Parametric Optimization Of Machining Parameters By Using Brass Wire .
International Journal of Mechanical and ProductionEngineering Research and Development (IJMPERD)ISSN (P): 2249–6890; ISSN (E): 2249–8001Vol. 10, Issue 3, Jun 2020, 1499-1512 TJPRC Pvt. Ltd.PARAMETRIC OPTIMIZATION OF MACHINING PARAMETERS BY USING BRASSWIRE ELECTRODE ON WIRE ELECTRIC DISCHARGE MACHININGI. HARISH1*, SANTOSH PATRO2 & P. SRINIVASA RAO3*12Department of Mechanical Engineering, Centurion University of Technology and Management, Odisha, IndiaAssistant professor, Mechanical Engineering, Centurion University of Technology and Management, Odisha, India3Professor, Mechanical Engineering, Centurion University of Technology and Management, Odisha, IndiaABSTRACTIn this work Brass wire is used to find performance of Wire Electrical Discharge Machining (WEDM) on D2 Steel withinput variables like T ON time, Input Current, T OFF time, spark gap set voltage, wire runoff time and Tension of thewire. The experiments are conducted as per the standard Taguchi’s L27 orthogonal array. The multiple performanceslike metal removal rate(MRR), Tool wear ratio(TWR), Surface roughness and kerf width(KW) are optimized byemploying a Multi criteria decision making method called Technique for order preference by similarity to idealsolution(TOPSIS). By the results, the optimal arrangement of input variables are T ON-time 120 µs, T OFF- time 45 µs,Spark gap set Voltage 15 volts, Input Current 180 amps, Wire Tension 8 Kg-f and Wire runoff 6 m/min. Later, Analysisperformances.KEYWORDS: T ON time, T OFF time, Kerf Width (KW), Metal Removal Rate (MRR), Surface Roughness, AHP andTOPSISReceived: Jun 03, 2020; Accepted: Jun 23, 2020; Published: Jun 29, 2020; Paper Id.: IJMPERDJUN2020133Original Articleof variance is implemented and it shows that the T ON-time is the most important parameter that affect the output1. INTRODUCTIONWire Electrical discharge machining (EDM) is a non-conventional machining which replaces the conventionalmachining and made revolutionary changes in the modern manufacturing industries. Now a days the use of thesemodern machines are very much useful to machine the hardest materials like High speed steels, heat treated steels,alloy steels, composites etc. As their capability of machining irregular shapes and geometrically complex shapesmakes these machines very needful in industries, at the same time the precision and surface quality obtained inthese machines is added advantage for the industries. Lot of research work was going on to optimize the machiningconditions which will help the industries. [1] Sarat Kumar Sahoo and Sunita Singh Naik, investigated on thematerial removal rate, surface roughness, and kerf width of the high-carbon and high-chromium steel during wireelectrical discharge machining process. For this Taguchi’s L9 orthogonal array was used in order to analyze theeffects of pulse on time, wire feed rate, and pulse off time on response variables, they observed that the machiningperformance is considerably changed by pulse on time and very less influenced by wire feed rate. [2] P. J. Pawarand M. Y. Khalkar uses multi-objective optimization of wire-electric discharge machining by using recentlydeveloped evolutionary optimization algorithm known as multi-objective artificial bee colony (MOABC)algorithm. In this work they considered MRR and wire wear ratio as responses to find the optimum processparameters in wire-electric discharge machining process. [3] Sanjeev Kumar Garg, Alakesh Manna & Ajai Jainpresents an experimental investigation of the machining characteristics and optimization of wire EDM processwww.tjprc.orgSCOPUS Indexed Journaleditor@tjprc.org
