Mô hình hóa thông số EDM cho độ nhám bằng phương pháp lập trình tiến hóa: Nghiên cứu mới nhất

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j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j m a t p r o t e c

Evolutionary programming method for modeling the EDM parameters for roughness

¨Ozlem Salman, M. Cengiz Kayacan ∗

a r t i c l e

i n f o

a b s t r a c t

Article history:

The method of electrical discharge machining (EDM), one of the processing methods based

Received 14 April 2006

on non-traditional manufacturing procedures, is gaining increased popularity, since it does

Received in revised form

not require cutting tools and allows machining involving hard, brittle, thin and complex

7 September 2007

geometry.

Accepted 10 September 2007

By using different EDM parameters (current, pulse on-time, pulse off-time, arc voltage), the Ra ((cid:2)m) roughness value as a result of application of a number of copper electrode-hardened powder metals (cold work tool steel) to a work piece has been investigated, in this study. At

the same time, roughness values obtained from the experiments that have been modeled by

Keywords:

using the genetic expression programming (GEP) method and a mathematical relationship

Electrical discharge machining

has been suggested between the GEP model and surface roughness and parameters affecting

Genetic expression programming

it. Moreover, EDM has been used by applying copper, copper–tungsten (W–Cu) and graphite

Surface roughness

electrodes to the same material with experimental parameters designed in accordance with

the Taguchi method. Results obtained from this study have been compared among each

other and similar studies in the literature.

Introduction

University of Suleyman Demirel, CAD/CAM Research and Application Center, 32300 Isparta, Turkey

A non-traditional manufacturing method, the electro ero- sion process does not depend on the hardness of material and offers a way to process materials of very complex geometry with very ﬁne and high precision by using cheap electrode materials, which makes it a preferred method.

∗ Corresponding author.

The most important advantage of this process is its inde- pendence of the machined material’s mechanical properties and independent from the cutting force. Thus, materials of high hardness, brittleness and strength that are difﬁcult-to- cut can be machined easily at desired shape (Tsai et al., 2003). EDM is a machining method based on the principle of con- trolled application of high-frequency electric discharge onto a work piece that conducts electricity thus detaching small particles from the work piece by melting and evaporating them. The machining performance in EDM processes consists The ﬁrst EDM application was carried out by Mr. and Mrs. Lazarenko in the Technical Institute of Moscow during the Second World War. The fundamentals of EDM can be traced as far back as 1770, when English chemist Joseph Priestly dis- covered the erosive effect of electrical discharges or sparks. However, it was only in 1943 at the Moscow University that Mr. and Mrs. Lazarenko exploited the destructive properties of electrical discharges for constructive applications (Puertas and Luis, 2004). The EDM method, one of the methods used in the machining industry, is becoming a preferred manufactur- ing method as it does not require the use of cutting tools for materials that conduct electricity and ensures low production costs.

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Fundamental principles of EDM and

2. surface roughness

of the material removal rate (MRR), electrode wear (EW), sur- face roughness (SR) and surface quality. The effort made in literature conducted so far has been to increase the mate- rial removal rate, with the studies aimed to erode as much material as possible. Technologies still face some difﬁculties in the increase of the MRR value. Studies to improve this pro- portion are going on (Puertas and Luis, 2004; Fuling et al., 2004; Amorim and Weingaertner, 2004; Valentincic and Junkar, 2004; Lin et al., 2000; Erden and Kaftano ˘glu, 1981).

One of the most important features of the EDM method is its ability to work independently of the mechanical prop- erties of the machined material. Once voltage is applied to the electrode and the work piece, electrons detached from the electrode (cathode) move accelerated towards the work piece. At the destination, they hit neutral dielectric molecules, removing more electrons. These electrons, in turn, accelerate the electron ﬂow towards the anode by similar collisions. This motion of electrons creates a leakage current in the dielec- tric, evaporating the dielectric ﬂuid in this region. The current increases in the evaporating ﬂuid. At the end, a “plasma” chan- nel is created between the electrode and the work piece. Due to its high temperature, this channel melts/evaporates a “crater” on both the work piece and the electrode. After the plasma channel extinguishes, all of the evaporated and a part of the melted material is ﬂushed away by the ﬂow of dielectric ﬂuid. A small “crater” is created on the surface of the electrode and the work piece. Craters created by a multitude of plasma chan- nels allow the surface machining.

