Analysis And Optimization Of Machining Parameters Of EN-47 .
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 6 Issue 04, April-2017Analysis and Optimization of MachiningParameters of EN-47 in Turning by TaguchiTechnique and Minitab-17 SoftwareJ.ChandrashekerM. Vijendhar ReddyAssociate professorDepartment of Mechanical EngineeringVaageswari College of EngineeringKarimnagar (TS), IndiaB. Tech StudentDepartment of Mechanical EngineeringVaageswari College of EngineeringKarimnagar (TS), IndiaB. VidyasagarS. SathishB. Tech StudentDepartment of Mechanical EngineeringVaageswari College of EngineeringKarimnagar (TS), IndiaB. Tech StudentDepartment of Mechanical EngineeringVaageswari College of EngineeringKarimnagar (TS), IndiaAbstract—This experimental study presents an effectiveapproach for the optimization of turning parameter usingMINITAB 17 and Taguchi Technique in varying condition. Theinformation about machining of difficult cutting materials isinadequate and complicated. Therefore an experimental studyhas to be conducted to come out with an optimum outcome. Inthis study, the machining parameters namely Depth of Cut,Cutting Speed, Feed Rate and cutting fluids are optimized withmultiple performance characteristics, such as maximummaterial removal rate and maximum surface finish. Theresponse table and response graph for each level of machiningparameters are obtained from the Taguchi Method and theoptimum levels of machining parameters are being selected.II MATERIALS AND METHODA. Work Piece MaterialThe work piece material used in this project was EN 42Stainless Steel of length of 250mm and diameter 40mm. Thework piece material is shown belowKeywords: - ANOVA, surface roughness, cutting tool, feed rateI. INTRODUCTIONTurning is a form of machining, a material removalprocess, which is used to create rotational parts by cuttingaway unwanted material as shown in Figure 1.The turningprocess requires a turning machine or lathe, work piece,fixture, and cutting tool. The work piece is a piece of preshaped material that is secured to the fixture, which itself isattached to the turning machine, and allowed to rotate at highspeeds. The cutter is typically a single-point cutting tool thatis also secured in the machine.Fig.2 EN-47 work piece materialTABLE I. CHEMICAL COMPOSITION OF EN-47 STEEL 20%0.06%0.06%TABLE II. PHYSICAL PROPERTIES OF EN-47 STEEL MATERIALSFig.1 Diagram for Turning ProcessIJERTV6IS040642Densitygm/cm3MeltingPoint( ̊C )Thermalconductivity(W/m K)Coefficient ofthermalexpansion(μm/m ̊C)77001450-15102510 x 10-6www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)975
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 6 Issue 04, April-2017TABLE III. MECHANICAL PROPERTIES OF EN-47 ldStrength(MPa)650-880%ofElongation350-5508-25%B. Carbide Coated Tip Cutting ToolCoatings are frequently applied to carbide tool tips toimprove tool life or to enable higher cutting speeds. Coatedtips typically have lives 10 times greater than uncoated tips.Common coating materials include titanium nitride, titaniumcarbide and aluminium oxide, usually 2-18 micro-m thick.Often several different layers may be applied, one on the topof another, depending upon the intended application of the tip.The techniques used for applying coatings include chemicalvapour deposition, plasma assisted CVD and physical vap ourdeposition.Fig.3 Carbide coated tip cutting toolC. Selections of Control FactorsCutting experiments are conducted considering fourcutting parameters: Cutting Speed (m/min), feed rate(mm/rev), Depth of Cut (mm) and cutting fluids. Overall 9experiments were carried out. Table shows the values ofvarious parameters used for experiments:Fig.4 Banka 40 Lathe MachineA. Taguchi ApproachProcess Steps of Taguchi Method Define the process objective Identify test conditions Identify the control factors and their alternativelevels Create orthogonal arrays for the parameter design Conduct the experiments indicated in the completedarray to collect data on the effect on the performancemeasure. Complete data analysis to determine the effect of thedifferent parameters on the performance