OPTIMIZATION OF MACHINING PARAMETERS OF OHNS STEEL BY USING EDM

Transcription

1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 7, July 2017, pp , Article ID: IJMET_08_07_090 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed OPTIMIZATION OF MACHINING PARAMETERS OF OHNS STEEL BY USING EDM V. Ramesh Assistant Professor, Mechanical Engineering, Veltech Dr RR & Dr SR University, Avadi, Chennai, India P. Anand Associate Professor, Mechanical Engineering, Veltech Dr RR & Dr SR University, Avadi, Chennai, India M. Soundar Assistant Professor, Mechanical Engineering, SRM University Ramapuram Campus, Chennai, India ABSTRACT The correct selection of manufacturing conditions is one of the most important aspects to take into consideration in the majority of manufacturing processes and, particularly, in processes related to Electrical Discharge Machining (EDM). It is a capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. being widely used in die and mould making industries, aerospace, aeronautics and nuclear industries. OHNS-EN-31 is a high car bon alloy steel which achieves high degree of hardness with compressive strength and abrasive resistance. OHNS-EN-31 steel, which is popularly used in automotive type applications, like axle, bearings, spindle and moulding dies etc. In this paper we have tried to investigate effect of machining parameter such as discharge current, pulse on time, and pulse of time on MRR in EDM while machining OHNS-EN- 31 STEEL using Cu tool. A well-designed experimental scheme was used to reduce the total number of experiments. Parts of the experiment were conducted with the L18 orthogonal array based on the Taguchi method. The results of analysis of variance (ANOVA) indicate that the proposed mathematical model can be adequately describe the performance within the limit of factors being studied. The optimal set of process parameters has also been predicted to maximize the MRR. Key words: OHNS, EDM, TWR, MRR editor@iaeme.com

2 V. Ramesh, P. Anand and M. Soundar Cite this Article: V. Ramesh, P. Anand and M. Soundar, Optimization of Machining Parameters of OHNS Steel by Using EDM, International Journal of Mechanical Engineering and Technology, 8(7), 2017, pp INTRODUCTION The history of EDM Machining Techniques goes as far back as the 1770s when it was discovered by an English Scientist. However, Electrical Discharge Machining was not fully taken advantage of until 1943 when Russian scientists learned how the erosive effects of the technique could be controlled and used for machining purposes. When it was originally observed by Joseph Priestly in1770, EDM Machining was very imprecise and riddled with failures. Commercially developed in the mid-1970s, wire EDM began to be a viable technique that helped shape the metal working industry we see today. In the mid-1980s.the EDM techniques were transferred to a machine tool. This migration made EDM more widely available and appealing over traditional machining processes. The new concept of manufacturing uses non-conventional energy sources like sound, light, mechanical, chemical, electrical, electrons and ions. With the industrial and technological growth, development of harder and difficult to machine materials, which find wide application in aerospace, nuclear engineering and other industries owing to their high strength to weight ratio, hardness and heat resistance qualities has been witnessed. New developments in the field of material science have led to new engineering metallic materials, composite materials and high tech ceramics having good mechanical properties and thermal characteristics as well as sufficient electrical conductivity so that they can readily be machined by spark erosion. Non-traditional machining has grown out of the need to machine these exotic materials. The machining processes are nontraditional in the sense that they do not employ traditional tools for metal removal and instead they directly use other forms of energy. The problems of high complexity in shape, size and higher demand for product accuracy and surface finish can be solved through non-traditional methods. Currently, non-traditional processes possess virtually unlimited capabilities except for volumetric material removal rates, for which great advances have been made in the past few years to increase the material removal rates. As removal rate increases, the cost effectiveness of operations also increase, stimulating ever greater uses of non-traditional process. The Electrical Discharge Machining process is employed widely for making tools, dies and other precision parts. EDM has been replacing drilling, milling, grinding and other traditional machining operations and is now a well-established machining option in many manufacturing industries throughout the world. And is capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. being widely used in die and mould making industries, aerospace, aeronautics and nuclear industries. Electric Discharge Machining has also made its presence felt in the new fields such as sports, medical and surgical, instruments, optical, including automotive R&D areas. Electro Discharge Machining (EDM) is an electro-thermal non-traditional machining Process, where electrical energy is used to generate electrical spark and material removal mainly occurs due to thermal energy of the spark. EDM can be used to machine difficult geometries in small batches or even on job-shop basis. Work material to be machined by EDM has to be electrically conductive. OHNS steel is an important tool and die material, mainly because of its high strength, high hardness, and high wear resistance. It has a high specific strength due to that it cannot be easily machinable by conventional machining techniques. EDM is a non-conventional machining process that removes material by thermal erosion, such as melting and vaporization of material. To understand the machining characteristics of OHNS steel by EDM were explored in this editor@iaeme.com

