Acta Polytechnica https://doi.org/10.14311/AP.2025.65.0361 Acta Polytechnica 65(3):361–370, 2025 © 2025 The Author(s). Licensed under a CC-BY 4.0 licence Published by the Czech Technical University in Prague ANALYSIS OF SURFACE ROUGHNESS AND MACHINING PERFORMANCE OF AZ91 MAGNESIUM ALLOY CUT BY WEDM Levent Urtekina, Faik Yılana,∗, İbrahim Baki Şahina, Kadir Gökb a Kırşehir Ahi Evran University, Faculty of Engineering and Architecture, Department of Mechanical Engineering, 40100 Kırşehir, Turkey b İzmir Bakırçay University, Engineering and Architecture Faculty, Department of Biomedical Engineering, 35660 İzmir, Turkey ∗ corresponding author: faik.yilan@ahievran.edu.tr Abstract. AZ91 magnesium alloys have poor machinability when conventional chip removal processes are used due to their low thermal stability and high susceptibility to softening and oxidation at elevated temperatures, which lead to excessive tool wear, poor surface quality, and deformation-induced machining challenges. This study investigated the impact of wire electrical discharge machining (WEDM) parameters on material removal rate (MRR) and surface roughness (SR) using magnesium. For this purpose, an analysis of variance (ANOVA) and Grey Relational Analysis (GRA) were performed to find the optimal settings. Findings indicate that pulse-on time (Ton) significantly affects both MRR and SR: higher Ton increases MRR but worsens SR, while shorter Ton improves SR but reduces MRR. Pulse-off time (Toff) and wire feed rate (WF) have secondary effects. Longer Toff improves surface quality but slightly reduces MRR, and lower WF improves cutting efficiency and MRR. The optimal settings identified by the Taguchi method were observed to be 123 µs Ton, 55 µs Toff , and 6 m min−1 WF for high MRR; and 123 µs Ton, 58 µs Toff , and 4 m min−1 WF for reduced SR. In summary, understanding how WEDM parameters affect MRR and SR allows manufacturers to achieve efficient material removal and desired surface quality. Keywords: AZ91, WEDM, material removal rate (MRR), surface roughness (SR), optimisation. 1. Introduction Biomaterials such as implants, plates, and screws used in orthopaedic and dental surgeries must be chosen carefully for stabilisation and fracture heal- ing. The biomechanical behaviour of these mate- rials affects stability and implant failure can lead to repeated surgeries, increasing risks for older pa- tients [1–4]. In 2019, there were 178 million new fractures globally, with 455 million cases of fracture symptoms, including 25.8 million years lived with dis- ability [5]. Successful fracture management is crucial, with fixation materials playing a key role. Studies have examined implant design, materials, and fatigue behaviour. Sykaras et al. [6] reviewed dental implant materials and designs, while other studies used three- dimensional analyses and finite element models for hip implants [7]. Third-generation biomaterials such as magnesium alloys are increasingly used in orthopaedic applica- tions due to their unique properties, including high biocompatibility, good biodegradability, and satisfac- tory mechanical properties [8–10]. The most popular magnesium alloy is AZ91, which has a high strength- to-weight ratio, reduced castability, and resistance to corrosion. Due to the low yield strength of AZ91 mag- nesium alloy, it is recommended for use in situations with minimal movement or low loads. However, its me- chanical properties can be improved through various heat treatments or by adding strength-enhancing alloy- ing elements [11, 12]. According to Kuyubasi et al. [13] the biomechanical behaviour of three different screw materials (Ti6Al4V, MgAZ91, and Ti6Al4V-HA) was analysed for fixing femoral neck fractures with trian- gular fixation under axial loading to determine which material performed better biomechanically. The au- thors believe that the investigated fixation materials could play a role in reducing the need for recurrent anesthesia and orthopaedic surgical risks. Magne- sium alloys such as AZ91 have attracted significant attention for their potential as biodegradable implant materials. However, their rapid corrosion rate re- mains a major drawback, as this can occur faster than bone healing, possibly compromising the mechanical integrity of the implant before the tissue regeneration completes [14, 15] Therefore, further research into the degradation mechanisms of these alloys is required. In addition to magnesium alloys, high-performance biomaterials such as titanium and stainless steel alloys are also widely used for biomedical implants due to their excellent mechanical properties and corrosion resistance. The applicability of this AZ91 alloy is, however, restricted due to its high processing costs, low worka- bility (because of its hexagonal close-packed (HCP) structure), and loss of mechanical qualities at high temperatures. Due to their physical and mechanical characteristics, magnesium alloys are highly machin- 361 https://doi.org/10.14311/AP.2025.65.0361 https://creativecommons.org/licenses/by/4.0/ https://www.cvut.cz/en L. Urtekin, F. Yılan, İ. B. Şahin, K. Gök Acta Polytechnica able by conventional machining processes; however, when it comes to producing complex and 3D intricate shapes with high geometrical accuracy and productiv- ity, or reaching the desired dimension with the least amount of material elimination, traditional machining processes fall short in terms of efficiency and precision. As a result, non-traditional machining methods need be developed [16, 17]. In WEDM processes, choosing the best machining parameters results in higher productivity and well- functioning machined components [18, 19]. WEDM, the most widely