer-Verlag; 2013. https://doi.org/10.1007/978-3-642-36458-7. ISO 3274:1996(E) geometrical product specifications (GPS) – surface texture: profile method – nominal characteristics of contact (stylus) instruments. Geneva, CH: Standard, International Organization for Standardization; 1996. Kang MC, Kim JS, Kim KH. Fractal dimension analysis of machined surface depending on coated tool wear. Surf Coat Technol 2005;193(1–3 SPEC. ISS): 259–65. https://doi.org/10.1016/j.surfcoat.2004.07.020. Reddy VV, Vedantha Krishna A, Schultheiss F, Ros´en B-G. Surface topography characterization of brass alloys: lead brass (CuZn 39 Pb 3) and lead free brass (CuZn 21 Si 3 P). Surf Topogr: Metrol Prop 2017;5(2):025001. https://doi.org/ 10.1088/2051-672X/aa612b. Guo F, Jia X, Gao Z, Wang Y. The effect of texture on the shaft surface on the sealing performance of radial lip seals. Sci China: Phys Mech Astron 2014;57(7): 1343–51. https://doi.org/10.1007/s11433-014-5404-6.