3 Optimization of Machining Parameters of OHNS Steel by Using EDM experimental study. Pichai Janmanee et al. (2012) studied considers the effect of a coppergraphite electrode material on tungsten carbide work pieces during machining by EDM. The experiment found that by increasing the discharge current there was led to the more material removal rate (MRR) and more electrode wear ratio (EWR). Dilshad Ahmad Khan et al. (2011) discussed the effect of tool polarity on the machining of silver steel by electric discharge machining. They concluded that direct polarity is suitable for higher MRR and lower relative EWR, but reverse polarity gives better surface finish. N.Arunkumar et al. (2012) presented the results of experimental work carried out in EDM of EN31 using three different tool materials namely copper, aluminium and EN24. They concluded that copper undergoes less tool wear rate and very high material removal rate. EXPERIMENT AND DATA COLLECTION Experiments are designed by using DOE. There are various important parameter of EDM. (a) Spark On-time (pulse time or Ton): The duration of time (μs) the current is allowed to flow per cycle. Material removal is directly proportional to the amount of energy applied during this on-time. This energy is really controlled by the peak current and the length of the on- time. (b) Spark Off-time (pause time or Toff): The duration of time (μs) between the sparks (that is to say, off-time). This time allows the molten material to solidify and to be wash out of the arc gap. This parameter is to affect the speed and the stability of the cut. Thus, if the off-time is too short, it will cause sparks to be unstable. (c) Arc gap (or gap): The Arc gap is distance between the electrode and work piece during the process of EDM. It may be called as spark gap. Spark gap can be maintained by servo feed system. (d) Discharge current (current Ip): Discharge current is directly proportional to the Material removal rate. (e) Duty cycle (t): It is a percentage of the on-time relative to the total cycle time. This parameter is calculated by dividing the on-time by the total cycle time (on-time pulse off- time). (f) Voltage (V): It is a potential that can be measure by volt it is also effect to the material removal rate and allowed to per cycle. Voltage is given by in this experiment is 50V. (g) Diameter of electrode (D): It is the electrode of Cu-tube there are two different size of diameter 4mm and 6mm in this paper. This tool is used not only as an electrode but also for internal flushing. EXPERIMENTAL SETUP Section I Machine Tool A) Technical Data: * Height : 2,075 mm * Width : 1,230 mm * Depth : 1,035 mm * Net Weight: 800 Kg editor@iaeme.com

4 V. Ramesh, P. Anand and M. Soundar Section II Power Supply Unit A) Technical Data: 1. Electrical data * Type : ELECTRONICA MODEL * Supply Voltage : 415 V, 3 Ph., 50 Hz. * Taps : 380 V, 415 V, 440 V. * Mains Voltage Tolerance : + 10% * Connected Load (KVA) : 3 KVA * Power Factor : Working Parameters * Machining Current Max. : 70 Amps * B.Pulse current : 2 Amps * Open Gap O/V : % * Current Range Selection : 10 Selection 1 = 1 Amp 2 = 2 Amps 3-10 = 4 Amps * B.Pulse Current : 2 Selection 1 = 1 Amp 1 = 1 Amp * Pulse on Duration : 2 to 1000 us. * Weight : 250 Kg. Approx. SELECTION OF EROSIVE PULSE PARAMETER According to the requirement of machining rate, surface finish, over cut and electrode, the positions of following switches are selected: a) Position of Push wheel (Pulse 'ON') time b) Position of Push wheel (Pulse 'OFF') time c) Current selection switches The setting of erosive pulse parameters could be obtained by selection of Pulse 'ON' time, Pulse 'OFF' time and Average Machining Current. It is then possible to adopt with precision the electrical parameters of pulse to the required for the machining conditions. All these adjustments influences the performance parameters such as conditions. All these adjustments influences the performance parameters such as - a) Machining speed and hence rate of removal b) Electrode tool wear c) Quality of machined surface d) Extent of overcut editor@iaeme.com