used unconventional machining tech- nique, removes material from the workpiece to create objects with intricate profiles and shapes. This process removes the material from the workpiece by creating a sequence of discrete spark discharges between the tool and the work electrode that are dipped in a liq- uid dielectric medium. A tiny quantity of the work material evaporates when the spark caused by the electrical discharge melts. The removed material is cleared and ejected using the dielectric [20, 21]. Some researchers have examined the performance of the WEDM process on Mg-based alloy materials [22– 26]. As clearly understood from these studies, the ob- tained results depend on the process input parameters according to various factors affecting the machining performance and productivity [27–30]. In addition to preliminary studies, the optimisation of the WEDM process parameters have been performed more re- cently. Kumar et al. [31] investigated the influence of WEDM process parameters on material removal, surface characteristics, and corrosion rate of ZE41A Mg alloy. The research used a Taguchi L16 orthog- onal array to measure the corrosion rate (CR) after a week of immersion in simulated body fluid. GRA was used to simultaneously optimise MRR, SR, and CR. Also, the studies indicated that optimum WEDM parameters improved performance, reduced SR, and reduced in-vitro CR, thereby mitigating mechanical integrity loss in bone design. In another study, Goyal et al. [32] used WEDM to process AZ31 alloy, focusing on input machining variables and response character- istics. The variables include servo feed, voltage, Ton, and Toff . ANOVA analyses was used to analyse each response, and empirical models were used to identify Pareto fronts in the various process. The multi ob- jective grey wolf optimisation algorithm (MOGWO) was improved using the Levy flight algorithm. The improved MOGWO efficiently detected uniformly dis- tributed Pareto fronts. Results were validated through confirmation experiments and the morphology was evaluated using scanning electron microscopy (SEM) to identify microcracks, globules, micropores, and lumps. Similarly, Muniappan et al. [33] investigated the impact of machining control parameters on kerf width (KW) and cutting speed (CS) in WEDM pro- cesses. Multi objective optimisation by ratio analysis (MOORA) can be achieved to optimise kerf width and cutting speed. The study found that Ton, Toff , peak pulse current, gap voltage, WF, and wire tension were the best combinations for improving CS and KW. Gotagunaki et al. [34] also investigated optimal values of the WEDM process parameters for milling AZ91D magnesium alloy using response surface methodology (RSM) and ANOVA. Surface flaws such as globules, recast layers, holes, and surface voids were identified on the machined material at higher cutting speeds. The ANOVA data indicate that the Ton is the most significant variable, with peak current being the sec- ond. According to the literature, machining parameters are crucial in determining the SR and MRR of WEDM processing. However, limited research has been car- ried out on the processing of AZ91 alloys. Therefore, the aim of this study is to investigate the effects of pulse on time, pulse off time, and wire feed speed as well as processing input parameters on the machin- ing performance of AZ91 alloys. The obtained MRR and SR values were optimised. Thus, the usability of these processes will be determined by optimising the parameters of the tested processes of AZ91 alloy, one of the most widely used metallic biomaterials in biomedical applications. 2. Experimental design 2.1. Materials The material chosen for this study is AZ91 magnesium (Mg) alloy, one of the most widely and successfully used commercial alloys among magnesium-based mate- rials, owing to its excellent combination of mechanical properties, lightweight characteristics, and corrosion resistance. Table 1 provides information on MgAZ91. The microstructure of the MgAZ91 alloy observed post-casting, as depicted in Figure 1, reveals a matrix composed primarily of α-Mg, along with a network of intermetallic phases, likely identified as γ. Addition- ally, non-equilibrium eutectic structures are evident in the vicinity of these intermetallic precipitates. Such microstructural inhomogeneities result in non-uniform deformation during mechanical loading. Furthermore, these hard intermetallic phases and precipitates can act as stress concentration sites, increasing the likeli- hood of crack initiation and propagation. 3. Taguchi experimental design The Design of Experiment (DOE) is a crucial method- ology used in experimental research to generate data that are statistically robust and analysable. It is a key tool for determining the best conditions while reduc- ing the number of experiments needed. Within the various experimental design techniques available, the Taguchi method is particularly notable for its struc- tured approach. This method begins with defining the response variable or objective function that needs to be studied. It then involves identifying the factors that affect this response variable. The next step is to set different levels for these factors. This is followed by 362 vol. 65 no. 3/2025 Analysis of surface roughness and machining performance of AZ91 . . . Element Mg Al Zn Mn Cu Ni Content [%] 89.52 8.50 0.45 0.17 0.03 0.002 Table 1. The chemical composition of AZ91. (a). Optical micrograph of etched AZ91 Mg alloy. (b). Scanning electron microscope (SEM) micrograph of the etched AZ91 Mg alloy sample. Figure 1. Optical microscope image of the MgAZ91 alloy. Units Levels 1 2 3 Pulse on time (Ton) µs 110 117 123 Pulse off