Performance study of electrical discharge machining process in burr removal of drilled holes in Al 7075

Deburring is the finishing technique that is very essential for the manufacturing of precise components. Although there are various methods to remove burrs but sometimes technology requires new innovative ideas for better outcome irrespective of cost. Deburring with greater efficiency and full automation is a difficult task and this thing leads to use unconventional approaches. This research paper is all about deburring through Electric Discharge Machining process (EDM), which can be useful for finishing materials with very high precision with no men interference. For successful utilization of deburring through EDM, more intensive research including control parameters analysis of the process, is still required. In this research, the drilling operation was done on the specimen to obtain burrs and comparative study was done with different parameters and different electrodes for deburring effectively through EDM. The variable parameters were discharge current, pulse time on and pulse time off and other parameters were remained constant. Material removal rate, tool wear rate and burr height were studied as output parameters to analyse *Corresponding author: Pretesh John, Department of Mechanical Engineering, Shepherd School of Engineering & Technology, SHIATS-DU, Allahabad, India E-mail: preteshjohn@yahoo.com Reviewing editor: Zude Zhou, Wuhan University of Technology, China Additional information is available at the end of the article ABOUT THE AUTHORS Pretesh John is a MTech. (Production & Industrial Engineering) research scholar in the Department of Mechanical Engineering, SSET, SHIATS, Allahabad, India, and has been working on deburring techniques, performed for burr removal on various engineering materials. He has published one article on deburring through EDM in an International Journal and has presented one research in an International Conference in Malaysia. Rahul Davis is an Assistant Professor in the Department of Mechanical Engineering, SSET, SHIATS, Allahabad, India. He has performed much research related to metal cutting and finishing operations on various engineering materials in last five years. He has published more than thirty Research Papers in reputed International Journals. He has been the part of various National/ International Conferences in India and abroad, as a Paper presenter, Keynote Speaker, Technical Committee member & Technical Session Chair. He is the member of Reviewer board of various International Journals such as IJAMT-Springer, JIEI-Springer, ILT-Emerald, Journal of MaterialsSAGE etc. PUBLIC INTEREST STATEMENT During the machining of many soft engineering materials, formation of raised and rough edges, called “burrs”, are natural. This may cause many negative effects on the dimensional accuracy, shape and size of the final product. Therefore, removal of these formed burrs is extremely essential. For deburring the burrs formed in drilled holes, there are many conventional methods available but new innovation and efficient outlooks are always welcomed by engineers and are therefore needed for the better outcome; therefore the intervention of unconventional processes comes into picture in this area. The present research is all about deburring through Electric Discharge Machining process (EDM), in which machining was done with the help of electric discharge and deburring with EDM results into better surface finish as well as good automation. In this research some of the machining parameters are studied to analyses their effects on material removal rate, tool wear rate and burr removal. Received: 06 February 2016 Accepted: 02 December 2016 First Published: 26 December 2016 Page 2 of 19 © 2017 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

ABOUT THE AUTHORS Pretesh John is a MTech. (Production & Industrial Engineering) research scholar in the Department of Mechanical Engineering, SSET, SHIATS, Allahabad, India, and has been working on deburring techniques, performed for burr removal on various engineering materials. He has published one article on deburring through EDM in an International Journal and has presented one research in an International Conference in Malaysia.
Rahul Davis is an Assistant Professor in the Department of Mechanical Engineering, SSET, SHIATS, Allahabad, India. He has performed much research related to metal cutting and finishing operations on various engineering materials in last five years. He has published more than thirty Research Papers in reputed International Journals. He has been the part of various National/ International Conferences in India and abroad, as a Paper presenter, Keynote Speaker, Technical Committee member & Technical Session Chair. He is the member of Reviewer board of various International Journals such as IJAMT-Springer, JIEI-Springer, ILT-Emerald, Journal of Materials-SAGE etc.

