The Influence of Selected Process Parameters on Wire Wear and Surface Quality of Nickel, Titanium and Steel Alloy Parts in WEDM

Abstract

Research on the WEDM process has traditionally focused on analyzing discharge initiation, material removal mechanisms and surface formation from the perspective of the machined part. However, the same phenomena also affect the tool, namely the wire electrode. A comprehensive understanding of the process requires to examine how these effects impact the electrode itself, particularly in terms of wear. Despite its significance, electrode wear in WEDM is not a topic frequently addressed in the literature. The most common method for evaluating wear involves determining the wire wear ratio (WWR), based on the electrode’s weight before and after machining. However, this approach does not provide insight into changes in the microstructure of the electrode surface. This study presents an alternative approach to interpreting wire electrode wear, using surface roughness parameters in relation to the surface texture of the machined workpiece. Measurements were conducted using an optical focus variation microscope. The influence of selected process parameters—including discharge current I_p, pulse-off time t_off and workpiece height h—on selected surface roughness parameters was investigated. The experimental tests were carried out for three alloys representing distinct material groups: 42CrMo4 steel, Inconel 718 nickel alloy, and Ti6Al4V titanium alloy. The results were compared with the roughness parameters of the corresponding machined surfaces. The presented interpretation of the key factors affecting the electrode surface condition after WEDM serves as an initial step in a broader research initiative. It lays the foundation for further studies on wire electrode wear and the development of new wear assessment parameters such as the electrode wear index based on surface texture parameters.

1. Introduction

Electrical Discharge Machining (EDM) is a material removal process alternative to traditional machining, allowing to manufacture parts primarily from electrically conductive materials with high precision and surface quality. The method is based on the phenomenon of spark erosion, where controlled electrical discharges occur between the tool and the workpiece [1]. As a result, EDM can be used to effectively process difficult-to-cut materials and intricate geometries [2,3,4]. In Wire Electrical Discharge Machining (WEDM), a continuously moving wire electrode, usually made from brass, is used as a tool. Both EDM and WEDM is conducted in dielectric fluid, typically deionized water. The discharges produce localized temperatures ranging from 8000 to 12,000 °C, causing material removal through melting and evaporation [5,6,7,8]. High temperatures during the process can lead to the formation of a white layer on the machined surface as well as microcracks in the surface layer which are among the shortcomings of the method [9,10].

Due to the ability to machine complex shapes from materials like nickel-based superalloys, titanium alloys and metal matrix composites with high accuracy and surface quality, which is difficult to achieve using conventional machining methods, WEDM is utilized in manufacturing injection molds in automotive industry, as well as biomedical parts and tool and die industry [11,12,13]. The latest developments in the design of numerically controlled WEDM machines allow for the reduction of the thickness of the heat affected zone (HAZ) close to 0 μm, allowing the method to be increasingly more often adopted in the aerospace industry for manufacturing critical parts, such as fir tree slots in turbine discs [14,15,16,17].

The wire electrode, which serves as a tool in WEDM, undergoes significant mechanical and thermal stresses during the machining process. Electrical discharges occurring between the tool and the part lead to erosion, melting and changes in the mechanical properties of the wire, which can collectively be referred to as wire wear [18,19]. Excessive wear of the wire can lead to lower shape and dimensional accuracy of the produced part due to the irregular shape of the wire and high variation in its diameter. It can also affect spark generation resulting in lower surface quality and increased thickness of a white (recast) layer [20,21,22,23,24]. Furthermore, one of the most important issues of excessive wire wear, especially in industrial practice is increased risk of wire breakage, as it leads to process interruption and increased manufacturing downtime [25,26,27]. Thus, understanding the phenomena occurring during WEDM and mitigating wire wear is of great importance, as it can improve stability of the process, workpiece accuracy and surface quality and reduce production costs. There are many factors influencing wire wear, including discharge energy E, pulse off time t_off, wire and workpiece material (Figure 1).