1500I. Harish, Santosh Patro & P. Srinivasa Raoparameters during machining of newly developed Al/ZrO2(p) metal matrix composite (MMC). They used full factorialapproach of response surface methodology (RSM) to design the experiment and they found maximum MRR and minimumspark gap. Al/ZrO2(p)-MMC can be effectively machined by wire EDM. [4] K.D. Mohapatraa* and S.K. Sahooa deals withthe experimental analysis and multi objective optimization of gear cutting process of Inconel 718 using WEDM and theyused to optimize the parameters in order to maximize the material removal rate and minimize the kerf in a gear cuttingprocess to get the optimum value in the wire EDM machine using brass wire as the electrode, Inconel 718 as the workpiece material and distilled water as the dielectric and they find good results by using TOPSIS method. [5]B.Ravindranadh and v.madhu uses multi response optimization based on taguchi coupled with grey relational analysis,experiments were performed with four variables namely pulseon time, pulse offtime, peak current and servo voltage bytaking responses as material removal rate, surface roughness and gap current in which they got the results that pulseontime, peak current and servo voltage were significant variables to grey relational grade. [6] Pujari srinivasa rao andkoona ramji done parametric analysis of wire edm machine by using taguchi method on surface roughness and materialremoval rateand they used hybrid genetic algorithm to optimize the parameters and obtained good agreement withexperimental values [7] M. Durairaj and D. Sudharsun done experimental investigation on wire electrical dischargemachining of Stainless Steel (SS304) by using brass wire of 0.25mm and optimized single objective taguchi techniqueand multi objective grey relational analysis to get the min surface roughness and min kerf width, ANOVA alsoimplemented to get the most influence factor of the above responses and they find it was pulse ontime which influencesthe most. [8] G. Selvakumar and G. Sornalatha done experimental analysis for the selection of most optimal machiningparameter combination for wire electrical discharge machining (WEDM) of 5083 aluminum alloy in which they usedTaguchi experimental design (L9 orthogonal array) method, a series of experiments were performed by considering pulseon time, pulse-off time, peak current and wire tension as input parameters and surface roughness and cutting speed wereresponses. Finally, they got the optimal machining parameters for maximum cutting speed and minimum surfaceroughness by using Taguchi methodology. [9] Reza Bagherian Azhiri and Reza Teimouri dry wire electrical dischargemachining (WEDM) process, the liquid dielectric is replaced with gaseous medium, experiments were designed andconducted based on L27 Taguchi's orthogonal array to study the effect of Ton time, Toff time, gap voltage, dischargecurrent, wire tension and wire feed on cutting velocity (CV) and surface roughness (SR). In order to correlate relationshipbetween process inputs and responses, adaptive neuro-fuzzy inference system has been utilized and a grey relationalanalysis has been used to maximize CV and minimize SR simultaneously, in ANOVA they found that Ton and dischargecurrent have significant effect on cutting Velocity and Surface Roughness. [10] Shivkant Tilekar and Sankha Shuvra Dasfinds the effect of process parameters on surface roughness and kerf width of aluminum and mild steel by using singleobjective taguchi method for process parameter optimization and found that spark on time and the input current havestatistically significant effect in case of aluminum and mild steel respectively. [11] G. Ugrasen and H.V. Ravindra usesMolybdenum wire having diameter of 0.18 mm as an electrode in wire edm to find the machining performance by takingthree responses namely accuracy, surface roughness and volumetric material removal rate, Experimentation wereperformed as per Taguchi's L’16 orthogonal array and based on this analysis, process parameters are optimized. [12]K.Ananda Babu and P.Venkataramaiah, wants to optimize the process parameters in wire electrical discharge machining(WEDM) of Al6061/SiCp composite using AHP-TOPSIS method. Taguchi L18 orthogonal array was used by consideringvarious process parameters namely Wire Type, T ON Time, T OFF Time, Wire Feed and Sensitivity for conductingWEDM experiments and found that Sensitivity (S) is the prevailing factor on the response characteristics of WEDM. [13]Tribeni Roy and Ranjit Kumar Dutta studied working of an electro discharge machining process by considering 4 variablesImpact Factor (JCC): 8.8746SCOPUS Indexed JournalNAAS Rating: 3.11