measure. Predict the performance at these levels Confirmation experiments.B. Selection of Orthogonal ArrayThe selection of orthogonal array for experiment was doneby use Minitab-17 statistical software. By putting parametervariation levels in Minitab-17 statistical software, the Minitabsuggests that L9 (3*3) fractional factorial orthogonal array ismost compatible for our experiment. This design reduces thenumber of experiments from 24 (i.e. factorial 4*3*2*1) to adesigned set of 9 experiments without compromise quality ofexperiment. The experiment table suggested by Minitab- 17for L9 Orthogonal array is shown in Table.TABLE V. EXPERIMENT DESIGN BY USE OF L9 ORTHOGONALARRAYTABLE IV. MACHINING PARAMETERS AND EED (mm/rev)DEPTH OF CUT(mm)CUTTING FLUIDS12345568310251100.3Sherol B1500.9Sherol ENF1751.2Straightcutting oilIII EXPERIMENTAL PROCEDURETurning is popularly used machining process. In thisproject work turning is done on the lathe machine which isshown in the 323193313In L9 (34) orthogonal array, five columns bearing thenumbers ‘1’, ‘2’, ‘3’, ‘4’, represents factors. And each set ofnumbers below these columns represent levels of that factorsrespectively. As the index in the first column depicts, eachrow represents an experiment.www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)976
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 6 Issue 04, April-2017TABLE VI. FACTOR ASSIGNMENT (EXPERIMENTAL gspeed(rpm)(levels)455455455110150175Depth 10251501751101500.90.30.90.3910251750.6Cutting fluids(levels)Sherol BSherol ENFStraight cuttingoilStraight cuttingoilSherol BSherol ENFSherol ENFStraight cuttingoilSherol BC. Measurement of Surface RoughnessIn this project stylus type surface roughness meterwas used to measure the surface roughness of the specimens.There were two main reasons behind selecting stylus typesurface roughness one is its easy availability and other is theease with which it can be operated. The surface roughnessmeasuring instrument used in this experiment is Talysurf.E. Optimization for Surface Roughness1) S/N ratio calculation of surface roughness: In this theobserve value of surface roughness is transform in S/N ratiovalues to find out the optimum combination of parameters forresponse variable. In surface roughness “smaller is better” isobjective characteristic, since the minimization of the qualitycharacteristic is interested.The S/N ratios are calculated using the below mentionedformula (smaller the better type formula).S/N ratio (η) -10Log10 []Wheren: no. of tests in trial (no. of repetitions regardless of noiselevels)yi : is the ith observation of the quality characteristic.For example the S/N ratio is calculated for first experiment isas follows:η1 -10 log10 [(1/1) (1.438)2]η1 -3.1551Similarly all the S/N ratios are calculated. These values ofS/N ratio and averages will then further be analyzed to detectthe most responsible factor and the percentage contribution ofeach factor on the surface roughness (response variable).TABLE VIII: S/N RATIO CALCULATION OF SRExpt NoFig.5 Stylus Movement on Work piece MaterialD. Procedure Followed To Measure Surface Roughness (Ra)For each and every experiment the surface roughness ofthe machined work material is found out. One point on thework material is considered for each sample and eachmeasurement is about 90 degrees apart. The stylus moves toand fro on the work material at this point. The Ra values aredisplayed on the digital meter and the three values of Ra areconsidered for that particular experiment. Similarly 9 (Ra)values are considered for 9 experiments. The S/N ratios ofsurface roughness are calculated.TABLE VII. RESULT TABLE FOR SURFACE ROUGHNESS (RA)VALUES IN ΜMCuttingFeedDepthCuttingSurfaceSpeed(mm/rev)of cutfluidsRoughness(rpm)(mm)(μm)4551100.3Sherol 4cutting oil6831100.6Straight3.650cutting oil6831500.9Sherol ENF10251500.3Straight3.593cutting oil10251750.6Sherol age of S/N ratios of SR (dB) (η)S/N ratio of 5.8893-11.1091-7.1396-6.92162) MINITAB statistical software: MINITAB statisticalsoftware has been used for the analysis of the experimentalwork. The Minitab software studies the experimental data andthen provides the predicted equations of surface roughness fora work piece material. After analysis of data, for the surfaceroughness based on the factors cutting speed, feed rate, depthof cut, cutting fluid for a work piece material i.e., EN-47stainless steel is given below.After analysis the entire procedure and final results are givendown in the form of screen shots from the mintab17 software:www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)977