One of the most important parameters in the EDM pro- cessing is surface roughness. To determine the most optimum material removal time, it should be ensured that the sur- face roughness stays within an acceptable range. Parameters affecting the surface roughness in the EDM are found to be discharge current, gap voltage, pulse on-time and pulse off- time. MRR depends on the properties of the dielectric ﬂuid used in the EDM as much as it depends on the properties of the work piece material and the electrode. Minimum wear of the electrode that removes the materials from the work piece by conducting the current is required in EDM applications. A number of researches have been made by adding various additives to the dielectric ﬂuid in order to decrease the elec- trode wear rate, obtaining better results compared to pure ﬂuids (Valentincic and Junkar, 2004; Luis et al., 2005; Wang et al., 1999). Volumetric relative wear has been observed in addition to the EW/MRR rates (Amorim and Weingaertner, 2004). Another performance indicator of the EDM process is the roughness formed on the surface of the work piece. One of the areas among studies involving the EDM most researches have been made on is the effort to decrease the surface roughness (Ho and Newman, 2003). While depending on the machining parameters during the EDM, the surface roughness is also greatly affected by the material proper- ties of the work piece and the electrode (Fuling et al., 2004; Pec¸ as and Henriques, 2003; Simao et al., 2003; Chow et al., 2000). Basic parameters affecting the manufacture process are brieﬂy deﬁned as follows (Puertas and Luis, 2004):

Use of the artiﬁcial intelligent methods (ANN, Fuzzy logic, hybrid intelligent method) in modeling studies to model roughness values obtained as a result of experiments involv- ing different materials and machining conditions is gaining demand (Valentincic and Junkar, 2004; Kaneko and Onodera, 2004; Fenggou and Dayong, 2004; Wang et al., 2003; Tsai and Wang, 2001).

• Discharge current (I): value of the current applied to the elec- trode during pulse on-time in the EDM. Discharge current is one of the primary input parameters of an EDM process and together with discharge duration and relatively con- stant voltage for given tool and work piece materials (Lee, 2001). • Gap voltage (V): voltage applied between the electrode and the work piece during the EDM.

Despite the effort to produce powder metal parts so as to give them ﬁnal shape features, different methods have to be implemented due to the locations and form of the use of some parts. Different processing methods are implemented espe- cially in the mold production to eliminate the negative impacts of distortion, etc. as a result of metal hardening. Probably the most important of such processing methods is the EDM pro- cess. • Pulse on-time (ton): time for which current is applied to the electrode during each EDM cycle. The material removed is directly proportional to the quantity of energy applied dur- ing pulse on-time. This energy is controlled by the current and the on-time.

• Off-time (toff): waiting interval during two pulse on-time periods. Melted and solidiﬁed particles are removed from the setting during this period.

The parameters explained above used as experimental variables deﬁne the value of roughness occurring on the sur- face of the work piece. There are various simple surface roughness amplitude parameters used in industry, such as roughness average (Ra), root-mean-square (rms) roughness (Rq), and maximum peak-to-valley roughness (Ry or Rmax), etc. (PDI Webmaster, 2000). The parameter Ra is used in this study. The average roughness (Ra) is the area between the rough- ness proﬁle and its mean line, or the integral of the absolute value of the roughness proﬁle height over the evaluation In this study, powder material (Assab79PM, DIN 1.2379) which has reached 57-58 Rc hardness by vacuuming in sim- ilar chemical properties to cold work steel especially used in cutting mold production has been machined in EDM process. Copper was used as the electrode material due to its low cost and high conductivity. Besides, the EDM process was applied using copper–tungsten (25–75%) and graphite as electrode materials to determination the effect of different electrode materials on the surface roughness. Moreover, data obtained from the EDM modeled using the copper electrode was used in genetic expression programming (GEP), an artiﬁcial intelli- gence technique. The roughness equation was derived based on the EDM parameters by using the GEP model and the rough- ness values calculated by using this equation were compared to studies of various researchers.

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length (C¸ olak et al., 2007). Therefore, the Ra is speciﬁed by the following equation: Table 1 – Experimental machining settings for copper electrodes Experimental parameters Value

(cid:2) L

(cid:3) (cid:3) (cid:3) (cid:3)Y(x)

Current (I, A) Pulse on-time (ton, (cid:2)s) Pulse off-time (toff, (cid:2)s) Gap voltage (V) Electrode polarity

7, 12, 22, 42 6, 12, 25, 50, 100 12, 25, 50, 100 40, 60, 80, 100 Positive (+)

Design of the experiments

dx (1) Ra = 1 L

Genetic expression programming (GEP)

where Ra is the arithmetic average deviation from the mean line, L the sampling length, and Y is the ordinate of the pro- ﬁle curve. There are many methods of measuring surface roughness, such as image processing, microscopes, stylus type instruments, proﬁle tracing instruments, etc. A Pocket Surf stylus type instrument (produced by Hommel Verke T500) was used in this study.

