Experimental Investigation of Material Removal Rate Parameters in ECM for Aluminum Hybrid Matrix Composites Using the RSM Technique

Naga Swapna Sri, M.; Anusha, P.; Rajendranrao Krishnajirao, Vittel Rao; Selvaraj, Manickam; Muthuvel, B.; Karthikeyan, N.

doi:https://doi.org/10.1155/2023/8406751

Advances in Materials Science and Engineering

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Abstract Introduction Results and Discussion Conclusion Data Availability Disclosure Conflicts of Interest Acknowledgments References Copyright Related Articles

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Research Article | Open Access

Volume 2023 | Article ID 8406751 | https://doi.org/10.1155/2023/8406751

Experimental Investigation of Material Removal Rate Parameters in ECM for Aluminum Hybrid Matrix Composites Using the RSM Technique

M. Naga Swapna Sri,¹P. Anusha,¹Vittel Rao Rajendranrao Krishnajirao,²Manickam Selvaraj,³B. Muthuvel,⁴and N. Karthikeyan⁵

Academic Editor: J. T. Winowlin Jappes

Received07 Sept 2022

Revised11 Oct 2022

Accepted13 Oct 2022

Published01 Feb 2023

Abstract

In the present work, the preparation of AA-6082/ZrSiO₄/TiC hybrid composite is studied along with an analysis of the effects of electrochemical machining parameters such as feed rate of electrode (FE), voltage (VO), electrolyte concentration (EL), and electrolyte discharge (ED) rate on the output responses of the material removal rate (MRR) and surface roughness (SR) for Al hybrid composites. The experiments are carried out based on the Taguchi L16 orthogonal array and the important process parameters are found for MRR and SR. Each parameter contains four different levels that are FE (0.10, 0.15, 0.20, and 0.25 mm/min), VO (10, 15, 20, and 25 V), EL (15, 20, 25, and 30 g/lit), and ED (1.5, 2, 2.5, and 3 lit/min).The optimization software, namely, Minitab-17 version helps to find the contribution of each parameter on MRR and SR. The ANOVA result reveals that the feed rate of electrode is the highest contributing parameter, trailed by the electrolyte discharge rate and other process parameters for MRR and SR. A linear model of regression and interaction plots is also included to show the relationship between the parameters. From the observational results, the highest MRR (0.00953 mg/min) is attained by the parameter combination level of the feed rate of electrode of 0.20 mm/min, voltage of 25 V, electrolyte concentration of 20 g/lit, and electrolyte discharge rate of 1.5 g/, whereas the lowest MRR is found at FE of 0.10 mm/min, VO of 10 V EL-15 g/lit and ED of 2.5 g/litre. For SR, the maximum and minimum are recognized at FE2-VO4-EL3-ED2 (0.15 mm/min, 25 V, 25 g/lit, and 2 lit/min) and FE1-VO1-EL1-ED1 (0.10 mm/min, 10 V, 15 g/lit, and 1.5 lit/min), respectively. Finally, the increment of MRR and SR values is mostly dependent on the feed rate of the electrode.