There are many studies focusing on the effects of process parameters in WEDM on the workpiece quality and machining efficiency. However, fewer experiments have been conducted on wire wear analysis. Most researchers focused on the analysis of WEDM with wire wear considered as one of the factors. Alternatively, some researchers have focused on mitigating the negative impact the wire wear has on the machining process. Kwon and Yang [28] introduced a model aimed at preventing wire breakage and improving process stability by real-time current monitoring. Gamage and DeSilva [29] studied the effects of energy consumption on unexpected wire breakage and resulting workpiece quality. Arunachalam et al. [30] developed a computational model that can evaluate the systematic effects that can lead to wire breakage by determining the stress induced by the electrical discharges as well as by the stress induced by wire erosion. The model is also capable of determining the wire deformation and vibrations in the feed direction. Kinoshita et al. [31] investigated the effect of pulse frequency and mean gap voltage on wire breakage. They developed a monitoring and control system detecting the rise of pulse frequency and prevent the wire from breaking by turning of the generator, thus affecting machining efficiency. Okada et al. [32] studied the effects of flow rate in nozzle jet flushing on wire breakage and the accuracy of the part. High flow rate allows for smooth extraction of debris from the machining gap, but leads to higher wire deflection and vibration. They conducted a numerical analysis of hydrodynamic stress distributions and wire deflection to determine the causes of wire breakage. Bufardi et al. [33] developed a fuzzy-logic hybrid process monitoring method. The model is capable of adjusting pulse off time in order to avoid surface damage. Cabanes et al. [34] proposed a method of wire breakages detection based on discharge energy and discharge current amplitude. Zhang et al. [21] developed a thermophysical model to explore the thermal deformation by calculating the temperature distribution and surface residual stress.

Most papers focused primarily on the analysis of wire wear investigated wire wear ratio (WWR), which is calculated using the following formula:

WWR = WWL/IWW

(1)

where WWL is the loss of the wire weight and IWW is the initial wire weight. Tosun and Cogun [35] investigated the effect of machining parameters, namely pulse duration, circuit voltage, dielectric flushing pressure and wire speed on the wire wear ratio. The effect was evaluated statistically and the level of significance of the parameters was determined with the use of analysis of variance (ANOVA) method. The authors found that increasing the circuit voltage and pulse duration led to an increase in WWR, whereas increasing the dielectric pressure and wire speed decreased WWR. Ramakrishnan and Karunamoorthy [6] proposed a multi response optimization method using Taguchi’s design approach. The authors performed each experiment under different conditions of wire tension, pulse on time, wire feed speed, delay time and current intensity. The machining parameters were optimized with the multi response characteristics of the material removal rate (MRR), surface roughness and wire wear ratio. Kneubühler et al. [36] investigated the wire consumption and wire wear ratio based on the radius and roundness deviation of the wire. They concluded that wire wear increased with more load e.g., higher pulse frequency and energy or lower wire speed. However, they reported that increased wire wear was not the primary cause of wire breakage. Kumar et al. [37] conducted an analysis of wire electrode wear ratio during machining of Al-metal matrix composite. Authors developed a prediction model for WWR using RSM-based Box-Behnken’s design (BBD) in order to obtain optimal machining conditions for minimizing wire wear ratio. Kumar et al. [38] proposed a RSM based desirability approach to optimize various operating parameters of WEDM and studied microstructural behavior at optimum combinations. Input parameters were pulse-off time, pulse-on time, spark gap voltage, peak current, wire feed and wire tension and performance were measured in terms of surface roughness, material removal rate and wire wear ratio.

While WWR provides a straightforward way to quantify wire wear, its use in process optimization and scientific analysis is limited by significant drawbacks. The chemical interactions between the materials of the part and electrode at high temperatures can lead to deposition of workpiece material or other particles in the dielectric fluid on the wire which may affect the accurate measurement of WWR and can result in incorrect assessments of wire wear [36,39]. In addition, WWR being an average measure does not provide any insights into where wear occurs and how it affects the shape of the wire. It does not allow for detection of peak wear zones, which are crucial to detect wire breakage [32]. Furthermore, coated wires are increasingly more common in WEDM due to their better performance in terms of material removal, cutting speed and surface quality as compared with uncoated wires [40]. However, wire wear ratio does not account for different erosion rates of coating and core material, potentially misrepresenting the amount of wire wear [41,42].

The literature review indicates the need for further evaluation of tool electrode wear in the WEDM process, particularly through alternative research approaches. Considering different electrical properties of conductive materials, wire electrode wear has become a subject of increasing research interest—not only in terms of machining parameters optimization, but also considering the type and height of the workpiece material. Accordingly, the following paper presents the results of wire wear analysis which takes into account effects of, beside discharge current I_p and pulse off time t_off, workpiece material and height. The impact of these parameters on selected topographic features of the wire electrode is discussed. To provide a more comprehensive assessment of erosive phenomena during machining, the obtained electrode surface topography results are compared with those of the corresponding machined workpiece surfaces.

