OPTIMIZATION FOR SURFACE ROUGHNESS, MRR, POWER CONSUMPTION .
Int. J. Mech. Eng. & Rob. Res. 2014M Adinarayana et al., 2014ISSN 2278 – 0149 www.ijmerr.comVol. 3, No. 1, January 2014 2014 IJMERR. All Rights ReservedResearch PaperOPTIMIZATION FOR SURFACE ROUGHNESS,MRR, POWER CONSUMPTION IN TURNING OFEN24 ALLOY STEEL USING GENETIC ALGORITHMM Adinarayana1*, G Prasanthi2 and G Krishnaiah3*Corresponding Author: M Adinarayana, adi mtech@yahoo.co.inDetermination of optimal cutting parameters is one of the most important elements in any processplanning of metal parts. The evolutionary algorithm Genetic Algorithm (GA) is used to improvemany solutions of optimization complex problems in many applications. The present paperreviewed the ideal selection of cutting parameters in turning operation of En24 work materialusing PVD coated tool using GA and its variants. This study deals with GA algorithm in differentmachining aspects in turning operation like surface roughness, material removal rate, and powerconsumption.Keywords: GA, Optimization, TurningINTRODUCTIONare required: knowledge of machining;empirical equations relating the tool life, forces,power, surface finish, etc., to develop realisticconstrains; specification of machine toolcapabilities; development of an effectiveoptimization criterion; and knowledge ofmathematical and numerical optimizationtechniques (Sonmez et al., 1999). In anyoptimization procedure, it is a crucial aspectto identify the output of chief importance, theso-called optimization objective oroptimization criterion. Some multi-objectiveThe selection of optimal cutting parameters,like the number of passes, depth of cut for eachpass, feed and speed, is a very importantissue for every metal cutting process. Inworkshop practice, cutting parameters areselected from machining databases orspecialized handbooks, but the range givenin this sources are actually starting values, andare not the optimal values (Dereli et al., 2001).Optimization of cutting parameters is usuallya difficult work, where the following aspects1Mechanical Engg., Sir Vishveswaraiah Institute of Technology & Science, Angallu, Madanapalli, India.2Mechanical Engg., JNTUA College of Engg., JNTUA, Anantapuram, Andhra Pradesh, India.3Mechanical Engg., SVU College of Engineering, S.V. University, Tirupati, Andhra Pradesh, India.20
Int. J. Mech. Eng. & Rob. Res. 2014M Adinarayana et al., 2014Process Parametersapproaches have been reported in cuttingparameters optimization (Lee and Tarng,2000; Zuperl and Cus, 2003; and Cus andBalic, 2003), but mainly they use a priortechniques, where the decision makercombines the different objectives into a scalarcost function. This actually makes the multiobjective problem, single-objective prior tooptimization (Van Veldhuizen and Lamont,2000). Comparing citations by technique, inthe last years, evidences the popularity of aposteriori techniques (Van Veldhuizen andLamont, 2000). In dealing with multiobjectiveoptimization problems, classical optimizationmethods (weighted sum methods, goalprogramming, min-max methods, etc.) are notefficient, because they cannot find multiplesolutions in a single run, thereby requiring themto be applied as many times as the number ofdesired Pareto-optimal solutions. On thecontrary, studies on evolutionary algorithmshave shown that these methods can beefficiently used to eliminate most of the abovementioned difficulties of classical methods(Soodamani and Liu, 2000). In this paper, amulti-objective optimization method, based ona posteriori techniques and using geneticalgorithms, is proposed to obtain the optimalparameters in turning processes.Genetic AlgorithmGA is an evolutionary algorithm techniquewhich borrows the idea of survival of the fittestamongst an interbreeding population to createa search strategy. It uses only the fitness valueand no other knowledge is required for itsoperation. It is a robust search techniquedifferent to the problem solving methods usedby more traditional algorithms which tend tobe more deterministic in