time (Toff) µs 55 58 62 Wire feed rate (WF) mm min−1 4 6 - Table 2. The variable parameters and levels used for the DOE. conducting experiments using an orthogonal array to evaluate the impact of these factor levels, as noted in references [35]. The final step in this process is to pin- point the optimal levels of these factors to improve the response variable. The Taguchi robust parameter de- sign method is well-regarded for its utility in designing experiments and optimising processes, often used to improve quality control measures offline, as discussed in literature [36–38]. The parameters and levels of parameters are presented in Table 2. The signal-to- noise ratio ( S N ratio), used as one of the performance functions in the Taguchi method, is generally divided into three main categories: smaller-the-better, larger- the-better, and nominal-the-best. In this study, the larger-the-better characteristic is used for the MRR response, while the smaller-the-better characteristic is used for the (SR) response. The MRR and SR are obtained using Equations (1) and (2). S N ratio for larger-the-better characteristic: S N ratio(η) = −10 log10 ( 1 n n∑ i=1 1 y2 ij ) , (1) S N ratio for smaller-the-better characteristic: S N ratio(η) = −10 log10 ( 1 n n∑ i=1 y2 ij ) , (2) where n corresponds to the number of experiments and yij refers to the response value for the corresponding experiments [39–41]. A key feature of the Taguchi method is the use of specially crafted orthogonal arrays that allow for a comprehensive analysis of all the parameters in- volved with fewer experiments. These arrays are or- ganised in a matrix format where rows represent the experiments and columns represent the factors affect- ing the response variable, as detailed in sources [42, 43]. This setup allows the determination of the best pa- rameter levels for the desired outcome. For example, a Taguchi L36 orthogonal array, which includes three levels for two factors, was used in the experimental framework outlined in Table 3. The choice of factors and their levels was influenced by previous research findings. The ANOVA output comprises several sta- tistical parameters, including the degree of freedom (DF), Fischer’s F distribution (F -value), sources of variation (control factors, error, and the total of all sources), adjusted mean squares (Adj MS), P -value, adjusted sum of squares (Adj SS), and the percentage contribution of the control factors. The statistical significance of the results is determined based on the P -value; specifically, a P -value below 0.05 indicates that the corresponding control factor has a statis- tically significant influence on the response param- 363 L. Urtekin, F. Yılan, İ. B. Şahin, K. Gök Acta Polytechnica # run Ton Toff WF [µs] [µs] [mm min−1] 1. 110 62 4 2. 110 58 4 3. 110 55 4 4. 110 62 4 5. 110 58 4 6. 110 55 4 7. 110 62 6 8. 110 58 6 9. 110 55 6 10. 110 62 6 11. 110 58 6 12. 110 55 6 13. 117 62 4 14. 117 58 4 15. 117 55 4 16. 117 62 4 17. 117 58 4 18. 117 55 4 19. 117 62 6 20. 117 58 6 21. 117 55 6 22. 117 62 6 23. 117 58 6 24. 117 55 6 25. 123 62 4 26. 123 58 4 27. 123 55 4 28. 123 62 4 29. 123 58 4 30. 123 55 4 31. 123 62 6 32. 123 58 6 33. 123 55 6 34. 123 62 6 35. 123 58 6 36. 123 55 6 Table 3. L36 Taguchi orthogonal array for the DOE. eter [44]. The percentage contribution reflects the extent to which a particular control factor impacts the response parameter, offering insight into its rel- ative importance. Furthermore, within the ANOVA model, S represents the standard deviation of the observed data and fitted values, R-sq denotes the pro- portion of variance in the response variable explained by the model, and R-sq (adj) provides a modified version of R-sq by considering the number of predic- tors relative to the number of observations, thereby preventing overfitting. Equations (3)–(6) are used to derive the key indicators of the ANOVA method, which include the degrees of freedom (DF), sum of squares (SS), mean squares (MS), F -values, and con- tribution ratio (CR) for each factor. These parameters serve as the fundamental components for assessing the influence and statistical significance of individual factors on the response variable, providing a com- prehensive quantitative analysis of the experimental data [40]. DF = nf∑ i=1 (Li − 1) + DFerror, (3) where Li is the number of levels, nf is the number of factors, and DFerror is the degree of freedom for the error. SSi = n∑ i=1 (ŷi − ȳ)2 , (4) where, ȳ is the mean response value, ŷi is the ith re- sponse value. The corresponding means of squares are given by: MSi = SSi DFi . (5) The F -factor, which reflects the statistical reliability of the results, is given by the ratio of the parameter’s mean square to the mean square error: Fi = MSi MSerror . (6) The contribution ratio (CR) is defined as the ratio of the sum of squared deviations to the total sum of squared deviations [45]: % Contributioni = SSi SST . (7) 3.1. Analysis and characterisation of the samples The surface images and the chemical element analysis of the samples were analysed and characterised using a Hitachi Regulus 8230 scanning electron microscope (SEM). The microstructures were examined by optical microscopy. The ZeGage optical profiler instrument was used to measure the average SR data. The sur- face roughness (SR) was measured perpendicular to the tool path following the ISO 1997 standard, with a sample length of 4 mm. The SR of the specimen was recorded as the average value obtained from three measurements. 