2. Materials and Methods

This section presents the experimental setup, materials, machining parameters and methods used in the experimental research.

2.1. Experimental Setup

The machining tests were carried out using a Mitsubishi FA10S (Mitsubishi Electric Corporation, Tokyo, Kantō region, Japan) wire electrical discharge machine. The process was conducted with deionized water as the dielectric fluid. Deionized water is commonly used in industrial wire electrical discharge machining aimed at achieving higher cutting speeds at the expense of the surface quality of the machined parts. The experimental setup is shown in Figure 2.

Machining parameters were primarily selected based on predefined machine settings. While the manufacturer provides recommended settings for material groups such as steels, aluminum alloys, bronzes and carbides, no specific guidelines are available for more challenging materials such as nickel- and titanium-based alloys. Therefore, in this study, the parameters developed for the steel group (material height: 50 mm) were adopted. The wire electrode used was made of brass with a diameter of 0.25 mm. The constant machining parameters are presented in

Table 1. Constant machining parameters.

Technological Parameter	Value
Mean gap voltage Um, notch	45
Wire running speed Ws, notch	12
Wire feedrate vf, mm/min	1.5
Dielectric flow rate Fr, L/min	10
Wire tension Wt, notch	14

.

Two electrical parameters were selected as variable factors in the experiment: discharge current I_p and pulse off time t_off. Additionally, the height of the workpiece h was varied. The machining tests were carried out for three alloys representing three distinct material groups. Discharge current settings ranged from the lowest level (I_p = 4), through intermediate levels (I_p = 11 and I_p = 12), up to the highest machine setting (I_p = 18). The pulse off time (t_off) was limited to two available values: t_off = 1 and t_off = 2. The height of the workpiece (h) was varied across a broad range, from the minimum height of 10 mm to the maximum of 160 mm. The variable parameters and materials are presented in

Table 2. Variable machining parameters and workpiece materials.

Technological Parameter	Value
Current Ip, notch	4, 11, 12, 18
Pulse off time toff, notch	1, 2
Workpiece height h, mm	10, 50, 70, 160
Material group	Alloy grade
Steel	42CrMo4
Nickel alloy	Inconel 718
Titanium alloy	Ti6Al4V

.

2.2. Tool and Workpiece Materials

A commonly adopted for both roughing and finishing machining in WEDM brass wire electrode with a tensile strength of 900 N/mm² and a diameter of 0.25 mm was used in the study. WEDM enables machining of all electrically conductive materials, regardless of their hardness. Three different materials, representing distinct material groups were selected for the experimental tests. Steels were represented by 42CrMo4 (PRO-MAR S.A., Poznań, Poland), a heat-treatable alloy steel commonly used for structural parts of i.e., vehicles or machine tools. Inconel 718 (Shanghai LANZHU super alloy Material Co., Ltd., Shanghai, China) was selected from the high-strength, thermal-resistant alloys (HRSA/HSTR). It is an alloy widely applied for components operating under high loads, such as aircraft engine and gas turbine parts. Ti6Al4V (Xi’an HST Metal Material Co., Ltd., Gaoxin District, Xi’an City, Shaanxi Province, China), a dual-phase α + β titanium alloy was included due to its low thermal expansion and fatigue resistance at high temperatures, which makes it suitable for aerospace applications. Thanks to its biocompatibility it is also widely used in medical applications, particularly for implants.

Table 3. Chemical composition of Inconel 718, Ti6Al4V and 42CrMo4 [43,44].

Alloy	Mass Percent (Mass%)
	C	Si	Mn	Cr	Mo	Ni	Co	Ti	Al	Nb + T	P	S	Fe	Cu
Inconel 718	Max 0.08	Max 0.35	Max 0.35	17.0–21.0	2.8–3.3	50.0–55.0	0.04	0.65–1.15	0.2–0.8	4.75–5.5	Max 0.015	Max 0.015	18.5	Max 0.3
	Ti	C	Fe	N	Al	O	V	H	Y	Other	-	-	-	-
Ti6Al4V	balance	0.0111	0.105	0.0065	6.41	0.176	4.17	0.0015	-	<0.40	-	-	-	-
	C	Si	Mn	P	S	Cr	Mo	Ni	-	-	-	-	-	-
42CrMo4	0.38–0.45	≤0.40	0.60–0.90	≤0.035	≤0.035	0.90–1.20	0.15–0.30	-	-	-	-	-	-	-

presents the chemical compositions of the selected materials.