1. Introduction

When constructing any components in the manufacturing and industrial engineering fields, Al is a chemical element that does not have sufficient strength on its own. So, to increase the materials’ strength and characteristics, reinforcements such as B₄C, TiB₂, TiC, and SiC were used. The advantages of AMCs are superior in a variety of industries and sectors [1] because it possesses high specific strength, stiffness, and wear resistance [2]. In the 6,000 series of alloys, Al 6082 is a moderately strong alloy with outstanding corrosion resistance. Al 6082 is the alloy that is most frequently machined when it is in plate form. The high strength alloy is created by control of grain structure and also adding a lot of Mn elements [3]. At a high temperature of about 1500°C, titanium dioxide and carbon undergo a chemical reaction to produce titanium carbide, a powder with a dark black appearance. When combined with Al alloy, TiC has been shown to acquire elastic and abrasion properties [4, 5]. For applications requiring resistance to corrosion by alkali materials, zirconium silicate is used as a refractory material. Electrochemical machining (ECM) is one of the modern machining techniques that removes material by dissolving atoms from the work piece’s molecular structure as a result of electrochemical action. This technique is based on Faraday’s principles [6]. Hard materials are machined using an electrochemical technique. Materials that are challenging to machine using conventional methods can be easily machined using the ECM with minimal heat production due to the lack of friction, no stresses produced because no contact is made between the tool and the work piece, and excellent surface finishes due to the removal of material at the atomic level. De Silva et al. [7], investigated the ECM approach using various electrolyte concentrations and concluded that lower quantities yielded greater accuracy. Burger et al. [8] investigated the ECM method for nickel-based materials which indicated that using the same electrochemical action for dissolving can produce high-quality products. Neto et al. [9] used several electrolytes to explain the primary variables in the ECM of SAEXEV-FValve-Steels and came to the conclusion that the electrode feed rate was the major factor determining the material removal rate (MRR). In order to find the ideal process parameter of the MRR, dimensional accuracy, and cost of machining, Rao et al. [10] evaluated the important ECM process characteristics of the electrode feed rate, voltage applied, and electrolyte discharge rate. The best option for processing variables is still difficult to choose in order to guarantee the greatest machining performance. To improve the processing performance, various researchers have conducted a number of investigations. Stir casing was used to create the 5,059 Al alloy reinforced with SiC particles with a size range of 10– 40 lm and a set molybdenum disulphide content of 2%. MRR and SR have been examined by altering the machining parameters on the L-27 OA experiments [11]. However, the optimization process used the following four parameters for the ECM: current, pulse-on time, pulse-off time, and voltage. The results of the ANOVA show that peak current and pulse-off time are the most important variables for MRR [12]. The impact of factors on the electrochemical machining properties of LM6 Al/B4C composites is studied. Twenty test cases are used to test the produced nonlinear regression models, which were developed after experiments were done using the central composite design of experiments. Based on RSM, the responses of MRR, SR, and ROC are each independently and simultaneously maximized. The nonlinear model’s average absolute percentage of error in predicting all replies is determined to be 9.657 [13]. The extremely narrow electrode space between the cathode and anode is where the DC voltage is applied. The anode serves as the work piece and the cathode as the tool. To complete the flow of current, transport the heat, and dissolve the metal, an electrolyte of free-toxic and dangerous wastage is delivered and filled in the IEG [14]. The Taguchi L9 design utilized by Jeykrishnan et al. [15] evaluated the impact of processed variables on the machined surface quality. The SR of D3 die steel was reduced as the electrolyte concentration dropped. For Al/B4C composites, Taguchi L-27 is also utilized to optimize the parameters. They claimed that one of the most important factors is tool feed rate. With 0.2 mm molybdenum wire and Taguchi’s pedagogy in consideration, Mohamed and Lenin [16] examined the WEDM characteristics of AA6082-T6. Investigations into the optimization of the parameters for electrochemical machining (ECM) require focus and address the technology gap for the upcoming ECM/nontraditional machining researchers. Many researchers selected mostly three factors in ECM process, and the improvement of MRR and SR is based on the concentration of electrolyte. Generally, the electrolyte performed the three important functions in the ECM process. It passes the current among the tool and work piece, removes a product of the response from the cutting region, and also removes the heat produced by the current flow in the operation. The novelty of this work is to implement four factors including electrolyte concentration and MRR and SR for Al 6082/ZrSiO₄/TiC composites which are measured with variations of four levels of NaCl electrolyte concentration, and also design of experiments is conducted according to Taguchi (L16) design to find out the optimal factor of electrolyte concentration, voltage, electrolyte discharge rate, and feed rate of the electrode on their responses of MRR and SR. This Al 6082 composite is practically used in bridges, cranes, transport applications, ore skips, beer drums, and milk blends.