4. WEDM experimental setup The dimensions of the MgAZ91 workpiece used in this study are 200 mm × 200 mm × 5 mm; how- ever, the specimen size that was removed from it is 10 mm × 10 mm × 5 mm. Ultracut F1, ELPULS 501 WEDM machine was used in this study. Using an electrode made of 250 µm-diameter brass wire, the workpiece of AZ91 Mg alloy (flat plate) was machined. Maximum machining current was recorded during the sample’s WEDM process when the wire had reached the sample’s full diameter. This number was recorded and used in the experiments. Throughout the studies, a wire tension of 8 N was applied. To flush the dirt 364 vol. 65 no. 3/2025 Analysis of surface roughness and machining performance of AZ91 . . . (a). MRR. (b). SR. Figure 2. MRR and SR for the S N graphs. Source DF Adj SS Adj MS F -Value P -Value Contribution % Ton 2 0.16722 0.083611 3.14 0.058 15.841828 Toff 2 0.09056 0.045278 1.7 0.199 8.5793323 WF 1 0 0 0 1 0 Error 30 0.79778 0.026593 75.57884 Total 35 1.05556 100 Table 4. ANOVA analysis results for MRR. out of the gap between the wire and the workpiece, distilled water was used as a dielectric fluid. The dif- ferences in the machining performance outputs (MRR and SR) were examined in order to modify the WEDM parameters (Ton, Toff and WF) for the samples. 5. Results and discussions 5.1. WEDM machining factors optimisation 5.1.1. ANOVA analysis of MRR and SR The main effects plot of the mean MRR and SR is presented in Figure 2. The ANOVA method is used to analyse experimental data and assess the impact of different parameters. It is a statistical tool that helps identify differences in the average performance of different groups of parts. ANOVA enables the determination of which parts are suitable for specific processes and their respective performance levels. The analysis of variance aims to assess the extent to which the elements being studied progress, the quality of the output values chosen for measurement, and the various levels that cause variations [46]. In the analysis of ANOVA results for MRR in MgAZ91 WEDM, as presented in Table 4, it is ob- served that the Ton significantly influences the MRR, accounting for 15.8 % of the variance. This signifi- cant impact can be attributed to the fact that longer pulse-on times allow for more energy to be discharged between the wire and the workpiece, thereby enhanc- ing the material removal rate. Similarly, the Toff contributes to 8.57 % of the variance in MRR. The pulse-off time is crucial as it allows the spark to cool down and debris to be flushed away, thus preventing excessive tool wear and workpiece damage, which can affect the efficiency of the machining process. However, the wire speed shows no significant effect on the MRR. This could be due to the specific properties of mag- nesium and the settings of the WEDM process used in this study, where variations in wire speed do not significantly alter the dynamics between the electrode wire and the magnesium workpiece, thus having a min- imal impact on the rate of material removal. In the machining of AZ91 magnesium alloy, it was observed that both the MRR and WF were higher compared to other materials. Specifically, the MRR reached 180 mm3 min−1, while the WF measured 0.450 mm. The primary factors contributing to this behaviour are the low melting point and high thermal conductivity of magnesium alloys. These characteristics facilitate easier material removal due to reduced cutting resis- tance and promote efficient thermal dissipation during 365 L. Urtekin, F. Yılan, İ. B. Şahin, K. Gök Acta Polytechnica Source DF Adj SS Adj MS F -Value P -Value Contribution % Ton 2 6.5646 3.28203 35.22 0 68.526455 Toff 2 0.2172 0.10851 1.16 0.326 2.2656117 WF 1 0.0025 0.00251 0.03 0.871 0.0262035 Error 30 2.7952 0.09318 29.18173 Total 35 9.5788 100 Table 5. ANOVA analysis results for SR. the machining process, which in turn affects the kerf dimensions [28]. An increase in pulse-off time leads to a reduction in the number of sparks generated per unit of time. Consequently, the energy available for material removal decreases. As a result, the MRR de- creases, although the reduction occurs with relatively small incremental changes in pulse-off time. This be- haviour contrasts with the effect of increasing pulse-on time, which typically results in a higher MRR due to the greater energy input during each spark [34]. In the analysis of ANOVA results for surface rough- ness of MgAZ91 WEDM, as presented in the Table 5, it is evident that the Ton has a substantial impact on sur- face roughness, accounting for 68.25 % of the variance. This pronounced effect is likely because longer pulse- on times increases the amount of energy discharged between the wire and the workpiece, which can lead to larger and deeper craters on the surface, thereby increasing roughness. Conversely, the Toff shows a rel- atively minor contribution, having only 2.2 % of the variance in surface roughness. The smaller influence of Toff on the surface roughness suggests that the cool- ing and debris flushing intervals, while essential for maintaining the integrity of the cut and preventing wire breakage, have a less direct effect on the textural quality of the surface. This analysis highlights the dominant role of Ton in determining the quality of the surface finish in WEDM processes, emphasising the need for careful setting of pulse durations to optimise surface characteristics.. Despite the high cutting rates observed during the WEDM of MgAZ91 alloys, the SR values remained within acceptable limits, highlighting the excellent machinability of these materials. The maximum recorded SR value was 4.683 µm, demon- strating that even at high material removal rates, the process produced surfaces of sufficient quality, further emphasising the suitability of MgAZ91 for precision machining applications [28]. During the investigation, it