Parametric Optimization of Machining Parameters by using Brass Wire Electrode on Wire Electric Discharge Machining1501like Ton time, duty cycle, discharge current, and gap voltage, each at three levels, to monitor 3 responses, like MRR,TWRand Tool overcut, integrated fuzzy AHP and fuzzy TOPSIS methods were applied in the scheme of multi-responseexperiment so that Taguchi’s OA technique was applied successfully for parametric optimization. [14] Santosh Patro, I.Harish develops a fuzzy model to get optimum values of machining parameters in wire edm machine and found thepredicted values were 90% accurate with experimental values. [15] Rajesh Kumar Bhuyan and B.C. Routara optimized theprocess parameters during electrical discharge machining (EDM) process of Al-SiC 24% metal matrix composite bytaking responses like MRR, TWR and surface roughness, In order to optimize the multiple responses problem TOPSISmethodology is used to get a single numerical index.This work mainly focus on process parameters optimization of WEDM by taking the output variables namelyKerf width, MRR, surface roughness &Tool wear ratio on D2 (High carbon high chromium) steel. Experimental trials aremade according to Taguchi’s L27 array by using input variables(Ton, Toff, spark voltage, wire runoff time, input current &wire tension) at three levels. MCDM(multi criteria decision making) technique namely Technique for Order Preference bySimilarity to Ideal Solution (TOPSIS) is used to optimize the responses. Later, Analysis of variance( ANOVA) applied toget the importance of input variables on output values.2. EXPERIMENTAL SETUP:In Present work, experiments are conducted on D2 tool steel with 10 mm thick on Wire Electric discharge machining(Electronica Sprint cut in “FIGURE. 1”) with a Brass wire having 0.25 mm dia electrode. D2 steel is used in manufacturingof Dies, shafts, gears, spindles, automotive components etc. Process parameters of Peak current (IP), TON, TOFF,ServoVoltage (SV), Wire runoff time (WF) and Wire Tension (WT) in the ranges as shown in Table 1. Taguchi’s L27 array wasused in the experimentation is shown in Table 2. The surface quality was taken by Surtronic 3 as shown in FIGURE 3.The surtronic 3 is a surface-roughness measuring instrument, which finds the surface of different machined surfaces andgives surface roughness values accurately which projects the values in μm. Cubes shown in “FIGURE 2” are tested forroughness at3 machined faces to get more accurate values we will take average and made it as final value.Figure 1: Sprint cut electric discharge Machine.www.tjprc.orgSCOPUS Indexed Journaleditor@tjprc.org
1502I. Harish, Santosh Patro & P. Srinivasa RaoFigure 2: Cubes of work pieces after machiningFigure 3: Surface roughness measuring machineTable 1: Input variables with levelsImpact Factor (JCC): 8.8746SCOPUS Indexed JournalNAAS Rating: 3.11
Parametric Optimization of Machining Parameters by using Brass Wire Electrode on Wire Electric Discharge Machining1503Table 2: L27 Orthogonal arrayTool Wear Ratio (TWR) is the ratio of change in diameter to original diameter of wire and the diameter wasmeasured by using screw gauge, Kerf width calculated by means of Profile projector. MRR can be defined as the rate ofmaterial removed per minute or the ratio of change in volume of work piece during machining divided by duration ofmachining. It was measured by the below formula in which cutting speed was taken as the ratio of total length cut to timetaken for the total cut.MRR Vc x B x HWhere, Vc Cutting speed of machining (mm/min)B Kerf width (mm)H Depth of cut (mm)3. METHODOLOGY1.1.Multi Criteria Decision Making Technique (TOPSIS):This technique(TOPSIS) was used to finds the optimum set of values which are nearest to positive ideal solution(PIS) andfarthest from negative ideal solution(NIS). Different stages involved in this technique are :www.tjprc.orgSCOPUS Indexed Journaleditor@tjprc.org