2. Material Selection and Methodology

Al 6082 is chosen as the primary material in this research process. Al alloy is combined with two reinforcements at a weight of 5%. Al 6082/ZrSiO₄/TiC is produced using the stir casting technique (SCT). The existing chemical elements and mechanical properties of Al 6082 are described in Tables 1 and 2, respectively. SCT is widely acknowledged as one of the several manufacturing methods available for discontinuous metal matrix composites[17, 18]. It is more beneficial in terms of cost-effectiveness, simplicity, and adaptability to large-scale production. ZrSiO₄/TiC acted as strengthen particles in the casting to produce Al 6082 hybrid composite. In the SCT process, A motor is driven by rotating the stirrer at varied speeds. A lift mechanism was employed to get the stirrer into contact with Al 6082 and particles of reinforcement. Al 6082 is first put in a furnace and heated to about 750–800°C in an electric furnace [19]. Zirconium silicate/titanium carbide (5 wt%) particles are heated at 400°C simultaneously in a second furnace. Then, melted 6,082 alloy and preheated reinforcements are mixed and heated to a temperature of 800°C. After being stirred continuously for 7 minutes by a stirrer, the prepared melted liquid is transferred into the die to produce Al 6082/ZrSiO₄/TiC-based composites. Unnecessary portions from the generated samples are removed using a grinding machine [20]. The complete setup of the stir casting process is displayed in Figure 1.

3. ECM Process and Design of Experiments

After samples are prepared, the electrochemical machining (ECM) process is used to remove metal from the work surface in accordance with our requirements and depends on a reverse electroplating mechanism. During the process, particles moved from the work specimen as an anode to the target machine tool as a cathode. The cavity is formed on the material as a result of current or voltage travelling through the setup observed in Figure 2. We use a weighing device to measure the sample weight before and after the ECM experiment, and the composites’ densities are computed using the following formulas:where ρ is the density of 6,082 Al and V_{Al 6082} is the volume of alloy and reinforcements.

The densities of ZrSiO₄ and TiC are 4.56 g/cm³ and 4.93 g/cm³. These two density values are more and almost very close to each other. The sample weight is increased due to the increase in reinforcement’s wt%. Two reinforcements (ZrSiO₄ and TiC) are taken equally and mixed with Al 6082 at 5 wt% to maintain a lower weight.

DOE and the use of numerical tools are the most effective methods for analysing the effects of frequent factors. This method’s purpose is to reduce the number of tests where DOE is left unchanged. The Taguchi methodology is a technique for determining the ideal process variables and parameters for a particular process response (output). The technique aims to deliver high-quality goods at a lower price, and it is also possible to determine the interaction between the variables and the result, which is very accurate when compared to other techniques. Four parameters with 4 levels are taken for designing the experiments and presented in Table 3. With the aid of Minitab software, the 16 experimental combinations are made by the interaction of the parameter levels as per Table 4.

To identify the influencing ECM process factors on the response, namely, the MRR, feed rate of the tool (FE), electrolyte concentration (EL), voltage (VO), and electrolyte discharge rate (ED) were studied. Both the data and the S/N ratios are used to determine the effects of each process factor on the machining performance and to identify the important process parameters. An analysis of variance is used to calculate the contribution percentage of the process parameters. A greater S/N ratio is preferable since it indicates that there are many settings that may be changed to reduce the impact of unwanted noise and loss [21].

4. Results and Discussion

4.1. Material Removal Rate

The overall MRR results are exposed in Figure 3 and Table 5. It was noticed that the maximum and minimum optimum combination parameters for material removal rate are acknowledged at FE3-VO4-EL3-ED1 (0.20 mm/min, 25 V, 20 g/lit, and 1.5 lit/min) and FE1-VO3-EL3-ED3 (0.10 mm/min, 20 V, 25 g/lit, and 2.5 lit/min), respectively. Four significant factors were nominated as important portions of this present study. Experimental tests were analysed with the help of ANOVA (95% confidence level), also assessing chosen variables (FE, EL, VO, and ED). The SN ratio mean plots and table results for MRR are mentioned in Figure 4 and Table 6. It was detected that the greatest optimal parameter groupings for MRR are FE3-VO4-EL4-ED2 (i.e., FE of 0.20 mm/min, VO of 25 V, EL of 30 g/lit, and ED of 2 g/litre) and least groupings variable are FE of 0.10 mm/min, VO of 10 V, EL of 15 g/lit, and ED of 2.5 g/litre.

