Modelling Surface Finish in WEDM using RSM

Transcription

1 Modelling Surface Finish in WEDM using RSM Amit Joshi 1, S. S. Samant 2 & K. K. S. Mer 3 Department of Mechanical Engineering; G. B. Pant Engineering College, Pauri, India amitjoshi_81@yahoo.co.in,sanjaysama@gmail.com,kks_mer@yahoo.com Abstract Wire electrical discharge machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the conventional machining processes. This practical technology of the WEDM process is based on the conventional EDM sparking phenomenon utilizing the widely accepted non-contact technique of material removal. Since the introduction of the process, WEDM has evolved from a simple means of making tools and dies to the best alternative of producing micro-scale parts with the highest degree of dimensional accuracy and surface finish quality. The effect of various parameters of WEDM like pulse on time (TON), pulse off time (TOFF), gap voltage (SV) have been investigated to reveal their impact on output parameter i.e., surface roughness of high carbon and high chromium steel using response surface methodology. Experimental plan is performed by standard RSM design called a BOX-BEHKEN DESIGN. The optimal set of process parameter has also been predicted to maximize the surface finish. Index Terms ANOVA, RSM, SN Ratio, WEDM. I. INTRODUCTION Wire electrical discharge machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This practical technology of the WEDM process is based on the conventional EDM sparking phenomenon utilizing the widely accepted non-contact technique of material removal. Since the introduction of the process, WEDM has evolved from a simple means of making tools and dies to the best alternative of producing micro-scale parts with the highest degree of dimensional accuracy and surface finish quality. Wire Electrical discharge machining (WEDM) is a non-traditional, thermoelectric process which erodes material from the work piece by a series of discrete sparks between a work and tool electrode immersed in a liquid dielectric medium. These electrical discharges melt and vaporize minute amounts of the work material, which are then ejected and flushed away by the dielectric. A wire EDM generates spark discharges between a small wire electrode (usually less than 0.5 mm diameter) and a work piece with deionized water as the dielectric medium and erodes the work piece to produce complex two- and three dimensional shapes according to a numerically controlled (NC) path. During the WEDM process, the material is eroded ahead of the wire and there is no direct contact between the work piece and the wire, eliminating the mechanical stresses during machining. In addition, the WEDM process is able to machine exotic and high strength and temperature resistive (HSTR) materials and eliminate the geometrical changes occurring in the machining of heat-treated steels. WEDM was first introduced to the manufacturing industry in the late 1960s. The development of the process was the result of seeking a technique to replace the machined electrode used in EDM. Fig. 1 Schematic representation of Wire EDM cutting process 1.1 Introduction to the RSM method: Many experimental programs are designed with a two-fold purpose in mind: to quantify the relationship between the values of some measurable response variable and those of a set of experimental factors presumed to affect the response and, to find the values 74

2 of the factors that produce the best value or values of the response(. Response surface methodology (RSM) is a set of techniques designed to find the best value of the response. If discovering the best value or values of the response is beyond the available resources of the experiment, then response surface methods are used to at least gain a better understanding of the overall response system. 1.2 Techniques of the Response surface methodology Three principal techniques: 1. Setting up a series of experiments. 2. Determining a mathematical model that best fits the data collected. 3. Determining the optimal settings of the experimental factors that produce the optimum value of the response. II. EXPERIMENTATION 2.1 Experiment design method: For this experiment design we use the response surface method. The three factors are taken as input parameter (control factors) and three levels of each factor are considered in this experiment. Therefore, Box-Behnken Design of RSM is selected for the experiment. 2.2 Input Parameters and their levels: The various input process parameters along with their three levels are taken for final experimentation as follows: Input Factors Table No. 1 Level 1 Level2 Level 3 Ton Toff SV Response of the experiment: In this work our objective is to model the surface finish so surface finish is the output parameter or response of the experiment. 2.4 Design of experiment: Table No. 2 Run Randomization T ON T OFF S.V (µs) (µs) Instruments used for measuring surface roughness One of the measurable output characteristics is surface Roughness. Instrument used in this work for measurement of surface Roughness is Mitutoyo Surftest SJ-201P. The surftest SJ-201P (mitutoyo) is a shop floor type surface-roughness measuring instrument, which traces the surface various machine parts and calculates the surface roughness based on roughness standards, and displays the results. The workpiece is attached to the detector unit of the SJ-201P will trace the minute irregularities of the workpiece surface. The vertical stylus displacement during the trace is processed and digitally displayed on the liquid crystal display of the SJ-201P. Another quality characteristic is MRR and it is calculated by the formula volume/time. The processing time of each cut will note down by the stop watch. 75

