dry sliding friction and wear studies of fly ash reinforced aa

Hindawi Publishing CorporationAdvances in TribologyVolume 2013 Article ID 365602 6 pageshttpdxdoiorg1011552013365602

Research ArticleDry Sliding Friction andWear Studies of Fly Ash ReinforcedAA-6351Metal Matrix Composites

M Uthayakumar1 S Thirumalai Kumaran1 and S Aravindan2

1Department of Mechanical Engineering Kalasalingam University Krishnankoil Tamil Nadu 626 126 India2Department of Mechanical Engineering Indian Institute of Technology New Delhi 110 016 India

Correspondence should be addressed to M Uthayakumar uthaykumargmailcom

Received 27 September 2012 Revised 28 December 2012 Accepted 30 December 2012

Academic Editor Huseyin Ccedilimenoğlu

Copyright copy 2013 M Uthayakumar et al is is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Fly ash particles are potentially used inmetal matrix composites due to their low cost low density and availability in large quantitiesas waste by-products in thermal power plantsis study describesmultifactor-based experiments that were applied to research andinvestigation on dry sliding wear system of stir-cast aluminum alloy 6351 with 5 10 and 15wt y ash reinforced metal matrixcomposites (MMCs) e effects of parameters such as load sliding speed and percentage of y ash on the sliding wear specicwear rate and friction coefficient were analyzed using Grey relational analysis on a pin-on-disc machine Analysis of variance(ANOVA) was also employed to investigate which design parameters signicantly affect the wear behavior of the composite eresults showed that the applied load exerted the greatest effect on the dry sliding wear followed by the sliding velocity

1 Introduction

Metal matrix composites (MMCs) have received substantialattention due to their reputation as stronger stiffer lighterand excellent wear properties over the monolithic alloys [1ndash3] ough MMCs possess superior properties they havenot been widely applied due to the complexity of fabrication[4] e conventional stir casting is an attractive processingmethod for fabrication as it is relatively inexpensive andoffers wide selection of materials and processing conditionsStir casting offers better matrix particle bonding due tostirring action of particles into melts [5]

Wear is one of the most commonly encountered indus-trial problems leading to frequent replacement of com-ponents particularly abrasion Abrasive wear occurs whenhard particles or asperities penetrate a soer surface anddisplace material in the form of elongated chips and slivers[6] Extensive studies on the tribological characteristics ofaluminumMMCs containing various reinforcements such assilicon carbide alumina and short steel ber are already doneby researchers [7ndash9] e variables such as composition of

the matrix particle distribution and interface between theparticles and the matrix affect the tribological behavior ofmetal matrix composites ese conditions include the typeof countersurface applied load sliding speed contact areageometry and environment [10] e principle tribologicalparameters such as applied load [11ndash13] sliding speed [1415] and percentage of y ash control the friction and wearperformance Fly ash is one of the residues generated inthe combustion of coal e addition of y ash leads tothe increase in wear resistance hardness elastic modulusand proof stress compared to unreinforced alloy e y ashparticle size and its volume fraction also signicantly affectthe wear and friction properties of composites [16 17]

e aim of the present study is to investigate the dry slid-ing wear of stir cast AA 6351 with 5 10 and 15wt y ashreinforcedMMCs using a pin-on-disc type of wear machineFurthermore ANOVA was employed to investigate whichdesign parameters signicantly affect thewear behavior of thecomposite Conrmation test was also conducted to verifythe improvement of the quality characteristic using optimallevels of the design parameters

2 Advances in Tribology

T 1 Elements of AA-6351 and its weight ()

Elements Al Si Mn MgWeight () 978 10 06 06

F 1 Micrograph of y ash

2 Materials and Experimental Procedures

21 Raw Material Description and Its Composition Alu-minum alloy 6351 is used in heavy engineering applicationsdue to its strength bearing capacity ease of workability andweldability It is also used in building ship column chimneyrod mould pipe tube vehicle bridge crane and roof [18]e elements of aluminum alloy 6351 and the weight in aretabulated in Table 1

Fly ash is an industrial by-product recovered from the uegas of coal burning electric power plants Fly ash particlesare mostly spherical in shape e y ash is collected fromTuticorin thermal power station India with an averageparticle size of 2ndash10 microns and its typical micrograph ispresented in Figure 1

22 Fabrication of Composite AA-6351 with 5 10 and15wt y ash reinforced MMCs are prepared by stir castingprocess e melting was carried out in a resistance furnacee y ash particles were preheated on an open hearthfurnace for one hour to remove the moisture Scraps of AA6351 were preheated at 450∘C for 3 to 4 h before meltinge preheated aluminum scraps were rst heated above theliquidus temperature to melt them completely ey werethen slightly cooled below the liquidus to maintain the slurryin the semisolid stateis procedure has been adopted whilestir-casting aluminum composites [19 20] Typical specimenis shown in Figure 2