5 Optimization of Machining Parameters of OHNS Steel by Using EDM The various technology charts represent the relation between the performance parameters and the erosive pulse parameters. (A)PULSE 'ON' TIME Different positions of switch Pulse 'ON' time are used for different machining rates as follows: a) Position 39 to 99 : Level 3 b) Position 12 to 38 : Level 2 c) Position 1 to 11 : Level 1 To achieve good machining stability, the following ranges of machining current recommended for different positions of Push Wheel (Pulse 'ON') Time. (B) PULSE 'OFF' TIME The pulse duration can be changed from minimum position (Position 9) to maximum (Position 1) in 9 positions by push wheel (Pulse 'OFF') time. Thus, one can obtain a full range of pulse duration from a minimum of 6 s to a maximum of 1680 s which largely covers the duration limits used in a Pulse Generator with a total power of 3 KVA. Decreasing the Pulse 'OFF' time, switch reduces the machining rate with a drastic increasing in the relative electrode tool wear. Too short a Pulse duration (T ON) position 3, 2 and 1, with copper electrode and steel work piece results in excessive accumulation of carbon in the machining zone with a subsequent instability of the machining process. (C) MACHINING CURRENT The increase in machining power is obtained by increasing the average machining current, indicated by Ammeter Gap Current and controlled by turning on one by one the switches. At the beginning of machining, the active electrode surface area is relatively small due to particular shape or misalignment of the electrode and the job at the start of machining. So it is always recommended to start with a low machining current, i.e., with a low machining power and then increase the current gradually. This is important in order to avoid frequent interruptions in machining process due to the excessive power being used for small area. Frequent interruption in the process reduces working speed, and unless a corrective action is taken the process remain in the state of instability. After machining to turn off the current selection switches without fail. With this, it is assured that the machine will restart under proper conditions in its next setting. (E) FLUSHING The product of Spark Erosion have to be removed from the work gap. The process by which this is accomplished is known as flushing. (F) OPERATIONAL DATA When a work piece is machined by EDM process, in order to attain optimum material removal rate and minimum electrode wear values of large number of parameters must be taken into consideration which can be derived only by experience. It also indicates the performance of the system under specified standard operating conditions given below and may vary within + 5% from generator to generator editor@iaeme.com

6 V. Ramesh, P. Anand and M. Soundar EXPERIMENTAL SETUP Section I Machine Tool A) Technical Data * Height : 2,075 mm * Width : 1,230 mm * Depth : 1,035 mm * Net Weight: 800 Kg. Section II Power Supply Unit A) Technical Data: 1. Electrical Data: * Type : ELECTRONICA MODEL * Supply Voltage : 415 V, 3 Ph., 50 Hz. * Taps : 380 V, 415 V, 440 V. * Mains Voltage Tolerance : + 10% * Connected Load (KVA) : 3 KVA * Power Factor : Working Parameters * Machining Current Max. : 70 Amps * B.Pulse current :2 Amps * Open Gap O/V : % * Current Range Selection : 10 Selection 1 = 1 Amp 2 = 2 Amps 3-10 = 4 Amps * B.Pulse Current : 2 Selection 1 = 1 Amp 1 = 1 Amp * Pulse on Duration : 2 to 1000 us. * Weight : 250 Kg. Approx editor@iaeme.com

