Research ArticleTaguchi Robust Design for Optimizing Surface Roughness ofTurned AISI 1045 Steel Considering the Tool Nose Radius andCoolant as Noise Factors

Adel T Abbas 1 Adham E Ragab 2 Faycal Benyahia1 and Mahmoud S Soliman 1

1Department of Mechanical Engineering College of Engineering King Saud University PO Box 800 Riyadh 11421Saudi Arabia

2Department of Industrial Engineering College of Engineering King Saud University PO Box 800 Riyadh 11421 Saudi Arabia

Correspondence should be addressed to Adel T Abbas aabbasksuedusa

Received 6 June 2018 Revised 20 September 2018 Accepted 22 October 2018 Published 11 November 2018

Academic Editor Fernando Lusquintildeos

Copyright copy 2018 Adel T Abbas et al+is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

AISI 1045 has been widely used in many industrial applications requiring good wear resistance and strength Surface roughness ofproduced components is a vital quality measure A suitable combination of machining process parameters must be selected toguarantee the required roughness values +e appropriate parameters are generally defined based on ideal lab conditions sincemost of the researchers conduct their experiments in closed labs and ideal conditions However when repeating these experimentsin industrial workshops different results are obtained Imperfect conditions such as the absence of a turning tool with definitespecifications as shown in know-how ldquotool nose radius 04mmrdquo and its replacement with the closest existence tool ldquotool noseradius 08mmrdquo as well as the interruption of cutting fluid during work as a result of sudden failure in the coolant pump lead to thementioned different lab-industrial conditions +ese complications are common among normal problems that happened duringthe metal cutting process in realistic conditions and are called noise factors In this paper Taguchi robust design is used to selectthe optimum combination of the cutting speed depth of cut and feed rate to enhance the surface roughness of turned AISI 1045steel bars while minimizing the effects of the two noise factors +e optimum parameters predicted by the developed modelshowed good agreement with the experimental results

1 Introduction

For its attractive characteristics from a stand point of viewof machinability strength wear resistance and impactproperties AISI 1045 steel is widely used in diverse in-dustrial applications Several widely used machine com-ponents are made of this material such as shafts gearscrankshafts connecting rods bolts and others Consid-erable researchers investigated the effect of processingparameters on the final machining quality represented bythe surface roughness of the machined products made ofdifferent materials such as AISI 1045 steel AISI 1015 andAL 6063Deepak and Rajendra [1] carried out an analysis of the

effect of the process parameters such us feed rate cuttingspeed and depth of cut on machined product surface

roughness Using Taguchi robust design method they foundthat the order of the most influential process parameters onthe surface roughness is the feed rate then the cutting speedfollowed by the depth of cutZhang et al [2] worked on the optimization of the

surface quality obtained in CNC milling operation using theTaguchi method An orthogonal array L9 (34) was adaptedfor the design of experiment of the study whose results wereanalyzed using ANOVA +e main control factors of thestudy were the spindle speed feed rate and depth of cutwhile the chamber temperature and different tool inserts(condition and dimensional variability) were considerednoise factors of the study +e ANOVA analyses looking forthe optimum surface roughness and signal-to-noise ratioconfirmed the test verified factors significant optimum forthe best response

HindawiAdvances in Materials Science and EngineeringVolume 2018 Article ID 2560253 9 pageshttpsdoiorg10115520182560253

Qasim et al [3] studied the optimization of processingparameters using several cutting tools +e focus was on thereduction of cutting forces and generated temperatureduring the cutting process of AISI 1045 steel material +emain parameters considered in this study are the feed ratecutting speed depth of cut and rake angle in the orthogonalmachining process Different analysis techniques have beencombined in this investigation namely the signal-to-noiseratio the Taguchi matrix the analysis of variance (ANOVA)and the finite element simulation +e latter was conductedusing the commercial FEA software ABAQUS whereas thestatistical study was conducted with Minitab package +eanalysis concluded that the depth of cut and the feed rate arethe most influential on the cutting force and hence should beconsidered for its optimization Nevertheless the cuttingspeed and rake angle are demonstrated to be the mostsignificant on the optimum resulting cutting temperatureMoreover the study concluded that for machining AISI1045 carbide cutting tools provide lower cutting forces andtemperatures compared than uncoated cemented carbidetoolsBhattacharya et al [4] conducted an experimental in-

vestigation driven by Taguchi techniques on the effects ofcutting parameters on the surface finish and processingpower consumption Processing of AISI 1045 steel at highspeed using coated carbide tools was also studied by theauthors +e analysis of variance and orthogonal array wereused to determine the contribution of the cutting speed feedrate depth of cut on the surface roughness and powerconsumption +e cutting speed was found to be the onlysignificant parameter on the surface finish and powerconsumptionMoganapriya et al [5] conducted an experimental study

on the effects of processing parameters on the quality ofCNC turning of AISI 1015 mild steel +e objective was tominimize the surface roughness while maximizing thematerial removal rate An optimization has been conductedusing the Taguchi method with an L9 orthogonal arrayrelated to the tool coating material (TiAlNWC-C TiAlN)depth of cut feed rate and spindle speed as input parametersand targeting to maximize the material removal rate forefficiency and the minimization of surface roughness forquality +e authors established a predictive correlation fordetermining the material removal rate and surface rough-ness for a given set of parameters +e optimal machiningconditions were identified for a cutting speed of 600mmina medium depth of cut of 15mm a high feed rate of015mmrev and a multilayer deposition from the selectedlevels Good agreement between experimental tests andprediction values of surface roughness and material removalrate were reportedMazarbhuiya et al [6] searched the optimal processing

factors of the electric discharge machining of an aluminumpart using a copper tool electrode Discharge currentflushing pressure polarity and the pulse ON time were themain input parameters of the study Two main machiningresponses were targeted the material removal rate and thesurface roughness +e experiments were designed based onthe Taguchi method and the results were analyzed using

ANOVA method +e best operating conditions based onlarge-the-better were reported for a discharge current of16A a pulse ON time of 463 μs a flushing pressure of10 kgfcm2 and a normal polarity Likewise the best op-erating conditions providing an optimum surface roughnessbased on smaller-the-best approach were reported fora discharge current of 8A a pulse ON time of 463 μsa reverse polarity and flushing pressure of 10 kgfcm2 +eobtained optimal settings were validated by SN ratio and thegenerated average performance graph Besides the analysisshowed that for the material removal rate the polarity is themost affecting parameter followed by the current levelwhereas the surface roughness is found to be only affected bythe current levelManivel and Gandhinathan [7] studied the optimization

of the cutting parameters in hard turning of ADI by carbideinserts CVD coated with Al2O3MT TICN Based on theTaguchi method dry conditions experiments have beendesigned using an L18 orthogonal array taking the cuttingspeed the feed rate the depth of cut and the nose radius asthe independent input parameters Two levels of the noseradius and three levels of all other parameters were con-sidered in the study while all other parameters were con-sidered constant +e ANOVA signal-to-noise ratio andregression analyses were used to optimize the processingparameters +e study confirms that the cutting speed is themost influential parameter on the surface roughness and thetool wear +e predicted optimum cutting parameters wereverified through experimental tests where surface roughnessand tool wear are found closer to 927 and 105 of de-viations respectivelyNalbant et al [8] conducted an experimental study

aiming to explore the effect of turning operations parameterson the surface roughness of AISI 1030 carbon steel material+e insert radius and feed rate parameters were found tohave a higher impact of the surface roughness compared tothat of the depth of cut parameter Taguchirsquos robust designmethod was used to prepare the experimental plan of ex-perience and to analyze the obtained results +e obtainedoptimum results obtained with the Taguchi method werevalidated experimentally confirming the validity of thedrawn conclusionsAsilturk and Akkus [9] investigated the effect of cutting

speed feed rate and depth of cut on the resultant surfaceroughness during CNC turning operations Nonlubricatedtests were conducted on AISI 4140 (51 HRC) materialsamples using coated carbide cutting tools A new cuttinginsert was used for each test for accurate reading Also testswere repeated three times each for better confidence +eanalysis of the results using ANOVA and signal-to-noiseratio highlighted the dominance of the feed rate effect on thesurface roughness Moreover the interaction of the feed ratespeed and depth of cut was found to play an important roleon machining surface qualityHwang and Lee [10] searched the minimum quantity

lubrication as well as the wet turning of AISI 1045material inthe perspective of developing a model capable of predictingthe cutting force along with the surface roughness for a givenset of processing parameters+e experiments were designed

2 Advances in Materials Science and Engineering

based on a fractional factorial and a central composite de-sign+e surface roughness and cutting force were measuredthrough the external cylindrical turning surface according tothe machining factors used in the established plan of ex-periments Optimal cutting parameters were determinedfrom the developed experimental relationshipsAbbas et al [11] conducted a multiobjective optimiza-

tion research of the processing factors in turning a heat-treated alloy steel material (J-steel) using uncoated tungsten-carbide tools under unlubricated conditions +e studyaimed at finding the adequate settings of the cutting pa-rameters (cutting speed depth of cut and feed rate) leadingto the optimum response combination of the surfaceroughness and the material removal rate +e experimentalstudy was conducted based on samples generated based ona five-level full factorial matrix +e analysis of the resultsidentified a Pareto tradeoff frontier among the objectives andshowed a ldquokneerdquo shape for which some processing settingcould reach both good surface finish and high materialremoval rate within certain limits Beyond those limitsimproving one of the objectives would be at the cost of thesecond objectiveAl Bahkali et al [12] studied the effect of machining

parameters (feed rate cutting speed depth of cut and toolnose radius) on the surface roughness of cast iron materialmachined in a turning operation Turning tools with carbideinserts and nose radii of 04 and 08mm were sued +esurface roughness was measured for each set of machiningfactors A design of experiment based on three levels of eachof the input parameters was implemented to establisha model relating the machining factors to the resultingsurface finish+e results revealed that the tool nose and feedrate are the most affecting parameters on the surfaceroughness of the machined product +e cutting speed andthe depth of cut are shown of lower influence +e lowestroughness was obtained at the lowest feed rate the highestcutting speed and the lowest depth of cut for higher noseradius A multiobjective optimization was also driven tomeasure the process productivity aiming at maximizing thematerial removal rate and minimize the surface roughnesssimultaneously Also a close look to the surface finish underoptical microscopy showed that for the lower nose radius(04mm) there are higher chances of graphite pullouts thatdeteriorated the surface roughnessIn this paper Taguchi robust design is used to evaluate

the effect of two noise factors (tool nose radius and coolantstatus) on the surface roughness of turned AISI 1045 steelbars for different levels of cutting speeds depth of cut andfeed rate Both of the noise factors are given two levels whilethe processing parameters range is divided into three levelseach

2 Materials and Methods

21 Materials and Testing +e chemical composition ofstudied AISI 1045 steel as measured by spectroscopicanalysis is shown in Table 1 +is material was heat treatedsuch that it was heated to 840degC held until the temperaturewas uniform and soaked for one hour and quenched in water

to produce martensitic structure +e hardening stage wasfollowed by tempering at 600degC holding until the temperatureis uniform soaking for two hours and then cooling in still air+e hardness of tempered steel was in the range of HV240ndash246 +e procedure of heat treatment was suggested bysteel manufacturer to have proper combination of strengthand ductility for the tempered martensite +e average tensilestrength and elongation (in 50mm) were 635MPa and 17respectively A sample of 20mm in diameter was prepared forthe microstructure study using standard methods of grindingpolishing and etching (2 nital) respectively +e micro-structure was observed using Olympus optical microscopewith the data base system and JOEL-SEM6600A CNC turning machine equipped with Sinumeric 840-

D was used to conduct all experiments +e drive power isequal 13 kW +e uncoated tungsten carbide insert wasclamped with the tool holder to carry out this work +e toolholder specification is SDJCL 2020 K11 while the two insertsspecifications are DCMT 11T3 04-KM H13A and DCMT11T3 08-KM H13A +e clearance and cutting edge anglesare 7deg and 55deg respectively +e tool nose radiuses are04mm and 08mm respectively +e test rig used formachining the specimens is shown in Figure 1+e test specimens have an initial diameter of 50mm and

a length of 135mm 30mm will be used for the chuckclamping 10mm for clearance grooving and 75mm will beused for applying the test experiment A standard conicalcenter was created from the other side for supporting therotary center of the tail stock +e test specimen drawing isshown in Figure 2A total of 108 tests were conducted +is test assembly

was divided into four different equal groups based on thecutting tool nose radius (04mm08mm) and on thecutting condition (wetdry) Each of the resultant sub-assemblies formed of 27 tests was divided into three maingroups of 9 tests each machined using three differentsurface speeds 150 125 and 100metermin +e groups ofthe 9 tests were in their turn divided into three subgroupswhere each was subjected to a different level-depth cut of075 05 and 025mm Each of the final subgroups op-erated with one cut depth level was machined using one ofthree different feed rates 015 010 and 005mmrev +etest rig for measuring the resultant surface roughness isshown in Figure 3

22 Robust Design Optimization Several factors affect thesurface roughness produced during machining processesSome of these factors are controllable during machining andhence called ldquocontrol factorsrdquo Others are impossible orcostly to control and are called ldquonoise factorsrdquo Robustoptimization aims at determining the combination ofcontrol factorsrsquo settings that minimize variations of theprocess output due to the existence of the noise factors

Table 1 Chemical composition of AISI 1045

C Mn Si Cu Cr Ni S Fe0462 0642 0220 0206 0079 0066 0004 Balance

Advances in Materials Science and Engineering 3

221 Selection of Control Factors +e effect of three controlfactors namely cutting speed depth of cut and feed rate onthe produced surface roughness is investigated in this study+e considered range of each factor was divided into threelevels as summarized in Table 2

222 Selection of Noise Factors Two noise factors wereinvestigated in this study namely the tool nose radius andcoolant operation ONOFF status +ese two factors affectthe surface roughness of produced parts while controllingthem during regular production is not always possible Fordifferent reasons workers in workshops may not followprecisely the selection of a particular nose radius Also if thecoolant system is broken down or does not functionproperly the machine operator will not necessarily turn off

the machine Two levels were selected for the tool noseradius (NR) 04 and 08mm and two levels for coolant wereselected coolant ON and coolant OFF

223 Orthogonal Array Taguchi L27 orthogonal array wasused in this study as the inner array that represents thecontrol variables (3 factors 3 levels each) L27 array can runup to 13 factors with 3 levels each It provides 26 degrees offreedom allowing the calculation of several factors in-teraction effects if needed L4 outer array was used torepresent the noise factors (2 factors 2 levels each)+e totalnumber of runs was of 108 runs as illustrated in Table 3 alongwith the related measured surface roughness Ra (microm)

3 Results and Discussion

A heat treatment of the as-received material has increased itshardness from range of 185ndash190HV to a value of 240ndash246HV that improved the surface quality of the machined