1504I. Harish, Santosh Patro & P. Srinivasa RaoStage 1: First we have to construct decision matrix by using the information available in the criteria. In which decisionmatrix gives the performance of ith alternative with respect to jth criterion.Stage 2: Then get normalized decision matrix, with equation given below:(i)Stage 3: Evaluate weights for the each criteria which is done by using Analytical Hierarchy Process (AHP) with belowStages:(ii) construct pair wise comparison matrix derive the Eigen value and Eigen vector find out the consistency index(CI) by formula (λmax –N) divided by (N-1). Later, get the consistency ratio as it is the ratio of CI to Random index and it should be less than or equal to 0.1which is a acceptable one.Stage 4: In this stage, we have to find weighted normalized matrix:(iii)Stage 5: Find out PIS and NIS values by the following equations:(iv)(v)Stage 6: Acquire the separation values namely Si and Si-.Stage 7: Find relative closeness values for each alternative and it is calculated as follows:(vi)Stage 8: Now arrange these Ci values for each alternative in descending order and prepare rankings from highestto lowest and the highest value is the one which is closer to ideal solution.Impact Factor (JCC): 8.8746SCOPUS Indexed JournalNAAS Rating: 3.11
Parametric Optimization of Machining Parameters by using Brass Wire Electrode on Wire Electric Discharge Machining15054. RESULTS AND DISCUSSIONSIn this work 27 experimental trails are conducted to find the influence of process parameters in wire electric dischargemachining on multiple responses namely kerf width, MRR, surface roughness and Tool wear ratio and they are shown inTable 7Table 3: output 0.2970.0950.10.2910.1000.12SR .763.593.02Now, these output responses are normalized by using equation (i) and it was shown in Table 4Exp.no123456789101112www.tjprc.orgTable 4: Normalized ValuesKerf WidthMetalSurfaceremoval PUS Indexed JournalTool 58031editor@tjprc.org
1506I. Harish, Santosh Patro & P. Srinivasa 0.191668Later, Analytical Hierarchy Process was implemented to find the weights for each criteria. In the below Table 5shows us the pair wise comparison matrix. By using equation (ii)calculated the weights as WMRR 0.509, WKF 0.173, WTWR 0.224 WSR 0.0912Table 5: Comparison of Output responses with their importanceMetal RemovalRateKerf WidthToolSurface(MRR)(K W)WearRoughness (SR)Ratio max 4.0455, Consistency Ratio 0.015Now, Take the above weights for each response and calculated the weighted normalized values by using theequation (iii) which are tabulated in below Table 6.S.No123Impact Factor (JCC): 8.8746Table 6: Values of Weighted Normalized equationKerf WidthMetalTool .0340740.0580260.0051710.010013SCOPUS Indexed JournalNAAS Rating: 3.11
Parametric Optimization of Machining Parameters by using Brass Wire Electrode on Wire Electric Discharge 07790.01748After getting the weighted normalized values by using equation (iv) & (v) get the PIS and NIS values for responsewhich is shown in Table 7.S.noPositive idealsolutionNegativeIdeal SolutionTable 7: Values of Positive and Negative ideal solutionsKerf Width Metal RemovalTool 710.0092610.0414550.0438270.0775650.028999Later stage find the Si and Si-(separation measures) values of output responses from PIS and NIS which aretabulated in Table 8.S.No12345678910111213www.tjprc.orgTable 8: Si and Si- Values.Si valueSi- 110.084159SCOPUS Indexed Journaleditor@tjprc.org
1508I. Harish, Santosh Patro & P. Srinivasa Finally, we will get Ci ( relative closeness)values by using Si and Si-values according to the equation (vi) and itwas shown in the below Table 9. From the Ci values we will get the optimal parameters as the highest value will gives thebest alternative and it is nearer to the Positive ideal solution.S.No.Impact Factor (JCC): 8.8746Table 9: Ci & S/N values of Ci Relative closeness valueSignal to noise ratio of Ci 758160.3896-8.18762779SCOPUS Indexed JournalNAAS Rating: 3.11
Parametric Optimization of Machining Parameters by using Brass Wire Electrode on Wire Electric Discharge 91044-8.15549275270.396248-8.040647421509Means of each input variable to the given levels are computed and shown in table 10. Figure 4 indicates the maineffects plot to the mean values. By the response table and the main effects plot we got the optimal combinations of inputvariables as TON-time of 120 µs, Servo Voltage of 15 volts, TOFF- time of 45 µs, Input Current of 180 amps, Wire runoffof 6 m per min and Wire Tension of 8 Kgf respectively.Later, Analysis of variance was applied to know the most influence factor that effects the responses which is astatistical tool to detect any differences occurred in the performance of multiple responses. The ANOVA result is shown intable 11, the result shows that TON is having more influence on output responses.Table 10: Response Table for Ci mean valuesLevel Pulse ON Pulse 03970.03970.03973555SCOPUS Indexed JournalWire Tension Wire Runoff0.47080.4639editor@tjprc.org