7 Optimization of Machining Parameters of OHNS Steel by Using EDM SELECTION OF EROSIVE PULSE PARAMETER According to the requirement of machining rate, surface finish, over cut and electrode, the positions of following switches are selected: a) Position of Push wheel (Pulse 'ON') time b) Position of Push wheel (Pulse 'OFF') time c) Current selection switches The setting of erosive pulse parameters could be obtained by selection of Pulse 'ON' time, Pulse 'OFF' time and Average Machining Current. It is then possible to adopt with precision the electrical parameters of pulse to the required for the machining conditions. All these adjustments influences the performance parameters such as conditions. All these adjustments influences the performance parameters such as - a) Machining speed and hence rate of removal b) Electrode tool wear c) Quality of machined surface d) Extent of overcut The various technology charts represent the relation between the performance parameters and the erosive pulse parameters. (A)PULSE 'ON' TIME Different positions of switch Pulse 'ON' time are used for different machining rates as follows: a) Position 39 to 99 : Level 3 b) Position 12 to 38 : Level 2 c) Position 1 to 11 : Level 1 To achieve good machining stability, the following ranges of machining current recommended for different positions of Push Wheel (Pulse 'ON') Time. (B) PULSE 'OFF' TIME The pulse duration can be changed from minimum position (Position 9) to maximum (Position 1) in 9 positions by push wheel (Pulse 'OFF') time. Thus, one can obtain a full range of pulse duration from a minimum of 6 s to a maximum of 1680 s which largely covers the duration limits used in a Pulse Generator with a total power of 3 KVA. Decreasing the Pulse 'OFF' time, switch reduces the machining rate with a drastic increasing in the relative electrode tool wear. Too short a Pulse duration (T ON) position 3, 2 and 1, with copper electrode and steel work piece results in excessive accumulation of carbon in the machining zone with a subsequent instability of the machining process. (C) MACHINING CURRENT The increase in machining power is obtained by increasing the average machining current, indicated by Ammeter Gap Current and controlled by turning on one by one the switches. At the beginning of machining, the active electrode surface area is relatively small due to particular shape or misalignment of the electrode and the job at the start of machining. So it is always recommended to start with a low machining current, i.e., with a low machining power and then increase the current gradually. This is important in order to avoid frequent interruptions in machining process due to the excessive power being used for small area. Frequent interruption in the process reduces working speed, and unless a corrective action is taken the process remain editor@iaeme.com

8 V. Ramesh, P. Anand and M. Soundar in the state of instability. After machining to turn off the current selection switches without fail. With this, it is assured that the machine will restart under proper conditions in its next setting. (D) FLUSHING The product of Spark Erosion have to be removed from the work gap. The process by which this is accomplished is known as flushing. (E) OPERATIONAL DATA When a work piece is machined by EDM process, in order to attain optimum material removal rate and minimum electrode wear values of large number of parameters must be taken into consideration which can be derived only by experience. It also indicates the performance of the system under specified standard operating conditions given below and may vary within + 5% from generator to generator. EXPERIMENT DETAILS Figure 1 Showing Experimental Setup Experiments will be conduct based on RSM and ANOVA method. The four factor i.e. Current, Pulse ON, Voltage Gap and Flushing Pressure were selected for conducting experiment with three levels each. This have three phases for the calculation and optimization, they are Planning phase. Conducting phase. Analysing phase. PLANNING PHASE In this phase following things are to be planned for the experiment. I. Selection of process variables and there levels for EDM. II. Selection of work piece material III. Design of experiment (DOE) editor@iaeme.com

9 Optimization of Machining Parameters of OHNS Steel by Using EDM A. Selection of process variables and three levels Selection of process variables CONTROL FACTOR SYMBOL FACTOR Current I(amp) A Pulse on-time Ton(µs) B Voltage V(volt) C Pressure P(kg/cm 2 ) D Level of parameters CONTROL FACTORS LEVEL 1 LEVEL 2 LEVEL 3 UNITS A Amp B μs C μs D Kg/min B. Selection of work piece material The material used for the experiments is grade OHNS EN-31 steel, which is popularly used in automotive type applications, like axle, bearings, spindle and moulding dies etc. OHNS steel refers to a variety of carbon and alloy steels that are particularly well-suited to be made into tools. Their suitability comes from their distinctive hardness, resistance to abrasion, their ability to hold a cutting edge, and/or their resistance to deformation at elevated temperatures (redhardness). Generally used in a heat-treated state. Many high carbon tool steels are also more resistant to corrosion due to their higher ratios of elements such as vanadium and niobium. With a carbon content between 0.7% and 1.5%, tool steels are manufactured under carefully controlled conditions to produce the required quality. The manganese content is often kept low to minimize the possibility of cracking during water quenching. However, proper heat treating of these steels is important for adequate performance, and there are many suppliers who provide tooling blanks intended for oil quenching. C. Response surface methodology/ DOE Response surface methodology (RSM) is a collection of mathematical and statistical techniques for empirical model building. By careful design of experiments, the objective is to optimize a response (output variable) which is influenced by several independent variables (input variables). An experiment is a series of tests, called runs, in which changes are made in the input variables in order to identify the reasons for changes in the output response. Originally, RSM was developed to model experimental responses (Box and Draper, 1987), and then migrated into the modelling of numerical experiments. The difference is in the type of error generated by the response. In physical experiments, inaccuracy can be due, for example, to measurement errors while, in computer experiments, numerical noise is a result of incomplete convergence of iterative processes, round-off errors or the discrete representation of continuous physical phenomena (Giunta et al., 1996; van Campen et al., 1990, Toropov et al., 1996). In RSM, the errors are assumed to be random. The application of RSM to design optimization is aimed at reducing the cost of expensive analysis methods (e.g. finite element method or CFD analysis) and their associated numerical noise. The problem can be approximated as described with smooth functions that improve the convergence of the optimization process because they reduce the effects of noise and they allow for the use of derivative-based algorithms. Venter et al. (1996) have discussed the advantages of using RSM for design optimization applications editor@iaeme.com

