Modeling and Optimization of Tool Wear Rate in Powder Mixed EDM of MMC

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1 Modeling and Optimization of Tool Wear Rate in Powder Mixed EDM of MMC Ashvarya Agrawal, Avanish Kumar Dubey, and Pankaj Kumar Shrivastava Abstract Machining of metal matrix composites (MMCs) is a big challenge for manufacturing industries due to it s superior mechanical properties. Unconventional machining processes (UMPs) are widely accepted to machine these MMCs. Electrical discharge machining (EDM) is one of such UMP which is most widely used to machine these advanced materials. However, the poor machining efficiency in terms of low material removal rate and high tool wear rate (TWR) are the circle of concern for manufacturing industries while using EDM to machine these materials. Powder mixed EDM (PMEDM) process may help to overcome above mentioned limitations up to some extent. The aim of present research is to investigate the machining performance of copper-iron-carbide MMC using PMEDM. A hybrid approach of artificial neural network and genetic algorithm has been used to develop the intelligent model for TWR and subsequent optimization with the experimental data obtained by central composite rotatable design. Keywords Artificial neural network, Genetic algorithm, Metal matrix composite, Powder mixed EDM. I. INTRODUCTION NCONVENTIONAL machining processes (UMPs) are Ugaining wide acceptance to machine advanced materials such as superalloys, composites and ceramics, which are otherwise difficult to machine by conventional techniques. Electrical discharge machining (EDM) is such an UMP widely applied during machining of such materials []. The performance of EDM may be enhanced by suitable process modifications such as combining EDM with other machining processes, applying suitable coatings on the workpiece surfaces and mixing additives in the dielectric. Powder mixed EDM (PMEDM) is such an modification of EDM process to enhance it s efficiency. PMEDM has different machining mechanism than that of conventional EDM. In this process, a suitable material in the powder form is mixed into the dielectric fluid. For better circulation of the powder mixed dielectric, a stirring system is employed. The various powders that can be added into the dielectric fluid include aluminum, chromium, graphite, copper and silicon carbide. Due to presence of the powder, the spark gap is filled up with additive particles. When a voltage of V is applied between the electrode and the workpiece facing each Ashvarya Agrawal is with the Atmiya University, Gujrat, India ( ash2agrawal@gmail.com). Avanish Kumar Dubey is with Motilal Nehru National Institute of Technology Allahabad, India (Corresponding author, Phone: ; avanishdubey@yahoo.com) Pankaj Kumar Shrivastava is with the AKS University, Satna (M.P.), India ( psiitd@yahoo.com). other with a gap of 25-50µm, an electric field of suitable magnitude is created [2]. The powder particles get energized and behave in a zigzag fashion. Under the sparking area, the particles come close to each other and arrange themselves in the form of chain like structures between both the electrodes. The interlocking between different powder particles occurred in the direction of flow of current. This chain formation helps in bridging the discharge gap between both the electrodes. Due to bridging effect, the insulating strength of the dielectric fluid decreases. The easy short circuit takes place, which causes early explosion in the gap. As a result, series discharge starts under the electrode area. The faster sparking within a discharge takes place, which causes fast erosion from the workpiece surface and hence increases the material removal. At the same time, the added powder modifies the plasma channel. The plasma channel becomes enlarged and widened. The sparking is uniformly distributed among the powder particles, hence electric density of the spark decreases. Consequently, uniform erosion (shallow craters) occurred on the workpiece surface. These results in improvement in surface finish as well. It is expected that weaker plasma channel in the interspaces after addition in powder form may lead to reduction in material removal rate (MRR) during normal single electric discharge process; however, increase in MRR has been observed by many researchers [3-9]. This was attributed to combined effect of increase in the sparking frequency coupled with mechanical thrust driven by gas explosion mainly from working fluid evaporation and ploughing effect caused by striking impact of suspended particles. The material removed by grinding effect of suspended particles within the inter spaces are negligible. Therefore, machining efficiency of the whole PMEDM process depends on striking effect of the particles and discharge transitivity. The discharge transitivity, especially decides the sparking frequency that governs the entire MRR, while the ploughing effect of particles has a minor cutting effect contributing mainly to the improvement of the surface finish. The efficiency of the EDM process with additives depends upon the type of additives, particle size, particle concentration and their properties like thermal conductivity, electrical resistivity, density, melting point, specific heat, etc. Many researchers have tried to explore the potential of PMEDM. Erden and Bilgin [3] investigated the effect of additives in dielectric during EDM of steel. It was observed that machining rate increases with increase in concentration of