1510I. Harish, Santosh Patro & P. Srinivasa RaoFigure 4: Main Effect plots for S /N ratiosParametersT ONT OFFSVInput currentWire TensionWire RunoffErrorTotalImpact Factor (JCC): 8.8746DOF2222221426Table 11: ANOVA .0081420.1073240.502090SCOPUS Indexed .0040710.007666Percentage %21.9247.684.161.621.621.6221.38100NAAS Rating: 3.11
Parametric Optimization of Machining Parameters by using Brass Wire Electrode on Wire Electric Discharge Machining1511Figure 5: Main residual plots for Ci CONCLUSIONSAccording to the experimental results, the below conclusions were drawn: Optimal arrangement of input variables as TON time of 120 µs, Servo Voltage of 15 volts, TOFF- time of 45 µs,Input Current of 180 amps, Wire runoff of 6 m per min and Wire Tension of 8 Kgf. By the ANOVA results we can conclude that TOFF time is the important factor having more influence onresponse variables. And finally we can conclude that TOPSIS is very useful to solve any MCDM problems and the results are veryeffective.6. REFERENCES1.Sarat Kumar Sahoo, Sunita Singh Naik "Experimental Analysis of Wire EDM Process Parameters for Micromachining ofHigh Carbon High Chromium Steel by Using MOORA Technique"Micro and Nano Machining with Non ConventionalMachining Techniques, Pages 137-1482.P. J. Pawar & M. Y. Khalkar," Multi-objective Optimization of Wire-Electric Discharge Machining Process Using Multiobjective Artificial Bee Colony Algorithm" Advanced Engineering Optimization Through Intelligent Techniques, Pages 39463.Sanjeev Kumar Garg, Alakesh Manna & Ajai Jain "Experimental investigation of spark gap and material removal rate ofAl/ZrO2(P)-MMC machined with wire EDM" Journal of the Brazilian Society of Mechanical Sciences and Engineering,38, pages481–491(2016)4.K.D. Mohapatraa* and S.K. Sahooa “A multi objective optimization of gear cutting in WEDM of Inconel 718 using TOPSISmethod” Decision Science Letters, Volume 7 Issue 2 pp. 157-170, 2018www.tjprc.orgSCOPUS Indexed Journaleditor@tjprc.org
15125.I. Harish, Santosh Patro & P. Srinivasa RaoB.Ravindranadh and v.madhu “Multi response optimization of wire-EDM process parameters of ballistic grade aluminiumalloy” Engineering Science and Technology, an International Journal, Volume 18, Issue 4, December 2015, Pages 720-7266.Pujari srinivasa rao and koona ramji “ Experimental investigation and optimization of wire edm parameters for surfaceroughness, MRR and white layer in machining of aluminium alloy, Procedia Materials Science 5 ( 2014 ) 2197 – 22067.M. Durairaj and D. Sudharsun, “Analysis of Process Parameters in Wire EDM with Stainless Steel using Single ObjectiveTaguchi Method and Multi Objective Grey Relational Grade”, Procedia Materials Science 64 ( 2013 ) 868 – 8778.G. Selvakumar and G. Sornalatha “ Experimental investigation and multi-objective optimization of wire electrical dischargemachining (WEDM) of 5083 aluminum alloy” Transactions of Nonferrous Metals Society of China, Volume 24, Issue2, February 2014, Pages 373-3799.Reza Bagherian Azhiri and Reza Teimouri “Application of Taguchi, ANFIS and grey relational analysis for studying,modeling and optimization of wire EDM process while using gaseous media”, The International Journal of AdvancedManufacturing Technology,volume 71, pages279–295(2014)10. Shivkant Tilekar and Sankha Shuvra Das, “Process Parameter Optimization of Wire EDM on Aluminum and Mild Steel byUsing Taguchi Method”, Procedia Materials Science,Volume 5, 2014, Pages 2577-2584.11. G. Ugrasen and H.V. Ravindra “Process Optimization and Estimation of Machining Performances Using Artificial NeuralNetwork in Wire EDM”, Procedia Materials Science, Volume 6, 2014, Pages 1752-1760.12. K.Ananda babu and P.Venkataramaiah,“Multi-response Optimization in Wire Electrical Discharge Machining (WEDM) ofAl6061/SiCp Composite Using Hybrid Approach”, Journal for Manufacturing Science and Production,Volume 