was found that pulse-on time has the major influ- ence on surface roughness while voltage and pulse-off time have a lesser influence on the surface roughness. The optimal parameters have been identified by examining the Signal-to-Noise ( S N ) ratio graphs for WEDM based on their effects on MRR and SR. For maximising MRR, the optimal settings are found to be Ton = 123 microseconds, Toff = 55 microseconds, and a WF rate of 6 metres per minute. These settings likely provide a balance between efficient material re- moval and maintaining operational stability, allowing for a higher discharge energy that improves the cut- ting efficiency without excessive tool wear or damage. When focusing on minimising SR, the optimal param- eters slightly differ. The best results are achieved with Ton = 123 microseconds, similar to the MRR settings, which suggests that this pulse duration effectively con- trols the energy input to both maximise the removal rates and manage the surface finish. However, the Toff is slightly increased to 58 microseconds, and the WF rate is reduced to 4 metres per minute. The increased Toff allows more time for debris expulsion and cooling, which helps in achieving a smoother surface by reduc- ing the thermal impact on the workpiece. The reduced wire feed rate decreases the interaction time between the wire and the workpiece, potentially leading to finer surface finishes. These findings illustrate the impor- tance of precisely adjusting the WEDM parameters to optimise different aspects of the machining pro- cess, depending on the specific machining outcomes desired. The surface plots showing the effects of Ton, Toff , and WF on MRR and SR are shown in Figure 3. The x and y-axes represent the machining parameters (Ton and Toff), while the z-axis denotes the response vari- ables (MRR in mm3 min−1 and SR in µm). A gradient colour scheme on the z-axis highlights the minima and maxima of the response values, aiding in the identifi- cation of optimal parameter combinations for WEDM. As depicted in Figure 3, distinct relationships be- tween machine settings and machining outcomes are observed in the exploration of parameter effects in WEDM, specifically concerning MRR and SR. The analysis reveals a positive correlation between Ton and MRR. As Ton increases, more energy is delivered to the workpiece, facilitating improved material removal rate. This relationship underscores the importance of Ton in optimising the efficiency of the WEDM process. Additionally, the data indicates that decreasing both the WF rate and the Toff contributes to an increase in MRR. A lower WF rate likely aids in maintaining the structural integrity of the wire, thus ensuring consis- tent cutting conditions, while a reduced Toff minimises idle time between discharges, maximising the material removal rate. Conversely, the study of SR demonstrates that in- creases in both Ton and Toff are associated with de- teriorated surface finishes. An extended Ton leads to larger energy discharges, which create deeper and more pronounced craters on the workpiece surface, 366 vol. 65 no. 3/2025 Analysis of surface roughness and machining performance of AZ91 . . . Figure 3. Surface plot for MRR and SR. thereby increasing roughness. Furthermore, a longer Toff allows the molten debris more time to resettle on the workpiece in an uneven manner before the next discharge, exacerbating the roughness of the fin- ished surface. These insights highlight the nuanced trade-offs between increasing MRR and minimising SR in WEDM. The findings from Figure 3 provide a comprehensive understanding of how WEDM pa- rameters must be meticulously managed to achieve the desired machining performance, emphasising the need for strategic parameter optimisation based on specific production goals. 5.1.2. Grey Relational Grade analysis Table 6 presents the outcomes of the Grey Relational Grade (GRG) analysis conducted on 36 distinct cases. The GRG values span from a minimum of 0.5062 to a maximum of 1.0000, demonstrating considerable variability in relational performance among the cases. The highest GRG value of 1.0000 signifies an optimal relationship, indicating that the corresponding case exhibits the most favourable performance within the dataset. Conversely, the lowest GRG value of 0.5062 reflects a weaker relational performance, highlighting potential areas for improvement. The distribution of GRG values across the dataset shows significant concentrations in the mid to upper ranges, suggesting that many cases exhibit moderate to high relational performance. This table offers a detailed overview of the relational efficiencies across the analysed cases, providing a crucial reference point for subsequent com- parisons and analytical evaluations. No. GRG No. GRG No. GRG 1. 1.0000 13. 0.8693 25. 0.7680 2. 0.8255 14. 0.7485 26. 0.6526 3. 0.7826 15. 0.7004 27. 0.6156 4. 0.9978 16. 0.8090 28. 0.7915 5. 0.7346 17. 0.6864 29. 0.6235 6. 0.7722 18. 0.6911 30. 0.6495 7. 0.8206 19. 0.7364 31. 0.6222 8. 0.6931 20. 0.6054 32. 0.5108 9. 0.6385 21. 0.5747 33. 0.5080 10. 0.7903 22. 0.7443 34. 0.6703 11. 0.5952 23. 0.5454 35. 0.4530 12. 0.6664 24. 0.5324 36. 0.5062 Table 6. GRG analysis results. 