In mold manufacturing, usage of powder metallurgy is gain- ing increased popularity. Powder metal is the material type preferred in manufacturing for its ability to yield the required mechanical properties. Despite the effort to produce powder metal parts so as to give them ﬁnal shape features, different machining methods have to be implemented due to the usage area and functionality of some powder metal parts. Different processing methods are implemented especially in the mold production to eliminate the negative impacts of distortion, etc. as a result of metal hardening. Probably the most important of such processing methods is the EDM.

GEP algorithm is a solution method which makes a global func- tion search for the problem, developed as a resultant of genetic algorithm (GA) and genetic programming (GP) algorithms. Characteristic of GA algorithms is a linear array of constant length chromosomes. Despite of they could be manipulated by genetic operators easily these are not functional in non-linear problems. GP algorithms try to ﬁnd a suitable solution using parse three which they create to deﬁne relations between dif- ferent size and shape non-linear variables. Advantages of GA and GP algorithms are jointed in GEP algorithm. Relationships of non-linear variables which are characteristically in differ- ent size and shape are derived in order to convert constant size and linear arrays using suitable function genetic operators (Chiang et al., 1995).

In this study used to observe the surface properties formed as a result of the EDM, Assab79PM, which demonstrates the chemical properties of the DIN 1.2379 cold work tool steel, has been selected as the work piece. Hardness of the whole material has been brought to 57–58 Rc by heat treatment. The EDM has been applied on the surface of work pieces possessing this hardness. The EDM has been done using three different electrode materials different numbers of times, namely cop- per, graphite and copper–tungsten. As indicated in Table 1, 320 tests have been designed using the copper electrode with different EDM parameters (discharge current, pulse on-time, pulse off-time and gap voltage) (Salman, 2005). The objective of the large number of EDM experiments using the copper electrode was to obtain sufﬁcient data for more effective mod- eling of the data obtained from the experiment system in the artiﬁcial intelligence techniques (GEP). To be more precise, a high number of EDM experiments ensure that more realistic results are obtained in modeling a problem using the artiﬁcial intelligence techniques (Fuzzy logic, ANN, GEP). The process parameters for the hardened powder metal material using a copper electrode at the depth of 2 mm are indicated in Table 1. Taguchi method was used to employ a more economical and effective experiment design using the same parame- ters with copper–tungsten and graphite electrodes. Nine EDM experiments were scheduled for each electrode type with this design method. GEP algorithm can be handled as a common application of GA and GP algorithms. The genetic algorithm (GA) is applied to solve the expression tree. GEP can be applied to the application of conventional genetic algorithm and genetic programming (GP). GEP genes are composed of a list of operators, func- tions, constants and variable names as chromosomes. Firstly GEP is produced randomly program depends on this opera- tors and data sets. The derived programs mutate until the program with best ﬁtness value among them is found. The program with the best ﬁtness value is taken as the last result. The results can be compared by applying the mathemati- cal relation obtained from the program with the best ﬁtness value in the proposed computer program (Chiang et al., 1995). Crossover is determined by choosing two ET (expression tree) based on ﬁtness and generating for each ET the crossover point (node) at random. For example: consider the following ETs (Fig. 1) with crossover points 2 and 3. The sub-tree of ET 1 start- ing from crossover point 2 will be swapped with the sub-tree of ET 2 at crossover point 3.

Fig. 1 – Example crossovers for GEP algorithm. (1) Program ET; (2) program ET; (3) crossover ET; (4) result program ET.

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Results and discussion

7 7 7 22 22 22 42 42 42

6 50 100 6 50 100 6 50 100

12 50 100 50 100 12 100 12 50

40 60 100 100 40 60 60 100 40

and its average value was calculated and used in the GEP model. Table 2 – Parameters of the EDM experiment using copper–tungsten and graphite as electrodes I (A) Gap V (V) ton ((cid:2)s) toff ((cid:2)s)

Some of the 320 surface roughness values measured as a result of the EDM applied with the copper electrode based on parameters such as the discharge current, pulse on-time, pulse off-time and gap voltage have been indicated in Table 3. For graphite and copper–tungsten electrodes, the experi- ments numbers and parameters design with Taguchi method are shown in Table 4.