Figure 1 Test rig for machining specimens

For clamping bythree jaw chuck

30

Oslash

10 25 5 25 10255

For supportingby rotary center

Feed rate005 mmrev

Feed rate015 mmrev

Feed rate025 mmrev

50

Figure 2 Test specimen drawing

Figure 3 Test rig for measuring the specimen surface roughness

Table 2 +e control factors and their levels used in the currentstudy

Designation Process parameter Level 1 Level 2 Level 3S Cutting speed (mmin) 100 125 150DOC Depth of cut (mm) 025 05 075FR Feed rate (mmrev) 005 010 015

4 Advances in Materials Science and Engineering

material +e increase of hardness has caused the material tobecome less ductile reducing its plastic flow capacity offeringa better surface finish A brittle interaction between a cuttingtool and a low ductility workpiece induces a material sep-aration during machining rather than a plastic flow causingsurface irregularitiesIt is well documented that the microstructure of

annealednormalized AISI 1045 is composed of ferrite andpearlite +e microstructure of tempered specimen is shownin Figure 4(a) It is composed of ferrite areas (white) andtempered martensite (dark) +e dark area in the opticalmicrostructure was enlarged using SEM as shown Figure 4(b)+e ferrite appears as dark areas and carbides as white areas

31 Signal-to-Noise Ratio In Taguchi analysis the signal-to-noise ratio (SN) refers to the ratio of the power of signal tothe power of noise [2 13] Maximizing the SN guaranteesthe minimum effects of noise on the measured output It iscalculated for three objective functions ldquothe nominal isbestrdquo ldquothe smaller is betterrdquo and ldquothe larger is betterrdquo asgiven in Equations (1)ndash(3) respectively [14]

S

N 10 log

y2

s2y (1)

S

N minus10 log

1n1113944y

21113872 1113873 (2)

S

N minus10 log

1n11139441y2

1113888 1113889 (3)

where y is the average of the observed data n is the number ofobservations and s2y is the variance of y SN for the surfaceroughness measure Ra is calculated using Equation (2) be-cause smaller Ra is better Table 4 shows the SN ratio for theL27 orthogonal inner array From the table it is found thatL03 has the highest SN ratio and hence provides the bestcombination with the minimum effects of noise factors withinthe tested levels of control factors Figure 5 shows the maineffect plot of SN ratio with respect to the control variablesand their levels +e results suggest that the SN ratio is moresensitive to the cutting speed and feed rate while the variationwith the depth of cut is of lower sensitivity

32 Analysis of Variance (ANOVA) ANOVA was con-ducted to test the significance of both control and noisefactors Table 5 illustrates the ANOVA results with p valueless than 005 being considered significant Terms with pvalues higher than 005 are removed from the model unlessthey are a part of significantly higher term ANOVA resultsprove the significance of all factors in combination withsome interactions of second and third order +e noseradius while not significant as a linear term is significant incombination of other factors as illustrated by the p value ofthe 2-way and 3-way interactions +e adjusted R-squared

Table 3 Orthogonal array with the measured Ra (microm)

Inner array Outer array

Test run Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

NR 04coolant on

NR 04coolant off

NR 08coolant on

NR 08coolant off

L01 150 075 015 18317 1970 1181 1654L02 150 075 010 1003 1157 0941 1302L03 150 075 005 0411 0855 0547 0783L04 150 05 015 1588 1530 1171 1231L05 150 05 010 1019 1337 0734 1109L06 150 05 005 0652 1174 0482 0653L07 150 025 015 1072 1539 1146 1484L08 150 025 010 0925 1416 0820 1393L09 150 025 005 0758 1310 0684 1062L10 125 075 015 1803 2287 1058 2039L11 125 075 010 0836 1291 0721 1621L12 125 075 005 0514 1000 0612 1351L13 125 05 015 1513 1663 1294 1689L14 125 05 010 1051 1290 1013 1667L15 125 05 005 0781 1163 0679 1294L16 125 025 015 1621 1780 1235 2576L17 125 025 010 1250 1362 1004 2214L18 125 025 005 1072 1143 0687 1590L19 100 075 015 1948 2053 1309 2128L20 100 075 010 1167 1828 1163 1978L21 100 075 005 1100 1350 0838 1763L22 100 05 015 1524 1895 1470 2450L23 100 05 010 1450 1695 1301 2226L24 100 05 005 1398 1445 1000 2145L25 100 025 015 1840 2702 2294 3082L26 100 025 010 1438 2659 1963 2272L27 100 025 005 0847 2243 1867 1984

Advances in Materials Science and Engineering 5

value is about 87 proving that the model is representativeand explains 97 of the variation in measured outputs

33 Regression and Optimization In this paper regressionanalysis is used to build a second-order relation between theresponse and control factors under dierent conditions ofthe L4 outer array e goal is to investigate the signicanceof quadratic terms on the selection of optimum machiningconditions to minimize the eect of noise factors Table 6summarizes the regression equations at the four combinations

of the outer array e results show that the value of expectedRa depends on quadratic terms in some cases

Composite desirability function was used to minimizethe expected Ra in the calculated four regression equationsTable 7 shows the predicted optimum conditions for eachequation and the corresponding expected Ra ese caseswere machined to validate the regression model output anda comparison between the predicted and measured valuesare presented in the table

Figure 6 represents the surface roughness prole pro-duced by the surface roughness tester It shows the increaseof the feed rate and depth of cut were found to diminish thesurface quality due the amount of forces and related frictioninvolved in cutting larger material volume incrementsSimilar high speed rates and depth of cut eects have beenreported by other studies [11 12 15]

10 μm

(a) (b)

Figure 4 (a) Optical micrograph of tempered steel it is composed of ferrite (white) and temperedmartensite (dark) (b) Secondary electronimage of the dark area in optical micrograph (tempered martensite) showing the presence of ferrite (dark areas) and carbides (white areas)

150125100

ndash1

ndash2

ndash3

ndash4

ndash5

075050025 015010005

S

Mea

n of

SN

ratio

s

DOC FR

Signal-to-noise Smaller is better

Figure 5 Main eect plot for SN ratio with respect to the controlfactors

Table 4 SN ratio for the L27 orthogonal inner array

SN SN SNL01 minus453567 L10 minus536497 L19 minus551797L02 minus090376 L11 minus139333 L20 minus396571L03 343843 L12 062490 L21 minus233181L04 minus287199 L13 minus379391 L22 minus546450L05 minus060185 L14 minus215754 L23 minus463266L06 210752 L15 minus010513 L24 minus382173L07 minus245098 L16 minus542733 L25 minus803586L08 minus136208 L17 minus367699 L26 minus656900L09 012517 L18 minus134791 L27 minus517539

Table 5 ANOVA results for control and noise factors

Source DF Adj SS Adj MS F value p valueModel 31 287059 0926 2367 0Linear 8 246965 308706 7892 0

S 2 82392 411961 10532 0DOC 2 14196 070979 1815 0FR 2 76466 382328 9774 0NR 1 00015 000151 004 0845Coolant 1 73896 738962 18891 0

2-way interactions 17 31721 018659 477 0S lowast DOC 4 09537 023843 61 0S lowast NR 2 04967 024834 635 0003S lowast coolant 2 05287 026436 676 0002DOC lowast FR 4 04059 010147 259 0043DOC lowast NR 2 02202 011011 281 0066FR lowast NR 2 01385 006925 177 0177NR lowast coolant 1 04284 042836 1095 0001

3-way interactions 6 08373 013955 357 0004S lowast NR lowast coolant 2 03999 019993 511 0008DOC lowast FR lowast NR 4 04374 010936 28 0032

Error 76 29728 003912Total 107 316787R-sq R-sq (adj) R-sq (pred)9062 8679 8105

6 Advances in Materials Science and Engineering

Composite desirability function was used to select theoptimum combination of control factors that optimizes Raunder different noise factors conditions ie the controlfactors levels that will minimize the effects of the noisefactors +is combination was S 150mminute DOC 064mm and FR 005mmrev with composite desirability 095 Figure 7 shows the optimization plot of Ra +eseoptimum conditions were run to validate the regression andoptimization process and the results are summarized in

Table 8 +e small error (le5) proves the match betweenexperimental and model predicted values

4 Conclusions

In this research a relation between the experiments done inclosed ideal lab conditions and the actual manufacturingprocesses in workshops is investigated Under real workingconditions uncontrolled parameters may interfere with

Table 6 Regression equations at the four conditions of the L4 outer array

Outer array combination Regression equationNR 04 Ra 1949 minus 000767S minus 1181DOC + 234FR + 1134DOC lowast FRCoolant onNR 04 Ra 1145 minus 01197S minus 836DOC + 044FR + 0000373S2 + 317DOC2 + 00279S lowast DOC + 1187DOC lowast FRCoolant offNR 08 Ra 1063 minus 01268S minus 5462DOC + 5291FR + 0000383S2 + 003777S lowast DOCCoolant onNR 08 Ra 4654 minus 002079S minus 365DOC + 6342FR + 298DOC2Coolant off

Table 7 Optimum machining conditions at each noise-factor combination

Testrun

Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

Screen shot fromsurface roughness tester

1 150 075 005 04 On 0440 0455 3 Figure 6(a)2 137 062 005 04 Off 0866 0848 2 Figure 6(b)3 150 025 005 08 On 0552 0541 2 Figure 6(c)4 150 064 005 08 Off 0752 0737 2 Figure 6(d)

(a) (b)

(c) (d)

Figure 6 Screenshots from surface roughness tester for optimum machining conditions at each noise factors combination as shown inTable 7 (a) Test run-1 (b) test run-2 (c) test run-3 (d) test run-4

Advances in Materials Science and Engineering 7

machining requirements inducing lower controlledproduct quality Two noise parameters have been con-sidered to evaluate their eects on the processing qualityand optimal control parameters have been determined tominimize their interference with the nal qualityrequirement

Taguchi robust design was used to minimize the eectof two noise factors tool nose radius and coolant oper-ation on the produced Ra ANOVA results showed that allinvestigated control and noise factors had signicant ef-fects on the output Ra proving the importance of thisinvestigation Regression analysis and composite de-sirability function were used to select the optimumcombination of control factors that will minimize theexpected Ra despite the eects of the noise factors Apredictive model has been developed to determine theoptimum combination of control factors for dierentcongurations of noise factors as illustrated in Tables 7 and8 e results show low prediction errors (le5) whencompared to real experimental tests An optimum com-bination of 150mmin surface speed 064mm depth ofcut and 005mmrev feed rate is used in case of uncertaincombination of tool nose radius and coolant condition

Data Availability

e data used to support the ndings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare no conicts of interest

Acknowledgments

e authors extend their appreciation to the Deanship ofScientic Research at King Saud University for funding thiswork through research group no RG-1439-020

References

[1] D Deepak and B Rajendra ldquoOptimization of machiningparameters for turning of Al6061 using robust design prin-ciple to minimize the surface roughnessrdquo Procedia Technol-ogy vol 24 pp 372ndash378 2016

[2] J Z Zhang J C Chen and E D Kirby ldquoSurface roughnessoptimization in an end-milling operation using the Taguchi

FR 0150

[0050]0050

DOC 0750

[06389]0250

S 1500

[1500]100

High CurLow

Optimal D 09458

Predict

Composite Desirability D 09458

8OFF Minimum y = 07338 d = 096673

8ON Minimum y = 06200 d = 092382

4OFF Minimum y = 09173 d = 096629

4ON Minimum y = 05230 d = 092713

Figure 7 Optimization plot for Ra under dierent combinations of noise factors

Table 8 Measured and expected values of Ra under optimum conditions for all combinations of noise factors

Test run Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

1 150 064 005 04 On 0509 0522 32 150 064 005 04 O 0964 0915 53 150 064 005 08 On 0591 0622 54 150 064 005 08 O 0766 0737 4

8 Advances in Materials Science and Engineering

design methodrdquo Journal of Materials Processing Technologyvol 184 no 1ndash3 pp 233ndash239 2007

[3] A Qasim S Nisar A Shah M S Khalid and M A SheikhldquoOptimization of process parameters for machining of AISI-1045 steel using Taguchi design and ANOVArdquo SimulationModelling Practice and 6eory vol 59 pp 36ndash51 2015

[4] A Bhattacharya S Das P Majumder and A Batish ldquoEsti-mating the effect of cutting parameters on surface finish andpower consumption during high speed machining of AISI1045 steel using Taguchi design and ANOVArdquo ProductionEngineering vol 3 no 1 pp 31ndash40 2009

[5] C Moganapriya R Rajasekar K Ponappa R Venkatesh andS Jeromec ldquoInfluence of coating material and cutting pa-rameters on surface roughness and material removal rate inturning process using Taguchi methodrdquo Materials TodayProceedings vol 5 no 2 pp 8532ndash8538 2018

[6] R M Mazarbhuiya P K Choudhury and P K Patowari ldquoAnexperimental study on parametric optimization for materialremoval rate and surface roughness on EDM by using Taguchimethodrdquo Materials Today Proceedings vol 5 no 2pp 4621ndash4628 2018

[7] D Manivel and R Gandhinathan ldquoOptimization of surfaceroughness and tool wear in hard turning of austemperedductile iron (grade 3) using Taguchi methodrdquo Measurementvol 93 pp 108ndash116 2016

[8] M Nalbant H Gokkaya and G Sur ldquoApplication of Taguchimethod in the optimization of cutting parameters for surfaceroughness in turningrdquo Materials and Design vol 28 no 4pp 1379ndash1385 2007

[9] I Asilturk and H Akkus ldquoDetermining the effect of cuttingparameters on surface roughness in hard turning using theTaguchi methodrdquoMeasurement vol 44 no 9 pp 1697ndash17042011

[10] Y K Hwang and C M Lee ldquoSurface roughness and cuttingforce prediction in MQL and wet turning process of AISI 1045using design of experimentsrdquo Journal of Mechanical Scienceand Technology vol 24 no 8 pp 1669ndash1677 2010

[11] A T Abbas K Hamza M F Aly and E A Al-BahkalildquoMultiobjective optimization of turning cutting parametersfor J-steel materialrdquo Advances in Materials Science and En-gineering vol 2016 Article ID 6429160 8 pages 2016

[12] E A Al Bahkali A E Ragab E A El Danaf and A T AbbasldquoAn investigation of optimum cutting conditions in turningnodular cast iron using carbide inserts with different noseradiusrdquo Proceedings of the Institution of Mechanical EngineersPart B Journal of Engineering Manufacture vol 230 no 9pp 1584ndash1591 2016

[13] K S Kim K T Jung J M Kim J P Hong and S I KimldquoTaguchi robust optimum design for reducing the coggingtorque of EPS motors considering magnetic unbalance causedby manufacturing tolerances of PMrdquo IET Electric PowerApplications vol 10 no 9 pp 909ndash915 2016

[14] A E Ragab ldquoMulti-objective optimization of profile accuracyin two point incremental forming using Taguchi-based greyrelation analysisrdquo International Journal of Collaborative En-terprise vol 6 no 1 pp 49ndash65 2018

[15] A T Abbas A E Ragab E A Al Bahkali and E A El DanafldquoOptimizing cutting conditions for minimum surfaceroughness in face milling of high strength steel using carbideinsertsrdquo Advances in Materials Science and Engineeringvol 2016 Article ID 7372132 14 pages 2016