nature and get stuckup at local optima. The three basic operatorsof GA are reproduction, crossover andmutation. Initially a finite population of feasiblesolutions to a specified problem is maintained.Through reproduction, it then iteratively createsnew populations from the old by ranking thesolutions according to their fitness values.Crossover leads to interbreeding the fittestsolutions to create new offsprings which areoptimistically closer to the optimum solutionto the problem at hand. As each generation ofsolutions is produced, the weaker ones fadeaway without producing off springs, while thestronger mate, combining the attributes of bothparents, to produce new and perhaps uniqueoff springs to continue the cycle. Occasionally,mutation is introduced into one of the solutionstrings to further diversify the population insearch for a better solution.MATERIALS AND METHODSSpecification of Work MaterialThe present work optimizes the desiredresponse and control parameters by writingthe mathematical models in Equations (1), (2)and (3) combined as single multi objectiveThe work material used for the present studyis En 24 alloy steel. The chemical compositionof the work material is shown in Table 1.Table 1: Chemical Composition of EN 24 Alloy 21
Int. J. Mech. Eng. & Rob. Res. 2014M Adinarayana et al., 2014function as .M-file and then solved by GA TOOLBOX using the MATLAB software. The initialpopulation size considered while running theGA is 20. A test of 10 runs with 50 generationseach was conducted. During the search, theresponse improved linearly with the numberof initial population size. The best responsewas measured with population size 20 afterwhich no improvement in the response valuewere recorded upon further increase ofpopulation size.Min Ra(s, f, d)MinimizingRa 3.158S 0.135f 0.110d 0.105 (1)MRR need to be maximum for increasingthe production rate(Higher is the better)The material removal rate, MRRMax MRR(s, f, d)MaximizingEXPERIMENTATIONMRR 0.003 S 1.23f 0.675 d 0.181The experiment is conducted for dry turningoperation of using EN24 Alloy steel as workmaterial and PVD as tool material on aconventional lathe PSG A141. The tests werecarried for a 500 mm length work material. Theprocess parameters used as spindle speed(rpm), feed (mm/rev), depth of cut (mm). Theresponse variables are surface roughness,material removal rate and power consumption.surface roughness of machined surface hasbeen measured by a stylus (surflest SJ201-P)instrument and power consumption ismeasured by using Watt meter, Materialremoval rate is calculated.(2)Power consumption need to be minimumfor reducing the cost of finished product,(Lower is the better)The Power consumption, PCMin PC(s, f, d)MinimizingPC 0.053 S1.01f 0.472d 0.156.(3)ConstraintsSmin S Smax,450 S 740 (4)fmin f fmax,Decision Variables0.05 f 0.09In the constructed optimization problem, threedecision variables are considered: cuttingspeed (v), feed (f), and cutting depth (d). Thesereally are the cutting parameters of theprocess. (5)dmin d dmax,0.05 d 0.15 (6)RESULTS AND DISCUSSIONObjective FunctionsIn order to satisfy the present day need ofmanufacturing industries carbide inserts withthe prescribed specifications were identified.The effect of surface roughness, Materialremoval rate and Power Consumption withPVD tool on EN 24 is considered. TheSurface roughness need to the minimum forgood quality product(Lower is the better)The surface roughness, Ra22
Int. J. Mech. Eng. & Rob. Res. 2014M Adinarayana et al., 2014Table 2: Process Parameters and Their LevelsLevelSpeed (s) (rpm)Feed Rate (f) (mm/rev)Depth of Cut (d) (mm)1.7400.090.152.5800.070.103.4500.050.05Table 3: Experimental Data and Results for 3 Parameters, CorrespondingRa, MRR and PC for PVD ToolSurfaceRoughnessRa (µm)MaterialRemoval Rate(mm3/min)PowerConsumptionin .0839163.709838S. No.Speed (Rpm)1.7400.092.7403.Feed (mm)Depth of Cut,(mm)23