23 Wear Tests e pin-on-disc machine was used to eval-uate the friction and wear response to the sliding contactsurface of the specimens as shown in Figure 3 Tests wereconducted under dry conditions as per ASTM G99-95standards e pin was initially cleaned with acetone andweighed accurately using a digital electronic balancee testwas carried out by applying load (981 1962 and 2943N)and run for a constant sliding distance (3000m) at differentsliding velocities (1 2 and 3ms)ediscmaterial wasmade

F 2 Typical specimen of AA 6351-y ash reinforced MMC

F 3 Pin-on-disc machine

of EN-32 steel with the hardness of 65 HRC At the endof each test the weight loss of the specimen was noticede results obtained during this work have been presentedin terms of sliding wear specic wear rate and frictioncoefficient Sliding wear is related to interactions betweensurfaces and more specically the removal and deformationof material on a surface as a result of mechanical action ofthe opposite surface Specic wear rate is the volume loss persliding distance and load Friction coefficient is the ratio ofthe force of friction between twobodies and the force pressingthem together

24 Selection of Testing Parameters and eir Levels eapplication of design-of-experiments (DoE) requires carefulplanning prudent layout of the experiment and expertanalysis of results Taguchi has standardizedmethods for eachof theseDoE application stepse experiment species threeprinciple wear testing conditions including applied load (A)sliding speed (B) and percentage of y ash (C) as the processparameters e experiments were carried out to analyzethe inuence of sliding wear specic wear rate and frictioncoefficient on the MMCs Control factors and their levels areshown in Table 2is table shows that the experimental planhad three levels

e standard Taguchi experimental plan with notationL9(3)

4 was chosen based upon the degree of freedom edegrees of freedom for the orthogonal array should be greater

Advances in Tribology 3

T 2 Control factors and their levels

Symbols Design parameters Level 1 Level 2 Level 3A Applied load N 981 1962 2943B Sliding speed ms 1 2 3C Percentage of y ash wt 5 10 15

T 3 L9 (3)4 orthogonal array

Control factorsEx A B C1 981 1 52 981 2 103 981 3 154 1962 1 105 1962 2 156 1962 3 57 2943 1 158 2943 2 59 2943 3 10

than or at least equal to those of the process parameters Table3 shows the L9(3)

4 orthogonal array

25 Experimental Details eexperimental results are trans-formed into a signal-to-noise (SN) ratio e-lower-the-better characteristics were selected for sliding wear specicwear rate and friction coefficient due to the wear resistanceof the tested samples A pin-on-disc type of apparatus wasemployed to evaluate the wear characteristics at a constantsliding distance of 3000m Table 4 shows the experimentalresults of sliding wear specic wear rate and friction coeffi-cient and their SN ratios

3 Grey Relational Analysis

31 Multiresponse Optimization Using Grey Relational Analy-sis Taguchi method is designed to optimize single responsecharacteristic e-higher-the-better performance for onefactormay affect the performance because another factormaydemand the-lower-the-better characteristics Hencemultire-sponse optimization characteristics are complex Here theGrey relational analysis optimizationmethodology formulti-response optimization is performed with the following steps

(i) Normalizing the experimental results(ii) Performing the Grey relational generating and calcu-

lating the Grey relational coefficient(iii) Calculating theGrey relational grade by averaging the

Grey relational coefficient(iv) Performing statistical analysis of variance (ANOVA)

for the input parameters with the Grey relationalgrade to ndwhich parameter signicantly affects theprocess

(v) Selecting the optimal levels of process parameters

(vi) Conducting conrmation experiment and verifyingthe optimal process parameters setting

InGrey relational analysis the complexmultiple responseoptimizations can be simplied into the optimization of asingle response Grey relational grade

32 Grey Relational Generation In the grey relational analy-sis when the range of the sequence is large or the standardvalue is enormous the function of factors is neglectedHowever if the factors goals and directions are different theGrey relational analysis might also produce incorrect resultserefore one has to preprocess the data which are relatedto a group of sequences which is called ldquoGrey relationalgenerationrdquo

For the-lower-the-better quality characteristics data pre-processing is calculated by