Advances in Materials Science and Engineering 9

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Polymer ScienceInternational Journal of

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Journal of

Hindawiwwwhindawicom Volume 2018

High Energy PhysicsAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

TribologyAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

Hindawiwwwhindawicom Volume 2018

Advances inPhysical Chemistry

Hindawiwwwhindawicom Volume 2018

BioMed Research InternationalMaterials

Journal of

Hindawiwwwhindawicom Volume 2018

Na

nom

ate

ria

ls

Hindawiwwwhindawicom Volume 2018

Journal ofNanomaterials

Submit your manuscripts atwwwhindawicom

based on a fractional factorial and a central composite de-sign+e surface roughness and cutting force were measuredthrough the external cylindrical turning surface according tothe machining factors used in the established plan of ex-periments Optimal cutting parameters were determinedfrom the developed experimental relationshipsAbbas et al [11] conducted a multiobjective optimiza-

tion research of the processing factors in turning a heat-treated alloy steel material (J-steel) using uncoated tungsten-carbide tools under unlubricated conditions +e studyaimed at finding the adequate settings of the cutting pa-rameters (cutting speed depth of cut and feed rate) leadingto the optimum response combination of the surfaceroughness and the material removal rate +e experimentalstudy was conducted based on samples generated based ona five-level full factorial matrix +e analysis of the resultsidentified a Pareto tradeoff frontier among the objectives andshowed a ldquokneerdquo shape for which some processing settingcould reach both good surface finish and high materialremoval rate within certain limits Beyond those limitsimproving one of the objectives would be at the cost of thesecond objectiveAl Bahkali et al [12] studied the effect of machining

parameters (feed rate cutting speed depth of cut and toolnose radius) on the surface roughness of cast iron materialmachined in a turning operation Turning tools with carbideinserts and nose radii of 04 and 08mm were sued +esurface roughness was measured for each set of machiningfactors A design of experiment based on three levels of eachof the input parameters was implemented to establisha model relating the machining factors to the resultingsurface finish+e results revealed that the tool nose and feedrate are the most affecting parameters on the surfaceroughness of the machined product +e cutting speed andthe depth of cut are shown of lower influence +e lowestroughness was obtained at the lowest feed rate the highestcutting speed and the lowest depth of cut for higher noseradius A multiobjective optimization was also driven tomeasure the process productivity aiming at maximizing thematerial removal rate and minimize the surface roughnesssimultaneously Also a close look to the surface finish underoptical microscopy showed that for the lower nose radius(04mm) there are higher chances of graphite pullouts thatdeteriorated the surface roughnessIn this paper Taguchi robust design is used to evaluate

the effect of two noise factors (tool nose radius and coolantstatus) on the surface roughness of turned AISI 1045 steelbars for different levels of cutting speeds depth of cut andfeed rate Both of the noise factors are given two levels whilethe processing parameters range is divided into three levelseach

2 Materials and Methods

21 Materials and Testing +e chemical composition ofstudied AISI 1045 steel as measured by spectroscopicanalysis is shown in Table 1 +is material was heat treatedsuch that it was heated to 840degC held until the temperaturewas uniform and soaked for one hour and quenched in water

to produce martensitic structure +e hardening stage wasfollowed by tempering at 600degC holding until the temperatureis uniform soaking for two hours and then cooling in still air+e hardness of tempered steel was in the range of HV240ndash246 +e procedure of heat treatment was suggested bysteel manufacturer to have proper combination of strengthand ductility for the tempered martensite +e average tensilestrength and elongation (in 50mm) were 635MPa and 17respectively A sample of 20mm in diameter was prepared forthe microstructure study using standard methods of grindingpolishing and etching (2 nital) respectively +e micro-structure was observed using Olympus optical microscopewith the data base system and JOEL-SEM6600A CNC turning machine equipped with Sinumeric 840-

D was used to conduct all experiments +e drive power isequal 13 kW +e uncoated tungsten carbide insert wasclamped with the tool holder to carry out this work +e toolholder specification is SDJCL 2020 K11 while the two insertsspecifications are DCMT 11T3 04-KM H13A and DCMT11T3 08-KM H13A +e clearance and cutting edge anglesare 7deg and 55deg respectively +e tool nose radiuses are04mm and 08mm respectively +e test rig used formachining the specimens is shown in Figure 1+e test specimens have an initial diameter of 50mm and

a length of 135mm 30mm will be used for the chuckclamping 10mm for clearance grooving and 75mm will beused for applying the test experiment A standard conicalcenter was created from the other side for supporting therotary center of the tail stock +e test specimen drawing isshown in Figure 2A total of 108 tests were conducted +is test assembly

was divided into four different equal groups based on thecutting tool nose radius (04mm08mm) and on thecutting condition (wetdry) Each of the resultant sub-assemblies formed of 27 tests was divided into three maingroups of 9 tests each machined using three differentsurface speeds 150 125 and 100metermin +e groups ofthe 9 tests were in their turn divided into three subgroupswhere each was subjected to a different level-depth cut of075 05 and 025mm Each of the final subgroups op-erated with one cut depth level was machined using one ofthree different feed rates 015 010 and 005mmrev +etest rig for measuring the resultant surface roughness isshown in Figure 3

22 Robust Design Optimization Several factors affect thesurface roughness produced during machining processesSome of these factors are controllable during machining andhence called ldquocontrol factorsrdquo Others are impossible orcostly to control and are called ldquonoise factorsrdquo Robustoptimization aims at determining the combination ofcontrol factorsrsquo settings that minimize variations of theprocess output due to the existence of the noise factors

Table 1 Chemical composition of AISI 1045

C Mn Si Cu Cr Ni S Fe0462 0642 0220 0206 0079 0066 0004 Balance

Advances in Materials Science and Engineering 3

221 Selection of Control Factors +e effect of three controlfactors namely cutting speed depth of cut and feed rate onthe produced surface roughness is investigated in this study+e considered range of each factor was divided into threelevels as summarized in Table 2

222 Selection of Noise Factors Two noise factors wereinvestigated in this study namely the tool nose radius andcoolant operation ONOFF status +ese two factors affectthe surface roughness of produced parts while controllingthem during regular production is not always possible Fordifferent reasons workers in workshops may not followprecisely the selection of a particular nose radius Also if thecoolant system is broken down or does not functionproperly the machine operator will not necessarily turn off

the machine Two levels were selected for the tool noseradius (NR) 04 and 08mm and two levels for coolant wereselected coolant ON and coolant OFF

223 Orthogonal Array Taguchi L27 orthogonal array wasused in this study as the inner array that represents thecontrol variables (3 factors 3 levels each) L27 array can runup to 13 factors with 3 levels each It provides 26 degrees offreedom allowing the calculation of several factors in-teraction effects if needed L4 outer array was used torepresent the noise factors (2 factors 2 levels each)+e totalnumber of runs was of 108 runs as illustrated in Table 3 alongwith the related measured surface roughness Ra (microm)

3 Results and Discussion

A heat treatment of the as-received material has increased itshardness from range of 185ndash190HV to a value of 240ndash246HV that improved the surface quality of the machined

Figure 1 Test rig for machining specimens

For clamping bythree jaw chuck

30

Oslash

10 25 5 25 10255

For supportingby rotary center

Feed rate005 mmrev

Feed rate015 mmrev

Feed rate025 mmrev

50

Figure 2 Test specimen drawing

Figure 3 Test rig for measuring the specimen surface roughness

Table 2 +e control factors and their levels used in the currentstudy

Designation Process parameter Level 1 Level 2 Level 3S Cutting speed (mmin) 100 125 150DOC Depth of cut (mm) 025 05 075FR Feed rate (mmrev) 005 010 015

4 Advances in Materials Science and Engineering

material +e increase of hardness has caused the material tobecome less ductile reducing its plastic flow capacity offeringa better surface finish A brittle interaction between a cuttingtool and a low ductility workpiece induces a material sep-aration during machining rather than a plastic flow causingsurface irregularitiesIt is well documented that the microstructure of

annealednormalized AISI 1045 is composed of ferrite andpearlite +e microstructure of tempered specimen is shownin Figure 4(a) It is composed of ferrite areas (white) andtempered martensite (dark) +e dark area in the opticalmicrostructure was enlarged using SEM as shown Figure 4(b)+e ferrite appears as dark areas and carbides as white areas

31 Signal-to-Noise Ratio In Taguchi analysis the signal-to-noise ratio (SN) refers to the ratio of the power of signal tothe power of noise [2 13] Maximizing the SN guaranteesthe minimum effects of noise on the measured output It iscalculated for three objective functions ldquothe nominal isbestrdquo ldquothe smaller is betterrdquo and ldquothe larger is betterrdquo asgiven in Equations (1)ndash(3) respectively [14]

S

N 10 log

y2

s2y (1)

S

N minus10 log

1n1113944y

21113872 1113873 (2)

S

N minus10 log

1n11139441y2

1113888 1113889 (3)

where y is the average of the observed data n is the number ofobservations and s2y is the variance of y SN for the surfaceroughness measure Ra is calculated using Equation (2) be-cause smaller Ra is better Table 4 shows the SN ratio for theL27 orthogonal inner array From the table it is found thatL03 has the highest SN ratio and hence provides the bestcombination with the minimum effects of noise factors withinthe tested levels of control factors Figure 5 shows the maineffect plot of SN ratio with respect to the control variablesand their levels +e results suggest that the SN ratio is moresensitive to the cutting speed and feed rate while the variationwith the depth of cut is of lower sensitivity

32 Analysis of Variance (ANOVA) ANOVA was con-ducted to test the significance of both control and noisefactors Table 5 illustrates the ANOVA results with p valueless than 005 being considered significant Terms with pvalues higher than 005 are removed from the model unlessthey are a part of significantly higher term ANOVA resultsprove the significance of all factors in combination withsome interactions of second and third order +e noseradius while not significant as a linear term is significant incombination of other factors as illustrated by the p value ofthe 2-way and 3-way interactions +e adjusted R-squared

Table 3 Orthogonal array with the measured Ra (microm)

Inner array Outer array

Test run Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

NR 04coolant on

NR 04coolant off

NR 08coolant on

NR 08coolant off

L01 150 075 015 18317 1970 1181 1654L02 150 075 010 1003 1157 0941 1302L03 150 075 005 0411 0855 0547 0783L04 150 05 015 1588 1530 1171 1231L05 150 05 010 1019 1337 0734 1109L06 150 05 005 0652 1174 0482 0653L07 150 025 015 1072 1539 1146 1484L08 150 025 010 0925 1416 0820 1393L09 150 025 005 0758 1310 0684 1062L10 125 075 015 1803 2287 1058 2039L11 125 075 010 0836 1291 0721 1621L12 125 075 005 0514 1000 0612 1351L13 125 05 015 1513 1663 1294 1689L14 125 05 010 1051 1290 1013 1667L15 125 05 005 0781 1163 0679 1294L16 125 025 015 1621 1780 1235 2576L17 125 025 010 1250 1362 1004 2214L18 125 025 005 1072 1143 0687 1590L19 100 075 015 1948 2053 1309 2128L20 100 075 010 1167 1828 1163 1978L21 100 075 005 1100 1350 0838 1763L22 100 05 015 1524 1895 1470 2450L23 100 05 010 1450 1695 1301 2226L24 100 05 005 1398 1445 1000 2145L25 100 025 015 1840 2702 2294 3082L26 100 025 010 1438 2659 1963 2272L27 100 025 005 0847 2243 1867 1984

Advances in Materials Science and Engineering 5

value is about 87 proving that the model is representativeand explains 97 of the variation in measured outputs

33 Regression and Optimization In this paper regressionanalysis is used to build a second-order relation between theresponse and control factors under dierent conditions ofthe L4 outer array e goal is to investigate the signicanceof quadratic terms on the selection of optimum machiningconditions to minimize the eect of noise factors Table 6summarizes the regression equations at the four combinations

of the outer array e results show that the value of expectedRa depends on quadratic terms in some cases

Composite desirability function was used to minimizethe expected Ra in the calculated four regression equationsTable 7 shows the predicted optimum conditions for eachequation and the corresponding expected Ra ese caseswere machined to validate the regression model output anda comparison between the predicted and measured valuesare presented in the table

Figure 6 represents the surface roughness prole pro-duced by the surface roughness tester It shows the increaseof the feed rate and depth of cut were found to diminish thesurface quality due the amount of forces and related frictioninvolved in cutting larger material volume incrementsSimilar high speed rates and depth of cut eects have beenreported by other studies [11 12 15]

10 μm

(a) (b)

Figure 4 (a) Optical micrograph of tempered steel it is composed of ferrite (white) and temperedmartensite (dark) (b) Secondary electronimage of the dark area in optical micrograph (tempered martensite) showing the presence of ferrite (dark areas) and carbides (white areas)

150125100

ndash1

ndash2

ndash3

ndash4

ndash5

075050025 015010005

S

Mea

n of

SN

ratio

s

DOC FR

Signal-to-noise Smaller is better

Figure 5 Main eect plot for SN ratio with respect to the controlfactors

Table 4 SN ratio for the L27 orthogonal inner array

SN SN SNL01 minus453567 L10 minus536497 L19 minus551797L02 minus090376 L11 minus139333 L20 minus396571L03 343843 L12 062490 L21 minus233181L04 minus287199 L13 minus379391 L22 minus546450L05 minus060185 L14 minus215754 L23 minus463266L06 210752 L15 minus010513 L24 minus382173L07 minus245098 L16 minus542733 L25 minus803586L08 minus136208 L17 minus367699 L26 minus656900L09 012517 L18 minus134791 L27 minus517539

Table 5 ANOVA results for control and noise factors

Source DF Adj SS Adj MS F value p valueModel 31 287059 0926 2367 0Linear 8 246965 308706 7892 0

S 2 82392 411961 10532 0DOC 2 14196 070979 1815 0FR 2 76466 382328 9774 0NR 1 00015 000151 004 0845Coolant 1 73896 738962 18891 0

2-way interactions 17 31721 018659 477 0S lowast DOC 4 09537 023843 61 0S lowast NR 2 04967 024834 635 0003S lowast coolant 2 05287 026436 676 0002DOC lowast FR 4 04059 010147 259 0043DOC lowast NR 2 02202 011011 281 0066FR lowast NR 2 01385 006925 177 0177NR lowast coolant 1 04284 042836 1095 0001

3-way interactions 6 08373 013955 357 0004S lowast NR lowast coolant 2 03999 019993 511 0008DOC lowast FR lowast NR 4 04374 010936 28 0032

Error 76 29728 003912Total 107 316787R-sq R-sq (adj) R-sq (pred)9062 8679 8105

6 Advances in Materials Science and Engineering

Composite desirability function was used to select theoptimum combination of control factors that optimizes Raunder different noise factors conditions ie the controlfactors levels that will minimize the effects of the noisefactors +is combination was S 150mminute DOC 064mm and FR 005mmrev with composite desirability 095 Figure 7 shows the optimization plot of Ra +eseoptimum conditions were run to validate the regression andoptimization process and the results are summarized in