PUBLIC INTEREST STATEMENT
During the machining of many soft engineering materials, formation of raised and rough edges, called "burrs", are natural. This may cause many negative effects on the dimensional accuracy, shape and size of the final product. Therefore, removal of these formed burrs is extremely essential. For deburring the burrs formed in drilled holes, there are many conventional methods available but new innovation and efficient outlooks are always welcomed by engineers and are therefore needed for the better outcome; therefore the intervention of unconventional processes comes into picture in this area. The present research is all about deburring through Electric Discharge Machining process (EDM), in which machining was done with the help of electric discharge and deburring with EDM results into better surface finish as well as good automation. In this research some of the machining parameters are studied to analyses their effects on material removal rate, tool wear rate and burr removal.

Introduction
Metal is frequently machined using many processes in order to create pieces of specific shape and size. Like Drilling is a cutting process. It is the creation or enlarging of a hole in a solid material with a drill (Parker, 2003). These procedures often create ragged edges or protrusions (burrs). Removal of burrs (Deburring) is important for quality, aesthetics, functionality, and the smooth operation of working parts. It greatly improves the quality and functionality of metal and wood pieces, making it a necessary use of time and a cost effective process. A burr is a raised edge or small pieces of material remaining attached to a work piece after a modification process (https://en.wikipedia.org/wiki/ Burr(edge)). It is usually an unwanted piece of material, removed with a deburring tool in a process called "deburring". Burrs are most commonly created after machining operations, such as grinding, drilling, milling, engraving, or turning. The ISO 13715:2000(International Standard ISO 13715:2000, 2000 defines the edge of a work piece as burred if it has an overhang greater than zero. A comprehensive definition was proposed by Beier (1999). According to it, a burr is a body created on a work piece surface during the manufacturing of a work piece, which extends over the intended and actual work piece surface and has a slight volume in comparison to the work piece, undesired, but to some extended, unavoidable (Obróbka skrawaniem, xxxx). Machining burrs typically form because of material being plastically deformed rather than cut towards the entrance or exit of a machined feature. Burrs are formed in most of the machining operations; they cannot be eliminated during the machining process, but can be minimized by controlling the process parameters, tool geometry, etc. (Jeong, HanYoo, Lee, & Min, 2009).
According to Gillespie (1999a), in drilling the burr formed at the entrance of the hole can be a result of tearing, a bending action followed by clean shearing, or lateral extrusion when chip forming and impacting of cutting edges. Kim, Min, and Dornfeld (2001) describe drilling burrs as uniform burr with or without a drill cap, crown burr, or petal burr according to their shapes and formation mechanism. When drilling stainless steel, Stein and Dornfeld (1997) revealed small or large uniform burrs as well as crown burrs, and when low alloyed steel drilling small or large uniform burrs, transient burrs and crown burrs were found. It was found that the constant ratio between burr height (BH) and undeformed chip thickness might be a fundamental property of work material for particular tool geometry (Stein & Dornfeld, 1997).
Burr shape is the most important because the burr size, and as a result, deburring cost is greatly dependent on it. Three different shapes of drilling burrs: (A) The uniform burr has relatively small and uniform BH and thickness around the hole periphery.
(B) The crown burr has a larger and irregular height distribution around the hole.
(C) The transient burr is a type of burr formed in the transient stage between the uniform burr and the crown burr (Dornfeld, 2006).
Deburring includes both the removal of burrs and maintenance of the proper edge condition. It is usually the last process during part production, therefore the loss of potential due to any failure in deburring process is very large (Dornfield & Kim, 2001). Deburring of inaccessible, an area via conventional methods does not ensure burr removal and edge conditioning; deburring via non-conventional techniques provides a better solution (Balasubramaniam, Krishnan, & Ramakrishnan, 1998). The burr removal methods can induce dimensioning errors to the work piece if improperly executed (Dornfeld & Lisiewicz, 1992). Finally, burrs may cause problems in further processes, such as handling and assembling operations. According Aurich (2006), a study carried out in the German automotive and machine tool industries showed that the deburring causes an increase of about 15% in work force and cycle times. In addition, a 2% share in the reject rate and a 4% share in machine breakdown times due to burrs. Averaging the presented distribution without any weight factors the share accounts of up to 9% of total manufacturing cost. An economic evaluation of the impact of burrs related Aurich has provided production cost too (Aurich, 2006): the costs are estimated at up to 500 million Euro expense per year only in Germany.
There is no standard procedure to remove burrs having different shape and dimensions. For removing burrs of different sizes and shapes. There are many ways of deburring to remove the burr, they are conventional deburring process namely manual deburring, hand deburring etc. and nonconventional deburring process (Prathap, Raghavendra, & Shetty, 2012). Conventional deburring processes necessitate time, labour and other associated costs. Manual deburring results in inconsistent quality. Repeatability is hard to achieve as it is done manually (Gillespie, 1999b). The limitations of traditional finishing led to the development of advanced finishing techniques like Abrasive Flow Machining, Magnetic Abrasive Finishing, and Ion Beam Machining, Electric Discharge Machining. Deburring through Electric Discharge Machining process (EDM) is one of the best options when better outcome is expected (in comparison to conventional methods) and overall cost of the process is not a constraint.

