Technical PaperThree-dimensional topography analysis of electrical discharge textured SS304 surfaces
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], orthopedic 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 , , and , 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 considering 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, Ramasawmy and Blunt [8] characterized the surface topographies generated on tool steel using EDM, in terms of areal texture parameters, such as arithmetic mean height (), root mean square height (), density of summits (), material volume (), core void volume ( or ), valley void volume ( or ), and core roughness depth (). They also analyzed the influence of operating factors, such as discharge current 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 () of the textured surface, and areal texture parameters. Since the measurement of usually required destructive methods, the authors aimed to develop regression models to evaluate 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 in terms of gave the best fit. 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 . They found that the copper electrode induced a larger variation in of these surfaces for varying pulse on-time and gap voltage compared to that with the other two electrode materials. Świercz and Świercz [12] performed EDM on high conductivity tool steel and characterized the resultant surface topographies in terms of areal texture parameters such as , , and arithmetic mean summit curvature (). 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 () 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 influence 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 (), 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 themselves. In addition to the experimental surface topography characterizations, 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 characterized 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 information 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 aspects of the surface topography they represent. Moreover, in the available 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 distinguishable features of the surface, such as points, lines, or areas, are scarcely analyzed [22]. These parameters are more influential on surface performance [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 to analyze different aspects of the surface, such as a quantitative representation of the textured surface, characterization of surface microstructures and their distributions, and the surface functionality in various applications, and to analyze the influence of discharge energy on these surface aspects.
Section snippets
Materials and methods
The experimental materials and methodologies used to perform characterization of surface topographies generated using EDT are discussed 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 exchangers. Thus, a comprehensive
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 distributions, and to analyze surface functionality in various applications.
Concluding remarks
In this work, three-dimensional characterization of surface topographies generated on SS304 using the EDT process is conducted. An analysis of surface topographies indicates that the microstructure distribution 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 parameters is performed
Acknowledgement
We want to express our gratitude towards Department of Science and Technology, Advance Manufacturing Technology Committee, Government 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 software for this study.
Conflict of
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