Table 8 +e small error (le5) proves the match betweenexperimental and model predicted values

4 Conclusions

In this research a relation between the experiments done inclosed ideal lab conditions and the actual manufacturingprocesses in workshops is investigated Under real workingconditions uncontrolled parameters may interfere with

Table 6 Regression equations at the four conditions of the L4 outer array

Outer array combination Regression equationNR 04 Ra 1949 minus 000767S minus 1181DOC + 234FR + 1134DOC lowast FRCoolant onNR 04 Ra 1145 minus 01197S minus 836DOC + 044FR + 0000373S2 + 317DOC2 + 00279S lowast DOC + 1187DOC lowast FRCoolant offNR 08 Ra 1063 minus 01268S minus 5462DOC + 5291FR + 0000383S2 + 003777S lowast DOCCoolant onNR 08 Ra 4654 minus 002079S minus 365DOC + 6342FR + 298DOC2Coolant off

Table 7 Optimum machining conditions at each noise-factor combination

Testrun

Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

Screen shot fromsurface roughness tester

1 150 075 005 04 On 0440 0455 3 Figure 6(a)2 137 062 005 04 Off 0866 0848 2 Figure 6(b)3 150 025 005 08 On 0552 0541 2 Figure 6(c)4 150 064 005 08 Off 0752 0737 2 Figure 6(d)

(a) (b)

(c) (d)

Figure 6 Screenshots from surface roughness tester for optimum machining conditions at each noise factors combination as shown inTable 7 (a) Test run-1 (b) test run-2 (c) test run-3 (d) test run-4

Advances in Materials Science and Engineering 7

machining requirements inducing lower controlledproduct quality Two noise parameters have been con-sidered to evaluate their eects on the processing qualityand optimal control parameters have been determined tominimize their interference with the nal qualityrequirement

Taguchi robust design was used to minimize the eectof two noise factors tool nose radius and coolant oper-ation on the produced Ra ANOVA results showed that allinvestigated control and noise factors had signicant ef-fects on the output Ra proving the importance of thisinvestigation Regression analysis and composite de-sirability function were used to select the optimumcombination of control factors that will minimize theexpected Ra despite the eects of the noise factors Apredictive model has been developed to determine theoptimum combination of control factors for dierentcongurations of noise factors as illustrated in Tables 7 and8 e results show low prediction errors (le5) whencompared to real experimental tests An optimum com-bination of 150mmin surface speed 064mm depth ofcut and 005mmrev feed rate is used in case of uncertaincombination of tool nose radius and coolant condition

Data Availability

e data used to support the ndings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare no conicts of interest

Acknowledgments

e authors extend their appreciation to the Deanship ofScientic Research at King Saud University for funding thiswork through research group no RG-1439-020

References

[1] D Deepak and B Rajendra ldquoOptimization of machiningparameters for turning of Al6061 using robust design prin-ciple to minimize the surface roughnessrdquo Procedia Technol-ogy vol 24 pp 372ndash378 2016

[2] J Z Zhang J C Chen and E D Kirby ldquoSurface roughnessoptimization in an end-milling operation using the Taguchi

FR 0150

[0050]0050

DOC 0750

[06389]0250

S 1500

[1500]100

High CurLow

Optimal D 09458

Predict

Composite Desirability D 09458

8OFF Minimum y = 07338 d = 096673

8ON Minimum y = 06200 d = 092382

4OFF Minimum y = 09173 d = 096629

4ON Minimum y = 05230 d = 092713

Figure 7 Optimization plot for Ra under dierent combinations of noise factors

Table 8 Measured and expected values of Ra under optimum conditions for all combinations of noise factors

Test run Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

1 150 064 005 04 On 0509 0522 32 150 064 005 04 O 0964 0915 53 150 064 005 08 On 0591 0622 54 150 064 005 08 O 0766 0737 4

8 Advances in Materials Science and Engineering

design methodrdquo Journal of Materials Processing Technologyvol 184 no 1ndash3 pp 233ndash239 2007

[3] A Qasim S Nisar A Shah M S Khalid and M A SheikhldquoOptimization of process parameters for machining of AISI-1045 steel using Taguchi design and ANOVArdquo SimulationModelling Practice and 6eory vol 59 pp 36ndash51 2015

[4] A Bhattacharya S Das P Majumder and A Batish ldquoEsti-mating the effect of cutting parameters on surface finish andpower consumption during high speed machining of AISI1045 steel using Taguchi design and ANOVArdquo ProductionEngineering vol 3 no 1 pp 31ndash40 2009

[5] C Moganapriya R Rajasekar K Ponappa R Venkatesh andS Jeromec ldquoInfluence of coating material and cutting pa-rameters on surface roughness and material removal rate inturning process using Taguchi methodrdquo Materials TodayProceedings vol 5 no 2 pp 8532ndash8538 2018

[6] R M Mazarbhuiya P K Choudhury and P K Patowari ldquoAnexperimental study on parametric optimization for materialremoval rate and surface roughness on EDM by using Taguchimethodrdquo Materials Today Proceedings vol 5 no 2pp 4621ndash4628 2018

[7] D Manivel and R Gandhinathan ldquoOptimization of surfaceroughness and tool wear in hard turning of austemperedductile iron (grade 3) using Taguchi methodrdquo Measurementvol 93 pp 108ndash116 2016

[8] M Nalbant H Gokkaya and G Sur ldquoApplication of Taguchimethod in the optimization of cutting parameters for surfaceroughness in turningrdquo Materials and Design vol 28 no 4pp 1379ndash1385 2007

[9] I Asilturk and H Akkus ldquoDetermining the effect of cuttingparameters on surface roughness in hard turning using theTaguchi methodrdquoMeasurement vol 44 no 9 pp 1697ndash17042011

[10] Y K Hwang and C M Lee ldquoSurface roughness and cuttingforce prediction in MQL and wet turning process of AISI 1045using design of experimentsrdquo Journal of Mechanical Scienceand Technology vol 24 no 8 pp 1669ndash1677 2010

[11] A T Abbas K Hamza M F Aly and E A Al-BahkalildquoMultiobjective optimization of turning cutting parametersfor J-steel materialrdquo Advances in Materials Science and En-gineering vol 2016 Article ID 6429160 8 pages 2016

[12] E A Al Bahkali A E Ragab E A El Danaf and A T AbbasldquoAn investigation of optimum cutting conditions in turningnodular cast iron using carbide inserts with different noseradiusrdquo Proceedings of the Institution of Mechanical EngineersPart B Journal of Engineering Manufacture vol 230 no 9pp 1584ndash1591 2016

[13] K S Kim K T Jung J M Kim J P Hong and S I KimldquoTaguchi robust optimum design for reducing the coggingtorque of EPS motors considering magnetic unbalance causedby manufacturing tolerances of PMrdquo IET Electric PowerApplications vol 10 no 9 pp 909ndash915 2016

[14] A E Ragab ldquoMulti-objective optimization of profile accuracyin two point incremental forming using Taguchi-based greyrelation analysisrdquo International Journal of Collaborative En-terprise vol 6 no 1 pp 49ndash65 2018

[15] A T Abbas A E Ragab E A Al Bahkali and E A El DanafldquoOptimizing cutting conditions for minimum surfaceroughness in face milling of high strength steel using carbideinsertsrdquo Advances in Materials Science and Engineeringvol 2016 Article ID 7372132 14 pages 2016

Advances in Materials Science and Engineering 9

CorrosionInternational Journal of

Hindawiwwwhindawicom Volume 2018

Advances in

Materials Science and EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Journal of

Chemistry

Analytical ChemistryInternational Journal of

Hindawiwwwhindawicom Volume 2018

ScienticaHindawiwwwhindawicom Volume 2018

Polymer ScienceInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Advances in Condensed Matter Physics

Hindawiwwwhindawicom Volume 2018

International Journal of

BiomaterialsHindawiwwwhindawicom

Journal ofEngineeringVolume 2018

Applied ChemistryJournal of

Hindawiwwwhindawicom Volume 2018

NanotechnologyHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

High Energy PhysicsAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

TribologyAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

Hindawiwwwhindawicom Volume 2018

Advances inPhysical Chemistry

Hindawiwwwhindawicom Volume 2018

BioMed Research InternationalMaterials

Journal of

Hindawiwwwhindawicom Volume 2018

Na

nom

ate

ria

ls

Hindawiwwwhindawicom Volume 2018

Journal ofNanomaterials

Submit your manuscripts atwwwhindawicom

221 Selection of Control Factors +e effect of three controlfactors namely cutting speed depth of cut and feed rate onthe produced surface roughness is investigated in this study+e considered range of each factor was divided into threelevels as summarized in Table 2

222 Selection of Noise Factors Two noise factors wereinvestigated in this study namely the tool nose radius andcoolant operation ONOFF status +ese two factors affectthe surface roughness of produced parts while controllingthem during regular production is not always possible Fordifferent reasons workers in workshops may not followprecisely the selection of a particular nose radius Also if thecoolant system is broken down or does not functionproperly the machine operator will not necessarily turn off

the machine Two levels were selected for the tool noseradius (NR) 04 and 08mm and two levels for coolant wereselected coolant ON and coolant OFF

223 Orthogonal Array Taguchi L27 orthogonal array wasused in this study as the inner array that represents thecontrol variables (3 factors 3 levels each) L27 array can runup to 13 factors with 3 levels each It provides 26 degrees offreedom allowing the calculation of several factors in-teraction effects if needed L4 outer array was used torepresent the noise factors (2 factors 2 levels each)+e totalnumber of runs was of 108 runs as illustrated in Table 3 alongwith the related measured surface roughness Ra (microm)

3 Results and Discussion

A heat treatment of the as-received material has increased itshardness from range of 185ndash190HV to a value of 240ndash246HV that improved the surface quality of the machined

Figure 1 Test rig for machining specimens

For clamping bythree jaw chuck

30

Oslash

10 25 5 25 10255

For supportingby rotary center

Feed rate005 mmrev

Feed rate015 mmrev

Feed rate025 mmrev

50

Figure 2 Test specimen drawing

Figure 3 Test rig for measuring the specimen surface roughness

Table 2 +e control factors and their levels used in the currentstudy

Designation Process parameter Level 1 Level 2 Level 3S Cutting speed (mmin) 100 125 150DOC Depth of cut (mm) 025 05 075FR Feed rate (mmrev) 005 010 015

4 Advances in Materials Science and Engineering

material +e increase of hardness has caused the material tobecome less ductile reducing its plastic flow capacity offeringa better surface finish A brittle interaction between a cuttingtool and a low ductility workpiece induces a material sep-aration during machining rather than a plastic flow causingsurface irregularitiesIt is well documented that the microstructure of

annealednormalized AISI 1045 is composed of ferrite andpearlite +e microstructure of tempered specimen is shownin Figure 4(a) It is composed of ferrite areas (white) andtempered martensite (dark) +e dark area in the opticalmicrostructure was enlarged using SEM as shown Figure 4(b)+e ferrite appears as dark areas and carbides as white areas

31 Signal-to-Noise Ratio In Taguchi analysis the signal-to-noise ratio (SN) refers to the ratio of the power of signal tothe power of noise [2 13] Maximizing the SN guaranteesthe minimum effects of noise on the measured output It iscalculated for three objective functions ldquothe nominal isbestrdquo ldquothe smaller is betterrdquo and ldquothe larger is betterrdquo asgiven in Equations (1)ndash(3) respectively [14]

S

N 10 log

y2

s2y (1)

S

N minus10 log

1n1113944y

21113872 1113873 (2)

S

N minus10 log

1n11139441y2

1113888 1113889 (3)

where y is the average of the observed data n is the number ofobservations and s2y is the variance of y SN for the surfaceroughness measure Ra is calculated using Equation (2) be-cause smaller Ra is better Table 4 shows the SN ratio for theL27 orthogonal inner array From the table it is found thatL03 has the highest SN ratio and hence provides the bestcombination with the minimum effects of noise factors withinthe tested levels of control factors Figure 5 shows the maineffect plot of SN ratio with respect to the control variablesand their levels +e results suggest that the SN ratio is moresensitive to the cutting speed and feed rate while the variationwith the depth of cut is of lower sensitivity

32 Analysis of Variance (ANOVA) ANOVA was con-ducted to test the significance of both control and noisefactors Table 5 illustrates the ANOVA results with p valueless than 005 being considered significant Terms with pvalues higher than 005 are removed from the model unlessthey are a part of significantly higher term ANOVA resultsprove the significance of all factors in combination withsome interactions of second and third order +e noseradius while not significant as a linear term is significant incombination of other factors as illustrated by the p value ofthe 2-way and 3-way interactions +e adjusted R-squared

Table 3 Orthogonal array with the measured Ra (microm)

Inner array Outer array

Test run Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

NR 04coolant on

NR 04coolant off

NR 08coolant on

NR 08coolant off

L01 150 075 015 18317 1970 1181 1654L02 150 075 010 1003 1157 0941 1302L03 150 075 005 0411 0855 0547 0783L04 150 05 015 1588 1530 1171 1231L05 150 05 010 1019 1337 0734 1109L06 150 05 005 0652 1174 0482 0653L07 150 025 015 1072 1539 1146 1484L08 150 025 010 0925 1416 0820 1393L09 150 025 005 0758 1310 0684 1062L10 125 075 015 1803 2287 1058 2039L11 125 075 010 0836 1291 0721 1621L12 125 075 005 0514 1000 0612 1351L13 125 05 015 1513 1663 1294 1689L14 125 05 010 1051 1290 1013 1667L15 125 05 005 0781 1163 0679 1294L16 125 025 015 1621 1780 1235 2576L17 125 025 010 1250 1362 1004 2214L18 125 025 005 1072 1143 0687 1590L19 100 075 015 1948 2053 1309 2128L20 100 075 010 1167 1828 1163 1978L21 100 075 005 1100 1350 0838 1763L22 100 05 015 1524 1895 1470 2450L23 100 05 010 1450 1695 1301 2226L24 100 05 005 1398 1445 1000 2145L25 100 025 015 1840 2702 2294 3082L26 100 025 010 1438 2659 1963 2272L27 100 025 005 0847 2243 1867 1984

Advances in Materials Science and Engineering 5

value is about 87 proving that the model is representativeand explains 97 of the variation in measured outputs

33 Regression and Optimization In this paper regressionanalysis is used to build a second-order relation between theresponse and control factors under dierent conditions ofthe L4 outer array e goal is to investigate the signicanceof quadratic terms on the selection of optimum machiningconditions to minimize the eect of noise factors Table 6summarizes the regression equations at the four combinations

of the outer array e results show that the value of expectedRa depends on quadratic terms in some cases

Composite desirability function was used to minimizethe expected Ra in the calculated four regression equationsTable 7 shows the predicted optimum conditions for eachequation and the corresponding expected Ra ese caseswere machined to validate the regression model output anda comparison between the predicted and measured valuesare presented in the table