Electric discharge machining
It is a manufacturing process whereby a desired shape is obtained using electrical discharges (sparks). Material is removed from the work piece by a series of rapidly recurring current discharges between two electrodes, separated by a dielectric liquid and subject to an electric voltage. One of the electrodes is called the tool-electrode, or simply the "tool" or "electrode", while the other is called the work piece-electrode, or "work piece". In this process, the metal is removing from the work piece due to erosion case by rapidly recurring spark discharge taking place between the tool and work-piece. The electrode and the work piece must have electrical conductivity in order to generate the spark (Abbas, Solomon, & Bahari, 2006). EDM is a non-conventional machining process but the technique of material erosion employed in EDM is still debatable (Tsai & Wang, 2001). The basic principal followed is the conversion of electrical energy into thermal energy through a series of discrete electrical discharges occurring between the electrode (tool) and work piece immersed in a dielectric fluid (Kalpajian & Schmid, 2003). Due to the insulating effect of the dielectric, which is used in EDM, process is very important because it avoids electrolysis of the electrodes during the EDM process. Spark is initiated when high voltage is applied between the electrode and work piece at smallest point distance. Metal starts eroding from both the surfaces of work piece as well as electrode. At the end, sparks spread over the entire work piece surface and it leads to the erosion, or machining to a shape, which is mirror image of the tool.  Surendra and Dabade (2011) investigated that the quality of the burr removed with brass electrode is better as compared with aluminium and copper electrodes. Secondly, it observed that the Aluminium electrodes wear is more irrespective of process parameters due to lower electrical conductivity than other electrodes. Finally, it is concluded that the brass electrodes gives better results in terms of quality, material removal rate (MRR), and electrode wear than aluminium and copper electrodes. Again Dabade (2013) investigated the effect of different EDM process parameters such as current, dielectric flow rate and pulse on time on the tool wear rate (TWR) and grading of deburred holes by visual inspection for burr removal in drilled holes of Inconel-718 material. Burr removal operation were carried out with two types of electrode materials such as Aluminum and Brass with cylindrical taper geometry at tip is used. Davis (2015, 2016) did the drilling operation on the OHNS specimen material and obtained burrs and comparative study was done with different parameters and different electrodes (copper and brass) for deburring effectively through EDM. Optimal levels of the parameters for deburring on OHNS with copper electrodes and brass electrodes were obtained respectively. Again  investigated the burr removal through EDM process on D2 steel with copper and brass electrodes and optimized combination of process parameters were obtained for MRR, TWR and BH removal.

Input process parameters of EDM Output parameters or responses
It is important to select machining parameters for achieving optimal machining performance (Tarng, Ma, & Chung, 1995). Usually, the desired machining parameters are determined based on experience or on handbook values. However, this does not ensure that the selected machining parameters result in optimal or near optimal machining performance for that particular electrical discharge machine and environment. So research is needed in this area to find out the optimal parameters for the deburring operation through EDM for various widely used specimen materials like titanium, aluminium alloys, steels, advanced materials etc.

Optimization technique
It is a mathematical results and numerical methods for finding and identifying the best candidate from a collection of alternatives without having to explicitly enumerate and evaluate all possible alternatives. The process of optimization lies at the root of engineering, since the classical function of the engineering is to design new, better, more efficient and less expensive systems as well as to devise plans and procedures for the improved operation of existing systems.