2.3. Measuring Stand

In the experimental tests surface topography was evaluated using an InfiniteFocus G4 (Alicona Imaging GmbH, Raaba-Grambach, Styria, Austria) focus variation microscope (Figure 3). Surface texture of new and used wire electrodes, as well as the machined workpiece, were measured. All measurements were performed using a ×20 magnification, with 0.2 vertical resolution and pixel size of 0.44 μm × 0.44 μm. For the wire electrodes, the measured area was approximately 0.2 mm × 2 mm, whereas for the machined workpiece 1 mm × 1.3 mm. Ten measurements were conducted for each wire regardless of machining conditions, while nine measurements were taken for each machined surface, except for the sample with a height of 10 mm, for which only six measurements were possible due to limited surface area.

2.4. Measurement Methodology

Data processing and surface texture parameters calculation were performed using SPIP 6.4.2 (Image Metrology A/S, Lyngby, Denmark) software. The following parameters were selected for analysis:

• Sa, µm—Arithmetical mean height
• Sq, µm—Root mean square height of the surface
• Sal, µm—Autocorrelation Length
• Sdr, %—Developed area ratio
• Sdq,—Root mean square gradient of the surface
• Spk + Sk + Svk, µm—sum of Core Roughness Depth, Reduced Peak Height, Reduced Valley Depth which corresponds to total surface height [45,46,47].

The influence of input factors such as t_off and material type on these surface texture parameters was analyzed using ANOVA (for homogeneity of variances) or Welch’s test (in case of heterogeneity of variances). Discharge current I_p and workpiece height h were continuous variables and thus their influence on surface texture parameters was analyzed using regression analysis. A linear model was assumed for I_p and a quadratic model for h. A significance level of α = 0.05 was adopted for all the tests. A methodology framework is presented in Figure 4.

3. Results

This section presents the results of experimental investigations of the effects of selected WEDM parameters on the surfaces of both the wire electrode and the machined workpiece. The analysis begins with the evaluation of the wire electrode surface texture after machining, Subsequently, to provide a complementary view of the discharge phenomena occurring within the spark gap, surface topography of the workpiece is examined. The discussion is limited to those surface texture parameters that exhibited statistically significant variation across the tested conditions.

3.1. Influence of Process Parameters on the Surface Topography of the Wire Electrode

Figure 2 illustrates the differences in surface topography between a new and a worn wire electrode. The surface of the new wire is significantly smoother, with fine irregularities aligned along the wire axis (Figure 5a). The average arithmetical mean height Sa of the new wire was 0.61 μm. The worn wire exhibited a distinctly more developed interfacial area ratio, with noticeably larger peaks and valleys (Figure 5b). The average Sa value across all worn electrode wires (regardless of machining conditions) was 4.04 μm—over 6.5 times higher than that of the new wire. A comparative summary of all analyzed surface texture parameters for new and worn wires is presented in

Table 4. Comparison of surface texture parameters for new and worn wire electrodes (average values from all measured surfaces).

Wire Electrode Condition	Sa, µm	Sq, µm	Spk + Sk + Svk, µm	Sdr, %	Sdq, -	Sal, µm
New	0.61	0.80	3.66	4.36	0.33	10.09
Worn	4.04	5.18	23.76	48.64	1.30	35.77

.

Analysis of the worn wire revealed the presence of isolated sections with no visible signs of electrical discharges (Figure 6). Such areas were observed at the end of the machining process on each of the analyzed wires. In the case of wires operating under continuous discharge conditions, these areas were rare. Along a 60 mm wire segment, only 1 to 2 such areas were typically found for each of the 4 machining conditions. The lengths of the regions without visible discharge marks were 0.2–0.3 mm.

In addition, individual craters resulting from electrical discharges were occasionally identified. An example is shown in Figure 7. The diameter of the crater (excluding material buildup) was approximately 28 μm, with a depth of 3 μm. The external diameter of the rim surrounding the crater formed by molten material was 50–55 μm. Furthermore, a heat-affected zone of approximately 95 μm can be seen.