Table 6 discloses the SN response of machining factors, level 3 of FE (−41.35) has a higher significant level of MRR, followed by level 4 of ED (−43.08), level 4 of EL (−42.69), and level 2 of VO (−43.06). ANOVA is a technique that assesses if there are statistical differences between two parameters, while it also assesses whether there are statistical differences between three or more parameters. F and values acted as controllable and probability of irrepressible to accomplish optimum MRR in analysis of variance. From Table 7, the value of process parameters FE (<0.005), ED (0.006), and EL (0.018) has been discovered to be less than 0.05. It is indicating that these parameters are more important parameters for increased MRR. The other process parameter, VO, and its combinations, on the other side, are less important process parameters. The peak contribution of FE (76.9%) is the first factor to increase MRR, trailed by ED (12.3%), EL (5.7%), and VO (4.6%). The feed rate of electrode is contributed as main parameters, and ED support is very less on MRR. From the observation of Figure 5, MRR is directly proportional to feed rate. This is due to the increase in electrode feed, which makes good contact with composite to remove material. Whenever parameters of electrolyte concentration, voltage, and MRR values are increasing, it is due to extra energy for oxidation that provides more anodic dissolution, more heat, and a good chemical reaction between the electrolyte and Al composite [22].

4.2. Surface Roughness

The experimental SR results are exposed in Figure 6. It was noticed that the maximum and minimum optimum combination parameters for surface roughness are recognized as FE2-VO4-EL3-ED2 (0.15 mm/min, 25 V, 25 g/lit, and 2 lit/min) and FE1-VO1-EL1-ED1 (0.10 mm/min, 10 V, 15 g/lit, and 1.5 lit/min), respectively. Table 8 and Figure 7 reveal that the SN response of machining parameters, level 2 of feed rate (5.024), has the highest influence level on SR, followed by level 4 electrolyte concentration (4.074), level 2 of voltage (4.338), and level 2 of electrolyte discharge rate (4.284). Based on the delta results of the SN table, the parameters are categorized from first rank to last rank. FE which is the most important variable (1^st rank) is determined as 3.540 delta followed by EL of 1.395 delta (2^nd rank), ED of 1.023 delta (3^rd rank), and VO of 1.016 (4^th rank). F and values acted as controllable and probability of irrepressible to achieve optimum surface roughness (SR) in the analysis of variance. From Table 9, the value of FE (0.0144) is discovered to be less than 0.05, indicating that these parameters are more important process parameters for increasing the SR. The VO, ED, and EL and their combinations on the other side have less important process parameters because value is more than 0.05. The highest contributions of FE are 62.4%, which is the most significant factor in increasing SR, followed by EL of 10.8%, ED of 5.2%, and finally VO of 5.1%. From the observation of Figure 8, the feed rate of electrode is contributed as the main parameters, and VO support is very low on the improvement of SR. SR is directly proportional to electrode feed rate until it reaches to 0.10–0.15 mm/min. Further increase in FE leads to decrease in surface roughness. The voltage effect on the surface roughness is also obtained more when increasing VO from 10 to 25 V. In case of EL and EL parameters, increasing the values of 20 g/lit and 2 lit/min will lead to decreasing surface roughness. The difference of SR values depends on the anodic dissolution and inappropriate blushing at the electrolyte flow, which cause the availability of chip materials that give a good or bad surface finish.

4.3. Regression Equation

In order to establish the connection between the input variables (FE, VO, EL, and ED) and response variables (MRR and SR), A linear equation was used to model the experimentally observed data using the multiple linear regression analysis examination method. A linear regression model presentation will be produced by the software Minitab-17 in view of the experimental findings [23]. For MRR and SR, the regression equations (2) and (3) were helpful.