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 6 Issue 04, April-2017Step1: Open minitab17 software and a window are displayedon the desktop as shown below. Analyzing the taguchi design Now various steps involved in getting the finalresults are as shown in the figures below. Create Taguchi DesignStep 2: Firstly in order to get results from Minitab software bytaguchi method we need to define the parameters consideredand obtained experimental values. And by going on clickingas the procedure shown below completes the definingprocedure in Minitab software.Click on STAT DOE TAGUCHI DEFINECUSTOM TAGUCHI DESIGN. The widow as shown in the figure below.Defining the custom Taguchi designIJERTV6IS040642www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)978
Published by :http://www.ijert.org International Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 6 Issue 04, April-2017Selection of Available DesignA1 – Cutting speed (455rpm)B3 – Feed (175 mm/rev)C3 – Depth of cut (0.9 mm)D1 –cutting fluid (Sherol B)F. Selection of Optimum Set of ConditionsThe objective is to maximize the S/N ratio, hence selectthe factor levels which have maximum S/N ratio values. Thebest condition for cutting speed factor is level 1 i.e. 455 rpm,for feed is level 3 i.e.175mm/rev, for depth of cut is level 3i.e. 0.9mm, for cutting fluids is level 1 i.e. Sherol B. Thusoptimum conditions chosen were: A1- B3- C3- D1combination.The optimized cutting parameters are shown in tableFinally click on ok in the window which shows theanalyses of Taguchi design. S/N ratio is generated as shownbelow in the form of screen shot:3) Main effects plot of surface roughness: The maineffects plot for S/N ratio of surface roughness verses cuttingspeed, feed rate, depth of cut and cutting fluid, which isgenerate form the value of S/N ratio of surface roughness asper Table in Minitabe-17 statistical software is useful to findout optimum parameter value for response variable. Thegraph generate by use of Minitab-17 statistical software forsurface roughness is shown in graph.TABLE VIII. OPTIMIZED CUTTING PARAMETERSControlFactorCuttingSpeed ( A)(rpm)Feed (B)(mm/rev)Depth ofcut (C)(mm)CuttingfluidsOptimumvalue455 rpm1750.9Sherol BThe analysed value of mean of surface roughness by use ofMinitab 17 statistical software is shown in Table.TABLE IX: RESPONSE TABLE OF S/N RATIO FOR SURFACEROUGHNESSLevelCuttingFeedDepth of -8.7093Delta1.2760.3860.6120.934Rank1432From Table, it is show that the value of delta for eachparameter A, B, C and D are 1.276, 0.386, 0.612 and 0.934for surface roughness. From delta value of each parameter itis conclude that for surface roughness the most effectiveparameter is cutting speed followed by cutting fluid, depth ofcut and feed.Fig.6 Mean Effect Plot Of Surface Roughness Vs Cutting Speed, Feed, DepthOf Cut And Cutting FluidFrom the Figure it is conclude that the optimumcombination of each process parameter for lower surfaceroughness is meeting at cutting speed (A1), feed rate (B3),depth of cut (C3), and cutting fluid (D1).The S/N of the surface roughness for each level of theeach machining parameters can be computed in Minitab 17and it is summarized for finding out rank of each effectiveparameter for response.The combination of factors and levels which give maximumS/N ratios give the optimum cutting parameters. That meansA1, B3, C3 and D1combination gives the optimum cuttingparameter which minimizes the surface roughness i.e. theselevels are applicable to the starting levels of the factors. Theseare:IJERTV6IS040642G. Prediction of Process Average for Optimum ConditionHaving determined the optimum condition from theorthogonal array experiment, the next step is to predict theanticipated process average η predicted under chosen optimumcondition. This is calculated by summing the effects of factorlevels in the optimum condition (the values of maximum S/Nratios in table). S/N ratios of optimum condition were used topredict the S/N ratio of the optimum condition using theadditive model.η predicted [A1 B3 C3 D1] - 3η [(-3.4822) (-6.3883) (-5.5980) (5.8089)] – (3 x – 6.9296) -0.9864 dB.Where η average of all S/N ratioswww.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)979