Designing the GEP model for Ra value estimation

5.1. for copper electrode EDM

A GEP model was created using the data obtained from the 320 EDM experiments done by using the copper electrodes. The main purpose of creating and using this model is to establish a mathematical relationship between the EDM parameters.

The quality design ﬁrst proposed by Taguchi in the 1960s is widely applied because of its proven success in greatly improv- ing industrial product quality (Taguchi et al., 1989; Bendell et al., 1989; Taguchi, 1981). Therefore, this study uses the Taguchi method to identify the optimum combination of machining parameters in EDM machining of powder metal. The method is used to formulate the experimental layout, analyze the effect of each machining parameter on the machining characteris- tics (Chang and Kuo, 2007).

In the GEP model design, the program parameters— number of terminals: 4; number of training values: 256; num- ber of test values: 50 have been selected as a constant. The values, Number of Chromosomes, Number of Genes, Head Size, Selection Range, Fitness Cases and Max. Fitness varied in each model.

In the study, surface roughness was set as the objective function of the EDM experiment, and four factors – current, pulse on-time, pulse off-time and gap voltage – were consid- ered the main machining parameters (Table 2). The number of experimental parameter levels was chosen based on the range of machining conditions, primarily current, pulse on- time, pulse off-time and gap voltage, resulting in different levels of each of the four controlled factors. Eighty percent of the data for each model established in the study was randomly selected and used for training data while 20% was used for test data. The data used for test were not used during the training stage. Table 5+ lists the data used for test stage.

A multitude of models was established using the EDM experiment by changing the GEP parameters to obtain the best trained model. The model reaching the highest regression (R2) value (0.95) was accepted as the solution from among the models constructed.

The model accepted as the most suitable solution model had its values selected as follows: number of chromosomes: 50; number of genes: 3; head size: 8; selection range: 100; ﬁt- ness cases: 256; max. ﬁtness: 25,600.

The view of the tabulated electrodes used in the experi- ments is given in Fig. 2 on a specially prepared board. The EDM experiments have been done by on an AjanEDM machine produced by AjanCNC corporation. Machined work piece at the depth of 2 mm by EDM, were cut on any of the sides for roughness measurement. The roughness of the machined sur- face is measured by using the Hommel Verke T500 surface measurement equipment. In the measurement, the sampling length (Lc) as 0.25 mm, measuring length (Lm) as 1.25 mm (5.Lc) and traverse length (Lt) as 1.5 mm is taken, respectively (Fig. 2). Surface roughness (Ra) that occurred on each part as a result of each EDM experiment was measured three times The relationship between the EDM parameters and Ra has been transformed into the C++ program code based on the model yielding the best result as follows:

Fig. 2 – Copper electrodes (∅10 mm × 10 mm) and roughness measurement.

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12 25 100 6 12 50 100 6 12 50 50 100 12 25 50 100 6 12 50

12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 100 100

6 12 25 50 100 12 25 100 6 12 50 12 25 50 1 12 25 12 50

12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 25 25

40 40 40 40 40 60 60 60 80 80 80 80 80 80 80 100 100 60 60

1.7 1.85 2.14 2.43 2.64 1.94 2.4 2.9 1.9 2.1 2.69 2.67 2.88 3.3 3.96 2.85 3.2 4.43 5.56

7 7 7 7 7 7 12 12 12 12 12 12 12 12 12 22 22 22 22

12 25 50 100 12 25 100 6 12 50 100 6 12 25 100 6 12 100 12

25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 50 50

40 40 40 40 60 60 60 80 80 80 80 100 100 100 100 40 40 40 60

1.68 1.93 2.28 2.68 1.85 2.1 3.77 2.12 2.46 3.21 3.82 2.26 2.63 2.77 3.94 2.7 3.15 4.3 3.26

12 12 12 22 22 22 22 22 22 22 42 42 42 42 42 7 7 22 22

2.85 3.2 4.03 2.76 3.31 4.65 4.8 3.06 3.61 4.8 5.6 5.83 4.5 5.14 5.73 3.0 2.0 2.86 3.6

100 100 100 40 40 40 40 60 60 60 40 40 60 60 60 80 100 40 40

100 12 100 6 12 50 100 6 12 25 100 6 25 50 6 100 6 12 25 100 6 25

100 100 100 100 100 100 100 100 100 100 50 50 50 50 50 12 12 12 12 12 12 12

22 22 22 22 22 22 22 42 42 42 7 12 12 12 12 22 22 22 22 22 22 22

4.0 3.1 4.23 3.03 3.39 3.9 4.43 3.61 3.76 3.96 2.97 1.8 2.26 2.6 1.98 5.29 3.23 3.61 4.34 5.53 3.4 4.67

