OPTIMIZATION OF MACHINING PARAMETERS WITH MINIMUM SURFACE .
http://doi.org/10.24867/JPE-2017-02-034JPE (2017) Vol.20 (2)Original Scientific PaperCica, Dj., Zeljkovic, M., Sredanovic, B., Tesic, S.OPTIMIZATION OF MACHINING PARAMETERS WITH MINIMUM SURFACEROUGHNESS FOR THREE-AXIS MILLING OF SCULPTURED PARTSReceived: 20 September 2017 / Accepted: 11 November 2017Abstract: The objective of this study is to identify optimum machining parameters on surface quality of sculpturedparts. The effect of various machining process parameters such as machining strategy, feed, depth of cut and spindlespeed on surface roughness during three-axis end milling of sculptured parts have been studied by performing anumber of experiments constructed according to standard Taguchi’s L9 orthogonal array design matrix. Greyrelational analysis method was used to find the optimal machining process parameters and analysis of variance wascarried out to find the significance and contribution of each machining parameter on performance characteristics.Finally, confirmation test was conducted to indicate the effectiveness of this proposed method.Key words: optimization, surface roughness, grey relational analysisOptimizacija parametara obrade troosnog glodanja složenih površina u cilju obezbeđenja minimalnehrapavosti obrađene površine. Cilj ovog rada je određivanje optimalnih parametara obrade koji obezbeđujuminimalnu hrapavost obrađene površine. Razmatran je uticaj različitih parametara obrade, kao što su strategijaobrade, brzina pomoćnog kretanja, dubina rezanja i broj obrtaja glavnog vretena, na hrapavost obrađene površinepri troosnom glodanju složenih površina. U radu je primenjen Tagučijev L9 ortogonalni plan za realizacijueksperimentalnih ispitivanja, dok je siva relaciona analiza korišćena za rešavanje problema optimizacije. Zaodređivanje značaja pojedinih parametara na performanse karakteristike kvaliteta korišćena je analiza varijanse.Konačno, u cilju potvrde predloženog modela optimizacije proveden je i konfirmacioni test.Ključne reči: optimizacija, hrapavost obrađene površine, siva relaciona analizatool life, savings in machining time, etc., therebyleading to higher productivity and efficiency.A large number of research papers about CNCmachining of sculptured surfaces have been ntation and selection of cutting path strategieswith appropriate cutting parameters had significanteffect on surface roughness in pocket milling which isoften encountered in plastic mould manufacture. Kimand Choi [3] proposed a machining time model thatconsiders the acceleration and deceleration of the CNCmachines and compare the machining efficiency of thedifferent tool paths currently employed in molds anddies manufacturing. Ghani et al. [4] applied Taguchioptimization methodology for optimization ofmachining parameters in end milling process whilemachining hardened steel AISI H13 with TiN coatedP10 carbide insert tool under semi-finishing andfinishing conditions of high speed cutting. Mladjenovicet al. [5] analyzed the impact of the chosen machiningstrategy on roughness of flat surfaces machined withthe ball end mill. Ramos et al. [6] analyze differentfinishing milling strategies of a complex geometry partcontaining concave and convex surfaces. dology for obtaining parameter values ofsculptured surface parts rough machining. Oktem et al.[8] presents method for determination of optimumcutting parameters leading to minimum surfaceroughness in milling mod surfaces by coupling neuralnetwork and genetic algorithm. Oktem et al. [9]1. INTRODUCTIONThe rapidly development of the aerospace,automotive die moulding industries brings along thedemand of new technological challenges related to thegrowing complexity of the products. Due to theimproved functionality mechanical parts havingsculptured surfaces are increasingly important in theseindustries. Sculptured surface machining, also calledfreeform surface machining is usually performing on a3- or 5-axis CNC machining centers with a ball-nosedcutters. However, machining of sculptured surfaces hasbeen a difficult problem addressed by numerousresearchers. In this process, several problems such asover- or under-cuts, inappropriate cutting parameters,non-optimized tool paths etc., usually produceinadequate products that require expensive reworkingwhich results in high production costs. Therefore,currently one of major areas of CAD/CAM systems isthe representation and manufacture of mechanical partshaving sculptured surfaces. In the field of ntation of various machining strategies (zigzag, true spiral, back forth, parallel spiral, high speedetc.) for roughing and finishing operation. A machiningstrategy is a methodology used to compute an operationwith the aim of carrying out a geometrical entity in itsfinal form [1]. Nevertheless, implementation andselection of a tool path generation strategy still remainsan expert field. Appropriate selection can lead toconsiderable improvement of surface roughness and34