Figure 6 represents the surface roughness prole pro-duced by the surface roughness tester It shows the increaseof the feed rate and depth of cut were found to diminish thesurface quality due the amount of forces and related frictioninvolved in cutting larger material volume incrementsSimilar high speed rates and depth of cut eects have beenreported by other studies [11 12 15]

10 μm

(a) (b)

Figure 4 (a) Optical micrograph of tempered steel it is composed of ferrite (white) and temperedmartensite (dark) (b) Secondary electronimage of the dark area in optical micrograph (tempered martensite) showing the presence of ferrite (dark areas) and carbides (white areas)

150125100

ndash1

ndash2

ndash3

ndash4

ndash5

075050025 015010005

S

Mea

n of

SN

ratio

s

DOC FR

Signal-to-noise Smaller is better

Figure 5 Main eect plot for SN ratio with respect to the controlfactors

Table 4 SN ratio for the L27 orthogonal inner array

SN SN SNL01 minus453567 L10 minus536497 L19 minus551797L02 minus090376 L11 minus139333 L20 minus396571L03 343843 L12 062490 L21 minus233181L04 minus287199 L13 minus379391 L22 minus546450L05 minus060185 L14 minus215754 L23 minus463266L06 210752 L15 minus010513 L24 minus382173L07 minus245098 L16 minus542733 L25 minus803586L08 minus136208 L17 minus367699 L26 minus656900L09 012517 L18 minus134791 L27 minus517539

Table 5 ANOVA results for control and noise factors

Source DF Adj SS Adj MS F value p valueModel 31 287059 0926 2367 0Linear 8 246965 308706 7892 0

S 2 82392 411961 10532 0DOC 2 14196 070979 1815 0FR 2 76466 382328 9774 0NR 1 00015 000151 004 0845Coolant 1 73896 738962 18891 0

2-way interactions 17 31721 018659 477 0S lowast DOC 4 09537 023843 61 0S lowast NR 2 04967 024834 635 0003S lowast coolant 2 05287 026436 676 0002DOC lowast FR 4 04059 010147 259 0043DOC lowast NR 2 02202 011011 281 0066FR lowast NR 2 01385 006925 177 0177NR lowast coolant 1 04284 042836 1095 0001

3-way interactions 6 08373 013955 357 0004S lowast NR lowast coolant 2 03999 019993 511 0008DOC lowast FR lowast NR 4 04374 010936 28 0032

Error 76 29728 003912Total 107 316787R-sq R-sq (adj) R-sq (pred)9062 8679 8105

6 Advances in Materials Science and Engineering

Composite desirability function was used to select theoptimum combination of control factors that optimizes Raunder different noise factors conditions ie the controlfactors levels that will minimize the effects of the noisefactors +is combination was S 150mminute DOC 064mm and FR 005mmrev with composite desirability 095 Figure 7 shows the optimization plot of Ra +eseoptimum conditions were run to validate the regression andoptimization process and the results are summarized in

Table 8 +e small error (le5) proves the match betweenexperimental and model predicted values

4 Conclusions

In this research a relation between the experiments done inclosed ideal lab conditions and the actual manufacturingprocesses in workshops is investigated Under real workingconditions uncontrolled parameters may interfere with

Table 6 Regression equations at the four conditions of the L4 outer array

Outer array combination Regression equationNR 04 Ra 1949 minus 000767S minus 1181DOC + 234FR + 1134DOC lowast FRCoolant onNR 04 Ra 1145 minus 01197S minus 836DOC + 044FR + 0000373S2 + 317DOC2 + 00279S lowast DOC + 1187DOC lowast FRCoolant offNR 08 Ra 1063 minus 01268S minus 5462DOC + 5291FR + 0000383S2 + 003777S lowast DOCCoolant onNR 08 Ra 4654 minus 002079S minus 365DOC + 6342FR + 298DOC2Coolant off

Table 7 Optimum machining conditions at each noise-factor combination

Testrun

Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

Screen shot fromsurface roughness tester

1 150 075 005 04 On 0440 0455 3 Figure 6(a)2 137 062 005 04 Off 0866 0848 2 Figure 6(b)3 150 025 005 08 On 0552 0541 2 Figure 6(c)4 150 064 005 08 Off 0752 0737 2 Figure 6(d)

(a) (b)

(c) (d)

Figure 6 Screenshots from surface roughness tester for optimum machining conditions at each noise factors combination as shown inTable 7 (a) Test run-1 (b) test run-2 (c) test run-3 (d) test run-4

Advances in Materials Science and Engineering 7

machining requirements inducing lower controlledproduct quality Two noise parameters have been con-sidered to evaluate their eects on the processing qualityand optimal control parameters have been determined tominimize their interference with the nal qualityrequirement

Taguchi robust design was used to minimize the eectof two noise factors tool nose radius and coolant oper-ation on the produced Ra ANOVA results showed that allinvestigated control and noise factors had signicant ef-fects on the output Ra proving the importance of thisinvestigation Regression analysis and composite de-sirability function were used to select the optimumcombination of control factors that will minimize theexpected Ra despite the eects of the noise factors Apredictive model has been developed to determine theoptimum combination of control factors for dierentcongurations of noise factors as illustrated in Tables 7 and8 e results show low prediction errors (le5) whencompared to real experimental tests An optimum com-bination of 150mmin surface speed 064mm depth ofcut and 005mmrev feed rate is used in case of uncertaincombination of tool nose radius and coolant condition

Data Availability

e data used to support the ndings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare no conicts of interest

Acknowledgments

e authors extend their appreciation to the Deanship ofScientic Research at King Saud University for funding thiswork through research group no RG-1439-020

References

[1] D Deepak and B Rajendra ldquoOptimization of machiningparameters for turning of Al6061 using robust design prin-ciple to minimize the surface roughnessrdquo Procedia Technol-ogy vol 24 pp 372ndash378 2016

[2] J Z Zhang J C Chen and E D Kirby ldquoSurface roughnessoptimization in an end-milling operation using the Taguchi

FR 0150

[0050]0050

DOC 0750

[06389]0250

S 1500

[1500]100

High CurLow

Optimal D 09458

Predict

Composite Desirability D 09458

8OFF Minimum y = 07338 d = 096673

8ON Minimum y = 06200 d = 092382

4OFF Minimum y = 09173 d = 096629

4ON Minimum y = 05230 d = 092713

Figure 7 Optimization plot for Ra under dierent combinations of noise factors

Table 8 Measured and expected values of Ra under optimum conditions for all combinations of noise factors

Test run Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

1 150 064 005 04 On 0509 0522 32 150 064 005 04 O 0964 0915 53 150 064 005 08 On 0591 0622 54 150 064 005 08 O 0766 0737 4

8 Advances in Materials Science and Engineering

design methodrdquo Journal of Materials Processing Technologyvol 184 no 1ndash3 pp 233ndash239 2007

[3] A Qasim S Nisar A Shah M S Khalid and M A SheikhldquoOptimization of process parameters for machining of AISI-1045 steel using Taguchi design and ANOVArdquo SimulationModelling Practice and 6eory vol 59 pp 36ndash51 2015

[4] A Bhattacharya S Das P Majumder and A Batish ldquoEsti-mating the effect of cutting parameters on surface finish andpower consumption during high speed machining of AISI1045 steel using Taguchi design and ANOVArdquo ProductionEngineering vol 3 no 1 pp 31ndash40 2009

[5] C Moganapriya R Rajasekar K Ponappa R Venkatesh andS Jeromec ldquoInfluence of coating material and cutting pa-rameters on surface roughness and material removal rate inturning process using Taguchi methodrdquo Materials TodayProceedings vol 5 no 2 pp 8532ndash8538 2018

[6] R M Mazarbhuiya P K Choudhury and P K Patowari ldquoAnexperimental study on parametric optimization for materialremoval rate and surface roughness on EDM by using Taguchimethodrdquo Materials Today Proceedings vol 5 no 2pp 4621ndash4628 2018

[7] D Manivel and R Gandhinathan ldquoOptimization of surfaceroughness and tool wear in hard turning of austemperedductile iron (grade 3) using Taguchi methodrdquo Measurementvol 93 pp 108ndash116 2016

[8] M Nalbant H Gokkaya and G Sur ldquoApplication of Taguchimethod in the optimization of cutting parameters for surfaceroughness in turningrdquo Materials and Design vol 28 no 4pp 1379ndash1385 2007

[9] I Asilturk and H Akkus ldquoDetermining the effect of cuttingparameters on surface roughness in hard turning using theTaguchi methodrdquoMeasurement vol 44 no 9 pp 1697ndash17042011

[10] Y K Hwang and C M Lee ldquoSurface roughness and cuttingforce prediction in MQL and wet turning process of AISI 1045using design of experimentsrdquo Journal of Mechanical Scienceand Technology vol 24 no 8 pp 1669ndash1677 2010

[11] A T Abbas K Hamza M F Aly and E A Al-BahkalildquoMultiobjective optimization of turning cutting parametersfor J-steel materialrdquo Advances in Materials Science and En-gineering vol 2016 Article ID 6429160 8 pages 2016

[12] E A Al Bahkali A E Ragab E A El Danaf and A T AbbasldquoAn investigation of optimum cutting conditions in turningnodular cast iron using carbide inserts with different noseradiusrdquo Proceedings of the Institution of Mechanical EngineersPart B Journal of Engineering Manufacture vol 230 no 9pp 1584ndash1591 2016

[13] K S Kim K T Jung J M Kim J P Hong and S I KimldquoTaguchi robust optimum design for reducing the coggingtorque of EPS motors considering magnetic unbalance causedby manufacturing tolerances of PMrdquo IET Electric PowerApplications vol 10 no 9 pp 909ndash915 2016

[14] A E Ragab ldquoMulti-objective optimization of profile accuracyin two point incremental forming using Taguchi-based greyrelation analysisrdquo International Journal of Collaborative En-terprise vol 6 no 1 pp 49ndash65 2018

[15] A T Abbas A E Ragab E A Al Bahkali and E A El DanafldquoOptimizing cutting conditions for minimum surfaceroughness in face milling of high strength steel using carbideinsertsrdquo Advances in Materials Science and Engineeringvol 2016 Article ID 7372132 14 pages 2016

Advances in Materials Science and Engineering 9

CorrosionInternational Journal of

Hindawiwwwhindawicom Volume 2018

Advances in

Materials Science and EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Journal of

Chemistry

Analytical ChemistryInternational Journal of

Hindawiwwwhindawicom Volume 2018

ScienticaHindawiwwwhindawicom Volume 2018

Polymer ScienceInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Advances in Condensed Matter Physics

Hindawiwwwhindawicom Volume 2018

International Journal of

BiomaterialsHindawiwwwhindawicom

Journal ofEngineeringVolume 2018

Applied ChemistryJournal of

Hindawiwwwhindawicom Volume 2018

NanotechnologyHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

High Energy PhysicsAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

TribologyAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

Hindawiwwwhindawicom Volume 2018

Advances inPhysical Chemistry

Hindawiwwwhindawicom Volume 2018

BioMed Research InternationalMaterials

Journal of

Hindawiwwwhindawicom Volume 2018

Na

nom

ate

ria

ls

Hindawiwwwhindawicom Volume 2018

Journal ofNanomaterials

Submit your manuscripts atwwwhindawicom

material +e increase of hardness has caused the material tobecome less ductile reducing its plastic flow capacity offeringa better surface finish A brittle interaction between a cuttingtool and a low ductility workpiece induces a material sep-aration during machining rather than a plastic flow causingsurface irregularitiesIt is well documented that the microstructure of

annealednormalized AISI 1045 is composed of ferrite andpearlite +e microstructure of tempered specimen is shownin Figure 4(a) It is composed of ferrite areas (white) andtempered martensite (dark) +e dark area in the opticalmicrostructure was enlarged using SEM as shown Figure 4(b)+e ferrite appears as dark areas and carbides as white areas

31 Signal-to-Noise Ratio In Taguchi analysis the signal-to-noise ratio (SN) refers to the ratio of the power of signal tothe power of noise [2 13] Maximizing the SN guaranteesthe minimum effects of noise on the measured output It iscalculated for three objective functions ldquothe nominal isbestrdquo ldquothe smaller is betterrdquo and ldquothe larger is betterrdquo asgiven in Equations (1)ndash(3) respectively [14]

S

N 10 log

y2

s2y (1)

S

N minus10 log

1n1113944y

21113872 1113873 (2)

S

N minus10 log

1n11139441y2

1113888 1113889 (3)

where y is the average of the observed data n is the number ofobservations and s2y is the variance of y SN for the surfaceroughness measure Ra is calculated using Equation (2) be-cause smaller Ra is better Table 4 shows the SN ratio for theL27 orthogonal inner array From the table it is found thatL03 has the highest SN ratio and hence provides the bestcombination with the minimum effects of noise factors withinthe tested levels of control factors Figure 5 shows the maineffect plot of SN ratio with respect to the control variablesand their levels +e results suggest that the SN ratio is moresensitive to the cutting speed and feed rate while the variationwith the depth of cut is of lower sensitivity

32 Analysis of Variance (ANOVA) ANOVA was con-ducted to test the significance of both control and noisefactors Table 5 illustrates the ANOVA results with p valueless than 005 being considered significant Terms with pvalues higher than 005 are removed from the model unlessthey are a part of significantly higher term ANOVA resultsprove the significance of all factors in combination withsome interactions of second and third order +e noseradius while not significant as a linear term is significant incombination of other factors as illustrated by the p value ofthe 2-way and 3-way interactions +e adjusted R-squared

Table 3 Orthogonal array with the measured Ra (microm)

Inner array Outer array

Test run Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

NR 04coolant on

NR 04coolant off

NR 08coolant on

NR 08coolant off

L01 150 075 015 18317 1970 1181 1654L02 150 075 010 1003 1157 0941 1302L03 150 075 005 0411 0855 0547 0783L04 150 05 015 1588 1530 1171 1231L05 150 05 010 1019 1337 0734 1109L06 150 05 005 0652 1174 0482 0653L07 150 025 015 1072 1539 1146 1484L08 150 025 010 0925 1416 0820 1393L09 150 025 005 0758 1310 0684 1062L10 125 075 015 1803 2287 1058 2039L11 125 075 010 0836 1291 0721 1621L12 125 075 005 0514 1000 0612 1351L13 125 05 015 1513 1663 1294 1689L14 125 05 010 1051 1290 1013 1667L15 125 05 005 0781 1163 0679 1294L16 125 025 015 1621 1780 1235 2576L17 125 025 010 1250 1362 1004 2214L18 125 025 005 1072 1143 0687 1590L19 100 075 015 1948 2053 1309 2128L20 100 075 010 1167 1828 1163 1978L21 100 075 005 1100 1350 0838 1763L22 100 05 015 1524 1895 1470 2450L23 100 05 010 1450 1695 1301 2226L24 100 05 005 1398 1445 1000 2145L25 100 025 015 1840 2702 2294 3082L26 100 025 010 1438 2659 1963 2272L27 100 025 005 0847 2243 1867 1984

Advances in Materials Science and Engineering 5

value is about 87 proving that the model is representativeand explains 97 of the variation in measured outputs