3.1.1. Influence of Workpiece Material

The type of machined material had no noticeable effect on the analyzed surface topography parameters of the wire.

Table 5. Surface texture parameters of the wire electrode depending on the workpiece material.

Material	Sa, µm		Sq, µm		Sdr, %		Sdq		Spk_Sk_Svk, µm		Sal, µm
	Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD
Ti6Al4V	3.99	0.32	5.06	0.43	46.74	12.12	1.29	0.19	23.39	2.26	36.85	3.35
Inconel 718	4.00	0.21	5.16	0.32	46.93	10.64	1.31	0.20	24.18	2.34	33.56	5.82
42CrMo4	4.02	0.24	5.09	0.37	42.36	14.36	1.21	0.24	23.39	2.04	35.40	5.99

presents the average values of the investigated parameters. A graphical comparison of Sq and Sdq depending on the workpiece material is presented in Figure 8. Both the values of the parameters and their variability were very similar across all the tested workpiece materials.

3.1.2. Influence of t_off Machine Setting

The t_off parameter influenced the height-related parameters Sa, Sq, Spk + Sk + Svk as well as the autocorrelation length Sal. When t_off = 2, a decrease of approximately 5–7% in height parameters was observed (

Table 6. Mean values and standard deviation of surface texture parameters of the wire electrode influenced by t_off setting.

Parameter	toff = 1		toff = 2		Diff, %	p-Value
	Mean	SD	Mean	SD
Sq, µm	5.09	0.37	4.76	0.26	−6.49	0.034
Spk + Sk + Svk, µm	23.39	2.04	21.67	1.50	−7.34	0.046
Sa, µm	4.02	0.24	3.81	0.21	−5.26	0.048
Sal, µm	35.40	5.99	43.21	10.09	+22.05	0.049

). Based on the calculated p-values, it can be noted that the greatest difference between the samples was observed for the root mean square height Rq. Meanwhile, Sal parameter increased by approximately 22% when machining with t_off = 2. The influence of t_off on the above-mentioned surface texture parameters is presented in Figure 9.

3.1.3. Influence of Discharge Current I_p

Regression analysis indicated a statistically significant influence of discharge current only on the parameters Spk + Sk + Svk and Sdq. An increase in I_p led to a noticeable reduction in both total surface height and the root mean square gradient. Increasing I_p from 4 to 18 caused a nearly 10% reduction in Spk + Sk + Svk and an 18% decrease in Sdq (

Table 7. Mean values and standard deviation of surface texture parameters of the wire electrode influenced by I_p setting.

Parameter	Ip = 4		Ip = 18		Diff, %	p-Value
	Mean	SD	Mean	SD
Spk + Sk + Svk, µm	24.95	1.76	22.53	2.20	−9.7%	0.038
Sdq	1.40	0.23	1.15	0.25	−18.0%	0.025

). Although the conducted regression analysis indicated the influence of I_p on the above-mentioned surface texture parameters, the calculated regression equations demonstrated very poor fit due to high intra-sample variability (Figure 10).

3.1.4. Influence of Workpiece Height h

Sample height had a statistically significant effect on all analyzed parameters except Sa (

Table 8. Mean values and standard deviation of surface texture parameters of the wire electrode influenced by sample height h.

Parameter	h = 10		h = 160		Diff, %	p-Value
	Mean	SD	Mean	SD
Sq, µm	5.41	0.38	5.52	0.57	+2.0%	0.017
Spk + Sk + Svk, µm	24.73	1.87	25.63	2.55	+3.6%	0.013
Sdr, %	53.06	15.24	77.51	25.31	+46.1%	<0.001
Sdq	1.39	0.23	1.69	0.27	+21.7%	<0.001
Sal, µm	32.68	5.37	28.91	2.94	−11.5%	<0.001

). The calculated plots of parameter = f(h) for Sq, Spk + Sk + Svk, Sdr and Sdq are concave—parameter values decreased in the range h (10, 70), and increased again up to h = 160 mm (Figure 11). In the lower range h (10, 70), surface height parameters, developed interfacial area ratio and the root mean square gradient decreased—indicating a smoother wire topography, the horizontal distance between peaks and valleys increased (based on Sal).