possible in high removal rates. Thus, minimummachining time of a sculptured surface part is set as anobjective. Simulation study was designed based onTaguchi L27 orthogonal array. Machining performancewas investigated according to the following machiningparameters: machining strategy, feed (f), depth of cut(a) and spindle speed (n). Levels of machiningparameters are shown in Table 1. Part havingsculptured surfaces used for simulation study is shownon the Fig. 1. Based on simulation analysis minimummachining time of 16 min 35 sec is obtained forparallel spiral machining strategy, f 650 mm/min, a 1.5 mm and n 4000 min-1.developed method for determination of the optimumcutting conditions leading to minimum surfaceroughness in milling of mold surfaces by couplingresponse surface methodology with a geneticalgorithm. Li et al. [10] presents a multi-objectiveoptimization approach, based on neural network, tooptimize the cutting parameters in sculptured partsmachining. Zain et al. [11] used genetic algorithmtechnique for estimation of the optimal cuttingconditions in end milling machining process that yieldthe minimum surface roughness value.The objective of this study is to identify the effectsof tool path strategies for rough and finish machiningof sculptured surfaces. Furthermore, second objective isto develop an optimization method in order to improvemachining quality in CNC finish milling. Themachining parameters considered were machiningstrategy, feed, depth of cut and spindle speed. The bothobjectives will be addressed by means of using Taguchiparameter design. The surface roughness optimizationmodel was developed by grey relational analysis and aconfirmation test was conducted to indicate theeffectiveness of this proposed method.Levels in coded hstrategyspiralspiralspiralFeed,350500650f [mm/min]Depth of cut,0.511.5a [mm]Spindle speed,300040005000n [min-1]Table 1. Design factors and their levels for roughmachining2. EXPERIMENTAL WORKTraditionally, dies and moulds are machined with aCNC machine where machining operation is usuallydecomposed in two main steps: rough and then a finishmachining. The main objective of rough machining isto remove the maximum amount of raw material assoon as possible, leaving a coarse approximation to thefinal shape. Finishing operation objective is to meet therequirements of the final shape for best surface qualityand outline precision. Since irregular scallops betweenfinishing tool passes are inevitably generated on themachined surface, manual polishing of sculpturedsurfaces is often required to obtain the desired surfacequality. While roughing as well as while finishing,implementation and selection of appropriate machiningstrategies with proper cutting parameters havesignificant effect on total production time, costs andproduct quality. Therefore, matters of support forselection of optimal machining strategy have theiractuality in the environments of each CAM system.Due to surface complexity optimal machining issignificantly more complicated for sculptured surfaceparts comparing to prismatic parts. Sculptured surfaceparts usually demand long tool paths, which result inhigh machining times. There are numerous options toefficiently machine a sculptured surface parts whichhave different impact on cutting process elementswherefore a set of objective function must be defined.Owing to possibility to simulating various alternatemachining scenarios and comparing them based on theobtained results the usage of CAM systems isconsidered today to be the most effective solution fortechnological preparation of production of sculpturedsurface parts. Therefore, first impact of machiningstrategy and machining parameters were examined inrough machining. In practice, in rough machiningoperations main objective is to achieve minimal costsof manufacturing removing as much material asFig. 1. Part having sculptured surfacesSecond objective of this paper is identify the effectsof machining strategies and cutting parametersemployed in finish machining of sculptured surfaces. Inpocket milling which is often encountered in mouldmanufacture, the main demand is associated withminimum surface roughness in order to eliminate as faras possible manual surface grinding and polishingoperation. In order to examine the influence ofmachining parameters on the surface roughness infinish milling, the experiments based on Taguchi L9orthogonal array have been conducted. The machiningparameters selected for the experimental work weremachining strategy (A), feed (B), finish allowance (C)and spindle speed (D). Rough machining wereperformed with machining parameters which providesminimum machining time. The milling operations wereperformed on a 3-axis milling machine EMCO ConceptMill 450 equipped with a Sinumerik 840D CNCcontroller. The experiments were performed onmachining aluminium alloy 7075-T6 with ball nosecutter of diameter 10 mm. The surface roughness of themachined surface of each specimen was measured35
