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1 This article was downloaded by: [Consorci de Biblioteques Universitaries de Catalunya] On: 21 December 2009 Access details: Access Details: [subscription number ] Publisher Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Materials and Manufacturing Processes Publication details, including instructions for authors and subscription information: Influence of Process Parameters and Electrode Geometry on Feature Micro-Accuracy in Electro Discharge Machining of Tool Steel N. Pellicer a ; J. Ciurana a ; T. Ozel b a Department of Mechanical Engineering and Industrial Construction, Universitat de Girona, Spain b Department of Industrial and System Engineering, Rutgers University, Piscataway, New Jersey, USA Online publication date: 16 December 2009 To cite this Article Pellicer, N., Ciurana, J. and Ozel, T.(2009) 'Influence of Process Parameters and Electrode Geometry on Feature Micro-Accuracy in Electro Discharge Machining of Tool Steel', Materials and Manufacturing Processes, 24: 12, To link to this Article: DOI: / URL: PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

2 Materials and Manufacturing Processes, 24: , 2009 Copyright Taylor & Francis Group, LLC ISSN: print/ online DOI: / Influence of Process Parameters and Electrode Geometry on Feature Micro-Accuracy in Electro Discharge Machining of Tool Steel N. Pellicer 1, J. Ciurana 1, and T. Ozel 2 1 Department of Mechanical Engineering and Industrial Construction, Universitat de Girona, Spain 2 Department of Industrial and System Engineering, Rutgers University, Piscataway, New Jersey, USA Agile manufacturing capabilities require fast responses in workshops to cope with different demands in product geometrical features and qualities in fabricating moulds. Therefore, direct machining of complex mould features using simple shape machined electrodes with the electro-discharge machining (EDM) process, empowers the flexibility and the capacity of the enterprises and dramatically reduces lead times resulting in much more efficient production processes. In the case of machining geometrical features with characteristic dimensions in the order of few millimeters, the EDM process requires further understanding. This article focuses on investigating the influence of EDM parameters and electrode geometry on feature micro-accuracy in tool steel for mould fabrication purposes. A set of designed experiments with varying EDM process parameters such as pulsed current, open voltage, pulse time, and pulse pause time is carried out in H13 steel using differently shaped copper electrodes. Microdimensional and geometrical accuracies are the measures of response. Artificial neural network and regression models have been constructed to capture the influence of the process parameters on the geometrical feature quality such as flatness, depth, slope, width, and dimension variation between the entrance and the exit (DVEE). Keywords Artificial neural network modeling; EDM technology; Mould making; Process parameters. 1. Introduction Electro-discharge machining (EDM) is a thermal material removal process which uses electric spark discharges to machine electrically conducting materials. A voltage is applied between the tool (or electrode) and the workpiece, which are both sunken into a dielectric liquid. During the process, both parts are placed very close to each other (gap distance is in the order of m), when a plasma channel is created between the anode and the cathode. The basis is the conversion of electrical energy created by electrical discrete sparks into thermal energy. When gap width between the tool and the electrode achieves an adequate sparking gap width, a micro-conductive ionized path appears, and the electric spark occurs [1], since the available energy is larger than the one needed to entreat the dielectric fluid. In consequence, enough heat is generated, and a plasma channel is created, achieving temperatures up to C [2]. As a result, the conductive material is molten and/or vaporized, hence material removal in the shape of the electrode is achieved. The fact of using thermal energy for material removing as well as the absence of direct contact between the tool and the workpiece provide an interesting advantage for EDM among conventional machining processes. Indeed, some important machining related problems, such as process induced Received October 18, 2008; Accepted January 18, 2009 Address correspondence to Prof. J. Ciurana, Department of Mechanical Engineering and Industrial Construction, Universitat de Girona, Av. Lluis Santalo s/n, Girona 17071, Spain; quim.ciurana@udg.edu 1282 mechanical (residual) stresses, chatter, and other vibrations, are avoided regardless of material s hardness [2]. The only limitation of EDM is the requirement of electrically conductivity for the work materials used [3, 4]. As reported by many authors, higher values of current