Details of the experiment
To obtain the burrs and to see the burr formation, each work piece was drilled by HSS cutting tool in the wet cutting conditions. Further, EDM operations were performed to measure MRR and TWR on each work piece, In order to study the effect of three different parameters (current supply, pulse time on, and pulse time off with different electrodes) on the burr removal of the specimens.

Details of the specimen materials selected for the proposed research work
Aluminium 7075 ( Figure 1) were chosen to be the specimen materials. The material composition of specimen material Al 7075 is shown in Table 1 and mechanical properties are shown in Table 2 respectively. Al 7075 is often used in transport applications, including marine, automotive and aviation, due to their high strength-to-density ratio. Its strength and lightweight is also desirable in other fields. Rock climbing equipment, bicycle components, inline skating-frames, and hang glider airframes are commonly made from 7075 aluminium alloy. Hobby grade RC models commonly use 7075 and 6061 for chassis plates. One interesting use for 7075 is in the manufacture of M16 rifles for the American military. In particular high quality M16 rifle lower and upper receivers as well as extension tubes are typically made from 7075-T6 alloy. Desert Tactical Arms and French armament company PGM use it for their precision rifles. It is also commonly used in shafts for lacrosse sticks, such as the STX sabre, and camping knife and fork sets. Due to its high strength, low density, thermal properties, and its ability to be highly polished, 7075 is widely used in mold tool manufacture (http:// www.azom.com/article.aspx?ArticleID=6652).

Brass
Brass is the generic term for a range of copper-zinc alloys with differing combinations of properties, including strength, machinability, ductility, wear-resistance, hardness, colour, antimicrobial, electrical, and thermal conductivity, corrosion resistance. Brass has higher malleability than bronze or zinc. The relatively low melting point of brass (900 to 940°C, 1,652 to 1,724°F, depending on composition) and its flow characteristics make it a relatively easy material to cast (Walker, xxxx).

Copper
Copper has properties, such as its high electrical conductivity, tensile strength, ductility, creep (deformation) resistance, corrosion resistance, low thermal expansion, high thermal conductivity, solder ability, and ease of installation. Copper is a chemical element with symbol Cu and atomic number 29. It is a ductile metal with very high thermal and electrical conductivity. Pure copper is soft and malleable. A freshly exposed surface has a reddish-orange colour (http://www.gsa.org.au/resources/ factites/factitesCopper.pdf).

Grey relational analysis
Deng proposed it in 1989 as cited in is widely used for measuring the degree of relationship between sequences by grey relational grade. Several researchers to Optimize control parameters having multi-responses through grey relational grade apply grey relational analysis. Deng (1989) had proposed Grey relational analysis in the Grey theory that was already proved to be a simple and accurate method for multiple attributes decision problems (Chang, 1996;Hsu, 1997;Luo & Kuhnell, 1993;Tzeng & Tsaur, 1994), especially for those problems with very unique characteristic (Chiang, 1997;Wu, 1998). This analytical model magnifies and clarifies the Grey relation among all factors. It also provides data to support quantification and comparison analysis (Sih, 1997). In other words, the Grey relational analysis is a method to analyze the relational grade for discrete sequences. This is unlike the traditional statistics analysis handling the relation between variables. The use of grey relational analysis to optimize the burr removal operations includes the following steps: (A) Design of experiment: • To identify the performance characteristics and machining parameters to be evaluated.
• To determine the number of levels for the process parameters.
• To select the appropriate orthogonal array and assign the machining parameters to the orthogonal array.
(C) Grey relational analysis: • To perform the grey relational generating (data pre-processing and deviation sequences) and calculate the grey relational coefficient.
• To calculate the grey relational grade by averaging the grey relational coefficient.
• To analyses the experimental results using the grey Relational grade.
• To select the optimal levels of machining parameters.
In the grey relational analysis method, experimental data (electrode wear ratio, material wear rate, surface roughness) are first normalized in the range between zero and one, which is also called the grey relational generation. Next, the grey relational coefficient is calculated from the normalized experimental data to express the relationship between the desired and actual experimental data. Then, the grey relational grade is computed by averaging the grey relational coefficient corresponding to each process response. The overall evaluation of the multiple process responses is based on the grey relational grade. As a result, optimization of complicated multiple process responses can be converted into optimization of a single grey relational grade. In other words, the grey relational grade can be treated as the overall evaluation of experimental data for the multi-response process. The optimal level of the process parameters is the level with the highest grey relational grade. In Figure 2 these steps are shown in flow chart to easily understand the various steps of GRA.