6. Surface roughness results Surface roughness is a paramount consideration in the finished cut of WEDM, impacting both the func- tional and aesthetic qualities of the machined compo- nents. This study aims to elucidate the influence of key machining parameters, which are the pulse-on time, pulse-off time, and wire speed, on surface roughness. A comprehensive understanding of these parameters is essential for optimising WEDM processes to achieve better surface finishes. Furthermore, the single dis- charge studies revealed that once the pulse energy was lowered to a specific value, craters on the workpiece surface could no longer be produced by a long pulse duration with a low peak value. Even yet, there is still a chance that the workpiece surface will have noticeable craters due to the short pulse length and high peak value. This suggests that longer pulses are 367 L. Urtekin, F. Yılan, İ. B. Şahin, K. Gök Acta Polytechnica Figure 4. Surface topography maps of samples depending on WEDM process parameters. unable to produce the same level of surface rough- ness as short pulses combined with high peak values. Roughness maps of the samples are shown in Figure 4. Higher pulse-on times produce better roughness met- ric SR. When comparing the surface roughness values of the samples, it is observed that the sample with Ton = 123, Toff = 58, and WF = 4 exhibits the best surface quality. In contrast, the surface roughness val- ues increase for the samples with Ton = 110, Toff = 55, and WF = 4 and Ton = 110, Toff = 62, and WF = 6. The lower surface roughness values of the sample with Ton = 123, Toff = 58, and WF = 4 are attributed to the reduction in heat intensity and the duration of heat application to the workpiece as the WEDM pulse on time increases. Consequently, it can be concluded that the number of pulsed sparks per unit area of the surface not only decreases but also prevents the formation of deep craters. 7. Conclusion The present work involved a detailed investigation analysed the effects of WEDM parameters on the MRR and SR for MgAZ91 magnesium alloy, and the following conclusions were made as a result of the investigation of its machining properties: • Ton is the most critical factor for both the MRR and SR. Increasing Ton increases MRR but worsens surface quality, while a shorter Ton improves surface quality but decreases MRR. • Toff has a secondary effect. A longer Toff improves surface smoothness, but slightly reduces MRR. • WF also affects MRR; a lower WF improves wire integrity and cutting efficiency, increasing MRR. • The Taguchi method identified the following settings as optimal for high MRR; Ton = 123 µs, Toff = 55 µs, and WF = 6 m min−1. For reduced surface quality, the optimal settings were found to be: Ton = 123 µs, Toff = 58 µs, and WF = 4 m min−1. These findings underscore the importance of se- lecting the optimal WEDM parameters to balance the MRR and SR based on the machining needs. In conclusion, this research offers valuable insights into optimising WEDM processes for magnesium, allow- ing efficient material removal while maintaining the desired surface quality. References [1] W. Pignaton, J. R. C. Braz, P. S. Kusano, et al. Perioperative and anesthesia-related mortality: An 8-year observational survey from a tertiary teaching hospital. Medicine (Baltimore) 95(2):e2208, 2016. https://doi.org/10.1097/MD.0000000000002208 [2] B. F. N. Pascal, A. Malisawa, A. Barratt-Due, et al. General anaesthesia related mortality in a limited resource settings region: A retrospective study in two teaching hospitals of Butembo. BMC Anesthesiology 21(1):60, 2021. https://doi.org/10.1186/s12871-021-01280-2 [3] M. S. Çömez, H. Demirkıran. Intraoperative anesthesia- related mortality: A 10-year survey in a tertiary teaching hospital. Van Medical Journal 28(2):280–287, 2021. https://doi.org/10.5505/vtd.2021.02259 368 https://doi.org/10.1097/MD.0000000000002208 https://doi.org/10.1186/s12871-021-01280-2 https://doi.org/10.5505/vtd.2021.02259 vol. 65 no. 3/2025 Analysis of surface roughness and machining performance of AZ91 . . . [4] Q. Chen, G. A. Thouas. Metallic implant biomaterials. Materials Science and Engineering: R: Reports 87:1–57, 2015. https://doi.org/10.1016/j.mser.2014.10.001 [5] A.-M. Wu, C. Bisignano, S. L. James, et al. Global, regional, and national burden of bone fractures in 204 countries and territories, 1990–2019: A systematic analysis from the Global Burden of Disease Study 2019. The Lancet Healthy Longevity 2(9):E580–E592, 2021. https://doi.org/10.1016/S2666-7568(21)00172-0 [6] N. Sykaras, A. M. Iacopino, V. A. Marker, et al. Implant materials, designs, and surface topographies: Their effect on osseointegration. A literature review. International Journal of Oral & Maxillofacial Implants 15(5):675–690, 2000. [7] V. Waide, L. Cristofolini, J. Stolk, et al. Modelling the fibrous tissue layer in cemented hip replacements: Experimental and finite element methods. Journal of Biomechanics 37(1):13–26, 2004. https://doi.org/10.1016/S0021-9290(03)00258-6 [8] K. Kumar, R. S. Gill, U. Batra. Challenges and opportunities for biodegradable magnesium alloy implants. Materials Technology 33(2):153–172, 2017. https://doi.org/10.1080/10667857.2017.1377973 [9] G. E. J. Poinern, S. Brundavanam, D. Fawcett. Biomedical magnesium alloys: A review of material properties, surface modifications and potential as a biodegradable orthopaedic implant. American Journal of Biomedical Engineering 2(6):218–240, 2012. https://doi.org/10.5923/j.ajbe.20120206.02 [10] Y. F. Zheng, X. Gu, F. Witte. Biodegradable metals. Materials Science and Engineering: R: Reports 77:1–34, 2014. https://doi.org/10.1016/j.mser.2014.01.001 [11] K. Majerski, E. Siemionek, M. Szucki, P. Surdacki. Investigations of the effect of heat treatment and plastic deformation parameters on the formability and microstructure of AZ91 alloy castings. Advances in Science and Technology Research Journal 18(1):1–9, 2024. https://doi.org/10.12913/22998624/174932 [12] E. Jonda, L. Łatka, A. Lont, et al. The effect of HVOF spray distance on solid particle erosion resistance of WC-based cermets bonded by Co, Co-Cr and Ni deposited on Mg-alloy substrate. Advances