discharge and open voltage clearly increase material removal rate (MRR), electrode wear ratio (EWR), and surface roughness [5 7] even at micromachining scale. Depending on the kind of material used and other requirements, positive or negative polarity can be applied. This is one of the most important process parameters that affect EWR, surface roughness, MRR, and expansion of microholes [8]. However, positive polarity (tool electrode as anode and workpiece as cathode) is generally employed when higher MRR is desired. The challenges associated with EDM for fabrication of features in the order of a few millimeters down to submillimeter dimensions are related to the dimensional and geometrical inaccuracies. These inaccuracies may be related to the electrode fabrication inaccuracies, electrode material, relative size of gap distance to the feature size, and both radial and axial electrode wear, among other factors. For this reason, this study focuses on investigating the influence of EDM parameters and electrode geometry on feature micro-accuracy in tool steel for mould fabrication purposes. The complex nature of physical process interaction between the electrode (tool) and the workpiece material poses difficulty to develop analytical or mechanistic models for EDM process. Experimental modeling using artificial neural networks (ANN) can capture the relationship between process parameters and performance measures of interests. Mandal et al. [9] and Gao et al. [10] used

3 FEATURE MICRO-ACCURACY IN ELECTRO DISCHARGE MACHINING 1283 artificial neural network modeling with genetic algorithms (GA). Their results demonstrate that the model is suitable for predicting the response parameters, such as MRR and EWR, with reasonable accuracy. Tsai et al. [11] tested and compared six different neural networks and a neurofuzzy network for modeling the surface roughness in EDM. This study concludes that Hyperbolic Tangent Sigmoid Multi-Layered Perceptron model (TANMLP), Radial Basis Function Network model (RBFN), adaptive RBFN model, and the Adaptive Neuro-Fuzzy Inference System model (ANFIS) can be used more efficiently. Although it is not a new technology, the complex nature of the process does not yet permit to predict, calculate, or simulate dimensional and geometrical accuracy with high precision. This article investigates the problem of dimensional and geometrical micro-accuracy dependence of different EDM process parameters. Four common process parameters (open voltage, current discharge, pulse-on, and pulse-off) combined with different shaped electrodes are used as input values in the process model, whereas microdimensional and microgeometrical features are treated as final process outputs. It is important to point out that input parameters have been chosen by considering the easiest to implement and facilitate its application in manufacturing workshops. Finally, regression models and artificial neural networks will be used for geometrical and dimensional feature accuracy predictions. 2. Experimentation EDM process efficiency can still be improved, especially when complex shapes and/or high surface quality are required. Therefore, the goal of the experiments is to find process parameter relations with the resultant geometrical and dimensional micro-accuracy to machine a simple feature (a groove) of 3 mm width and 1 mm depth. Next, the experimentation steps, material characteristics, and design of experiments (DOE) are presented Methodology All electrodes are designed with a solid modeler and implemented in computer-aided manufacturing (CAM) application to create computer numerical control (CNC) programs for each electrode shape (see Fig. 1). They are fabricated in a Deckel Maho 64 V Linear CNC machining center. Erowa clamping device is aligned with the CNC machine s X, Y, and Z axis, using a dial indicator with 1 m accuracy. This configuration provides a fine adjustment between the machined electrode axis and the subsequent machining path in the EDM machine. Once the electrodes are machined, a finish grinding phase is required to obtain an adequate surface roughness (R a )on the top surface of the electrodes. GER SCA 60/40 grinding machine with a grinding wheel (Ref: 88A46J7V217) and Erowa clamping device have been used [see Fig. 2(a)]. Finally, electrode surface roughness and width are measured by using a measurement probe [see Fig. 2(b)]. It was found that surface roughness of the electrodes ranged between 0.26 and 0 59 m. At this stage, tool electrodes are ready to be used in EDM machining of the AISI H13 tool steel workpiece (hardness of 95 HRB). All experiments are carried out consecutively in order to minimize alignment and repeatability errors. ONA DB-300 is used in the experimentation combining several process parameters (see Fig. 3). Once the experiments are finished, final workpiece dimensional and geometrical accuracy are measured using a Mitutoyo