Int. J. Mech. Eng. & Rob. Res. 2014M Adinarayana et al., 2014experiments were conducted by Taguchiorthogonal array L27. These experimentalresults are modeled as multi linearlogarithmic Equations (1), (2) and (3) forsurface roughness, Material removal rate andPower Consumption for PVD tool. By usingthese logarithmic equations, the cuttingconstraints formulated in Equations (4), (5)and (6) and with GA parameters, the geneticalgorithm solver get the inputs like fitnessfunction (objective function), variablesconstraints, population size, crossover rate,mutation probability and the plot function. W1,W2 and W3 are the weights assigned to thethree objective functions and weights areassigned to the objective functions randomlysuch that the summation of weights should beequal to one (1). Run the Genetic solver inthe MATLAB optimization toolbox software.After running several iterations the optimumcutting conditions for the minimum surfaceroughness (Ra), Maximum Material Removalrate (MRR) and for minimum PowerConsumption (PC) were displayed in thegenetic solver. The results given by theGenetic solver for different weights given tothe objective functions such as Ra, MRR andPC are tabulated as follows for PVD tool. Thefollowing Table 4 is for optimum cuttingcondition levels that are obtained fromGATOOL for minimum Ra, maximum MRRand minimum PC for PVD tool on EN 24 workpiece material.Table 4: Optimized Cutting Condition Levels for Ra, MRR and PC for PVD ToolOptimal Cutting Condition LevelsWeightsS.No.Speed (S)Feed (f)DOC 0.5550.207718.0610.090.14999Table 5: Optimal Cutting Conditions and Response Valuesfor Different Weighting Factors (PVD)S.No.Optimal Cutting Condition LevelsWeightsGAW1W2W3Speed RpmFeed mmDOC mmRa(µm)MRR(mm3)PowerConsumed 077400.090.254.82470.60679.87324
Int. J. Mech. Eng. & Rob. Res. 2014M Adinarayana et al., 2014CONCLUSIONThe optimal values that are estimated bythe GA technique for different cutting conditionsare in the range of actual Machining cuttingconditions. These Optimal cutting conditionlevels are interpreted into the regressionEquations (1), (2) and (3) for different weights,where we will obtain optimized surfaceroughness Ra, MRR and PC values. Thefollowing Table 5 is for optimized Surfaceroughness (Ra), Material removal Rate (MRR)and Power Consumption (PC) values from GAfor PVD tool obtained from GA tool usingMATLAB. As can be remarked in the result, a multiobjective optimization offers greatestamount of information in order to make adecision on selecting cutting parameters inturning. The Genetic Algorithm (GA) is tested to findoptimal values of parameters with varyingweight factors for the three objectivefunctions with less deviation. In this study the GA techniques wasadopted. GA technique gives effectivemethodology in order to find out the effectiveperformance output and machiningconditions.Figure 1: General Procedurefor Genetic Algorithm The assigned weights to the objectivefunction shows insignificant by entrophymethod.REFERENCES1. ASM Metals Handbook – Machining,9th Edition, Vol. 7, USA, 1980.2. Bonifacio M E R and Diniz A E (1994),“Correlating Tool Wear Tool Life, SurfaceRoughness and Tool Vibration in FinishTurning with Coated Carbide Tools”,Wear, Vol. 173, pp. 137-144.3. Cus F and Balic J (2003), “Optimizationof Cutting Process by GA Approach”,Robotics and Computer IntegratedManufacturing, Vol. 19, pp. 113-121.4. Dereli D, Filiz I H and Bayakosoglu A(2001), “Optimizing Cutting Parametersin Process Planning of Prismatic Parts byUsing Genetic Algorithms”, InternationalJournal of Production Research, Vol. 39,No. 15, pp. 3303-3328.25