Data pre-processing
In grey relational analysis, the data pre-processing is the first step performed to normalize the random grey data with different measurement units to transform them to dimensionless parameters. Thus, data pre-processing converts the original sequences to a set of comparable sequences. Different methods are employed to pre-process grey data depending upon the quality characteristics of the original data. The original reference sequence and pre-processed data (comparability sequence) are represented by where m is the number of experiments and n is the total number of observations of data. Here, X i represents the ith experimental results and is called the comparative sequence in grey relational analysis. X 0 (k) and X i (k) represent the numeric value of kth element in the reference sequence and the comparative sequence, respectively.
Depending upon the quality characteristics, the three main categories for normalizing the original sequence: If the original sequence data has quality characteristic as "larger-the-better" then the original data is pre-processed as "larger-the-best" If the original data has the quality characteristic as "smaller-the-better", then original data is preprocessed as "smaller-the-best" However, if the original data has a target optimum value (OV) then quality characteristic is "nominal-the-better" and the original data is pre-processed as "nominal-the-better" In addition, the original sequence is normalized by a simple method in which all the values of the sequence are divided by the first value of the sequence.
where max X i (0) (k) and min X i (0) (k) are the maximum and minimum values respectively of the original sequence X i (0) (k). Comparable sequence X i *(k) is the normalized sequence of original data of the kth element in the ith sequence.

Grey relation grade
Next step is the calculation of deviation sequence, ΔO i (k) from the reference sequence of pre-processes data X i *(k) and the comparability sequence X i *(k). The grey relational coefficient is calculated from the deviation sequence using the following relation: where ΔO i (k) is the deviation sequence of the reference sequence X 0 *(k) and comparability sequence X i *(k).
ξ is the distinguishing coefficient ξ ∈ [0, 1]. The distinguishing coefficient (ξ) value is chosen to be 0.5. A grey relational grade is the weighted average of the grey relational coefficient and is defined as follows: The grey relational grade (X 0 * , X i * ) represents the degree of correlation between the reference and comparability sequences. If two sequences are identical, then grey relational grade value equals unity. The grey relational grade implies that the degree of influence related between the comparability sequence and the reference sequence. In case, if a particular comparability sequence has more influence on the reference sequence than the other ones, the grey relational grade for comparability and reference sequence will exceed that for the other grey relational grades. Hence, grey relational grade is an accurate measurement of the absolute difference in data between sequences and can be applied to appropriate the correlation between sequences.
A higher value of the grey relational grade represents a stronger relational degree between the reference sequence X 0 (k) and the given sequence X i (k). A higher value of the grey relational grade means that the corresponding process parameter is closer to the optimal one. In other words, optimization of the complicated multiple process responses can be converted into optimization of a single grey relational grade.

Methodology
For the proposed work, the following methodology was adopted: (I) To prepare the specimen.
(II) To prepare the electrode tools.
(III) To perform drilling operation on the specimen.
(IV) To investigate the following: • Burrs height measurement.
(V) To perform EDM operation to remove burrs with define parameters.
(VI) To investigate the following after EDM process: • Burrs height measurement.
(VII) To analyse tool electrode weight.
(VIII) To find optimal results through Grey relational analysis based calculation.
Al 7075 was cut in desired dimensions by handsaw. The dimensions are shown in Table 3. The brass and copper electrode rods also were cut into small pieces as per required dimensions shown in Table 4. Then drilling operations were performed on each work piece to obtain burrs. Two holes were drilled on each work piece and BH were measured with digital Vernier callipers. Varity of burrs were formed as shown in Figures 3-5. Then average BHs, weight of the specimen were calculated for calculation purpose. Later EDM machining were done on each work piece according to L4 array as shown in Table 5 with three factors and two levels as shown in Table 6. The experimental setup of EDM machine is shown in Figure 6.