3 Fig.2 Mitutoyo Surftest SJ-201P Instrument 2.6 Experimental Data for surface roughness: Experimental data for surface roughness is obtained by Mitutoyo Surftest instrument and data is shown in table no. 3 Run T ON (µs) T OFF (µs) Table No. 3 S.V (volt) S. R (µm) III. RESULTS 3.1 Description of the experiment with results The influences of the T on, T off and SV on machined surface roughness in wire electrical discharge machining process have been examined. Experiments have been performed on high carbon high chromium steel with a wire of diameter mm and the obtained data has been analyzed using Response Surface Methodology. The results of the performed experiment show that pulse on time (Ton), pulse off time (Toff) and the servo voltage (SV) influence on surface roughness. 3.2 Effects Of Machining Parameters On Surface Roughness: 1. Effects of Pulse on time on surface roughness:- It is shown from the graph.1 that machining speed increases with the pulse on time. On the contrary, surface accuracy decreases with increasing the pulse on time. This is because the discharge energy increases with the pulse on time. As a result, machining speed becomes faster with the increase of discharge energy Graph No Effect of Pulse off time on surface roughness :- From the graph no 2 it is shown that as the pulse off time is shorter, the number of discharges within a given period becomes more. This will lead to a higher machining speed. But, surface accuracy becomes poor because of a large number of discharge

4 Graph No Effect of Servo reference voltage on surface roughness :- From the Graph 3 we seen that Higher the servo reference voltage, the longer the discharge wait time. To obtain the longer discharge wait time, the machining speed needs to be slowed down. This will lead to a wider average discharge gap. Therefore, the discharge condition becomes more stable but the number of discharge cycles becomes within a given period. Owing to this stable machining, surface accuracy becomes better. 3.4 Discussion of results Graph No. 5 Graph No. 6 The experiment was carried out as per the experimental plan, discussed earlier, and Analysis of Variance (ANOVA) is achieved by design expert software. Table No. 4 ANOVA for Surface Roughness Source Sum Of Squares Dof Mean square F-value P-value 3.3 Three-Dimensional views of all Graphs: Three dimensional graph shows the variation of output parameter i.e surface finish with respect to two input parameters. Model Ton (A) Toff(B) SV(C) SV (C)sq Residual Lack Of Fit Pure Error Graph No. 4 Cor Total

5 The Model F-value of implies the model is significant. There is only a 0.04% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than indicate model terms are significant. In this case A, B, C, C2 are significant model terms. Values greater than indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve the model. Final Equation in Terms of Actual Factors: S = * Ton * Toff * SV E-003* SV2 With the help of this equation the optimal value of surface roughness is 3.91µm. IV. CONCLUSION The following are the conclusions drawn from the work done in this investigation. 1. Surface roughness increases with an increase of pulse on time. 2. surface roughness decreases with increase of pulse off time. 3. surface roughness increases with an increase of SV 4. In WEDM Process, use of high pulse on time (125 units), low Pulse off time (43 units) and low Servo voltage (10 units) are recommended to obtained better surface finish for the specific test range in a High carbon high chromium material. The optimal value of surface roughness is 3.91 units. V. REFERENCES [1] M.J. Haddada, A. Fadaei Tehranib, Material removal rate (MRR) study in the cylindrical wire electrical discharge turning (CWEDT) process, journal of materials processing technology 199(2008) [2] Te-Chang Tsai, Jenn-Tsong Horng, The effect of heterogeneous second phase on the machinability evaluation of spheroidal graphite cast irons in the WEDM process, Materials and Design 29 (2008) [3] Aminollah Mohammadi, Alireza Fadaei Tehrani, Statistical analysis of wire electrical discharge turning on material removal rate, journal of materials processing technology ( ) [4] Jin Yuan, Kesheng Wang, Reliable multiobjective optimization of high-speed WEDM process based on Gaussian process regression, International Journal of Machine Tools & Manufacture 48 (2008) [5] Probir Saha & Abhijit Singha, Soft computing models based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite, Int J Adv Manuf Technol(2007). [6] S. Sarkar, M. Sekh, S. Mitra, Modeling and optimization of wire electrical discharge machining of _-TiAl in trim cutting operation, Journal of Materials Processing Technology (2007). [7]. R. Ramakrishnan L. Karunamoorthy, Multi response Optimization of WEDM processes using robust design of experiment, Int J Adv Manuf Technol (2006) 29: [8] S. S. Mahapatra & Amar Patnaik, Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method, Int J Adv Manuf Technol(2006). [9] A. Manna, B. Bhattacharyya, Taguchi and Gauss elimination method: A dual response approach for parametric optimization of CNC wire cut EDM of PRAlSiCMMC, Int J Adv Manuf Technol (2006) 28: [10] A.B. Puri B. Bhattacharyya, Modeling and analysis of white layer depth in a wire-cut EDM process through response surface methodology, Int J Adv Manuf Technol (2005) 25: [11] M.S. Hewidy, T.A. El-Taweel, Modelling the machining parameters of wire electrical discharge machining of Inconel 601 using RSM, Journal of Materials Processing Technology 169 (2005) [12] K.H. Ho, S.T. Newman,. Rahimifard, R.D. Allen, State of the art in wire electrical discharge machining (WEDM), International Journal of Machine Tools & Manufacture 44 (2004)