40 60 80 100 100 100 100 40 40 40 100 40 40 40 60 60 80 80 80 80 100 100

100 6 50 100 6 12 25 100 6 12 25 100 6 12 50 100 6 12 25 50 25 50 100 6

7 7 7 7 7 7 7 7 7 7 7 12 12 12 12 12 12 42 42 . . . 42 42 7 7 7 7 7 7 12 12 7 7 7 7 7 7 7 7 7 7 42 42 42 42

25 25 25 25 25 25 25 25 25 25 100 100 100 100 100 100 100 100 100 100 50 50 50 50

60 80 80 80 100 100 100 100 40 40 40 40 60 60 60 60 80 80 80 80 40 40 40 60

5.9 4.33 2.41 2.92 1.7 1.96 2.14 3.05 2.01 2.16 1.79 2.35 1.4 1.67 2.2 2.42 1.44 1.74 2.14 2.32 4.41 4.78 5.3 3.93

22 22 22 42 42 42 42 42 42 42 42 42 42 42 12 22 22 22 22 42 42 42 22 22

25 50 12 25 50 100 12 25 50 100 100 12 25 100 100 6 25 50 100 12 60 12 100 50

50 50 50 50 50 50 50 50 50 50 100 100 100 100 50 50 50 50 50 50 12 12 12 12

60 60 100 60 60 60 80 80 80 80 80 100 100 100 40 80 100 100 100 40 40 40 100 100

3.53 3.86 3.58 4.46 4.91 5.64 4.48 4.7 4.97 5.72 5.01 4.45 4.65 5.16 3.24 2.9 3.87 4.3 4.87 4.06 4.1 4.2 5.9 5.12

Table 3 – Results of the EDM experiment done by copper electrodes I (A) I (A) I (A) ton ((cid:2)s) ton ((cid:2)s) ton ((cid:2)s) Ra ((cid:2)m) toff ((cid:2)s) V (V) Ra ((cid:2)m) toff ((cid:2)s) V (V) Ra ((cid:2)m) toff ((cid:2)s) V (V)

7 7 7 22 22 22 42 42 42

6 50 100 6 50 100 6 50 100

12 50 100 50 100 12 100 12 50

40 60 100 100 40 60 60 100 40

1.74 2.93 2.52 2.6 3.6 4.04 2.28 3.6 4.67

1.49 2.41 3.14 1.8 3.44 3.86 1.3 4.18 5.04

Table 4 – Experimental results obtained in this study with using graphite and W–Cu I (A) Gap V (V) ton ((cid:2)s) Cu–W Ra ((cid:2)m) Graphite Ra ((cid:2)m) toff ((cid:2)s)

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40 100 80 40 100 100 40 80

12 42 7 7 12 22 22 12 42 22 12 22 22 42 7 12

12 12 5 5 5 12 12 100 25 5 12 6 25 50 50 25

100 100 100 100 12 12 25 100 100 100 100 100 100 100 12 100

60 60 40 100 100 100 60 80 80 60 80 60 100 100 60 40

2.1 4.03 2.18 2.41 3.55 3.9 3.5 3.38 4.46 3.63 2.16 2.77 3.6 4.92 2.7 2.32

7 12 42 7 12 42 7 22 42 12 22 42 7 7 22 42 7

25 12 25 25 100 25 50 6 6 50 12 6 6 50 50 60 60

12 12 12 25 25 25 50 50 50 100 100 100 12 12 12 12 25

80 60 40 80 40 60 80 60 80 100 80 100 60 100 80 80 60

2.5 2.5 5.0 2.1 3.35 5.05 2.41 2.87 4.21 3.1 3.2 4.22 1.83 3.0 4.76 4.6 1.7

22 22 42 12 12 22 42 7 12 22 42 42 7 7 12 12 42

25 12 50 12 25 6 6 100 25 25 6 100 50 50 50 50 50

25 25 25 50 50 50 50 100 12 12 12 12 25 25 25 25 25

0.4 0.6 0.6 0.8 0.6 1 0.6 1 0.4

3.89 3.73 5.57 1.96 2.59 3.24 3.8 2.64 2.64 4.22 4.31 6.21 2.35 2.66 3.1 3.42 5.42