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 6 Issue 04, April-2017IV CONFIRMATION TESTConducting a verification experiment is a crucial final stepof the robust design methodology. The predicted results mustbe conformed to the verification test, with the optimum set ofconditions. In this final step, the optimum cutting conditionsof cutting speed 455 rpm, cutting feed 175 mm/rev, depth ofcut 0.9 mm, cutting fluid are obtained for EN 47 work piecematerial.A conformation test is performed with the obtainedoptimum cutting parameters (cutting speed 455rpm, feed 175mm/rev, depth of cut 0.9 mm, and cutting fluid is Sherol B)By using these optimum conditions an experiment isconducted on a newly ground tool. The surface roughnessvalues at a sample specimen were taken using Talysurf(surface roughness measuring instrument) and the S/N ratio iscalculated by using the smaller-the-better type characteristicformula for this condition. These values are shown in Table.Hence this conformation test performed verifies the obtainedresults i.e. the optimized cutting parameters which minimizethe surface roughness.Fig.7 the main effects plot for means vs process parameters 0.9S/N ratio175SR(μm)Depth uttingSpeed(rpm)ControlFactorTABLE X: CONFORMATION TEST RESULTSSherol B0.8076-1.856 The S/N ratio of predicted value and verification testvalues were compared for validity of the optimum condition.These values are shown in figure. It is found that the S/N ratiovalue of verification test is within the limits of the predictedvalue and the objective is fulfilled. As the conformation andprojected improvements matched, suggested optimumconditions can be adopted. S/N ratio is calculated by using theformula given below.TABLE XI. COMPARISON OF S/N RATIOSηpredicted (dB)0.9864ηconfirmation test (dB)-1.856VI REFEANCES[1][2][3][4]The main effects plot for means vs process parameters areshown in below graph:[5][6][7][8][9]IJERTV6IS040642V CONCLUSIONSTaguchi design of experiment can be very efficientlyused in the optimization of machining parameters inmetal cutting process.The optimum set of process parameters found are Cuttingspeed: 1025rpm, Cutting feed: 175 mm/rev, Depth of cut:0.9mm, cutting oil (Sherol B) for EN-47 steel material.With this optimum set of control factors the surface finishon the work piece materials improved. This combinationwas successfully tested for its validity.The significant factors concluded that the effect ofCutting speed and cutting fluid are more on the qualitycharacteristic.In this work, the analysis of conformation experimentshas shown that Taguchi parameter design cansuccessfully verify the optimum cutting parameters.Alagarsamy.S. V, Raveendran. P, Arockia Vincent Sagayaraj.S,Tamilvendan. S, Optimization of Machining Parameters For Turning ofAluminium Alloy 7075 Using Taguchi Method, International ResearchJournal of Engineering and Technology, 2016.Saurabh Singhvi, M. S. Khidiya, S. Jindal, M. A. Saloda, Optimizationof Rake Angle and Turning Process Parameters for Cutting Force: AReview, Imperial Journal of Interdisciplinary Research, 2016.Arshad Qureshi, Prof Madhukar Sorte, Prof S.N. Teli A LiteratureReview on Optimization of Cutting Parameters for Surface Roughnessin Turning Process, International Journal of Engineering Research andDevelopment, 2015.Aswathy V G, Rajeev N, Vijayan K,Effect of Machining Parameters onSurface Roughness, Material Removal Rate and Roundness ErrorDuring The Wet Turning of Ti-6Al-4V Alloy, Int. Journal of AppliedSciences and Engineering Research, 2015.Narendra Kumar Verma, Ajeet