10 V. Ramesh, P. Anand and M. Soundar 1. Approximate model function Generally, the structure of the relationship between the response and the independent variables is unknown. The first step in RSM is to find a suitable approximation to the true relationship. The most common forms are low-order polynomials (first or second-order). In this thesis a new approach using genetic programming is suggested. The advantage is that the structure of the approximation is not assumed in advance, but is given as part of the solution, thus leading to a function structure of the best possible quality. In addition, the complexity of the function is not limited to a polynomial but can be generalised with the inclusion of any mathematical operator (e.g. trigonometric functions), depending on the engineering understanding of the problem. The regression coefficients included in the approximation model are called the tuning parameters and are estimated by minimizing the sum of squares of the errors (Box and Draper, 1987): 2. Design of experiments An important aspect of RSM is the design of experiments (Box and Draper, 1987), usually abbreviated as DoE. These strategies were originally developed for the model fitting of physical experiments, but can also be applied to numerical experiments. The objective of DoE is the selection of the points where the response should be evaluated. Most of the criteria for optimal design of experiments are associated with the mathematical model of the process. Generally, these mathematical models are polynomials with an unknown structure, so the corresponding experiments are designed only for every particular problem. The choice of the design of experiments can have a large influence on the accuracy of the approximation and the cost of constructing the response surface. In a traditional DoE, screening experiments are performed in the early stages of the process, when it is likely that many of the design variables initially considered have little or no effect on the response. The purpose is to identify the design variables that have large effects for further investigation. Genetic Programming has shown good screening properties (Gilbert et al., 1998), as will be demonstrated which suggests that both the selection of the relevant design variables and the identification of the model can be carried out at the same time. A detailed description of the design of experiments theory can be found in Box and Draper (1987), Myers and Montgomery (1995) and Montgomery (1997), among many others. Schoofs (1987) has reviewed the application of experimental design to structural optimization, Unal et al. (1996) discussed the use of several designs for response surface methodology and multidisciplinary design optimization and Simpson et al. (1997) presented a complete review of the use of statistics in design. A particular combination of runs defines an experimental design. The possible settings of each independent variable in the Ndimensional space are called levels. A comparison of different methodologies is given in the next section Full factorial design To construct an approximation model that can capture interactions between N design variables, a full factorial approach (Montgomery, 1997) may be necessary to Response surface methodology investigate all possible combinations. A factorial experiment is an experimental strategy in which design variables are varied together, instead of one at a time. The lower and upper bounds of each of N design variables in the optimization problem needs to be defined. The allowable range is then discretized at different levels. If each of the variables is defined at only the lower and upper bounds (two levels), the experimental design is called 2N full factorial. Similarly, if the midpoints are included, the design is called 3N full factorial and shown in Figure editor@iaeme.com