The conducted regression analysis indicated the influence of sample height h on the above-mentioned surface texture parameters. However, the calculated regression equations demonstrated poor and very poor fit due to high data variability within a given sample.

3.2. Influence of Process Parameters on the Surface Topography of the Workpiece

In addition to standard machining tests conducted under varying process conditions, additional experiments were performed in order to induce only a limited number of discharges. These tests allowed for the observation of craters formed by individual discharges on the workpiece surface. Representative images of such craters are presented in Figure 12.

3.2.1. Influence of the Workpiece Material

The type of machined material had a significant impact on all analyzed parameters. Post hoc analysis (

Table 9. Results of post hoc analysis indicating statistically significant differences in surface texture parameters for different workpiece materials.

Parameter	A	B	Mean_A	SD_A	Mean_B	SD_B	Diff, %	p-Value
Sa, µm	Ti6Al4V	Inconel 718	4.07	0.19	3.94	0.21	3.3	0.229
	Ti6Al4V	42CrMo4	4.07	0.19	3.6	0.09	13.06	0
	Inconel 718	42CrMo4	3.94	0.21	3.6	0.09	9.44	0.001
Sq, µm	Ti6Al4V	Inconel 718	5.15	0.22	5.11	0.3	0.78	0.943
	Ti6Al4V	42CrMo4	5.15	0.22	4.55	0.11	13.19	0
	Inconel 718	42CrMo4	5.11	0.3	4.55	0.11	12.31	0
Spk + Sk + Svk, µm	Ti6Al4V	Inconel 718	23.82	1.06	23.69	1.64	0.55	0.979
	Ti6Al4V	42CrMo4	23.82	1.06	20.78	0.44	14.63	0
	Inconel 718	42CrMo4	23.69	1.64	20.78	0.44	14	0.001
Sdq	Ti6Al4V	Inconel 718	1.02	0.09	1.21	0.15	18.63	0.002
	Ti6Al4V	42CrMo4	1.02	0.09	0.95	0.06	7.37	0.409
	Inconel 718	42CrMo4	1.21	0.15	0.95	0.06	27.37	0
Sdr, %	Ti6Al4V	Inconel 718	34.57	5.32	46.13	9.29	33.44	0.017
	Ti6Al4V	42CrMo4	34.57	5.32	30.12	2.91	14.77	0.11
	Inconel 718	42CrMo4	46.13	9.29	30.12	2.91	53.15	0.002
Sal, µm	Ti6Al4V	Inconel 718	30.06	1.44	29.62	1.77	1.49	0.836
	Ti6Al4V	42CrMo4	30.06	1.44	33.08	1.67	10.05	0.002
	Inconel 718	42CrMo4	29.62	1.77	33.08	1.67	11.68	0

) indicated that for height parameters (Sa, Sq, Spk + Sk + Svk) and the Sal parameter, the 42CrMo4 steel samples differed from those made from Inconel 718 and Ti6Al4V alloys. The latter two materials did not exhibit statistically significant differences between each other (Figure 13). For Sdq and Sdr parameters, no statistically significant differences were observed between 42CrMo4 and Ti6Al4V. Figure 14 presents true color images and topography maps of the sample surfaces.

3.2.2. Influence of t_off Setting

The t_off setting influenced surface parameters including height parameters Sa, Sq, developed interfacial area ratio Sdr and root mean square gradient Sdq (

Table 10. Mean values and standard deviation of surface texture parameters of the workpiece influenced by t_off setting.

Parameter	toff = 1		toff = 2		Diff, %	p-Value
	Mean	SD	Mean	SD
Sa, µm	3.60	0.09	3.5	0.08	−2.78	0.018
Sq, µm	4.55	0.11	4.42	0.10	−2.86	0.023
Sdq	0.95	0.06	0.89	0.01	−6.32	0.018
Sdr, %	30.12	2.91	27.21	0.68	−9.66	0.017

). All these parameters were higher for samples machined at t_off = 1. Notably, the developed interfacial area ratio (Sdr) decreased by approximately 10% when the machining setting was changed from t_off = 1 to t_off = 2. The influence of toff on these parameters is illustrated in Figure 15.