15: Issue 413. Tribeni Roy and Ranjit Kumar Dutta, “Integrated fuzzy AHP and fuzzy TOPSIS methods for multi-objective optimization ofelectro discharge machining process” Soft Computing, volume 23, pages5053–5063(2019)14. Santosh Patro, I. Harish, P. Srinivasa Rao “A Fuzzy Modelling for Selection of Machining Parameters in Wire ElectricalDischarge Machining of D2 Steel”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878,Volume-8 Issue-5, January 202015. Rajesh Kumar Bhuyan and B.C. Routara “ An approach for optimization the process parameter by using TOPSIS Method ofAl–24%SiC metal matrix composite during EDM”, Volume 2, Issues 4–5, 2015, Pages 3116-3124.16. Hanaa Elgohari, Mohammed Abdulmajeed & Ahmed Elrefaey, “Application Sarima Models on Time Series to Forecast theNumber of death in hospital”, International Journal of Applied Mathematics Statistical Sciences (IJAMSS), Vol. 7, Issue 4, pp.9-1817. M. M. A. El-Sheikh, R. Sallam & Nahed A. Mohamady, “New Criteria for Oscillation of Second Order Nonlinear DynamicEquations with Damping on Time Scales”, IMPACT: International Journal of Research in Applied, Natural and SocialSciences (IMPACT: IJRANSS), Vol. 3, Issue 3, pp. 79-8618. Durgesh Agnihotri & Pallavi Chaturvedi, “A Study on Customer Preference and Attitude Towards Major E-floors withSpecial Reference to Kanpur”, BEST: International Journal of Management, Information Technology and Engineering (BEST:IJMITE), Vol. 3, Issue 12, pp. 21-2819. Abhinav Jain & Monika Mittal, “haar wavelet based computationally efficient optimization of linear time varying systems”,international Journal of Electrical and Electronics Engineering (IJEEE), Vol . 3, Issue 3, pp. 11-20Impact Factor (JCC): 8.8746SCOPUS Indexed JournalNAAS Rating: 3.11
Selvakumar and G. Sornalatha done experimental analysis for the selection of most optimal machining parameter combination for wire electrical discharge machining (WEDM) of 5083 aluminum alloy in which they used . Harish develops a fuzzy model to get optimum values of machining parameters in wire edm machine and found the .
Surface is partitioned into parametric patches: Watt Figure 6.25 Same ideas as parametric splines! Parametric Patches Each patch is defined by blending control points Same ideas as parametric curves! FvDFH Figure 11.44 Parametric Patches Point Q(u,v) on the patch is the tensor product of parametric curves defined by the control points
parametric models of the system in terms of their input- output transformational properties. Furthermore, the non-parametric model may suggest specific modifications in the structure of the respective parametric model. This combined utility of parametric and non-parametric modeling methods is presented in the companion paper (part II).
equation 2, the S/N values of machining performance of the obtained MRR values are computed. In order to obtain the effects of machining parameters for each level, the S/N values of each fixed parameter and level in each machining performance were summed up. 1.2 Process Parameters Selection In this analysis, WEDM parameters such as T on
There are different types of machining process used for sapphire material. The fig. 1 shows a graphical representation of sapphire machining processes i.e. laser machining process, grinding process, polishing process, lapping process, new developed machining process, compound machining process and electro discharge machining process. Fig.1.
Machining metals follows a predictable pattern with minimal creep. When machining plastics, quick adjustments must be made to accommodate substantial creep — not to mention that the material has a strong propensity for chipping and melting during machining. Simply stated, the basic principles of machining metals do not apply when machining
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Machining metals follows a predictable pattern with minimal creep. When machining plastics, quick adjustments must be made to accommodate substantial creep — not to mention that the material has a strong propensity for chipping and melting during machining. Simply stated, the basic principles of machining metals do not apply when machining
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