Singh Sikarwar Optimizing TurningProcess by Taguchi Method under Various Machining Parameters,International Research Journal of Engineering and Technology, 2015.S. Magibalan, M. Prabu, P. Vignesh, P.Senthil Kuma,R. ExperimentalStudy on The Cutting Surface Roughness in CNC Turning Operationsby Using Taguchi Technique, International Journal of Latest Trends inEngineering and Technology, 2015.Sujit Kumar Jha andPramod K Shahabadkar, ExperimentalInvestigation of CNC Turning of Aluminum Using Taguchi Method,International Journal of Engineering, Science and Technology, 2015.S. Mohanty, A. Das, A. Khan, Optimization Of Machining ParametersUsing Taguchi Approach During Hard Turning of Alloy Steel withUncoated Carbide Under Dry Cutting Environment, InternationalJournal of Lean Thinking, 2015.Sonowal, Thuleswar Nath, Dhrupad Sarma, a Review on Optimizationof Cutting Parameters on Turning, International Journal of EngineeringTrends and Technology, 2015.www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)980
Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 6 Issue 04, April-2017[10] X.M.Antho, Analysis of Cutting Force and Chip Morphology DuringHard Turning of AISI D2 Steeln, Iternational Journal Of Mechanicaland Industrial Engineering, 2015.[11] Sourabh Waychal, Anand V. Kulkarni, Investigation of The Effect ofMachining Parameters on Surface Roughness and Power ConsumptionDuring The Machining of AISI 304 Stainless Steel by Doe Approach,International Research Journal of Engineering and Technology, 2015.[12] Taquiuddin Quazi, Pratik Gajanan, Optimization of Turning ParametersSuch as Speed Rate, Feed Rate, Depth of Cut For Surface Roughness byTaguchi Method, Asian Journal of Engineering and TechnologyInnovation,2014.[13] Rony Mohan, Josephkunju Paul C, George Mathew, Optimization ofSurface Roughness of Bearing Steel During CNC Hard TurningProcess, International Journal of Engineering Trends and Technology,2014.[14] M. Venkata Ramana, G. Krishna Mohan Rao, D. Hanumantha Rao,Optimization and Effect of Process Parameters on Tool Wear inTurning of Titanium Alloy Under Different Machining Conditions,International Journal of Materials, Mechanics and Manufacturing,2014.[15] Sachin C Borse, Optimization of Turning Process Parameter in DryTurning of Sae52100 Steel, International Journal of EngineeringScience & Advanced Technology, 2014.IJERTV6IS040642www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)981
by use Minitab-17 statistical software. By putting parameter variation levels in Minitab-17 statistical software, the Minitab suggests that L9 (3*3) fractional factorial orthogonal array is most compatible for our experiment. This design reduces the nu
where the use of 5-axis simultaneous machining brings unequalled surface quality. Moreover, it is targeted at prototype machining, 5-axis trimming and special machining where full 5-axis machining is the requirement for quick and accurate manufacturing. Multi-Axis Surface Machining is also an add-on product to Prismatic Machining and Lathe .
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
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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The milling machining is limited to the XY plane and can thus follow 2-dimensional contours. The machining itself is limited to 2-dimensional contours. The third dimension is achieved by tilting and securing the machining plane. To machine the free -form surfaces, the 5 axes move dynamically and simultaneously. 2D machining . 2½D machining
Therefore, the automatic programming system of NC lathe is studied. In particular, the optimization and Simulation of machining parameters have important practical significance. Analysis and Parameter Optimization of Machining Process for Typical Parts of 2 CNC Lathe . Analysis of Machining Process for Typical Parts of CNC Lathe.
process parameters, analysis of machining, response characteristics, Applications of the Abrasive Jet Machining, Water Jet Machining, Abrasive water Jet Machining, Ultra sonic machining. . Ghosh, Amitabh., Manufacturing Processes. New Delhi: Tata McGraw Hill