33 Regression and Optimization In this paper regressionanalysis is used to build a second-order relation between theresponse and control factors under dierent conditions ofthe L4 outer array e goal is to investigate the signicanceof quadratic terms on the selection of optimum machiningconditions to minimize the eect of noise factors Table 6summarizes the regression equations at the four combinations

of the outer array e results show that the value of expectedRa depends on quadratic terms in some cases

Composite desirability function was used to minimizethe expected Ra in the calculated four regression equationsTable 7 shows the predicted optimum conditions for eachequation and the corresponding expected Ra ese caseswere machined to validate the regression model output anda comparison between the predicted and measured valuesare presented in the table

Figure 6 represents the surface roughness prole pro-duced by the surface roughness tester It shows the increaseof the feed rate and depth of cut were found to diminish thesurface quality due the amount of forces and related frictioninvolved in cutting larger material volume incrementsSimilar high speed rates and depth of cut eects have beenreported by other studies [11 12 15]

10 μm

(a) (b)

Figure 4 (a) Optical micrograph of tempered steel it is composed of ferrite (white) and temperedmartensite (dark) (b) Secondary electronimage of the dark area in optical micrograph (tempered martensite) showing the presence of ferrite (dark areas) and carbides (white areas)

150125100

ndash1

ndash2

ndash3

ndash4

ndash5

075050025 015010005

S

Mea

n of

SN

ratio

s

DOC FR

Signal-to-noise Smaller is better

Figure 5 Main eect plot for SN ratio with respect to the controlfactors

Table 4 SN ratio for the L27 orthogonal inner array

SN SN SNL01 minus453567 L10 minus536497 L19 minus551797L02 minus090376 L11 minus139333 L20 minus396571L03 343843 L12 062490 L21 minus233181L04 minus287199 L13 minus379391 L22 minus546450L05 minus060185 L14 minus215754 L23 minus463266L06 210752 L15 minus010513 L24 minus382173L07 minus245098 L16 minus542733 L25 minus803586L08 minus136208 L17 minus367699 L26 minus656900L09 012517 L18 minus134791 L27 minus517539

Table 5 ANOVA results for control and noise factors

Source DF Adj SS Adj MS F value p valueModel 31 287059 0926 2367 0Linear 8 246965 308706 7892 0

S 2 82392 411961 10532 0DOC 2 14196 070979 1815 0FR 2 76466 382328 9774 0NR 1 00015 000151 004 0845Coolant 1 73896 738962 18891 0

2-way interactions 17 31721 018659 477 0S lowast DOC 4 09537 023843 61 0S lowast NR 2 04967 024834 635 0003S lowast coolant 2 05287 026436 676 0002DOC lowast FR 4 04059 010147 259 0043DOC lowast NR 2 02202 011011 281 0066FR lowast NR 2 01385 006925 177 0177NR lowast coolant 1 04284 042836 1095 0001

3-way interactions 6 08373 013955 357 0004S lowast NR lowast coolant 2 03999 019993 511 0008DOC lowast FR lowast NR 4 04374 010936 28 0032

Error 76 29728 003912Total 107 316787R-sq R-sq (adj) R-sq (pred)9062 8679 8105

6 Advances in Materials Science and Engineering

Composite desirability function was used to select theoptimum combination of control factors that optimizes Raunder different noise factors conditions ie the controlfactors levels that will minimize the effects of the noisefactors +is combination was S 150mminute DOC 064mm and FR 005mmrev with composite desirability 095 Figure 7 shows the optimization plot of Ra +eseoptimum conditions were run to validate the regression andoptimization process and the results are summarized in

Table 8 +e small error (le5) proves the match betweenexperimental and model predicted values

4 Conclusions

In this research a relation between the experiments done inclosed ideal lab conditions and the actual manufacturingprocesses in workshops is investigated Under real workingconditions uncontrolled parameters may interfere with

Table 6 Regression equations at the four conditions of the L4 outer array

Outer array combination Regression equationNR 04 Ra 1949 minus 000767S minus 1181DOC + 234FR + 1134DOC lowast FRCoolant onNR 04 Ra 1145 minus 01197S minus 836DOC + 044FR + 0000373S2 + 317DOC2 + 00279S lowast DOC + 1187DOC lowast FRCoolant offNR 08 Ra 1063 minus 01268S minus 5462DOC + 5291FR + 0000383S2 + 003777S lowast DOCCoolant onNR 08 Ra 4654 minus 002079S minus 365DOC + 6342FR + 298DOC2Coolant off

Table 7 Optimum machining conditions at each noise-factor combination

Testrun

Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

Screen shot fromsurface roughness tester

1 150 075 005 04 On 0440 0455 3 Figure 6(a)2 137 062 005 04 Off 0866 0848 2 Figure 6(b)3 150 025 005 08 On 0552 0541 2 Figure 6(c)4 150 064 005 08 Off 0752 0737 2 Figure 6(d)

(a) (b)

(c) (d)

Figure 6 Screenshots from surface roughness tester for optimum machining conditions at each noise factors combination as shown inTable 7 (a) Test run-1 (b) test run-2 (c) test run-3 (d) test run-4

Advances in Materials Science and Engineering 7

machining requirements inducing lower controlledproduct quality Two noise parameters have been con-sidered to evaluate their eects on the processing qualityand optimal control parameters have been determined tominimize their interference with the nal qualityrequirement

Taguchi robust design was used to minimize the eectof two noise factors tool nose radius and coolant oper-ation on the produced Ra ANOVA results showed that allinvestigated control and noise factors had signicant ef-fects on the output Ra proving the importance of thisinvestigation Regression analysis and composite de-sirability function were used to select the optimumcombination of control factors that will minimize theexpected Ra despite the eects of the noise factors Apredictive model has been developed to determine theoptimum combination of control factors for dierentcongurations of noise factors as illustrated in Tables 7 and8 e results show low prediction errors (le5) whencompared to real experimental tests An optimum com-bination of 150mmin surface speed 064mm depth ofcut and 005mmrev feed rate is used in case of uncertaincombination of tool nose radius and coolant condition

Data Availability

e data used to support the ndings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare no conicts of interest

Acknowledgments

e authors extend their appreciation to the Deanship ofScientic Research at King Saud University for funding thiswork through research group no RG-1439-020

References

[1] D Deepak and B Rajendra ldquoOptimization of machiningparameters for turning of Al6061 using robust design prin-ciple to minimize the surface roughnessrdquo Procedia Technol-ogy vol 24 pp 372ndash378 2016

[2] J Z Zhang J C Chen and E D Kirby ldquoSurface roughnessoptimization in an end-milling operation using the Taguchi

FR 0150

[0050]0050

DOC 0750

[06389]0250

S 1500

[1500]100

High CurLow

Optimal D 09458

Predict

Composite Desirability D 09458

8OFF Minimum y = 07338 d = 096673

8ON Minimum y = 06200 d = 092382

4OFF Minimum y = 09173 d = 096629

4ON Minimum y = 05230 d = 092713

Figure 7 Optimization plot for Ra under dierent combinations of noise factors

Table 8 Measured and expected values of Ra under optimum conditions for all combinations of noise factors

Test run Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

1 150 064 005 04 On 0509 0522 32 150 064 005 04 O 0964 0915 53 150 064 005 08 On 0591 0622 54 150 064 005 08 O 0766 0737 4

8 Advances in Materials Science and Engineering

design methodrdquo Journal of Materials Processing Technologyvol 184 no 1ndash3 pp 233ndash239 2007

[3] A Qasim S Nisar A Shah M S Khalid and M A SheikhldquoOptimization of process parameters for machining of AISI-1045 steel using Taguchi design and ANOVArdquo SimulationModelling Practice and 6eory vol 59 pp 36ndash51 2015

[4] A Bhattacharya S Das P Majumder and A Batish ldquoEsti-mating the effect of cutting parameters on surface finish andpower consumption during high speed machining of AISI1045 steel using Taguchi design and ANOVArdquo ProductionEngineering vol 3 no 1 pp 31ndash40 2009

[5] C Moganapriya R Rajasekar K Ponappa R Venkatesh andS Jeromec ldquoInfluence of coating material and cutting pa-rameters on surface roughness and material removal rate inturning process using Taguchi methodrdquo Materials TodayProceedings vol 5 no 2 pp 8532ndash8538 2018

[6] R M Mazarbhuiya P K Choudhury and P K Patowari ldquoAnexperimental study on parametric optimization for materialremoval rate and surface roughness on EDM by using Taguchimethodrdquo Materials Today Proceedings vol 5 no 2pp 4621ndash4628 2018

[7] D Manivel and R Gandhinathan ldquoOptimization of surfaceroughness and tool wear in hard turning of austemperedductile iron (grade 3) using Taguchi methodrdquo Measurementvol 93 pp 108ndash116 2016

[8] M Nalbant H Gokkaya and G Sur ldquoApplication of Taguchimethod in the optimization of cutting parameters for surfaceroughness in turningrdquo Materials and Design vol 28 no 4pp 1379ndash1385 2007

[9] I Asilturk and H Akkus ldquoDetermining the effect of cuttingparameters on surface roughness in hard turning using theTaguchi methodrdquoMeasurement vol 44 no 9 pp 1697ndash17042011

[10] Y K Hwang and C M Lee ldquoSurface roughness and cuttingforce prediction in MQL and wet turning process of AISI 1045using design of experimentsrdquo Journal of Mechanical Scienceand Technology vol 24 no 8 pp 1669ndash1677 2010

[11] A T Abbas K Hamza M F Aly and E A Al-BahkalildquoMultiobjective optimization of turning cutting parametersfor J-steel materialrdquo Advances in Materials Science and En-gineering vol 2016 Article ID 6429160 8 pages 2016

[12] E A Al Bahkali A E Ragab E A El Danaf and A T AbbasldquoAn investigation of optimum cutting conditions in turningnodular cast iron using carbide inserts with different noseradiusrdquo Proceedings of the Institution of Mechanical EngineersPart B Journal of Engineering Manufacture vol 230 no 9pp 1584ndash1591 2016

[13] K S Kim K T Jung J M Kim J P Hong and S I KimldquoTaguchi robust optimum design for reducing the coggingtorque of EPS motors considering magnetic unbalance causedby manufacturing tolerances of PMrdquo IET Electric PowerApplications vol 10 no 9 pp 909ndash915 2016

[14] A E Ragab ldquoMulti-objective optimization of profile accuracyin two point incremental forming using Taguchi-based greyrelation analysisrdquo International Journal of Collaborative En-terprise vol 6 no 1 pp 49ndash65 2018

[15] A T Abbas A E Ragab E A Al Bahkali and E A El DanafldquoOptimizing cutting conditions for minimum surfaceroughness in face milling of high strength steel using carbideinsertsrdquo Advances in Materials Science and Engineeringvol 2016 Article ID 7372132 14 pages 2016

Advances in Materials Science and Engineering 9

CorrosionInternational Journal of

Hindawiwwwhindawicom Volume 2018

Advances in

Materials Science and EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Journal of

Chemistry

Analytical ChemistryInternational Journal of

Hindawiwwwhindawicom Volume 2018

ScienticaHindawiwwwhindawicom Volume 2018

Polymer ScienceInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Advances in Condensed Matter Physics

Hindawiwwwhindawicom Volume 2018

International Journal of

BiomaterialsHindawiwwwhindawicom

Journal ofEngineeringVolume 2018

Applied ChemistryJournal of

Hindawiwwwhindawicom Volume 2018

NanotechnologyHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

High Energy PhysicsAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

TribologyAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

Hindawiwwwhindawicom Volume 2018

Advances inPhysical Chemistry

Hindawiwwwhindawicom Volume 2018

BioMed Research InternationalMaterials

Journal of

Hindawiwwwhindawicom Volume 2018

Na

nom

ate

ria

ls

Hindawiwwwhindawicom Volume 2018

Journal ofNanomaterials

Submit your manuscripts atwwwhindawicom

value is about 87 proving that the model is representativeand explains 97 of the variation in measured outputs

33 Regression and Optimization In this paper regressionanalysis is used to build a second-order relation between theresponse and control factors under dierent conditions ofthe L4 outer array e goal is to investigate the signicanceof quadratic terms on the selection of optimum machiningconditions to minimize the eect of noise factors Table 6summarizes the regression equations at the four combinations

of the outer array e results show that the value of expectedRa depends on quadratic terms in some cases

Composite desirability function was used to minimizethe expected Ra in the calculated four regression equationsTable 7 shows the predicted optimum conditions for eachequation and the corresponding expected Ra ese caseswere machined to validate the regression model output anda comparison between the predicted and measured valuesare presented in the table

Figure 6 represents the surface roughness prole pro-duced by the surface roughness tester It shows the increaseof the feed rate and depth of cut were found to diminish thesurface quality due the amount of forces and related frictioninvolved in cutting larger material volume incrementsSimilar high speed rates and depth of cut eects have beenreported by other studies [11 12 15]

10 μm

(a) (b)

Figure 4 (a) Optical micrograph of tempered steel it is composed of ferrite (white) and temperedmartensite (dark) (b) Secondary electronimage of the dark area in optical micrograph (tempered martensite) showing the presence of ferrite (dark areas) and carbides (white areas)

150125100

ndash1

ndash2

ndash3

ndash4

ndash5

075050025 015010005

S

Mea

n of

SN

ratio

s

DOC FR

Signal-to-noise Smaller is better

Figure 5 Main eect plot for SN ratio with respect to the controlfactors

Table 4 SN ratio for the L27 orthogonal inner array

SN SN SNL01 minus453567 L10 minus536497 L19 minus551797L02 minus090376 L11 minus139333 L20 minus396571L03 343843 L12 062490 L21 minus233181L04 minus287199 L13 minus379391 L22 minus546450L05 minus060185 L14 minus215754 L23 minus463266L06 210752 L15 minus010513 L24 minus382173L07 minus245098 L16 minus542733 L25 minus803586L08 minus136208 L17 minus367699 L26 minus656900L09 012517 L18 minus134791 L27 minus517539

Table 5 ANOVA results for control and noise factors

Source DF Adj SS Adj MS F value p valueModel 31 287059 0926 2367 0Linear 8 246965 308706 7892 0

S 2 82392 411961 10532 0DOC 2 14196 070979 1815 0FR 2 76466 382328 9774 0NR 1 00015 000151 004 0845Coolant 1 73896 738962 18891 0

2-way interactions 17 31721 018659 477 0S lowast DOC 4 09537 023843 61 0S lowast NR 2 04967 024834 635 0003S lowast coolant 2 05287 026436 676 0002DOC lowast FR 4 04059 010147 259 0043DOC lowast NR 2 02202 011011 281 0066FR lowast NR 2 01385 006925 177 0177NR lowast coolant 1 04284 042836 1095 0001

3-way interactions 6 08373 013955 357 0004S lowast NR lowast coolant 2 03999 019993 511 0008DOC lowast FR lowast NR 4 04374 010936 28 0032