Results and discussion
The factors were varied at two levels for burr removal machining operations in EDM process. Analysis of the results was carried out analytically as well as graphically. All the statistical calculations and plots were generated by MINITAB17 software.
These were four optimal combinations of the parameters at which experimental processes were performed and the corresponding MRR, TWR and BH were found out by the following formulas: where E 1 = weight of material before EDM in gm; E 2 = weight of material after EDM in gm; ρ = density of the material in gm/cm 3 ; T = machining time in seconds.
where E 1 = weight of material before EDM in gm; E 2 = weight of material after EDM in gm; ρ = density of the material in gm/cm 3 ; T = machining time in seconds.
where Y 1 = burr height before EDM machining; Y 2 = burr height after EDM machining. Table 7 presents the experimental result and effect of three input control parameters (discharge current, pulse time on and pulse time off) on Al 7075 with copper electrode in terms of three output parameters (material removal rate, TWR and burr height). It is observed by the Table 7 that Copper Electrode gives High MRR and Low TWR in comparison of brass electrode.  Figure 6. Experimental setup of EDM. Table 8 represent data pre-processing results for EDM operation on specimen with copper electrode. For MRR "largest is best" (Equation (1)) and for TWR and BH "smallest is best" (Equation (2)) is used. Here in this case k = 1, 2, 3 and i = 1, 2, 3, 4. Table 9 represents the Deviation Sequence for EDM Operation for out parameters. The deviation sequences ∆O i , ∆O i max(k) and ∆O i min(k) were calculated. The deviation sequences ∆O 1 (1) were calculated as: ∆Oi(k)=|x 0 *(k) − x i *(k)| = |1.0000 − 0.000| = 1.0000.

With copper electrode
In Table 10 grey relational grades are shown, the Grey relational grades show the level of correlation between the reference and the comparability sequences, the larger Grey relational grade means the comparability sequence exhibiting a stronger correlation with the reference sequence. So here, combination 1, 2 and 4 tells the optimal combination of parameters.
The response table was used to calculate the average Grey relational grades for each factor level, as listed in Table 11. Based on this study, a combination of the levels that provide the largest average response can be selected. Here from Table 11 the optimal levels A 1 , B 2 , and C 1 were found.
From MINITAB 17 software graphs were obtained for each output, parameters (Figures 7-9). Contour graphs are surface graphs plotted in 2D space. Viewing a contour graph is the same as viewing a 3D surface graph from a vantage point perpendicular to the XZ plane. In contour graphs, different colours or levels of gray scale, labelled contour lines, or both distinguish ranges of Z values.

With brass electrode
With brass electrode, all calculations were done as it was done with copper electrode. Here, Table 12 presents the experimental result and effect of three input control parameters on Al 7075 with brass electrode in terms of output parameters. It was investigated by Table 12 that brass electrode gives Low MRR and High TWR in compare of Copper Electrode. Table 13 represent data pre-processing results for EDM operation on specimen with copper electrode.       Here in Table 15, by the grey relational grade the optimal combination of machining was found No. 4.
The response table was used to calculate the average Grey relational grades for each factor level, as listed in Table 16. Based on this study, a combination of the levels that provide the largest average response can be selected. Here from response Table 16 the optimal levels A 2 , B 1 , and C 1 were found.
From MINITAB17 software graphs were obtained for each output, parameters (Figures 10-12).
Here also in contour plot shows the effect on output parameters with different level of input parameters. Figure 10(A) and (B) depicts that maximum MRR was found when the discharge current values

Conclusion
In the present research work, drilling operations were performed on specimen material Al 7075 to investigate the formation of burr and their removal to the maximum extent as per the recommendations of latest research works. The research work consisted of removal of burrs (deburring) though EDM unconventional machining method. Optimum combinations were obtained for machining parameters such as discharge current, pulse time on and pulse time off for BH, TWR, MRR through Grey Relational Analysis.
Following were the conclusions: • Copper Electrode gives high MRR in compare of Brass Electrode while machining Al 7075.
• Brass Electrode gives high TWR in compare of Copper Electrode while machining Al 7075.