The grey relational grade indicate the degree ofsimilarity between comparability sequence and thereference sequence. Hence, higher grey relational gradefor particular comparability sequence shows that thiscomparability sequence is most similar to the referencesequence.using Surftest SJ-210 Mitotoyo. Three measurementswere conducted along the longitudinal and transversedirection for each specimen and the average surfaceroughness parameter values from those reading wasrecorded. Experimental plan consists values ofmachining parameters is shown in Table 2 whileobserved responses are shown in Table 3. Analyzingthe results it can be concluded that, depending on thedifferent machining strategy, different values of surfaceroughness were obtained for the same surface.4. RESULTS AND DISCUSSIONAccording to the implementation steps of greyrelational analysis method presented in the previoussection, the experimental results for surface roughnessin Table 3 were first normalized according to the"lowest-is-the-best" approach and then, deviations fromthe reference series were calculated. Afterwards, thegrey relational coefficient for each machining responsewas calculated and listed in Table 3.Levels in coded ntourstrategyFeed,150250350f [mm/min]Depth of cut,0.10.20.3a [mm]Spindle speed,500065008000n [min-1]Table 2. Design factors and their levels for finishmachiningNo.1.2.3.4.5.6.7.8.9.3. GREY RELATIONAL ANALISYSSimilar to the fuzzy set theory, the grey systemtheory is an effective mathematical model to deal withincomplete and uncertain information. In grey systemstheory, a color spectrum from black to white is used todescribe the degree of clearness of the availableinformation. Black color means complete absence ofinformation, whereas white color means having all theinformation. Grey color is used for intermediate levelof information, i.e. for systems with partially knownand partially unknown information. Therefore, greycolor means the deficiency of information anduncertainty. The intensity of the shade of the greydetermines the clarity of the available information,where lower intensity represent higher quality of theknown information.The grey relational analysis can be expressed in thefollowing steps: (i) conversion of experimental datainto normalized values, (ii) calculation of greyrelational coefficients, (iii) generating grey relationalgrading. In the procedure of grey relational analysis,the first step is to normalize the data being input in thesystem in the range between 0 and 1, which is alsocalled grey relational generation. Due to the differentmeasurement units and scales of response attributes,pre-processing of all data in quantitative way into acomparability sequence is very important. Thenormalized type depends upon the characteristics ofattributes including the smaller-the-better, larger-thebetter and nominal-the-better characteristic. The secondstep of grey relational analysis involves thedetermination of grey relation coefficient to representthe correlation between the desired and actualnormalized experimental results. Finally, the greyrelational grades were computed by averaging the greyrelational coefficient corresponding to selectedresponses. The overall evaluation of the multipleprocess responses is based on the grey relational grade.A111222333MachiningparameterB 70.4690.4490.5671.5281.4331.513Grey .3540.336135426978Table 3. Experimental results for finish machining andgrey relational coefficientsIn this study, it is observed that the experimental no.1 has the highest grey relational coefficent among thenine experiments in Table 3. The response table wasobtained from the average value of the grey relationalcoefficent for each level of the control parameters inorder to find the optimal level of each parameter. Theoptimal setting of control parameters is to select thelevel with higher value of grey relational coefficent.From the results shown in Table 4. the optimal levelsetting of four control parameters for minimizingsurface roughness among the nine experiments areidentified as surface finish parallel machining strategy,feed as 250 mm/min, depth of cut as 0.2 mm andspindle speed as 5000 min-1, represented as A1B2C2D1.Machining parameterBCDFeed,Depth Spindleof cut, speed,fn[mm/min] a [mm][min-1]Level 10.9420.7640.724 0.777Level 20.9240.7700.755 0.707Level 30.3410.6700.728 0.723Max-Min0.6010.1000.027 0.054Rank1243Table 4. Response table for the grey relationalcoefficientLevel36AMachiningstrategy
Main effects plot of grey relational coefficent is drawnfrom response table, as shown in Fig. 2. The mostinfluential factors affecting the surface roughness insequence can be listed as: factor A (machiningGrey relational grade1160 200 240 280 320 360Feed 80.6632Machining strategyGrey relational gradeGrey relational gradeGrey relational grade10.90.80.70.60.50.40.3strategy), factor B (feed), factor D (spindle speed) andfactor C (depth of cut). The interaction plot between theinput parameters over calculated grey relationalcoefficent is shown in Fig. 3.0.1 0.14 0.18 0.22 0.26 0.3Depth of cut [mm]0.780.760.740.720.75000 600070008000-1Spindle speed [min ]Fig. 2. Main effects plot for grey relational Depth of cut0.60.60.410.40.8Spindle speed0.60.41101001502503500.10.20.3Fig. 3. Interaction plot for grey relational coefficientResults of ANOVA are presented in Table 5. andindicate that the machining strategy is most significantparameter, and feed is the next significant parameterthat affecting the total performance characteristics.The error that may due to experiment