11 Optimization of Machining Parameters of OHNS Steel by Using EDM 2.2. Central composite design A second-order model can be constructed efficiently with central composite designs (CCD) (Montgomery, 1997). CCD are first-order (2N) designs augmented by additional centre and axial points to allow estimation of the tuning parameters of a second-order model. Figure 3.4 shows a CCD for 3 design variables. CONDUCTING PHASE After DOE 14 experiments are carried out in electrode discharge machining. After each experiment MRR is calculated. A quality characteristic for MRR is larger is better. The readings which noted during each cycle are as follows along with their current, pulse on-time, voltage and flushing pressure. I Ton V P MRR(mm3/min) TWR(mm3/min) ANALYSIS PHASE The output characteristic, MRR & TWR are analysed by software Design Expert 8 and is formed, which shows the percentage contribution of each influencing factor on MRR & TWR. Main effect plot for means and main effect plots for S-N ratio are plotted with the help of software Design Expert 8. The observations made by the software for every steps are as follows: DESIGN SUMMARY File Version Study Type Response Surface Runs 29 Design Type Box-Behnken Blocks No Blocks Design Model Quadratic Build Time (ms) 1.43 EVALUATION RESULT 4 Factors: A, B, C, D Design Matrix Evaluation for Response Surface Quadratic Model No aliases found for Quadratic Model Aliases are calculated based on your response selection, taking into account missing data points, if necessary. Watch for aliases among terms you need to estimate. Degrees of Freedom for Evaluation Model 14 Residuals 14 Lack of Fit 10 Pure Error 4 Corr Total 28 A recommendation is a minimum of 3 lack of fit df and 4 df for pure error. This ensures a valid lack of fit test. Fewer df will lead to a test that may not detect lack of fit. Standard errors should be similar within type of coefficient. Smaller is beter. Ideal VIF is 1.0. VIFs above editor@iaeme.com

12 V. Ramesh, P. Anand and M. Soundar are cause for alarm, indicating coefficients are poorly estimated due to multicollinearity. Ideal Ri-squared is 0.0. High Ri-squared means terms are correlated with each other, possibly leading to poor models. If the design has multi linear constraints multi collinearity will exist to a greater degree, thus increasing the VIFs and the Ri-squareds, rendering these statistics useless. Power is an inappropriate tool to evaluate response surface designs. Use precision-based metrics provided in this program via fraction of design space (FDS) statistics. Click on the Graphs button at the top of this screen, look for the [?] button on the FDS Tool. Be sure to set the Model (on previous screen) to be an estimate of the terms you expect to be significant. Measures Derived From the (X'X)-1 Matrix StdL Point type IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact IBFact Center Center Center Center Center Average = Watch for leverages close to 1.0. Consider replicating these points or make sure they are run very carefully editor@iaeme.com

13 Optimization of Machining Parameters of OHNS Steel by Using EDM GRAPHS ANALYSIS PROCESS After you have entered your response data in the Design layout view, choose a response by clicking on the corresponding node under analysis. Now following are the steps displayed as buttons across the top of the view: Transformation. Select response node and choose transformation A. fit summary (RSM/mix). Use this to evaluate models for RSM and mixture. B. effects (Factorials). Choose significant effects from graph or list. Model (RS/Mix). Choose model order and desired terms from the list. Analysis of Variance (ANOVA). Analyse the chosen model and view results. Diagnostics. Evaluate model fil and transformation choice with graphs. Model Graphs. Use this to interpret and evaluate your model. Final Equation in Terms of Coded Factors: MRR = ( * A) (1.31 * B) - (0.24 * C) - (6.88 * D) (4.68 * A * B) (1.02 * A * C) + (0.93 * A * D) - (0.35 * B * C) (1.33 * B * D) + (7.71 * C * D) + (59.82 * A 2 ) + (7.54 * B 2 ) + (1.03 * C 2 ) - (8.89 * D 2 ) Final Equation in Terms of Actual Factors MRR = ( * current) + ( * pulse on time) + ( * voltage) + ( * pressure) + ( * current * pulse on time) + ( * current * voltage) + ( * current * pressure) + ( E-003* pulse on time * voltage) + ( * pulse on time * pressure) + ( * voltage * pressure) + ( * current 2 ) + ( * pulse on time 2 ) + ( * voltage 2 ) + ( * pressure 2 ) The Diagnostics Case Statistics Report has been moved to the Diagnostics Node. In the Diagnostics Node, Select Case Statistics from the View Menu. Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the: 1) Normal probability plot of the student zed residuals to check for normality of residuals. 2) Student zed residuals versus predicted values to check for constant error. 3) Externally Student zed Residuals to look for outliers, i.e., influential values. 4) Box-Cox plot for power transformations editor@iaeme.com

14 V. Ramesh, P. Anand and M. Soundar If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon. DIAGNOSTICS MODEL GRAPHS