4.4. Interaction Plots for MRR and SR

Figures 9 and 10 illustrate the effect of various factors on MRR and SR from the produced composites. Figure 9 shows that when a parameter is changed, either in terms of its levels or interactions, the MRR varies. The level of these parameters will rise while the interaction between FE ∗ ED, FE ∗ EL, and EL ∗ ED varies with nearly constant values. These interactions increase very slightly, and it is possible to consider them to be constant deviations.For the case of other parameter interactions such as FE ∗ VO, VO ∗ EL, and VO ∗ ED, when the value of these parameters increase MRR ,value decreses. In these combinations, the MRR is mostly signified by the feed rate and electrolyte concentration. Additionally, only small differences in the value of each case were observed owing to a change in parameter levels seen, which is essentially the same phenomenon as these results. Some data suggest that some parameter arrangements or interactions have only a very small impact on the MRR of Al hybrid composites. From the observation of Figure 9, the SR will change when a parameter is changed in the collaborations of ED ∗ FE and FE ∗ EL. However, for the case of parameter interactions of VO ∗ FE, EL ∗ V, and other combinations, when these parameter values increase, the SR value decrease. However, feed rate and electrolyte discharge rate play a prevailing role in SR.

4.5. Surface Morphology and EDX Analysis

The maximum MRR and SR were obtained with Experiment No. 12 and 8, and the SEM image of their machined surface is presented in Figures 11 and 12. The presence of reinforcement, microcraters, and microcracks is investigated in all the surface regions. It is evident from the presence of more reinforcement (ZrSiO₄ + TiC) noticed in the left end region while compared to other regions. Additionally, the accumulation of reinforcements in the left region is shown in Figure 11, and this is due to the solidification process of AMCs. The pattern of ridges is formed uniformly due to energy generation in a particular direction, and also an extra feed rate by the electrode leads for the creation of microridges. It is noticed that microvoids and gas or air bubbles are visible at some regions of the surface due to the addition of reinforcement particles into melted Al 6082 during the stir casting process, and air enters the materials along with the reinforcement; however, gas is released from the material during machining. The microcraters and gas bubbles are visible in Figure 12. Many literature studies have stated that the main reason for microcrack instigation is de-bonding at the reinforcement matrix boundary. The EDX map with respect to machine surfaces in Experiment 12 is presented in Figure 13, which reveals the different chemical composition of machine surfaces. The composition percentage is changed due to addition of reinforcements. EDX analysis concerning the localized distortion portion of the SEM image is identified as the combination of Al, Mg, Zn, Si, and Fe elements with wt% of 89.66%, 1.53%, 3.48%, 0.22%, and 0.18%, respectively.

5. Conclusion

In this research work, the influence of the electrochemical machining parameters of the electrode feed (FE) rate, voltage (VO), electrolyte discharge (ED) rate, and electrolyte concentration (EL) on the responses of MRR and SR is examined by the optimization technique, namely, TaguchiL16 orthogonal array. The following conclusions are discussed:(i)The maximum material removal rate is attained by selecting the best combination level that is at FE3-VO4-EL4-ED2 (i.e., feed rate of electrode of 0.20 mm/min, voltage of 25 V, electrolyte concentration of 20 g/lit, and electrolyte discharge rate of 1.5 g/litre), and the minimum value is obtained at FE of 0.10 mm/min, VO of 10 V, EL of 15 g/lit, and ED of 2.5 g/litre.(ii)From the ANOVA result on material removal rate, the highest contributions of FE are 76.9% which is the most significant factor in increasing the MRR, followed by ED 12.3%, EL 5.7%, and VO 4.6%.(iii)The maximum and minimum optimum combination parameters for surface roughness are recognized at FE2-VO4-EL3-ED2 (0.15 mm/min, 25 V, 25 g/lit, and 2 lit/min) and FE1-VO1-EL1-ED1 (0.10 mm/min, 10 V, 15 g/lit, and 1.5 lit/min), respectively.(iv)From the ANOVA result on surface roughness, the highest contributions of FE are 62.4%, which is the most significant factor in increasing the surface roughness, trailed by EL of 10.8%, ED of 5.2%, and finally VO of 5.1%.(v)The increment of MRR and SR values mainly depends on the increment of feed rate of electrode.

Data Availability

The data used to support the findings of this study are included in the article and are available from the corresponding author upon request.

Disclosure

This study was performed as a part of the Employment Bule Hora University, Ethiopia.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

The authors appreciate the technical assistance to complete this experimental work from Department of Mechanical Engineering, Bule Hora University, Ethiopia. The author thanks P V P Siddhartha Institute of Technology, Vijayawada, India, for the support of draft writing.

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Copyright © 2023 M. Naga Swapna Sri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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