CMM machine (model Crysta Apex 544) with an error lower than 1 9 m (certified by ENAC), as shown in Fig. 4. Surface flatness, slope, and average depth are determined for each groove by using 12 point CMM measurements on the bottom of the groove (see Fig. 5). Machined surface slope is also obtained considering the top plane as reference, as shown in Fig. 5. Some researchers, such as Liu et al. [12, 13], use the parameter dimension variation between the entrance and exit (DVEE) to measure the difference between top and bottom diameters in microhole EDM machining. It is also known as taper parameter since it provides a measure for the taper effect [14]. It is caused by electrode wear, provoking a smaller diameter at the bottom of the hole (or groove). Even though the same idea is used in this study, some modifications have been considered. In this case, the XY plane becomes the reference plane, whereas in Liu s study [12] the ZY or ZX were the ones used. Thus, by measuring groove width by 10 point CMM measurements, the deviation between entrance and exit angles caused by the electrode wear can be obtained. DVEE measurement depicts the parallelism angle along the machined groove, mainly caused by the radial electrode wear (see Fig. 6). The tool (electrode) wear is characterized by both corner wear and end wear (affecting material removal in radial and axial directions, respectively) deteriorating both the depth and the shape of a machined feature in EDM [1]. For this reason, it becomes very interesting to analyze different electrode shapes, which will deplete differently depending on the sparking affected area. Figure 1. Solid model design for the electrodes (units are in mm).

4 1284 N. PELLICER ET AL. Figure 2. (a) Electrode grinding and (b) Electrode being measured, ready to be used in EDM Materials AISI H13 steel is used as the workpiece as a common material in mould and die manufacturing industry. Its composition is presented in Table 1. The most common materials used for electrode manufacturing are copper, graphite, aluminium, and related alloys, such as copper-tungsten. In this case, electrolytic copper is used because it offers a good overall behavior when tool steels are machined. Indeed, higher MRR with low relative tool wear (RTW) and good surface roughness can be achieved [15]. Tool electrode and workpiece hardness are measured as 77 HRB and 95 HRB, respectively (tested in a Hoytom durometer, model Minor-69) Design of Experiments To determine influential parameters for EDM groove machining Taguchi DOE has been applied in order to reduce the number of experiments. L 16 (4 5 ) orthogonal array has been chosen as the test matrix [16]. Figure 7 illustrates the 16 required experiments. Each machined groove is made by inducing a 9 mm path to the tool electrode, except for Figure 4. Dimensional and geometrical accuracy measurements. the rectangle shaped electrode, which only penetrates the workpiece vertically (grooves 4, 5, 11, 14 in Fig. 7) with 1 mm depth. Open voltage, current discharge, and pulse-on and pulse-off times are clear influential process parameters to the common performance measures such as MRR, surface roughness, and EWR [5 7, 12, 17, 18]. In this study, these input parameters have been selected because Figure 3. EDM process using ONA DB-300. Figure 5. Machined surface slope.

5 FEATURE MICRO-ACCURACY IN ELECTRO DISCHARGE MACHINING 1285 Table 2. EDM process parameters and their levels. Parameter Level Polarity [ ] + Servo [%] 70 Capacitors [ ] 0 Dielectric flow [l/min] 4.6 T w [s] 1.5 T p [s] 0.5 Figure 6. Parallelism between edges. Table 1. AISI H13 steel chemical composition (UNE-EN ISO 4957:2000 Norm). Composition C Si Mn <P <S Cr Mo V % Weight <0 03 < they can be easily programmed by machine operators. In addition, tool (electrode) geometry is considered as an input parameter to investigate its performance during the process and its effect on the final machined feature microaccuracy. Table 2 presents five different EDM process parameters selected and their levels. The rest of EDM parameters presented in Table 3 must be kept constant during the experimentation to ensure a correct Table 3. Constant EDM process parameters. Level Parameter L1 L2 L3 L4 P1. Open voltage [V] P2. Intensity [A] P3. T on [ s] P4. T off [ s] P5. Tool geometry G1 G2 G3 G4 comparison between the 16 tests (where T w is working time, and T p is pause time). 3. Artificial neural network modeling At first, multiple linear regression models are constructed to relate the output features with the input process parameters. These regression models are found unacceptable to be utilized in process planning for EDM of similar grooves, since the confidence level values (R 2 ) are low (between 0.15 and 0.25). This observation justifies the need and the importance of using artificial neural network modeling for such applications. Figure 7. Workpiece design (units are in mm).