Int. J. Mech. Eng. & Rob. Res. 2014M Adinarayana et al., 20145. Diniz A E and Micaroni R (2002), “CuttingConditions for Finish Turning ProcessAiming: the Use of Dry Cutting”,International Journal of Machine Toolsand Manufacture, pp. 899-904.Hardness Alloy Steel by Ceramic andCBN Tools”, Journal of MaterialsProcessing Technology, Vol. 88,pp. 114-121.10. Sönmez A I, Baykasoglu A, Dereli T andFiliz I H (1999), “Dynamic Optimization ofMultipass Milling Operation viaGeometric Programming”, InternationalJournal of Machine Tools &Manufacturing, Vol. 39, pp. 297-320.6. Juneja B L, Sekhon G S and Nitin Seth(2005), “Fundamentals of Metal Cuttingand Machine Tools”, NewageInternational.7. Kuriakose S and Shunmugam M S(2005), “Multi-Objective Optimization ofW ire-Electro Discharge MachiningProcess by Non- dominated SortingGenetic Algorithm”, Journal of MaterialsProcessing Technology, Vol. 170,Nos. 1-2, pp. 133-141 [doi:10.1016/j.jmatprotec.2005.04.105].11. Soodamani R and Liu Z Q (2000), “GABased Learning for a Model-BasedObject Recognition System”, InternationalJournal of Approximate Reasoning,Vol. 23, pp. 85-109.12. Van Veldhuizen D A and Lamont G B(2000), “Multiobjective EvolutionaryAlgorithms: Analizing the State-of-the-Art”,Evolutionary Computation, Vol. 8,pp. 125-147.8. Lee B Y and Tarng Y S (2000), “CuttingParameter Selection for MaximizingProduction Rate or MinimizingProduction Cost in Multistage TurningOperations”, Journal of MaterialsProcessing Technology, Vol. 105,Nos. 1-2, pp. 61-66.13. Zuperl U and Cus F (2003), “Optimizationof Cutting Conditions During Cutting byUsing Neural Networks”, Robotics andComputer Integrated Manufacturing,Vol. 19, pp. 189-199.9. Luo S Y, Liao Y S and Tsai Y Y (1999),“Wear Characteristics in Turning High26
In the constructed optimization problem, three decision variables are considered: cutting speed (v), feed (f), and cutting depth (d). These really are the cutting parameters of the process. Objective Functions Surface roughness need to the minimum for good quality product (Lower is the better) The surface roughness,Ra Min R a (s,f, d) Minimizing
surface roughness can be seen in Figures 3 through 5 as examples of the observations. Fig. 4. Example of a surface with relatively average surface roughness. (0.335 μm) Fig. 3. Example of a surface with relatively low surface roughness. (0.141 μm) Table 1. 3D Surface roughness parameters Parameters Name Definition Comments Sa Average Roughness
programmed surface roughness and non-programmed surface region are given, respectively. The AFM surface roughness value in the non-programmed surface roughness region in Fig. 3a showed a similar value as the first roughness value measured in the programmed surface roughness region (y -60mm). It means there is no Cr film at that location. This
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Introduction Types of surfaces Nomenclature of surface texture Surface roughness value Machining symbols Roughness grades and symbols Surface roughness indication Direction of lay Roughness symbol on drawing Surface roughness for various machining processes SURFACE QUALITY & MACHINIG SYMBOLS 2 Engineering components are manufactured by
roughness declined with the discharge energy, associated with the current simultaneous reduction and the discharge time. In addition, the electrode roughness also shows influence on the machined surface roughness. Keywords: Electrical Discharge Machining, Optimization, Surface Roughness, Electrode Roughness Influence.
have neglected the effect of changing displacement height. In this experiment two surface roughness changes are investigated, both consisting of a change from upstream 3D Lego type roughness elements to downstream 2D bar type roughness. The downstream surface roughness bar height to canyon width ratio is different in each roughness change case
regression analysis in modelling and optimization of surface roughness in the turning roughness has a clear downward trend feed rate and the depth of cut. Keywords: turning, surface roughness, regression analysis, optimization Introduction 1 The key demands in the case of cutting technology include: reducing component size and weights, enhancing surface quality, tolerances and manufacturing .
Fig. 6 Relationship between surface roughness/oil viscosity and friction coefficient (Blend 1) 3.2 Surface roughness Figs. 7 and 8 show the time-series variations of the surface roughness of the roller and discs, respectively, when the Blend 1 oil was used. A sharp decrease in the surface roughness of the roller was observed on the roller at an .