119909119909119894119894119894119894 =1007650100765011991011991011989411989411989411989410076661007666max

minus 1199101199101198941198941198941198941007650100765011991011991011989411989411989411989410076661007666max

minus 1007650100765011991011991011989411989411989411989410076661007666min

(1)

where 119910119910119894119894119894119894 is the 119894119894th experimental results in the jth experi-ment Table 5 shows the preprocessed data results

e grey relational coefficients of each performancecharacteristic are calculated in Table 6 using (2)

e grey relation coefficient is

120574120574 100765010076501199091199090119894119894 11990911990911989411989411989411989410076661007666 =Δmin + 120577120577ΔmaxΔ119894119894119894119894 + 120577120577Δmax

(2)

for 119894119894 = 119894 119894119894 119894119894 119894119894 = 119894 119894119894 119894119894 where

Δ119894119894119894119894 = 100925010092501199091199090119894119894 minus 11990911990911989411989411989411989410092501009250

Δmin = Min 10076821007682Δ119894119894119894119894 119894119894 = 119894 119894119894 119894119894119894 119894119894 = 119894 119894119894 11989411989410076981007698

Δmax = Max 10076821007682Δ119894119894119894119894 119894119894 = 119894 119894119894 119894119894119894 119894119894 = 119894 119894119894 11989411989410076981007698

(3)

120577120577 is the distinguishing coefficient 120577120577 120577 1205770 119894120577

33 Grey Relational Grade Table 7 shows the inuence ofprocess parameters of Grey relational gradee higher valueof the Grey relational grade represents the stronger relationaldegree of the reference sequence and the given sequence

e grey relational grade is obtained by

120572120572119894119894 =119894119894119894

11989411989410055761005576119894119894=119894

120575120575119894119894119894119894 (4)

where 120572120572119894119894 is the Grey relational grade for the jth experimentandm is the number of performance characteristics

As a result optimization of the complicated multiple per-formance characteristics can be converted into optimizationof single grey relational grade e higher Grey relationalgrade represents that the experimental result is closer to theideal normalized value


T 4 Experimental results of wear and friction coefficient and their SN ratios

ExExperimental results SN ratios

Sliding wear(120583120583)

Specic wear rate(m2N) times 10minus15 Friction coefficient Sliding wear

(dB)Specic wear rate

(dB)Friction coefficient

(dB)1 146 2494 0254 minus4328 minus4793 11872 104 237123 0560 minus4034 minus4749 5023 82 1843 0244 minus3827 minus4531 12224 261 237123 0198 minus4833 minus4749 14035 298 21907 0214 minus4948 minus4681 13386 160 1813 0147 minus4408 minus4516 16607 369 22893 0383 minus5134 minus4719 8318 384 2904 0217 minus5168 minus4925 13259 414 32348 0224 minus5234 minus5019 1298

T 5 Preprocessed data results

Ex Sliding wear(120583120583)

Specic wear rate(m2N) times 10minus15 Friction coefficient

1 0807 0521 07412 0934 0607 00003 1000 0979 07654 0461 0607 08775 0349 0734 08406 0765 1000 10007 0136 0665 04288 0090 0233 08319 0000 0000 0815

T 6 Grey relational coefficient of each performance character-istic



1 0722 0511 06592 0883 0560 03333 1000 0960 06814 0481 0560 08025 0435 0653 07576 0680 1000 10007 0366 0599 04668 0355 0395 07489 0333 0333 0730

4 WearMechanism

e typical wear mechanism of composites is presented inthis section At lower loads the sliding wear specic wearrate and friction coefficient decrease with increase in y ashpercentage but as the load increases they tend to increasewith increase in y ash percentage e composites were alsonoticed to have mild wear as the sliding speed increases eresults were related and veried to the tests conducted byvarious researches [21 22]

T 7 nuence of process parameters of Grey relational grade

Ex Grey relational grade Order1 0630 32 0592 63 0880 24 0614 55 0615 46 0893 17 0477 88 0499 79 0465 9

T 8 Response table for Grey relational grade

Symbols Response tableLevel 1 Level 2 Level 3 MaxminusMin

A 0701 0708 0481 0227B 0574 0569 0746 0178C 0674 0557 0657 0117Error 0570 0654 0664 0094

Mean value of Grey relational grade = 0629

Examinations of theworn surfaces of the composites weredone by scanning electronmicroscope Figure 4(a) shows theworn surface of the AA-6351-yash composite ( 1962N3ms and 5wt yash) featuring some shallow groovesthat are produced when there is abrasion in the tribologicalpair Figure 4(b) shows the worn surface of AA 6351-yashcomposite ( 2943N 1ms and 15wt yash) featuringsome shallow grooves and damages Among the experimentsconducted there was no seizure condition noticed