Table 5 – Data used for test stage in the GEP model I (A) I (A) I (A) ton ((cid:2)s) ton ((cid:2)s) ton ((cid:2)s) Ra ((cid:2)m) toff ((cid:2)s) V (V) Ra ((cid:2)m) toff ((cid:2)s) V (V) Ra ((cid:2)m) toff ((cid:2)s) V (V)

written as follows using abbreviation of parameters (Eq. (2)):

⎛

⎞

(cid:8)(cid:8)

(cid:9)

(cid:8)

(cid:9)ton

⎝

+ C++ program code: double APSCfunction(double d[]) {

⎠ + sin

cos ton

Ra = sin ton I I 10(I/I)

(cid:3) (cid:3) (cid:3)

(cid:3) (cid:3) (cid:3)(1010

)

(cid:11)

(cid:10)(cid:10)

double dblTemp = 0; dblTemp +=

(cid:8)

(cid:9)(cid:11)

sin((d[0]/fabs(pow(pow(10,pow(10,d[0])),cos(d[1]))))); + + V (2) I ton ton 10(t off) dblTemp += sin((d[0]/(pow(10,(d[0]/d[0])) + pow((d[1]/d[0]),d[1])))); dblTemp += ((d[1]/pow(10,(pow(d[2],d[0]) + (d[0] + d[1]))))*d[3]); return dblTemp; }

To determine similarities or differences between the results obtained from the using copper electrodes at EDM and the results calculated by using the GEP function for rough and ﬁn- ish machining parameters have been presented in Table 6 and Figs. 3–6. The results obtained from the EDM experiments are close to those calculated.

The following variables in the program code stand for the values assigned to them as follows: d[] = Surface rough- ness ((cid:2)m), d[0] = Discharge current (I), d[1] = Pulse on-time ((cid:2)s), d[2] = Pulse off-time ((cid:2)s), d[3] = Gap voltage (V).

The mathematical formula of the C++ program code obtained as a result of the evolutionary programming has been The results of the ﬁnish EDM with a constant 7 A current, 6 (cid:2)s pulse on-time and 50 (cid:2)s pulse off-time and variable gap voltage of 40, 60, 80 and 100 V and the results obtained from the GEP calculation have been compared in Fig. 3. As can be under- stood from the ﬁgure, the surface roughness results obtained

Finish EDM

7 7 7 7

6 6 6 6

50 50 50 50

40 60 80 100

1.3 1.4 1.5 1.6

1.4 1.45 1.52 1.64

Rough EDM

22 22 22 22 42 42 42 42

50 50 50 50 100 100 100 100

50 50 50 50 50 50 50 50

40 60 80 100 40 60 80 100

3.2 3.47 3.73 4 5.25 5.39 5.52 5.66

3.76 3.86 4.13 4.3 5.3 5.64 5.72 5.88

Table 6 – GEP calculated and EDM experiment Ra ((cid:2)m) results (for copper) I (A) Gap V (V) ton ((cid:2)s) GEP calculated results, Ra ((cid:2)m) Experimental results, Ra ((cid:2)m) toff ((cid:2)s)

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Fig. 3 – Surface roughness results obtained from the EDM experiment and GEP calculations. Fig. 5 – Surface roughness results obtained from the rough EDM experiment and GEP calculations.

Fig. 4 – Surface roughness results obtained from the rough EDM and GEP calculations. Fig. 6 – Change in surface roughness with the pulse on-time.

in the experiment and the calculated results are considerably similar.

Fig. 4. As can be seen from the ﬁgure, the difference EDM pro- cess experiment results with, the largest different of 0.6 (cid:2)m in the surface roughness value. So that this difference is unim- portant.

Rough EDM process experiment results with a constant 22 A current, 50 (cid:2)s pulse on-time and 50 (cid:2)s pulse off-time and variable gap voltage of 40, 60, 80 and 100 V and the results obtained from the GEP calculation have been compared in The results of the rough EDM with constant a 42 A current, 100 (cid:2)s pulse on-time and 50 (cid:2)s pulse off-time and variable gap

1 2

10 7

20 25

D2 tool steel Hardened (PM tool steel)

100 100

Copper Copper

7.5 2.14

Guu et al. (2003) In this study

Tungsten-carbide

Lee (2001)

200

100

Copper W–Cu Graphite

2 5.6 6.1

100

Hardened (PM tool steel)

100

Copper

2.68

In this study

Tungsten-carbide

Lee (2001)