2 additives. The increase in machining rates was obtained due to decrease in time lags with additives concentrations. It was further reported that machining becomes unstable at excessive concentration of abrasive particles due to occurrence of short circuits. Jeswani et al. [4] investigated the effect of graphite powder during EDM of steel and reported that addition of about 4 gram of graphite powder per liter of kerosene, improved machining stability, thereby improving the MRR by 60% and tool wear rate (TWR) by 28%. This effect was attributed to the increase in interspaces for electric discharge initiation and reduction in the breakdown strength of dielectric. Tzeng and Lee [5] studied the effect of aluminium, chromium, copper, and silicon carbide powder as additives in dielectric medium during EDM of SKD steel and found that concentration, density, size, electrical resistivity, and thermal conductivity significantly affect the EDM process performances such as MRR and TWR. Zhao et al. [6] reported that machining efficiency of PMEDM can be significantly increased by selecting proper discharging parameters. Singh et al. [7, 8] performed PMEDM on cast aluminium MMC, using silicon carbide powder in dielectric and reported better machining rate as compared to simple die sinking. Further, they find the optimum values of control factor to maximize the MRR using Taguchi methodology. Kansal et al. [9] performed PMEDM on MMCs and reported increase in the MRR as well as a reduction in TWR using silicon carbide, aluminium, copper, and chromium powders as additives. Further, they established optimum process parameters in the rough machining phase using response surface methodology. Yan and Chen [0] studied the effect of aluminium powder additive and concluded that the enlargement of spark gap and improvement in energy dispersion lead to better MRR. Yu et al. [] reported the same results during EDM of tungsten carbide with fine aluminium powder additive in kerosene. The literature survey revealed that most of the research work in the field of PMEDM has been done on metals, alloys and Al/SiC MMC. No work has been reported on copper-irongraphite MMC, which is being widely used in automobile and aviation industries. Also, the effect of graphite powder mixed dielectric fluid on TWR of MMCs is not well reported. So, the aim of present research is to investigate the effect of graphite powder mixed dielectric on the TWR of MMC. An experimental investigation has been done on copper based MMC with varying peak current, pulse-on time, pulse-off time and powder concentration to study the process performance such as TWR. Further, a hybrid approach of artificial neural network (ANN) and genetic algorithm (GA) has been used to minimize TWR. II. EXPERIMENTAL SETUP AND DESIGN OF EXPERIMENTS The experiments have been performed on Electronica EMS 5030 EDM machine. The workpiece material selected was copper-iron-graphite MMC. The tool material used was mild steel rod of 20 mm diameter and the dielectric fluid used was SEO 450. Fine graphite powder of 200 mesh size was used for mixing into the dielectric. A stirrer has been employed in the machining tank to avoid particle settling and ensure proper mixing of the powder into the dielectric fluid. Magnetic forces were used to separate the debris from the dielectric fluid. For this purpose, few permanent magnets were placed in the machining tank. The setup used for experimentation is shown in Fig.. (a) (b) Fig. Experimental setup (a) EDM machine tool with rotation assembly for stirrer (b) stirrer employed for proper mixing of powder Chemical composition of copper-iron-graphite MMC is shown in Table I below. TABLE I COMPOSITION OF WORKPIECE (% VOLUME) Copper Iron Graphite The different control factors and their levels are given in Table II. The range of the control factors have been decided by exhaustive pilot experiments. The experiments have been performed using central composite rotatable design of experiments [2]. Each experiment was carried out for 30 minutes and the quality characteristic; TWR in each experimental run was obtained by measuring the mass difference before and after the experiment 2

3 using precision electronic digital weight balance with 0. mg resolution. TABLE II CONTROL FACTORS AND THEIR LEVELS Factors pulse-on pulse-off peak current powder time (µs) time (µs) (A) con.(g/l) Symbol X X 2 X 3 X 4 Level Level Level Level Level TWR (g/min) has been calculated by using following formula: TWR = m i m f t p () Where m i and m f are the mass of tool in gram before and after machining, respectively; t p is machining time in minutes. The observed values of TWR have been shown in Table III. TABLE III EXPERIMETNAL VALUIES OF TWR Ex. No Factor TWR (g/min) X X 2 X 3 X III. MODELING AND OPTIMIZATION METHODOLOGY A. Artificial neural network ANN is artificial intelligence based modeling technique, which is based on parallel processing of highly interconnected neurons. The ANN architecture consists of input layer, hidden layer and output layer. The number of neuron in the input and output layer are fixed, whereas the number of hidden layers and the number of neurons in each hidden layer is determined by trial and error [3]. The net input to a node in layer j is gives as: net j = w ji o i + θ j (2) Where w ji is the weight on the link between nodes in layer j and i. o i is the input to node in layer j and θ j is the bias to the layer. The output of node j is given by: o j =f(net j ) (3) Here f is the activation function such as log sigmoid and hyperbolic tangent as mentioned below: o j = f net j = +e net (4) j o j = f net j = enetj e net j e netj + e net (5) j During training, the calculated output is compared with the target output, and the mean square error is calculated. If the mean square error is more than a prescribed limiting value of the error, it is back propagated to modify the interconnection weights and the process continues until the prescribed error limit is reached. The mean square error is calculated by the equation: MSE = N (t N i= p y p ) 2 (6) Where t p, y p the target value and actual value respectively, N is is the number of data sample. B. Genetic algorithm GA is a generalized search and optimization technique inspired by the theory of biological evolution. GA maintains a population of individuals that represent candidate solutions. Each individual is evaluated to give some measure of its fitness to the problem from the objective function. In each generation, a new population is formed by selecting the more fit individuals based on a particular selection strategy. Some members of the new population undergo genetic operations to form new solutions. The two commonly used genetic operators are crossover and mutation. Crossover is a mixing operator that combines genetic material from selected parents. Mutation acts as a background operator and is used to search the unexplored search space by randomly changing the values at one or more positions of the selected chromosome [4]. 3