6 1286 N. PELLICER ET AL. In this study, multilayer artificial neural networks are utilized to model the relations between process parameters selected and the output measures of interests. A multilayer neural network consists of at least three layers: input, hidden, and output layer where inputs, applied at the input layer, and outputs are obtained at the output layer, and learning is achieved when the associations between a specified set of input output (target) pairs are established. Furthermore, several backpropagation training algorithms including gradient descent with momentum and adaptive learning method, resilient backpropagation algorithm, and Levenberg Marquardt algorithm have tested in various artificial neural network (ANN) architectures. In addition, the Levenberg Marquardt with Bayesian regularization is used to improve the generalization capability of the neural networks. In all of those ANN models, the nonlinear tanh activation functions are used in the hidden layer, and input data are normalized in the range of 1 1. Linear activation functions are used in the output layer. The weights and biases of the network are initialized to small random values to avoid immediate saturation in the activation functions. The data set is divided into two sets as training and testing sets. Neural networks are trained by using the training data set and examined by using testing sets. The training data never is used in the testing data. Matlab s neural network toolbox is used to train neural networks. Simulations with test data repeated many times with different weight and bias initializations Prediction of Dimensional and Geometrical Accuracy By using ANN models developed, geometrical and dimensional features (flatness, depth, slope, width, and DVEE) are predicted with a trained feed-forward neural network as shown in Fig. 8. Open voltage, current discharge, pulse-on time, pulse-off time, and tool shape are used as inputs to neural networks. Various training data sets were selected and tested using a trail-and-error method, then, the training data set was found as the most suitable training data sets. The remaining data sets are used in testing the prediction capability of the trained ANN models. Training algorithms and network architectures are selected for minimum squared error (MSE) for best predictions using a training procedure. Selection process for the ANN architecture includes identifying first most optimum training Figure 8. Architecture of multilayer feed-forward neural network used for predictions. Table 4. Selection of ANN architecture and backpropagation training algorithms. Gradient descent Resilient w/momentum & back- Levenberg Bayesian adaptive learning propagation Marquart regularization MSE R 2 MSE R 2 MSE R 2 MSE R 2 Flatness Structure Structure Structure Depth Structure Structure Structure Slope Structure Structure Structure Width Structure Structure Structure DVEE Structure Structure Structure algorithm and most optimum number of hidden layer neurons for a minimized MSE. The results of these tests are summarized in Table 4. The resilient backpropagation algorithm is found the most suitable for training ANN models with structures of and designed for predicting depth and DVEE, respectively. The Bayesian regularization algorithm has resulted in better training of the ANN model for predicting Slope parameter with a structure of On the other hand, the Levenberg Marquardt algorithm has performed better for training ANN models with a structure of for both predicting Flatness and Width. This approach decreased the size of each neural network thus enabled faster convergence Table 5. Experimental results. T on T off Flatness Depth Slope Width DVEE Groove [V] [A] [ s] s Tool [ m] [mm] [ ] [mm] [ ]

7 FEATURE MICRO-ACCURACY IN ELECTRO DISCHARGE MACHINING 1287 and better predictions of output values [19 21]. Predictions using these neural networks are given in Section Results and discussions Sixteen grooves have been machined with EDM by following the experimental plan discussed in Tables 2 and 3. [5] presents five different inputs (voltage, current discharge, pulse-on time, pulse-off time, and electrode shape) and five measured outputs after investigating the machined features (flatness, depth, slope, width, and DVEE). A set of 10 experimental data set selected from Table 5 was used for the training of ANN models, whereas the remaining data sets were utilized in validation of ANN models for predictions Prediction of Geometrical and Dimensional Features Using ANN In order to obtain a fine prediction capability, the experimental data for training the neural network models must be selected with care. Although the data set presented in Table 5 is not large, it is found adequate after a careful evalutation to train neural networks for modeling the relationships among the EDM machining parameters and the geometrical and dimensional features of grooves for this study. Therefore, the experimental data is utilized in training the ANN as described in Section 3.1. Prediction simulations are performed with respect to geometrical and dimensional features by using trained ANN models. Measured and predicted flatness, depth, slope, width, and DVEE values using the ANN are given in Fig. 9. The graphs indicate Figure 9. Comparison of ANN predictions with testing data.