200

100

Copper W–Cu Graphite

2.4 3.25 3.9

6 7 8 9 10

22 12 12 12 12

25 25 25 50 50

100 25 25 25 25

Hardened (PM tool steel) 2080 tool steel Hardened (PM tool steel) 2080 tool steel Hardened (PM tool steel)

100 80 80 80 80

Copper Copper Copper Copper Copper

3.6 5.72 2.66 6.31 3.21

In this study C¸ o ˘gun et al. (2004) In this study C¸ o ˘gun et al. (2004) In this study

Table 7 – The studies with using copper electrode and tool steel No. I (A) Gap V. (V) Workpiece material Electrode material References ton ((cid:2)s) Ra ((cid:2)m) toff ((cid:2)s)

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als with similar properties very close to the actual values, which may substitute high-cost experimental work that would take a lot of time.

Acknowledgements

voltage of 40, 60, 80 and 100 V and the results obtained from the GEP calculation have been compared in Fig. 5. The high level of consistency between the experimental results in the rough machining and the GEP results points to the success of this study.

This work was supported by Suleyman Demirel University Research Project Department (Project No. 03yl787) and Ass- abKorkmaz Company.

r e f e r e n c e s

Surface roughness values obtained with the same gap volt- age but different pulse on-time periods have been indicated in Fig. 6. The gap voltage and pulse off-time have been kept con- stant here at 60 V and 100 (cid:2)s, respectively. It can be clearly observed that, as pulse on-time (6, 12, 25, 50, 100 (cid:2)s) and the current applied (7, 12, 22, 42 A) increased, the surface rough- ness also increased.

Comparison between surface roughness values

Amorim, F.L., Weingaertner, W.L., 2004. Die-sinking electrical

5.2. obtained as a result of EDM with different electrode materials

discharge machining of a high-strength copper-based alloy for injection molds. J. Braz. Soc. Mech. Sci. Eng. XXVI, 137–144.

Bendell, A., Disney, J., Pridmore, W.A., 1989. Taguchi

Methods—Application in World Application: Industries, Productivity, Improvement and Case Study. IFS Publication, UK.

Chang, C.W., Kuo, C.P., 2007. Evaluation of surface roughness in laser-assisted machining of aluminum oxide ceramics with Taguchi method. Int. J. Mach. Tool Manuf. 47, 141–147. Chiang, S.T., Liu, D.I., Lee, A.C., Chieng, W.H., 1995. Adaptive

control optimization in end milling using neural networks. Int. J. Mach. Tool Manuf. 35 (4), 637–660.

Chow, H.M., Yan, B.H., Huang, F.Y., Hung, J.C., 2000. Study of

added powder in kerosene for the micro-slit machining of titanium alloy using electro-discharge machining. J. Mater. Process. Technol. 101, 95–103.

C¸ o ˘gun, C., Kocabas¸, B., ¨Ozgedik, A., 2004. Experimental and theoretical investigation of workpiece surface proﬁles in electrical discharge machining (EDM). J. Fac. Eng. Arch. 19 (1), 97–106.

C¸ olak, O., Kurbano ˘glu, C., Kayacan, M.C., 2007. Milling surface roughness prediction using evolutionary programming methods. Mater. Des. 28 (2), 657–666.

The results have been systematically summarized in Table 7 in consideration of similarities between electrodes and EDM parameters involved in studies published in the literature on the subject. When the table is examined carefully, the sim- ilar properties between the rows 1–2, 3–4, 7–8, 9–10 are seen respect to work piece material, electrode and EDM parameters. Comparing surface roughness values in these lines (Table 7) to each other shows clear success of the surface roughness values obtained as a result of machining the powder metal part. Despite the fact that the roughness values obtained with tungsten–carbide workpiece (3 and 5 rows) and powder mate- rial yielded a better roughness value, the difference between the two values was insigniﬁcant.

Conclusion

Erden, A., Kaftano ˘glu, B., 1981. Thermo-mathematical modeling and optimization of energy pulse forms in electric discharge machining (EDM). Int. J. Mach. Tool Des. Res. 21 (1), 11–22. Fenggou, C., Dayong, Y., 2004. The study of high efﬁciency and

intelligent optimization system in EDM sinking process. J. Mater. Process. Technol. 149 (1–3), 83–87.