Error 76 29728 003912Total 107 316787R-sq R-sq (adj) R-sq (pred)9062 8679 8105

6 Advances in Materials Science and Engineering

Composite desirability function was used to select theoptimum combination of control factors that optimizes Raunder different noise factors conditions ie the controlfactors levels that will minimize the effects of the noisefactors +is combination was S 150mminute DOC 064mm and FR 005mmrev with composite desirability 095 Figure 7 shows the optimization plot of Ra +eseoptimum conditions were run to validate the regression andoptimization process and the results are summarized in

Table 8 +e small error (le5) proves the match betweenexperimental and model predicted values

4 Conclusions

In this research a relation between the experiments done inclosed ideal lab conditions and the actual manufacturingprocesses in workshops is investigated Under real workingconditions uncontrolled parameters may interfere with

Table 6 Regression equations at the four conditions of the L4 outer array

Outer array combination Regression equationNR 04 Ra 1949 minus 000767S minus 1181DOC + 234FR + 1134DOC lowast FRCoolant onNR 04 Ra 1145 minus 01197S minus 836DOC + 044FR + 0000373S2 + 317DOC2 + 00279S lowast DOC + 1187DOC lowast FRCoolant offNR 08 Ra 1063 minus 01268S minus 5462DOC + 5291FR + 0000383S2 + 003777S lowast DOCCoolant onNR 08 Ra 4654 minus 002079S minus 365DOC + 6342FR + 298DOC2Coolant off

Table 7 Optimum machining conditions at each noise-factor combination

Testrun

Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

Screen shot fromsurface roughness tester

1 150 075 005 04 On 0440 0455 3 Figure 6(a)2 137 062 005 04 Off 0866 0848 2 Figure 6(b)3 150 025 005 08 On 0552 0541 2 Figure 6(c)4 150 064 005 08 Off 0752 0737 2 Figure 6(d)

(a) (b)

(c) (d)

Figure 6 Screenshots from surface roughness tester for optimum machining conditions at each noise factors combination as shown inTable 7 (a) Test run-1 (b) test run-2 (c) test run-3 (d) test run-4

Advances in Materials Science and Engineering 7

machining requirements inducing lower controlledproduct quality Two noise parameters have been con-sidered to evaluate their eects on the processing qualityand optimal control parameters have been determined tominimize their interference with the nal qualityrequirement

Taguchi robust design was used to minimize the eectof two noise factors tool nose radius and coolant oper-ation on the produced Ra ANOVA results showed that allinvestigated control and noise factors had signicant ef-fects on the output Ra proving the importance of thisinvestigation Regression analysis and composite de-sirability function were used to select the optimumcombination of control factors that will minimize theexpected Ra despite the eects of the noise factors Apredictive model has been developed to determine theoptimum combination of control factors for dierentcongurations of noise factors as illustrated in Tables 7 and8 e results show low prediction errors (le5) whencompared to real experimental tests An optimum com-bination of 150mmin surface speed 064mm depth ofcut and 005mmrev feed rate is used in case of uncertaincombination of tool nose radius and coolant condition

Data Availability

e data used to support the ndings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare no conicts of interest

Acknowledgments

e authors extend their appreciation to the Deanship ofScientic Research at King Saud University for funding thiswork through research group no RG-1439-020

References

[1] D Deepak and B Rajendra ldquoOptimization of machiningparameters for turning of Al6061 using robust design prin-ciple to minimize the surface roughnessrdquo Procedia Technol-ogy vol 24 pp 372ndash378 2016

[2] J Z Zhang J C Chen and E D Kirby ldquoSurface roughnessoptimization in an end-milling operation using the Taguchi

FR 0150

[0050]0050

DOC 0750

[06389]0250

S 1500

[1500]100

High CurLow

Optimal D 09458

Predict

Composite Desirability D 09458

8OFF Minimum y = 07338 d = 096673

8ON Minimum y = 06200 d = 092382

4OFF Minimum y = 09173 d = 096629

4ON Minimum y = 05230 d = 092713

Figure 7 Optimization plot for Ra under dierent combinations of noise factors

Table 8 Measured and expected values of Ra under optimum conditions for all combinations of noise factors

Test run Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

1 150 064 005 04 On 0509 0522 32 150 064 005 04 O 0964 0915 53 150 064 005 08 On 0591 0622 54 150 064 005 08 O 0766 0737 4

8 Advances in Materials Science and Engineering

design methodrdquo Journal of Materials Processing Technologyvol 184 no 1ndash3 pp 233ndash239 2007

[3] A Qasim S Nisar A Shah M S Khalid and M A SheikhldquoOptimization of process parameters for machining of AISI-1045 steel using Taguchi design and ANOVArdquo SimulationModelling Practice and 6eory vol 59 pp 36ndash51 2015

[4] A Bhattacharya S Das P Majumder and A Batish ldquoEsti-mating the effect of cutting parameters on surface finish andpower consumption during high speed machining of AISI1045 steel using Taguchi design and ANOVArdquo ProductionEngineering vol 3 no 1 pp 31ndash40 2009

[5] C Moganapriya R Rajasekar K Ponappa R Venkatesh andS Jeromec ldquoInfluence of coating material and cutting pa-rameters on surface roughness and material removal rate inturning process using Taguchi methodrdquo Materials TodayProceedings vol 5 no 2 pp 8532ndash8538 2018

[6] R M Mazarbhuiya P K Choudhury and P K Patowari ldquoAnexperimental study on parametric optimization for materialremoval rate and surface roughness on EDM by using Taguchimethodrdquo Materials Today Proceedings vol 5 no 2pp 4621ndash4628 2018

[7] D Manivel and R Gandhinathan ldquoOptimization of surfaceroughness and tool wear in hard turning of austemperedductile iron (grade 3) using Taguchi methodrdquo Measurementvol 93 pp 108ndash116 2016

[8] M Nalbant H Gokkaya and G Sur ldquoApplication of Taguchimethod in the optimization of cutting parameters for surfaceroughness in turningrdquo Materials and Design vol 28 no 4pp 1379ndash1385 2007

[9] I Asilturk and H Akkus ldquoDetermining the effect of cuttingparameters on surface roughness in hard turning using theTaguchi methodrdquoMeasurement vol 44 no 9 pp 1697ndash17042011

[10] Y K Hwang and C M Lee ldquoSurface roughness and cuttingforce prediction in MQL and wet turning process of AISI 1045using design of experimentsrdquo Journal of Mechanical Scienceand Technology vol 24 no 8 pp 1669ndash1677 2010

[11] A T Abbas K Hamza M F Aly and E A Al-BahkalildquoMultiobjective optimization of turning cutting parametersfor J-steel materialrdquo Advances in Materials Science and En-gineering vol 2016 Article ID 6429160 8 pages 2016

[12] E A Al Bahkali A E Ragab E A El Danaf and A T AbbasldquoAn investigation of optimum cutting conditions in turningnodular cast iron using carbide inserts with different noseradiusrdquo Proceedings of the Institution of Mechanical EngineersPart B Journal of Engineering Manufacture vol 230 no 9pp 1584ndash1591 2016

[13] K S Kim K T Jung J M Kim J P Hong and S I KimldquoTaguchi robust optimum design for reducing the coggingtorque of EPS motors considering magnetic unbalance causedby manufacturing tolerances of PMrdquo IET Electric PowerApplications vol 10 no 9 pp 909ndash915 2016

[14] A E Ragab ldquoMulti-objective optimization of profile accuracyin two point incremental forming using Taguchi-based greyrelation analysisrdquo International Journal of Collaborative En-terprise vol 6 no 1 pp 49ndash65 2018

[15] A T Abbas A E Ragab E A Al Bahkali and E A El DanafldquoOptimizing cutting conditions for minimum surfaceroughness in face milling of high strength steel using carbideinsertsrdquo Advances in Materials Science and Engineeringvol 2016 Article ID 7372132 14 pages 2016

Advances in Materials Science and Engineering 9

CorrosionInternational Journal of

Hindawiwwwhindawicom Volume 2018

Advances in

Materials Science and EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Journal of

Chemistry

Analytical ChemistryInternational Journal of

Hindawiwwwhindawicom Volume 2018

ScienticaHindawiwwwhindawicom Volume 2018

Polymer ScienceInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Advances in Condensed Matter Physics

Hindawiwwwhindawicom Volume 2018

International Journal of

BiomaterialsHindawiwwwhindawicom

Journal ofEngineeringVolume 2018

Applied ChemistryJournal of

Hindawiwwwhindawicom Volume 2018

NanotechnologyHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

High Energy PhysicsAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

TribologyAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

Hindawiwwwhindawicom Volume 2018

Advances inPhysical Chemistry

Hindawiwwwhindawicom Volume 2018

BioMed Research InternationalMaterials

Journal of

Hindawiwwwhindawicom Volume 2018

Na

nom

ate

ria

ls

Hindawiwwwhindawicom Volume 2018

Journal ofNanomaterials

Submit your manuscripts atwwwhindawicom

Composite desirability function was used to select theoptimum combination of control factors that optimizes Raunder different noise factors conditions ie the controlfactors levels that will minimize the effects of the noisefactors +is combination was S 150mminute DOC 064mm and FR 005mmrev with composite desirability 095 Figure 7 shows the optimization plot of Ra +eseoptimum conditions were run to validate the regression andoptimization process and the results are summarized in

Table 8 +e small error (le5) proves the match betweenexperimental and model predicted values

4 Conclusions

In this research a relation between the experiments done inclosed ideal lab conditions and the actual manufacturingprocesses in workshops is investigated Under real workingconditions uncontrolled parameters may interfere with

Table 6 Regression equations at the four conditions of the L4 outer array

Outer array combination Regression equationNR 04 Ra 1949 minus 000767S minus 1181DOC + 234FR + 1134DOC lowast FRCoolant onNR 04 Ra 1145 minus 01197S minus 836DOC + 044FR + 0000373S2 + 317DOC2 + 00279S lowast DOC + 1187DOC lowast FRCoolant offNR 08 Ra 1063 minus 01268S minus 5462DOC + 5291FR + 0000383S2 + 003777S lowast DOCCoolant onNR 08 Ra 4654 minus 002079S minus 365DOC + 6342FR + 298DOC2Coolant off

Table 7 Optimum machining conditions at each noise-factor combination

Testrun

Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

Screen shot fromsurface roughness tester

1 150 075 005 04 On 0440 0455 3 Figure 6(a)2 137 062 005 04 Off 0866 0848 2 Figure 6(b)3 150 025 005 08 On 0552 0541 2 Figure 6(c)4 150 064 005 08 Off 0752 0737 2 Figure 6(d)

(a) (b)

(c) (d)

Figure 6 Screenshots from surface roughness tester for optimum machining conditions at each noise factors combination as shown inTable 7 (a) Test run-1 (b) test run-2 (c) test run-3 (d) test run-4

Advances in Materials Science and Engineering 7

machining requirements inducing lower controlledproduct quality Two noise parameters have been con-sidered to evaluate their eects on the processing qualityand optimal control parameters have been determined tominimize their interference with the nal qualityrequirement

Taguchi robust design was used to minimize the eectof two noise factors tool nose radius and coolant oper-ation on the produced Ra ANOVA results showed that allinvestigated control and noise factors had signicant ef-fects on the output Ra proving the importance of thisinvestigation Regression analysis and composite de-sirability function were used to select the optimumcombination of control factors that will minimize theexpected Ra despite the eects of the noise factors Apredictive model has been developed to determine theoptimum combination of control factors for dierentcongurations of noise factors as illustrated in Tables 7 and8 e results show low prediction errors (le5) whencompared to real experimental tests An optimum com-bination of 150mmin surface speed 064mm depth ofcut and 005mmrev feed rate is used in case of uncertaincombination of tool nose radius and coolant condition

Data Availability

e data used to support the ndings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare no conicts of interest

Acknowledgments

e authors extend their appreciation to the Deanship ofScientic Research at King Saud University for funding thiswork through research group no RG-1439-020

References

[1] D Deepak and B Rajendra ldquoOptimization of machiningparameters for turning of Al6061 using robust design prin-ciple to minimize the surface roughnessrdquo Procedia Technol-ogy vol 24 pp 372ndash378 2016

[2] J Z Zhang J C Chen and E D Kirby ldquoSurface roughnessoptimization in an end-milling operation using the Taguchi

FR 0150

[0050]0050

DOC 0750

[06389]0250

S 1500

[1500]100

High CurLow

Optimal D 09458

Predict

Composite Desirability D 09458

8OFF Minimum y = 07338 d = 096673

8ON Minimum y = 06200 d = 092382

4OFF Minimum y = 09173 d = 096629

4ON Minimum y = 05230 d = 092713

Figure 7 Optimization plot for Ra under dierent combinations of noise factors

Table 8 Measured and expected values of Ra under optimum conditions for all combinations of noise factors

Test run Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

1 150 064 005 04 On 0509 0522 32 150 064 005 04 O 0964 0915 53 150 064 005 08 On 0591 0622 54 150 064 005 08 O 0766 0737 4

8 Advances in Materials Science and Engineering

design methodrdquo Journal of Materials Processing Technologyvol 184 no 1ndash3 pp 233ndash239 2007

[3] A Qasim S Nisar A Shah M S Khalid and M A SheikhldquoOptimization of process parameters for machining of AISI-1045 steel using Taguchi design and ANOVArdquo SimulationModelling Practice and 6eory vol 59 pp 36ndash51 2015

[4] A Bhattacharya S Das P Majumder and A Batish ldquoEsti-mating the effect of cutting parameters on surface finish andpower consumption during high speed machining of AISI1045 steel using Taguchi design and ANOVArdquo ProductionEngineering vol 3 no 1 pp 31ndash40 2009

[5] C Moganapriya R Rajasekar K Ponappa R Venkatesh andS Jeromec ldquoInfluence of coating material and cutting pa-rameters on surface roughness and material removal rate inturning process using Taguchi methodrdquo Materials TodayProceedings vol 5 no 2 pp 8532ndash8538 2018

[6] R M Mazarbhuiya P K Choudhury and P K Patowari ldquoAnexperimental study on parametric optimization for materialremoval rate and surface roughness on EDM by using Taguchimethodrdquo Materials Today Proceedings vol 5 no 2pp 4621ndash4628 2018

[7] D Manivel and R Gandhinathan ldquoOptimization of surfaceroughness and tool wear in hard turning of austemperedductile iron (grade 3) using Taguchi methodrdquo Measurementvol 93 pp 108ndash116 2016

[8] M Nalbant H Gokkaya and G Sur ldquoApplication of Taguchimethod in the optimization of cutting parameters for surfaceroughness in turningrdquo Materials and Design vol 28 no 4pp 1379ndash1385 2007

[9] I Asilturk and H Akkus ldquoDetermining the effect of cuttingparameters on surface roughness in hard turning using theTaguchi methodrdquoMeasurement vol 44 no 9 pp 1697ndash17042011

[10] Y K Hwang and C M Lee ldquoSurface roughness and cuttingforce prediction in MQL and wet turning process of AISI 1045using design of experimentsrdquo Journal of Mechanical Scienceand Technology vol 24 no 8 pp 1669ndash1677 2010