15 Optimization of Machining Parameters of OHNS Steel by Using EDM Final Equation in Terms of Coded Factors: TWR = (20.04 * A) + (0.55 * B) - (0.057 * C) - (2.92 * D) Final Equation in Terms of Actual Factors: TWR = ( * current) + ( * pulse on time) ( * voltage) ( * pressure) The Diagnostics Case Statistics Report has been moved to the Diagnostics Node. In the Diagnostics Node, Select Case Statistics from the View Menu. Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the: 1) Normal probability plot of the studentized residuals to check for normality of residuals. 2) Studentized residuals versus predicted values to check for constant error. 3) Externally Studentized Residuals to look for outliers, i.e., influential values. 4) Box-Cox plot for power transformations. If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon. DIAGNOSTICS editor@iaeme.com

16 V. Ramesh, P. Anand and M. Soundar MODEL GRAPHS OPTIMIZATION Numerical optimization set goals for each response then click on solutions to generate optimal conditions. Graphical optimization set minimum or maximum limits for each response then create an overlay graph highlighting the area of operability. Point prediction enter your desired operating conditions and discover your predicted response values with confidence intervals. Confirmation compare the results predicted by the models to overcome of a confirmation experiment. NUMERICAL OPTIMIZATION GRAPH for the selected reading editor@iaeme.com

17 Optimization of Machining Parameters of OHNS Steel by Using EDM CONCLUSIONS Following conclusions were made for optimum MRR and TWR during the machining of OHNS steel on EDM For OHNS steel optimum machining condition for material removal rate (MRR) During machining on EDM were Current, Pulse on, Voltage Gap and flushing pressure with positive polarity For OHNS steel optimum machining condition for better hardness were, Current (12 amp.), Pulse-on (73 μs), voltage gap (35.50 volt) and flushing pressure (0.20 kg/cm2) MRR increases with increase of current MRR first increases and then after getting a peak value it decreases with increase of Ton MRR increases partially and then decreases with increase of Toff Optimum value of MRR is calculated as1.6348mm3/min. REFERENCES [1] Dhar, S Purohit, R., Saini, n., Sharma, a. and Kumar, G.H., Mathematical modeling of electric discharge machining of cast Al-4Cu-6Si alloy-10 wt. % SICP composites. Journal of Materials Processing Technology, 193(1-3), [2] Karthikeyan R, Lakshmi Narayanan, P.R. and Naagarazan, R.S., Mathematical modeling for electric discharge machining of aluminium-silicon carbide particulate composites. Journal of Materials Processing Technology, 87(1-3), [3] El-Taweel, T.A., Multi-response optimization of EDM with Al-Cu-Si-tic P/M composite electrode. International Journal of Advanced Manufacturing Technology, 44(1-2), [4] Mohan, B., Rajadurai, A. and Satyanarayana, K.G., Effect of sic and rotation of electrode on electric discharge machining of Al-sic composite. Journal of Materials Processing Technology, 124(3), [5] Lin, y, Cheng, C. Su, B.-. and Hwang, L, Machining characteristics and optimization of machining parameters of SKH 57 high-speed steel using electrical-discharge machining based on Taguchi method. Materials and Manufacturing Processes, 21(8), [6] J. Simao, H.G. Lee, D.K. Aspinwall, R.C. Dewes, and E.M. Aspinwall 2003.Workpiece surface modification using electrical discharge machining, 43 (2003) [7] Dr. S. Sreenivasulu, M. Venkatesulu, T. Vijaya Kumar Comparisons of Machining Parameters in Electro Discharge Machining of Aluminum 6082 and Hybrid Nano Metal Matrix Composite. International Journal of Mechanical Engineering and Technology, 8(5), 2017, pp [8] Chandra Shekar, N B D Pattar and Y Vijaya Kumar, Design and Study of the Effect of Multiple Machining Parameters in Turning of AL6063T6 Using Taguchi Method. International Journal of Design and Manufacturing Technology 7(3), 2016, pp [9] D. Jeya Prakash, P. Vijaya Kumar, P. Renuka Devi, M. Ganesan and N. Baskar, Optimization of Electrical Discharge Machining Parameters For Machining of Titanium Grade 2 Using Design of Experiments Approach, International Journal of Mechanical Engineering and Technology, 8(4), 2017, pp [10] Singh, P.N., Raghukandan, K., Rathinasabapathi, M. and Pai, B.C., 2004.Electric discharge machining of Al-10%sicp as-cast metal matrix composites. Journal of Materials Processing Technology, (1-3), editor@iaeme.com