8 1288 N. PELLICER ET AL. fairly acurate predictions when ANN models are utilized for selection of the most suitable EDM machining parameters Tool Geometry Influence As discussed in the beginning of this article, tool geometry standardization is likely to be crucial for lower cost production. The analysis of geometries with better machining performance becomes important to achieve smaller electrode stocks in workshops. Figure 10 shows effects of tool geometry input factor used in this work on five different output data by using notched box plots. Box plots have several graphic elements. Lower and upper lines of the box are the 25th and 75th percentiles of the process error samples. The distance between the top and the bottom of the box is the interquartile range. The line in the middle of the box is the sample median. The whiskers are lines extending above and below the box. They show the extent of the rest of the sample (without the outliers). Whiskers are defined as one time of the interquartile range lengths. The plus sign at the top or bottom of the plot is an indication of an outlier in the data. This point may be the result of a data error such as a poor measurement or a change in the system that generated the data. Experimental data presented in Table 5 is directly utilized in a Matlab code to generate box plots shown in Fig. 10. Regarding the flatness analysis [Fig. 10(a)], rectangular and square shaped electrodes offer the best performance because they have more concentrated flatness accuracy between 20 m and 30 m. Figure 10(b) presents the relation between depth (whose target value is 1 mm) and tool geometries. Figure 10. Interrelation between electrode shape and machined groove quality.

9 FEATURE MICRO-ACCURACY IN ELECTRO DISCHARGE MACHINING 1289 Rectangular and cylindrical shaped electrodes have similar data variations, but the rectangular electrode provides depth closer dimension to 1 mm. When slope value is analyzed [Fig. 10(c)], square and cylindrical shaped electrodes provide more stable behavior than the others. However, a squared electrode performs slightly better due to its lower values of the whole data box plot. Lower width variation is achieved by using squared electrodes, as shown in Fig. 10(d). Although width target value is 3 mm, all obtained data present higher values due to gap distance, with the exception of triangular shaped electrode. Finally, for DVEE analysis two figures are required [Figs. 10(e) and (f)]. The first one presents scale problems, caused by excessive tool wear in experiment number 16. For this reason, data from this experiment has been removed in the second box plot graph. Taking this into account, square and rectangle shaped electrodes present lower wear ratios and, consequently, better process performance. As it is clearly shown in all figures, triangular shaped electrodes offer the worse performance independently of the analyzed performance measure. Therefore, in the mould design, geometrical features requiring triangular shapes should be avoided. 5. Conclusions Influence of different process parameters (pulse current, open voltage, pulse time, and pulse pause time) and tool electrode shape on performance measures (flatness, slope, depth, width, and DVEE) have been analyzed for copper electrode and AISI H13 steel workpiece in sinking type EDM process. Obtained regression models posses low values of R 2 factor, what could provoke poor predictions. Therefore, advanced process models using ANNs are required to obtain a better process prediction. Controlling the electrode wear is the main challenge to achieve good dimensional and accuracy in machined cavities (grooves or pockets). Square-shaped and rectangle-shaped electrodes offer the best global performance for high-accuracy groove machining due to the better EWR and process stability. The triangular-shaped electrode is found highly inefficient since the edges of the electrode wears fast, and geometrical accurcy deteoriates accordingly. However, other possible electrode geometries and more complex machined features must be considered for further discussions. References 1. Jeong, Y.H.; Min, B. Geometry prediction of EDM-drilled holes and tool electrode shapes of micro-edm process using simulation. International Journal of Machine Tools and Manufacture 2007, 47, Ho, K.H.; Newman. S.T. State of the art electrical discharge machining (EDM). Int. J. Mach. Tools Manuf. 2003, 43, Groover, M.P. Fundamentals of Modern Manufacturing. 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