Evolutionary programming method for modeling the EDM parameters for roughness

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(BQ) The method of electrical discharge machining (EDM), one of the processing methods based on non-traditional manufacturing procedures, is gaining increased popularity, since it does not require cutting tools and allows machining involving hard, brittle, thin and complex geometry

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Nội dung Text: Evolutionary programming method for modeling the EDM parameters for roughness

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 Assab79PM, which demonstrates the chemical properties of the DIN 1.2379 cold j o u r n a l h o m e p a g e : w w tool 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 work w . e steel Evolutionary programming method for modeling the EDM parameters for roughness ¨ Ozlem Salman, M. Cengiz Kayacan ∗ University of Suleyman Demirel, CAD/CAM Research and Application Center, 32300 Isparta, Turkey 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 (␮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 Keywords: the same time, roughness values obtained from the experiments that have been modeled by Electrical discharge machining using the genetic expression programming (GEP) method and a mathematical relationship Genetic expression programming has been suggested between the GEP model and surface roughness and parameters affecting Surface roughness it. Moreover, EDM has been used by applying copper, copper–tungsten (W–Cu) and graphite 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. © 2007 Published by Elsevier B.V. 1. Introduction 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 discovered 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 manufacturing method as it does not require the use of cutting tools for materials that conduct electricity and ensures low production costs. ∗ Corresponding author. E-mail address: ckayacan@mmf.sdu.edu.tr (M.C. Kayacan). 0924-0136/$ – see front matter © 2007 Published by Elsevier B.V. doi:10.1016/j.jmatprotec.2007.09.022 A non-traditional manufacturing method, the electro erosion 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. The most important advantage of this process is its independence of the machined material’s mechanical properties and independent from the cutting force. Thus, materials of high hardness, brittleness and strength that are difﬁcult-tocut can be machined easily at desired shape (Tsai et al., 2003). EDM is a machining method based on the principle of controlled 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 348 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 of the material removal rate (MRR), electrode wear (EW), surface roughness (SR) and surface quality. The effort made in literature conducted so far has been to increase the material 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 proportion 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 Kaftanoglu, 1981). 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 electrode 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 properties of the work piece and the electrode (Fuling et al., 2004; Pecas and Henriques, 2003; Simao et al., 2003; Chow et al., ¸ 2000). 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 involving 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). 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 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 process. In this study, powder material (Assab79PM, DIN 1.2379) which has reached 57-58 Rc hardness by vacuuming in similar 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 intelligence technique. The roughness equation was derived based on the EDM parameters by using the GEP model and the roughness values calculated by using this equation were compared to studies of various researchers. 2. Fundamental principles of EDM and surface roughness One of the most important features of the EDM method is its ability to work independently of the mechanical properties 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 dielectric, evaporating the dielectric ﬂuid in this region. The current increases in the evaporating ﬂuid. At the end, a “plasma” channel 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 channels allow the surface machining. One of the most important parameters in the EDM processing is surface roughness. To determine the most optimum material removal time, it should be ensured that the surface 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 offtime. Basic parameters affecting the manufacture process are brieﬂy deﬁned as follows (Puertas and Luis, 2004): • Discharge current (I): value of the current applied to the electrode 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 constant 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. • 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 during 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 surface 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 roughness proﬁle and its mean line, or the integral of the absolute value of the roughness proﬁle height over the evaluation 349 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 length (Colak et al., 2007). Therefore, the Ra is speciﬁed by the ¸ following equation: 1 Ra = L Experimental parameters L Y(x) dx (1) 0 where Ra is the arithmetic average deviation from the mean line, L the sampling length, and Y is the ordinate of the proﬁle 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. 3. Table 1 – Experimental machining settings for copper electrodes Genetic expression programming (GEP) GEP algorithm is a solution method which makes a global function 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 different 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 different size and shape are derived in order to convert constant size and linear arrays using suitable function genetic operators (Chiang et al., 1995). 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, functions, constants and variable names as chromosomes. Firstly GEP is produced randomly program depends on this operators 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 mathematical 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 starting from crossover point 2 will be swapped with the sub-tree of ET 2 at crossover point 3. Current (I, A) Pulse on-time (ton , ␮s) Pulse off-time (toff , ␮s) Gap voltage (V) Electrode polarity 4. Value 7, 12, 22, 42 6, 12, 25, 50, 100 12, 25, 50, 100 40, 60, 80, 100 Positive (+) Design of the experiments In mold manufacturing, usage of powder metallurgy is gaining 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. 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 copper, 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 modeling 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 parameters with copper–tungsten and graphite electrodes. Nine EDM experiments were scheduled for each electrode type with this design method. Fig. 1 – Example crossovers for GEP algorithm. (1) Program ET; (2) program ET; (3) crossover ET; (4) result program ET. 350 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 Table 2 – Parameters of the EDM experiment using copper–tungsten and graphite as electrodes I (A) 7 7 7 22 22 22 42 42 42 ton (␮s) 6 50 100 6 50 100 6 50 100 toff (␮s) Gap V (V) 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. The quality design ﬁrst proposed by Taguchi in the 1960s is widely applied because of its proven success in greatly improving 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 characteristics (Chang and Kuo, 2007). 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 considered the main machining parameters (Table 2). The number of experimental parameter levels was chosen based on the range of machining conditions, primarily current, pulse ontime, pulse off-time and gap voltage, resulting in different levels of each of the four controlled factors. The view of the tabulated electrodes used in the experiments 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 surface 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 5. Results and discussion 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 experiments numbers and parameters design with Taguchi method are shown in Table 4. 5.1. Designing the GEP model for Ra value estimation 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. In the GEP model design, the program parameters— number of terminals: 4; number of training values: 256; number 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. 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; ﬁtness cases: 256; max. ﬁtness: 25,600. 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. 351 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 Table 3 – Results of the EDM experiment done by copper electrodes I (A) 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 ton (␮s) toff (␮s) V (V) Ra (␮m) I (A) ton (␮s) toff (␮s) V (V) Ra (␮m) I (A) ton (␮s) toff (␮s) V (V) Ra (␮m) 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 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 12 100 100 100 100 100 40 40 40 40 60 60 60 40 40 60 60 60 80 100 40 40 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 6 50 100 6 12 25 100 6 12 25 100 6 12 50 100 6 12 25 50 25 50 100 6 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 22 22 22 22 22 22 22 42 42 42 7 12 12 12 12 22 22 22 22 22 22 22 100 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 40 60 80 100 100 100 100 40 40 40 100 40 40 40 60 60 80 80 80 80 100 100 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 Table 4 – Experimental results obtained in this study with using graphite and W–Cu I (A) 7 7 7 22 22 22 42 42 42 ton (␮s) toff (␮s) Gap V (V) Cu–W Ra (␮m) Graphite Ra (␮m) 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