3.2.3. Influence of Discharge Current I_p

Regression analysis revealed a statistically significant influence of discharge current only on the total surface height Spk + Sk + Svk. Higher discharge currents were associated with lower total surface height. While this trend was observed on the wire surface as well, the corresponding reduction in workpiece surface roughness was less pronounced (approximately 4%) compared to the wire (approximately 10%) as I_p increased from 4 to 18 (

Table 11. Mean values and standard deviation of surface texture parameters of the workpiece influenced by I_p setting.

Parameter	Ip = 4		Ip = 18		Diff, %	p-Value
	Mean	SD	Mean	SD
Spk + Sk + Svk, µm	20.73	1.17	19.93	0.75	−3.9	0.046

). The conducted regression analysis indicated the influence of I_p on the above-mentioned surface texture parameters. However, the calculated regression equations demonstrated very poor fit due to high intra-sample variability (Figure 16).

3.2.4. Influence of Workpiece Height h

Sample height had a statistically significant effect on all analyzed parameters except Sal (

Table 12. Mean values and standard deviation of surface texture parameters of the workpiece influenced by sample height h.

Parameter	h = 10		h = 160		Diff, %	p-Value
	Mean	SD	Mean	SD
Sa, µm	4.34	0.11	3.09	0.07	−28.7%	<0.001
Sq, µm	5.45	0.12	3.91	0.10	−28.3%	<0.001
Spk + Sk + Svk, µm	24.82	0.83	18.33	0.76	−26.2%	<0.001
Sdr, %	41.13	4.71	19.94	1.32	−51.5%	<0.001
Sdq	1.14	0.08	0.74	0.03	−35.1%	<0.001

). For all other parameters, a monotonic dependency was observed (Figure 17). Values of these parameters decreased with increasing sample height. The higher the sample, the smoother the workpiece surface after machining was observed—height parameters, root mean square gradient and developed interfacial area ratio decreased. The most significant changes occurred at lower heights, with the influence of height diminishing between h = 75 mm and h = 160 mm. The regression equations calculated for Sa, Sq and Spk + Sk + Svk demonstrated strong fit (R² > 0.9). Figure 18 presents surface images in true color and topography maps of the lowest and the highest sample. 3D maps indicate a smoother surface of samples with h = 160 mm.

3.3. Summary

A single discharge forming a crater on the wire electrode leaves a machining mark with clearly visible remelted metal and resolidified outflow. This phenomenon results from the absence of disturbances in the erosion process of the molten material by subsequent discharges in the immediate vicinity. Once the electrode has passed through the full height of the workpiece (Figure 4), the resulting surface of the wire shows a distinct lack of regular craters. Instead, it comprises overlapping discharge marks with well-visible resolidified material, commonly referred to as the recast layer or white layer. This layer consists of molten and resolidified material of the electrode and the workpiece, and even some dielectric fluid particles. It is found on both the tool and the workpiece surface and is associated with disrupted dielectric flow, typically caused by insufficient flushing of the spark gap. This may influence the initiation of subsequent discharges in regions closer to the lower wire guide. The surfaces of the workpieces exhibited various machining marks. Regularly shaped craters with visible remelted metal were observed (Figure 12-1) as well as irregular craters where shape distortion was largely due to an accumulation of unflushed material (Figure 12-2), which then resolidified on the surface, forming a recast layer. In addition, large craters with regular edges and no visible resolidified material were noted (Figure 12-3), indicating efficient flushing of the spark gap in that region. Surfaces of different machined materials (Figure 13 and Figure 17) exhibited a similar, irregular distribution of peaks and valleys. The overall surface is the result of superimposed, non-directional machining marks—craters. The steel sample revealed distinct discolored remelted zones, likely due to a significantly higher carbon content in the alloy. This may also explain the higher susceptibility of the steel surface to microcracking compared to other tested materials—nickel and titanium alloys.