[11] A T Abbas K Hamza M F Aly and E A Al-BahkalildquoMultiobjective optimization of turning cutting parametersfor J-steel materialrdquo Advances in Materials Science and En-gineering vol 2016 Article ID 6429160 8 pages 2016

[12] E A Al Bahkali A E Ragab E A El Danaf and A T AbbasldquoAn investigation of optimum cutting conditions in turningnodular cast iron using carbide inserts with different noseradiusrdquo Proceedings of the Institution of Mechanical EngineersPart B Journal of Engineering Manufacture vol 230 no 9pp 1584ndash1591 2016

[13] K S Kim K T Jung J M Kim J P Hong and S I KimldquoTaguchi robust optimum design for reducing the coggingtorque of EPS motors considering magnetic unbalance causedby manufacturing tolerances of PMrdquo IET Electric PowerApplications vol 10 no 9 pp 909ndash915 2016

[14] A E Ragab ldquoMulti-objective optimization of profile accuracyin two point incremental forming using Taguchi-based greyrelation analysisrdquo International Journal of Collaborative En-terprise vol 6 no 1 pp 49ndash65 2018

[15] A T Abbas A E Ragab E A Al Bahkali and E A El DanafldquoOptimizing cutting conditions for minimum surfaceroughness in face milling of high strength steel using carbideinsertsrdquo Advances in Materials Science and Engineeringvol 2016 Article ID 7372132 14 pages 2016

Advances in Materials Science and Engineering 9

CorrosionInternational Journal of

Hindawiwwwhindawicom Volume 2018

Advances in

Materials Science and EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Journal of

Chemistry

Analytical ChemistryInternational Journal of

Hindawiwwwhindawicom Volume 2018

ScienticaHindawiwwwhindawicom Volume 2018

Polymer ScienceInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Advances in Condensed Matter Physics

Hindawiwwwhindawicom Volume 2018

International Journal of

BiomaterialsHindawiwwwhindawicom

Journal ofEngineeringVolume 2018

Applied ChemistryJournal of

Hindawiwwwhindawicom Volume 2018

NanotechnologyHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

High Energy PhysicsAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

TribologyAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

Hindawiwwwhindawicom Volume 2018

Advances inPhysical Chemistry

Hindawiwwwhindawicom Volume 2018

BioMed Research InternationalMaterials

Journal of

Hindawiwwwhindawicom Volume 2018

Na

nom

ate

ria

ls

Hindawiwwwhindawicom Volume 2018

Journal ofNanomaterials

Submit your manuscripts atwwwhindawicom

machining requirements inducing lower controlledproduct quality Two noise parameters have been con-sidered to evaluate their eects on the processing qualityand optimal control parameters have been determined tominimize their interference with the nal qualityrequirement

Taguchi robust design was used to minimize the eectof two noise factors tool nose radius and coolant oper-ation on the produced Ra ANOVA results showed that allinvestigated control and noise factors had signicant ef-fects on the output Ra proving the importance of thisinvestigation Regression analysis and composite de-sirability function were used to select the optimumcombination of control factors that will minimize theexpected Ra despite the eects of the noise factors Apredictive model has been developed to determine theoptimum combination of control factors for dierentcongurations of noise factors as illustrated in Tables 7 and8 e results show low prediction errors (le5) whencompared to real experimental tests An optimum com-bination of 150mmin surface speed 064mm depth ofcut and 005mmrev feed rate is used in case of uncertaincombination of tool nose radius and coolant condition

Data Availability

e data used to support the ndings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare no conicts of interest

Acknowledgments

e authors extend their appreciation to the Deanship ofScientic Research at King Saud University for funding thiswork through research group no RG-1439-020

References

[1] D Deepak and B Rajendra ldquoOptimization of machiningparameters for turning of Al6061 using robust design prin-ciple to minimize the surface roughnessrdquo Procedia Technol-ogy vol 24 pp 372ndash378 2016

[2] J Z Zhang J C Chen and E D Kirby ldquoSurface roughnessoptimization in an end-milling operation using the Taguchi

FR 0150

[0050]0050

DOC 0750

[06389]0250

S 1500

[1500]100

High CurLow

Optimal D 09458

Predict

Composite Desirability D 09458

8OFF Minimum y = 07338 d = 096673

8ON Minimum y = 06200 d = 092382

4OFF Minimum y = 09173 d = 096629

4ON Minimum y = 05230 d = 092713

Figure 7 Optimization plot for Ra under dierent combinations of noise factors

Table 8 Measured and expected values of Ra under optimum conditions for all combinations of noise factors

Test run Surface speed(mmin)

Depth ofcut (mm)

Feed rate(mmrev)

Tool noseradius (mm) Coolant Measured

Ra (microm)PredictedRa (microm)

()Error

1 150 064 005 04 On 0509 0522 32 150 064 005 04 O 0964 0915 53 150 064 005 08 On 0591 0622 54 150 064 005 08 O 0766 0737 4

8 Advances in Materials Science and Engineering

design methodrdquo Journal of Materials Processing Technologyvol 184 no 1ndash3 pp 233ndash239 2007

[3] A Qasim S Nisar A Shah M S Khalid and M A SheikhldquoOptimization of process parameters for machining of AISI-1045 steel using Taguchi design and ANOVArdquo SimulationModelling Practice and 6eory vol 59 pp 36ndash51 2015

[4] A Bhattacharya S Das P Majumder and A Batish ldquoEsti-mating the effect of cutting parameters on surface finish andpower consumption during high speed machining of AISI1045 steel using Taguchi design and ANOVArdquo ProductionEngineering vol 3 no 1 pp 31ndash40 2009

[5] C Moganapriya R Rajasekar K Ponappa R Venkatesh andS Jeromec ldquoInfluence of coating material and cutting pa-rameters on surface roughness and material removal rate inturning process using Taguchi methodrdquo Materials TodayProceedings vol 5 no 2 pp 8532ndash8538 2018

[6] R M Mazarbhuiya P K Choudhury and P K Patowari ldquoAnexperimental study on parametric optimization for materialremoval rate and surface roughness on EDM by using Taguchimethodrdquo Materials Today Proceedings vol 5 no 2pp 4621ndash4628 2018

[7] D Manivel and R Gandhinathan ldquoOptimization of surfaceroughness and tool wear in hard turning of austemperedductile iron (grade 3) using Taguchi methodrdquo Measurementvol 93 pp 108ndash116 2016

[8] M Nalbant H Gokkaya and G Sur ldquoApplication of Taguchimethod in the optimization of cutting parameters for surfaceroughness in turningrdquo Materials and Design vol 28 no 4pp 1379ndash1385 2007

[9] I Asilturk and H Akkus ldquoDetermining the effect of cuttingparameters on surface roughness in hard turning using theTaguchi methodrdquoMeasurement vol 44 no 9 pp 1697ndash17042011

[10] Y K Hwang and C M Lee ldquoSurface roughness and cuttingforce prediction in MQL and wet turning process of AISI 1045using design of experimentsrdquo Journal of Mechanical Scienceand Technology vol 24 no 8 pp 1669ndash1677 2010

[11] A T Abbas K Hamza M F Aly and E A Al-BahkalildquoMultiobjective optimization of turning cutting parametersfor J-steel materialrdquo Advances in Materials Science and En-gineering vol 2016 Article ID 6429160 8 pages 2016

[12] E A Al Bahkali A E Ragab E A El Danaf and A T AbbasldquoAn investigation of optimum cutting conditions in turningnodular cast iron using carbide inserts with different noseradiusrdquo Proceedings of the Institution of Mechanical EngineersPart B Journal of Engineering Manufacture vol 230 no 9pp 1584ndash1591 2016

[13] K S Kim K T Jung J M Kim J P Hong and S I KimldquoTaguchi robust optimum design for reducing the coggingtorque of EPS motors considering magnetic unbalance causedby manufacturing tolerances of PMrdquo IET Electric PowerApplications vol 10 no 9 pp 909ndash915 2016

[14] A E Ragab ldquoMulti-objective optimization of profile accuracyin two point incremental forming using Taguchi-based greyrelation analysisrdquo International Journal of Collaborative En-terprise vol 6 no 1 pp 49ndash65 2018

[15] A T Abbas A E Ragab E A Al Bahkali and E A El DanafldquoOptimizing cutting conditions for minimum surfaceroughness in face milling of high strength steel using carbideinsertsrdquo Advances in Materials Science and Engineeringvol 2016 Article ID 7372132 14 pages 2016

Advances in Materials Science and Engineering 9

CorrosionInternational Journal of

Hindawiwwwhindawicom Volume 2018

Advances in

Materials Science and EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Journal of

Chemistry

Analytical ChemistryInternational Journal of

Hindawiwwwhindawicom Volume 2018

ScienticaHindawiwwwhindawicom Volume 2018

Polymer ScienceInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Advances in Condensed Matter Physics

Hindawiwwwhindawicom Volume 2018

International Journal of

BiomaterialsHindawiwwwhindawicom

Journal ofEngineeringVolume 2018

Applied ChemistryJournal of

Hindawiwwwhindawicom Volume 2018

NanotechnologyHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

High Energy PhysicsAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

TribologyAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

Hindawiwwwhindawicom Volume 2018

Advances inPhysical Chemistry

Hindawiwwwhindawicom Volume 2018

BioMed Research InternationalMaterials

Journal of

Hindawiwwwhindawicom Volume 2018

Na

nom

ate

ria

ls

Hindawiwwwhindawicom Volume 2018

Journal ofNanomaterials

Submit your manuscripts atwwwhindawicom

design methodrdquo Journal of Materials Processing Technologyvol 184 no 1ndash3 pp 233ndash239 2007

[3] A Qasim S Nisar A Shah M S Khalid and M A SheikhldquoOptimization of process parameters for machining of AISI-1045 steel using Taguchi design and ANOVArdquo SimulationModelling Practice and 6eory vol 59 pp 36ndash51 2015

[4] A Bhattacharya S Das P Majumder and A Batish ldquoEsti-mating the effect of cutting parameters on surface finish andpower consumption during high speed machining of AISI1045 steel using Taguchi design and ANOVArdquo ProductionEngineering vol 3 no 1 pp 31ndash40 2009

[5] C Moganapriya R Rajasekar K Ponappa R Venkatesh andS Jeromec ldquoInfluence of coating material and cutting pa-rameters on surface roughness and material removal rate inturning process using Taguchi methodrdquo Materials TodayProceedings vol 5 no 2 pp 8532ndash8538 2018

[6] R M Mazarbhuiya P K Choudhury and P K Patowari ldquoAnexperimental study on parametric optimization for materialremoval rate and surface roughness on EDM by using Taguchimethodrdquo Materials Today Proceedings vol 5 no 2pp 4621ndash4628 2018

[7] D Manivel and R Gandhinathan ldquoOptimization of surfaceroughness and tool wear in hard turning of austemperedductile iron (grade 3) using Taguchi methodrdquo Measurementvol 93 pp 108ndash116 2016

[8] M Nalbant H Gokkaya and G Sur ldquoApplication of Taguchimethod in the optimization of cutting parameters for surfaceroughness in turningrdquo Materials and Design vol 28 no 4pp 1379ndash1385 2007

[9] I Asilturk and H Akkus ldquoDetermining the effect of cuttingparameters on surface roughness in hard turning using theTaguchi methodrdquoMeasurement vol 44 no 9 pp 1697ndash17042011

[10] Y K Hwang and C M Lee ldquoSurface roughness and cuttingforce prediction in MQL and wet turning process of AISI 1045using design of experimentsrdquo Journal of Mechanical Scienceand Technology vol 24 no 8 pp 1669ndash1677 2010

[11] A T Abbas K Hamza M F Aly and E A Al-BahkalildquoMultiobjective optimization of turning cutting parametersfor J-steel materialrdquo Advances in Materials Science and En-gineering vol 2016 Article ID 6429160 8 pages 2016

[12] E A Al Bahkali A E Ragab E A El Danaf and A T AbbasldquoAn investigation of optimum cutting conditions in turningnodular cast iron using carbide inserts with different noseradiusrdquo Proceedings of the Institution of Mechanical EngineersPart B Journal of Engineering Manufacture vol 230 no 9pp 1584ndash1591 2016

[13] K S Kim K T Jung J M Kim J P Hong and S I KimldquoTaguchi robust optimum design for reducing the coggingtorque of EPS motors considering magnetic unbalance causedby manufacturing tolerances of PMrdquo IET Electric PowerApplications vol 10 no 9 pp 909ndash915 2016

[14] A E Ragab ldquoMulti-objective optimization of profile accuracyin two point incremental forming using Taguchi-based greyrelation analysisrdquo International Journal of Collaborative En-terprise vol 6 no 1 pp 49ndash65 2018

[15] A T Abbas A E Ragab E A Al Bahkali and E A El DanafldquoOptimizing cutting conditions for minimum surfaceroughness in face milling of high strength steel using carbideinsertsrdquo Advances in Materials Science and Engineeringvol 2016 Article ID 7372132 14 pages 2016

Advances in Materials Science and Engineering 9

CorrosionInternational Journal of

Hindawiwwwhindawicom Volume 2018

Advances in

Materials Science and EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Journal of

Chemistry

Analytical ChemistryInternational Journal of

Hindawiwwwhindawicom Volume 2018

ScienticaHindawiwwwhindawicom Volume 2018

Polymer ScienceInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Advances in Condensed Matter Physics

Hindawiwwwhindawicom Volume 2018

International Journal of

BiomaterialsHindawiwwwhindawicom

Journal ofEngineeringVolume 2018

Applied ChemistryJournal of

Hindawiwwwhindawicom Volume 2018

NanotechnologyHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

High Energy PhysicsAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

TribologyAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

Hindawiwwwhindawicom Volume 2018

Advances inPhysical Chemistry

Hindawiwwwhindawicom Volume 2018

BioMed Research InternationalMaterials

Journal of

Hindawiwwwhindawicom Volume 2018

Na

nom

ate

ria

ls

Hindawiwwwhindawicom Volume 2018

Journal ofNanomaterials

Submit your manuscripts atwwwhindawicom

CorrosionInternational Journal of

Hindawiwwwhindawicom Volume 2018

Advances in

Materials Science and EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Journal of

Chemistry

Analytical ChemistryInternational Journal of

Hindawiwwwhindawicom Volume 2018

ScienticaHindawiwwwhindawicom Volume 2018

Polymer ScienceInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Advances in Condensed Matter Physics

Hindawiwwwhindawicom Volume 2018

International Journal of

BiomaterialsHindawiwwwhindawicom

Journal ofEngineeringVolume 2018

Applied ChemistryJournal of

Hindawiwwwhindawicom Volume 2018

NanotechnologyHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

High Energy PhysicsAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

TribologyAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

Hindawiwwwhindawicom Volume 2018

Advances inPhysical Chemistry

Hindawiwwwhindawicom Volume 2018

BioMed Research InternationalMaterials

Journal of

Hindawiwwwhindawicom Volume 2018

Na

nom

ate

ria

ls

Hindawiwwwhindawicom Volume 2018

Journal ofNanomaterials

Submit your manuscripts atwwwhindawicom