determined fromthis ANOVA test was only 0.23%, which isstatistically acceptable.In this study, the analysis of variance (ANOVA) isperformed to investigate significance of machiningparameters that affect the performance characteristics.An ANOVA table consists the parameters such asdegree of freedom (DOF), sum of squares (SS), meansquare (MS), Fisher's ratio (F) and probability value (P).In this analysis, depth of cut having the least significanteffect on surface roughness, hence it is pooled.5. CONFIRMATION EXPERIMENTSource DOFSSMSFPMach.20.70104 0.35052 405.33 0.0025strategyFeed20.01966 0.00983 11.37 0.0809Spindle20.00813 0.00406 4.70.1754speedResidual20.00173 0.00086errorTotal80.73055Table 5. Results of the analysis of varianceFinally, after obtaining optimal levels of fourmachining parameters a confirmation test wasconducted to verify the improvement of surfaceroughness. Using optimal levels of machiningparameters the minimal value of surface roughnesswas calculated as follows:N Y Ym Yi Ymi 137 (1)
pp. 7-15, 2008.where Ym is total mean of experimental results for[3] Kim, B.H., Choi, B.K.: Machining efficiencyperformance characteristic, Yi is the mean of theexperimental results at the optimal level and N is thenumber of the machining parameters. The predictedsurface roughness at optimal setting is found to be 0.413µm.Finally a confirmation test was conducted to verifythe improvement in the required performancecharacteristic that is surface roughness, using theoptimal level of four machining parameters. Table 6shows the comparisons of initial and optimal levels ofmachining parameters. The initial designate levels ofmachining parameters are A1B1C1D1 which isexperiment no. 1 in Table 3. Based on the confirmationexperiments, for the final optimal combination ofparameters A1B2C2D1 surface roughness (Ra) isdecreased from 0.446 µm to 0.427 µm. Hence there is asignificant improvement in surface roughness afteroptimization.comparison direction-parallel tool path withcontour-parallel tool path, Computer AidedDesign, Vol. 34(2), pp. 89-95, 2002.[4] Ghani, J.A., Choudhury, I.A., Hassan, H.H.:Application of Taguchi method in theoptimization of end milling parameters, Journal ofMaterials Processing Technology, Vol. 145(1),pp. 84-92, 2004.[5] Mlađenović, C., Kalentić, N., Zeljković, M.,Tabaković, S.: The influence of milling strategieson roughness of complex surfaces, Journal ofProduction Engineering, Vol, 17(1), pp. 51-54,2014.[6] Ramos, A.M., Relvas, C., Simoes, J.A.: Theinfluence of finishing milling strategies ontexture, roughness and dimensional deviations onthe machining of complex surfaces, Journal ofMaterials Processing Technology, Vol. 136(1-3),pp. 209-216, 2003.[7] Krimpenis, A., Vosniakos, G.C.: Rough millingoptimisation for parts with sculptured surfacesusing genetic algorithms in a Stackelberg game,Journal of Intelligent Manufacturing, Vol. 20(4)pp. 447-461, 2009.[8] Oktem, H., Erzurumlu, T., Erzincanli, F.:Prediction of minimum surface roughness in endmilling mold parts using neutral network andgenetic algorithm, Journal of Material andDesign, 27(9), pp. 735-744, 2006.[9] Oktem, H., Erzurumlu, T. Kurtaran, H.:Application of response surface methodology inthe optimization of cutting conditions for surfaceroughness, Journal of Material ProcessingTechnology, Vol. 170(1-2), pp. 11-16, 2005.[10] Li, L. Liu, F., Chen, B., Li, C.B.: Multi-objectiveoptimization of cutting parameters in sculpturedparts machining based on neural network, Journalof Intelligent manufacturing, Vol. 26(5), pp. 891898, 2015.[11] Zain, A.M., Haron, H., Sharif, S.: Application ofGA to optimize cutting conditions for minimizingsurface roughness in end milling machiningprocess, Expert Systems with Applications, Vol.37(6), pp. 4650-4659, .LevelA1B1C1D1 A1B2C2D1 A1B2C2D1Surface roughness,0.4460.4130.427Ra [µm]Table 6. Initial and optimal level performance6. CONCLUSIONSFirst objective of this study is to identify
The surface roughness optimization model was developed by grey relational analysis and a confirmation test was conducted to indicate the effectiveness of this proposed method. 2. EXPERIMENTAL WORK Traditionally, dies and moulds are machined with a CNC machine where machining operation is usually decomposed in two main steps: rough and then a finish machining. The main objective of rough .
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
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 .
on the parameter optimization of CNC lathe machining for surface roughness using the Taguchi method, where surface roughness generated during machining. In the parameter optimization, the parameters are cutting speed, feed, and depth of cut. After selecting parameters turning on CNC lathe is to be done and selected orthogonal array and parameters used for the optimum set of combined controlled .
Optimization of machining parameters using Taguchi coupled GRA 167 Table 3: Input parameters with Taguchi design Machining Parameters Levels of Parameters 1 (low) 2 (medium) 3 (high) Cutting speed (v) m/min 42 60 108 Feed rate (f) mm/rev 0.1 0.2 0.3 Depth-of-cut (d) mm 0.25 0.5 0.75 Evaluation of optimal cutting parameters