in Science and Technology Research Journal 18(2):115–128, 2024. https://doi.org/10.12913/22998624/184025 [13] A. Gok, L. Urtekin, K. Gok, et al. Computer aided analysis of biomechanical performance of schanz screw with different additive manufacturing materials used in pertrochanteric fixator on an intertrochanteric femoral fracture (corrosion resistance approach). International Journal for Numerical Methods in Biomedical Engineering 39(12):e3763, 2023. https://doi.org/10.1002/cnm.3763 [14] B. Barani, A. K. Lakshminarayanan, R. Subashini, et al. Microstructural characteristics of chitosan deposited AZ91. Materials Today: Proceedings 16:456–462, 2019. https://doi.org/10.1016/j.matpr.2019.05.115 [15] D. Bairagi, S. Mandal. A comprehensive review on biocompatible Mg-based alloys as temporary orthopaedic implants: Current status, challenges, and future prospects. Journal of Magnesium and Alloys 10(3):627–669, 2022. https://doi.org/10.1016/j.jma.2021.09.005 [16] F. Klocke, M. Schwade, A. Klink, A. Kopp. EDM machining capabilities of magnesium (Mg) alloy WE43 for medical applications. Procedia Engineering 19:190–195, 2011. https://doi.org/10.1016/j.proeng.2011.11.100 [17] J. Xu, K. Xia, Z. Lian, et al. Surface properties on magnesium alloy and corrosion behaviour based high-speed wire electrical discharge machine power tubes. Micro & Nano Letters 11(1):15–19, 2016. https://doi.org/10.1049/mnl.2015.0204 [18] K. H. Ho, S. T. Newman, S. Rahimifard, R. D. Allen. State of the art in wire electrical discharge machining (WEDM). International Journal of Machine Tools and Manufacture 44(12–13):1247–1259, 2004. https: //doi.org/10.1016/j.ijmachtools.2004.04.017 [19] V. Kavimani, K. S. Prakash, T. Thankachan. Influence of machining parameters on wire electrical discharge machining performance of reduced graphene oxide/magnesium composite and its surface integrity characteristics. Composites Part B: Engineering 167:621–630, 2019. https: //doi.org/10.1016/j.compositesb.2019.03.031 [20] H. Singh, R. Garg. Effects of process parameters on material removal rate in WEDM manufacturing and processing. Journal of Achievements in Materials and Manufacturing Engineering 32(1):70–74, 2009. [21] M. E. Asgar, A. K. S. Singholi. Parameter study and optimization of WEDM process: A review. IOP Conference Series: Materials Science and Engineering 404(1):012007, 2018. https://doi.org/10.1088/1757-899X/404/1/012007 [22] L. Urtekin, H. B. Özerkan, C. Cogun, et al. Experimental investigation on wire electric discharge machining of biodegradable AZ91 Mg alloy. Journal of Materials Engineering and Performance 30(10):7752–7761, 2021. https://doi.org/10.1007/s11665-021-05939-2 [23] R. Panwar, N. Sharma, A. Kumar, R. Khanna. Experimental investigation of WEDM control parameters for AZ61 Mg alloy using ANN modeling. Materials Today: Proceedings 62:1397–1401, 2022. https://doi.org/10.1016/j.matpr.2021.12.381 [24] T. U. Siddiqui, J. Ramkumar. Micro-wire electric discharge machining of Mg alloy used in biodegradable orthopaedic implants. Materials Today: Proceedings 4(9):10273–10277, 2017. https://doi.org/10.1016/j.matpr.2017.06.363 [25] N. Ahuja, U. Batra, K. Kumar. Experimental investigation and optimization of wire electrical discharge machining for surface characteristics and corrosion rate of biodegradable Mg alloy. Journal of Materials Engineering and Performance 29(6):4117–4129, 2020. https://doi.org/10.1007/s11665-020-04905-8 [26] M. Somasundaram, J. P. Kumar. Multi response optimization of EDM process parameters for biodegradable AZ31 magnesium alloy using TOPSIS and grey relational analysis. Sadhana – Academy Proceedings in Engineering Sciences 47(3):136, 2022. https://doi.org/10.1007/s12046-022-01908-0 369 https://doi.org/10.1016/j.mser.2014.10.001 https://doi.org/10.1016/S2666-7568(21)00172-0 https://doi.org/10.1016/S0021-9290(03)00258-6 https://doi.org/10.1080/10667857.2017.1377973 https://doi.org/10.5923/j.ajbe.20120206.02 https://doi.org/10.1016/j.mser.2014.01.001 https://doi.org/10.12913/22998624/174932 https://doi.org/10.12913/22998624/184025 https://doi.org/10.1002/cnm.3763 https://doi.org/10.1016/j.matpr.2019.05.115 https://doi.org/10.1016/j.jma.2021.09.005 https://doi.org/10.1016/j.proeng.2011.11.100 https://doi.org/10.1049/mnl.2015.0204 https://doi.org/10.1016/j.ijmachtools.2004.04.017 https://doi.org/10.1016/j.ijmachtools.2004.04.017 https://doi.org/10.1016/j.compositesb.2019.03.031 https://doi.org/10.1016/j.compositesb.2019.03.031 https://doi.org/10.1088/1757-899X/404/1/012007 https://doi.org/10.1007/s11665-021-05939-2 https://doi.org/10.1016/j.matpr.2021.12.381 https://doi.org/10.1016/j.matpr.2017.06.363 https://doi.org/10.1007/s11665-020-04905-8 https://doi.org/10.1007/s12046-022-01908-0 L. Urtekin, F. Yılan, İ. B. Şahin, K. Gök Acta Polytechnica [27] F. Han, J. Jiang, D. Yu. Influence of machining parameters on surface roughness in finish cut of WEDM. International Journal of Advanced Manufacturing Technology 34(5–6):538–546, 2007. https://doi.org/10.1007/s00170-006-0629-9 [28] A. Mostafapor, H. Vahedi. Wire electrical discharge machining of AZ91 magnesium alloy; Investigation of effect of process input parameters on performance characteristics. Engineering Research Express 1(1):015005, 2019. https://doi.org/10.1088/2631-8695/ab26c8 [29] N. Lenin, M. Sivakumar, G. Selvakumar, et al. Optimization of process control parameters for WEDM of Al-LM25/Fly Ash/B4C hybrid composites using evolutionary algorithms: A comparative study. Metals 11(7):1105, 2021. https://doi.org/10.3390/met11071105 [30] D. K. Ammisetti, S. S. H. Kruthiventi. Experimental analysis and artificial neural network teaching – learning-based optimization modeling