Fuling, Z., Zhozhu, L., Baorang, Z., 2004. Study on the

technological characteristics of powder-mixed electrical discharge machining. In: Proceedings of the Third International Conference and Exhibition on Design and Production of Dies and Molds, Bursa, Turkey, pp. 240–244. Guu, Y.H., Hocheng, H., Chou, C.Y., Deng, C.S., 2003. Effect of

Despite all these interpretations, the change is observed in the values listed in the last two rows of Table 4. When Table 4 is examined, it is seen that W–Cu electrodes give better results in terms of surface roughness in the last two rows, where rough EDM process is applied, when compared to ﬁnish EDM process. This result can be overlooked since it is rough EDM process.

electrical discharge machining on surface characteristics and machining damage of AISI D2 tool steel. Mater. Sci. Eng. A 358, 37–43.

Ho, K.H., Newman, S.T., 2003. State of the art electrical discharge machining (EDM). Int. J. Mach. Tool Manuf. 43, 1287–1300.

Kaneko, T., Onodera, T., 2004. Improvement in machining

In this study, surface roughness values resulting from the EDM process done on the powder metal material with different machining parameters using different materials as the elec- trode have been investigated and a mathematical relationship has been established between EDM machining parameters and surface roughness by using GEP, an artiﬁcial intelligence technique. The following conclusions can be drawn as a result of this investigation:

performance of die-sinking EDM by using self-adjusting fuzzy control. J. Mater. Process. Technol. 149 (1–3), 204–211. Lee, S.H., Li, X.P., 2001. Study of the effect of machining

parameters on the machining characteristics in electrical discharge machining of tungsten carbide. J. Mater. Process. Technol. 115, 344–358.

• From among tool steels undergoing the EDM with similar parameters, powder metal material yields smaller rough- ness. • Roughness values close to each other have been obtained in experiments using tungsten and graphite electrodes.

Lin, J.L., Wang, K.S., Yan, B.H., Tarng, Y.S., 2000. Optimization of the electrical discharge machining process based on the Taguchi method with Fuzzy logics. J. Mater. Process. Technol. 102 (1–3), 48–55.

• Roughness obtained in the powder metal material did not demonstrate signiﬁcant difference in the EDM with the use different electrode materials.

• The mathematical relationship obtained with the GEP approach allows determine roughness values of materi-

j o u r n a l o f m a t e r i a l s p r o c e s s i n g t e c h n o l o g y 2 0 0 ( 2 0 0 8 ) 347–355

355

Taguchi, G., Elsayed, E.A., Hsiang, T., 1989. Quality Engineering in

Luis, C.J., Puertas, I., Villa, G., 2005. Material removal rate and

Production Systems. McGraw-Hill, New York.

Tsai, K.M., Wang, P.J., 2001. Predictions on surface ﬁnish in

electrode wear study on the EDM of silicon carbide. J. Mater. Process. Technol. 164/165, 889–896.

Pec¸ as, P., Henriques, E., 2003. Inﬂuence of silicon powder-mixed

electrical discharge machining based upon neural network models. Int. J. Mach. Tool Manuf. 41, 1385–1403.

Tsai, H.C., Yan, B.H., Huang, F.Y., 2003. EDM performance of

dielectric on conventional electrical discharge machining. Int. J. Mach. Tool Manuf. 43, 1465–1471.

Cr/Cu-based composite electrodes. Int. J. Mach. Tool Manuf. 43, 245–252.

Valentincic, J., Junkar, M., 2004. On-line selection of rough

Puertas, I., Luis, C.J., 2004. A study on the machining parameters optimisation of electrical discharge machining. J. Mater. Process. Technol. 143/144, 521–526.

Salman, ¨O., 2005. Determination Surface Roughness Parameters

machining parameters. J. Mater. Process. Technol. 149, 256– 262.

for Electro Discharge Machining with Using Genetic Programming. Suleyman Demirel University, Natural Science Institute.

Wang, C.C., Yan, B.H., Chow, H.M., Suzuki, Y., 1999. Cutting austempered ductile iron using an EDM sinker. J. Mater. Process. Technol. 88, 83–89.

Simao, J., Lee, H.G., Aspinwall, D.K., Dewes, R.C., Aspinwall, E.M.,

Wang, K., Gelgele, H.L., Wang, Y., Yuan, Q., Fang, M., 2003. A

2003. Workpiece surface modiﬁcation using electrical discharge machining. Int. J. Mach. Tool Manuf. 43, 121–128.

hybrid intelligent method for modelling the EDM process. Int. J. Mach. Tool Manuf. 43, 995–999.

Taguchi, G., 1981. On-line Quality Control During Production.

Japan Standard Association, Tokyo.