The surface roughness parameters of the wire electrode, relative to the electrical parameters of the process (I_p and t_off) were comparable to or slightly higher than those measured on the workpiece surface, typically within the range of several to a dozen percent. This might suggest a lower or similar amount of energy transferred to the workpiece during discharge, but is more likely a result of the duration for which a given section of the wire electrode remains in the machining zone (a specific section of the wire contributes to discharge generation and surface texture formation over a larger area of the workpiece before exiting the machining zone). The Sdr and Sdq parameters, when considered in relation to the workpiece height h, are particularly noteworthy. For a low workpiece height h = 10 mm, the wire electrode demonstrated slightly higher values of the parameters—approximately 30% increase in Sdr and approximately 20% increase in Sdq. This difference became much more pronounced for the taller sample (h = 160 mm), where Sdr was nearly four times higher (increased by 290%) and Sdq was more than twice as high (increased by 130%) for the wire electrode. This is most likely due to the extended exposure time of a given electrode section in the machining zone. For low-height samples, the duration is considerably shorter. With taller workpieces, a given segment of the wire electrode is involved in shaping a much larger surface area, thereby experiencing significantly more discharges. Analyzing the roughness parameters of the wire electrode as a function of workpiece height, a clear increasing trend, particularly for Sdr (approx. +50%) and Sdq (approx. +20%), can be observed. This may be attributed to the longer presence of the electrode in the machining zone for taller samples. Conversely, for the machined workpiece, roughness parameters decrease with increasing height, most notably Sdr (by approx. 105%) and Sdq (by approx. 55%). This trend is most likely associated with the higher concentration of discharges over the shorter height (h = 10 mm) compared with the taller sample (h = 160 mm).

The analysis of the wire electrode’s surface roughness in relation to the machined material revealed similar values regardless of the material type. Likewise, for the workpiece surface roughness, most parameters were comparable between materials. However, the Sdr parameter showed noticeable variation. The lowest value was obtained for 42CrMo4 steel (Sdr = 30.12%), slightly higher for Ti6Al4V alloy (Sdr = 34.57%), and the highest for Inconel 718 alloy (Sdr = 46.13%). These differences are likely related to the volume of the material resolidified on the surface (particularly visible on the steel), which filled surface irregularities and thus reduced the true developed surface texture area.

The conducted investigations constitute preliminary research aimed at exploring the potential of using surface topography analysis to monitor wire wear during WEDM. Due to the limited area of the electrode surface available for analysis, the selection of surface roughness parameters was necessarily narrowed to a specific group, primarily height-based parameters. This constraint should be considered when interpreting the results, as a broader surface mapping could yield additional insights info wear mechanisms. Moreover, to establish a stronger correlation between the wire surface after machining (and thus its wear) and process parameters, the design of the experiment should be expanded to include multifactorial plans that account for interactions between process variables. In addition, a broader range of alloys within the studied material groups should be considered.

4. Conclusions

The presented study demonstrated the potential of utilizing surface topography analysis to evaluate the wear of the wire electrode. Surface measurements were carried out using a focus variation microscope. The influence of the workpiece material (42CrMo4 steel, Inconel 718 nickel alloy, and Ti6Al4V titanium alloy), as well as selected process parameters—discharge current I_p, pulse off time t_off and workpiece height h—on selected roughness parameters of both the worn wire and the machined surface was examined. The height-related topography parameters were selected for the evaluation.

The analysis particularly emphasized the Sdq and Sdr parameters. The most significant changes of these parameters, for both the wire electrode and the machined surface were observed when analyzing various workpiece heights. In the case of the wire electrode, increasing the workpiece height led to an increase in these parameters by 20% (Sdq) and 50% (Sdr). Conversely, the surface roughness parameters of the machined workpieces indicated a decreasing trend when increasing height of the part, with reductions ranging from 55% (Sdq) to 105% (Sdr). This suggests that the phenomena responsible for the development of surface texture may influence differently the wire surface compared to the machined surface.

The presented surface texture analysis of the electrode can complement the wire wear ratio (WWR) measurements. Analyzing changes in surface roughness parameters with respect to the workpiece material may serve as a criterion for optimizing process parameters for a given material group, particularly in high-speed cutting, where the wire electrode is more prone to breakage than under conventional cutting conditions. This is particularly relevant for materials with low carbon content, which can be machined at higher parameters while still maintaining acceptable surface quality due to a relatively thin recast layer.

Further research should focus on investigating the discharge frequency in relation to the workpiece height and the extent of electrode wear, taking into account the wire feed rate. Future studies could also explore additional material groups such as copper and aluminum alloys, as well as coated wire electrodes. A comparative assessment of electrode wear during roughing and finishing passes would also offer valuable insights.

Given the nature of the surface irregularities on the worn wire, it would also be beneficial to extend the research scope to include alternative surface topography analyses, such as “particles and pores analysis” using i.e., watershed segmentation.