on electrical discharge machining characteristics of AZ91 composites. Journal of Materials Engineering and Performance 33(21):11718–11735, 2024. https://doi.org/10.1007/s11665-023-08795-4 [31] R. Kumar, P. Katyal, S. Mandhania. Grey relational analysis based multiresponse optimization for WEDM of ZE41A magnesium alloy. International Journal of Lightweight Materials and Manufacture 5(4):543–554, 2022. https://doi.org/10.1016/j.ijlmm.2022.06.003 [32] K. K. Goyal, N. Sharma, R. D. Gupta, et al. Measurement of performance characteristics of WEDM while processing AZ31 Mg-alloy using Levy flight MOGWO for orthopedic application. International Journal of Advanced Manufacturing Technology 119(11–12):7175–7197, 2022. https://doi.org/10.1007/s00170-021-08358-8 [33] A. Muniappan, M. Sriram, C. Thiagarajan, et al. Optimization of WEDM process parameters on machining of AZ91 magnesium alloy using MOORA method. IOP Conference Series: Materials Science and Engineering 390(1):012107, 2018. https://doi.org/10.1088/1757-899X/390/1/012107 [34] S. Gotagunaki, V. S. Mudakappanavar, R. Suresh. Wire electrical discharge machining characteristics of rare earth oxides reinforced AZ91D magnesium alloy hybrid composite using Taguchi-grey relational analysis approach. Hybrid Advances 4:100116, 2023. https://doi.org/10.1016/j.hybadv.2023.100116 [35] S. S. Mahapatra, A. Patnaik. Parametric optimization of wire electrical discharge machining (WEDM) process using Taguchi method. Journal of the Brazilian Society of Mechanical Sciences and Engineering 28(4):422–429, 2006. https://doi.org/10.1590/S1678-58782006000400006 [36] T. Lokeswara Rao, N. Selvaraj. Optimization of WEDM process parameters on titanium alloy using Taguchi method. International Journal of Modern Engineering Research 3(4):2281–2286, 2013. [37] R. Karthik, R. Viswanathan, J. Balaji, et al. Optimization of WEDM parameters for machining of AZ31B Mg alloy using Taguchi method. IOP Conference Series: Materials Science and Engineering 1013(1):012005, 2021. https://doi.org/10.1088/1757-899X/1013/1/012005 [38] A. Muniappan, R. Solomon, V. Jayakumar, et al. An estimating the effect of control process variables on kerf width in wire EDM of AZ91 magnesium alloy by Taguchi method. IOP Conference Series: Materials Science and Engineering 402(1):012171, 2018. https://doi.org/10.1088/1757-899X/402/1/012171 [39] P. Sivaiah, D. Chakradhar. Modeling and optimization of sustainable manufacturing process in machining of 17-4 PH stainless steel. Measurement 134:142–152, 2019. https: //doi.org/10.1016/j.measurement.2018.10.067 [40] A. Ustaoglu, B. Kursuncu, M. Alptekin, M. S. Gok. Performance optimization and parametric evaluation of the cascade vapor compression refrigeration cycle using Taguchi and ANOVA methods. Applied Thermal Engineering 180:115816, 2020. https: //doi.org/10.1016/j.applthermaleng.2020.115816 [41] L. Urtekin, F. Yılan, İ. B. Şahin. Optimization of rheology parameters for feedstock by powder injection molding (PIM) via Taguchi analysis. International Journal of Integrated Engineering 15(7):89–101, 2023. [42] A. Ikram, N. A. Mufti, M. Q. Saleem, A. R. Khan. Parametric optimization for surface roughness, kerf and MRR in wire electrical discharge machining (WEDM) using Taguchi design of experiment. Journal of Mechanical Science and Technology 27(7):2133–2141, 2013. https://doi.org/10.1007/s12206-013-0526-8 [43] U. K. uz Zaman, U. A. Khan, S. Aziz, et al. Optimization of wire electric discharge machining (WEDM) process parameters for AISI 1045 medium carbon steel using Taguchi design of experiments. Materials 15(21):7846, 2022. https://doi.org/10.3390/ma15217846 [44] İ. B. Şahin, F. Yılan, L. Urtekin. Optimisation on machining parametres by EDM of TiN coated Ti6Al4V alloys. Advances in Materials and Processing Technologies 10(2):960–970, 2024. https://doi.org/10.1080/2374068X.2023.2184585 [45] M. A. B. Taher, U. Pelay, S. Russeil, D. Bougeard. A novel design to optimize the optical performances of parabolic trough collector using Taguchi, ANOVA and grey relational analysis methods. Renewable Energy 216:119105, 2023. https://doi.org/10.1016/j.renene.2023.119105 [46] L. Urtekin, İ. B. Şahin, F. Yılan, et al. Investigation and optimization of cutting performance of high chrome white cast iron by wire erosion. Arabian Journal for Science and Engineering 49(2):1585–1596, 2023. https://doi.org/10.1007/s13369-023-07930-6 370 https://doi.org/10.1007/s00170-006-0629-9 https://doi.org/10.1088/2631-8695/ab26c8 https://doi.org/10.3390/met11071105 https://doi.org/10.1007/s11665-023-08795-4 https://doi.org/10.1016/j.ijlmm.2022.06.003 https://doi.org/10.1007/s00170-021-08358-8 https://doi.org/10.1088/1757-899X/390/1/012107 https://doi.org/10.1016/j.hybadv.2023.100116 https://doi.org/10.1590/S1678-58782006000400006 https://doi.org/10.1088/1757-899X/1013/1/012005 https://doi.org/10.1088/1757-899X/402/1/012171 https://doi.org/10.1016/j.measurement.2018.10.067 https://doi.org/10.1016/j.measurement.2018.10.067 https://doi.org/10.1016/j.applthermaleng.2020.115816 https://doi.org/10.1016/j.applthermaleng.2020.115816 https://doi.org/10.1007/s12206-013-0526-8 https://doi.org/10.3390/ma15217846 https://doi.org/10.1080/2374068X.2023.2184585 https://doi.org/10.1016/j.renene.2023.119105 https://doi.org/10.1007/s13369-023-07930-6 Acta Polytechnica 65(3):361–370, 2025 1 Introduction 2 Experimental design 2.1 Materials 3 Taguchi experimental design 3.1 Analysis and characterisation of the samples 4 WEDM experimental setup 5 Results and discussions 5.1 WEDM machining factors optimisation 5.1.1 ANOVA analysis of MRR and SR 5.1.2 Grey Relational Grade analysis 6 Surface roughness results 7 Conclusion References