4 IV. RESULTS AND DISCUSSIONS A. Parametric analysis The result of analysis of variance [5] has been shown in Table IV. Peak current has been identified as most significant control factor affecting the TWR, followed by powder concentration and pulse-on time. TABLE IV ANALYIS OF VARIANCE FOR TWR Source Degree of Sum of square F Contribution (%) freedom Pulse-on time # Pulse-off time Peak current # Powder con # 3.96 Error Total # Significant control factor Fig. 3 Variation of TWR with peak current and powder concentration Input layer layer Fig. 2 shows the variation of TWR with the peak current and pulse-on time. It is evident that with the increase in the pulse-on time, the TWR increases for all the values of peak current and vice-a-versa. With the increase in peak current and pulse-on time, the discharge energy per spark increases, which results in more melting from the tool surface and hence increase in TWR. x x 2 x 3 Output layer TWR x 4 w xy b x Fig. 4 ANN architecture used for TWR model The values of weights and biases have been used to develop ANN model in mathematical form. The ANN model in mathematical form for TWR can be expressed as: Fig. 2 Variation of TWR with peak current and pulse-on time Fig. 3 shows the response plot for the TWR with respect to peak current and powder concentration. It can be concluded from the response surface plot that with the increase in the powder concentration in the dielectric, the TWR decreases in general. The increase in the powder concentration enlarges the plasma channel and spark is also distributed uniformly over the tool surface. That may be the reason for reduction in TWR. B. Modeling MATLAB 7.4 was used for developing ANN model and optimization. After trying different activation functions and their combinations, the log sigmoid and pure linear activation function, has been found best for hidden layer and output layer, respectively for TWR. Also, the hidden layer with 3 neurons has been found most appropriate for TWR. The used ANN architecture is shown in Fig. 4. TWR = y y y (7) Where, y = [ + e ( x x x )] y 2 = [ + e (0.289 x x x x )] y 3 = [ + e (0.02 x.27 x x x )] ] Here, x, x 2, x 3, and x 4 denotes input control factors, pulseon time, pulse-off time, peak current and powder concentration, respectively. y i=,2, 3 represents the output of hidden layer. 4

5 Fig. 5 compares the experimental values of TWR with the ANN predicted values. It can be concluded that ANN predicted values of TWR are in closed agreement with experimental values. The mean square error for TWR has been found to be , which is almost negligible. Also, the correlation coefficient between experimental values and ANN predicted values for TWR has been found to be Therefore, ANN models are reliable and capable to predict the quality characteristics. Fitness value Best: Mean: Best fitness Mean fitness 0. TWR (g/min) Experiment number Experimental v of TWR ANN predicted values of TWR Generation Fig. 6 Generation- fitness graphics for TWR The optimization result been shown in Table V. The comparison of optimum results with that of results obtained at initial level of control factors show considerable reduction of TWR. TABLE V OPTIMIZATION RESULT Fig. 5 Comparison of experimental values with the ANN predicted values of TWR C. Optimization The objective function of optimization problem can be stated as below: Find: X, X 2, X 3 and X 4 Minimize: TWR = y y y (8) With range of process input parameters: 5 X 25 5 X 2 35 X X 4 4 The population size 50, cross over rate 0.8, mutation rate 0.0 and number of generation of 52 are taken to find the minimum value of TWR. The objective function in Eq. (8) has been solved without any constraint. In Fig. 6, the best and mean fitness curves are illustrated in the search space. The fitness function is optimized when the mean curve converges with the best curve after 4 generation. The corresponding optimum values of control factors pulse-on time, pulse-off time, peak current, and powder concentration have been found to be 5.07 µs, µs,.27 A, and.25 g/l, respectively. At this optimum value of control factors, the value of TWR has been obtained as g/min. Factors Initial Optimum Improvement (%) value value X X X X TWR % V. CONCLUSIONS Following conclusions can be drawn from the powder mixed electrical (PMEDM) discharge machining of metal matrix composite (MMC):. Mixing graphite powder in dielectric significantly reduces the tool wear rate (TWR) during machining of MMC. 2. The peak current has been identified as most significant control factor affecting TWR, followed by powder concentration. 3. The developed artificial neural network model is reliable and adequate to predict the TWR with negligible prediction error. 4. The optimization result shows considerable reduction of % in TWR. REFERENCES [] V.K. Jain, Advanced Machining Processes, Allied Publishers, New Delhi, [2] A. Kumar, S. Maheshwari, C. Sharma, and N. Beri, Research development in additives mixed electrical discharge machining (AEDM): A state of art review, Materials and Manufacturing Processes, 200, 25, pp [3] A. Erden, and S. Bilgin, Role of impurities in electric discharge machining, Proceedings of the 2th International Machine Tool 5

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