5 Analysis of Variance

e analysis of variance (ANOVA) is used to investigatewhich design parameters signicantly aect the quality char-acteristice traditional statistical technique can only obtain


T 9 Results of analysis of variance

Design parameters Degree of freedom (DOF) Sum of squares (SS) Mean of square (MS) F-test Contribution ()Applied Load N 2 0100 0050 625 4971Sliding speed ms 2 0061 0031 388 3043Percentage of y ash 2 0024 0012 150 1189Error 2 0016 0008 797Total 8 0201 10000

(a) (b)

F 4 (a) SEM of worn surface with grooves (b) SEM of worn surface with grooves and damages

T 10 Results of the conrmation experiment

Initial design parameters Optimal design parametersPredicted Experimental

Level A1B1C1 A2B3C1 A2B3C1Grey relational grade 0630 0870 0893Improvement in grade mdash 0240 0263

07010708

04810574

0569

07460674

0557

0657

0

01

02

03

04

05

06

07

08

A1 A2 A3 B1 B2 B3 C1 C2 C3

Ava

rage

SN

rat

io

Parameter

SN ratio chart

F 5 Main effects plot for SN ratios (dB)

one parameter in a single sequence one has to do the analysisrepeatedly to obtain other factors for the experiment

Since the experimental design is orthogonal it is thenpossible to separate the effects of each process parameterat different levels For example the mean of grey relationalgrade for the applied load at level 1 2 and 3 can be calculatedby taking the average of the grey relational grade for theexperiments 1ndash3 4ndash6 and 7ndash9 respectively e mean of

the grey relational grade for each level can be computed insimilar manner e mean of the grey relational grade foreach level of the combining parameters is summarized in themultiresponse performance which is shown in Table 8 Figure5 shows the main effects plot for SN ratios (dB)

ANOVA of the response quality characteristics is showninTable 9 and it is observed that applied load is the signicantfactor for minimizing the sliding wear specic wear rate andfriction coefficient

Conion ss

e nal step is to verify the improvement of the qualitycharacteristic using optimal levels of the design parameters(A2B3C1) Table 10 shows a comparison of the predictedand the actual design parameters According to Table 10the improvement of the performance is noticed when theoptimum conditions were used

7 Conclusions

(i) Aluminum matrix reinforced with 5 10 and 15wty ash was successfully prepared by stir-casting pro-cess and the wear behavior of the composites wasinvestigated using pin-on-disc machine

(ii) e L9(3)4 orthogonal array was adopted to investi-

gate the effects of operating variables on the abrasivewear of various composites

(iii) e experimental results show that the compositesretain the wear resistance properties at lower loads


with increase in yash percentage Mild wear wasalso observed in the composites as the sliding speedincreases

(iv) For all the trials it is observed that mild-to-severewear exists and it is witnessed by the microscopicresults

(v) e applied load and sliding speed are the mostinuencing factors and it is observed that their con-tributions to wear behavior are 4971 and 3043respectively

(vi) e optimum design parameters were predictedthrough Grey relational analysis (applied load =1962N sliding speed = 3ms and percentage ofyash = 5wt)

(vii) e conrmation experiment is conducted with thelevel A2B3C1 to verify the optimal design parameterand it exhibits better wear performance

References

[1] Y Sahin ldquoOptimization of testing parameters on the wearbehaviour of metal matrix composites based on the TaguchimethodrdquoMaterials Science and Engineering A vol 408 no 1-2pp 1ndash8 2005

[2] Y Sahin ldquoWear behaviour of aluminium alloy and its com-posites reinforced by SiC particles using statistical analysisrdquoMaterials and Design vol 24 no 2 pp 95ndash103 2003

[3] M K Surappa S V Prasad and P K Rohatgi ldquoWear andabrasion of cast Al-Alumina particle compositesrdquoWear vol 77no 3 pp 295ndash302 1982

[4] C iagarajan R Sivaramakrishnan and S SomasundaramldquoCylindrical grinding of SiC particles reinforced AluminumMetal Matrix compositesrdquo ARPN Journal of Engineering andApplied Sciences vol 6 pp 14ndash20 2011

[5] K Kalaiselvan N Muruganand and P Siva ldquoProd and char-acterization of AA6061ndashB4C stir cast compositerdquoMaterials andDesign vol 32 pp 4004ndash4009 2011

[6] Y Sahin andKOumlzdin ldquoAmodel for the abrasivewear behaviourof aluminium based compositesrdquoMaterials and Design vol 29no 3 pp 728ndash733 2008

[7] A T Alpas and J Zhang ldquoEffect of SiC particulate rein-forcement on the dry sliding wear of aluminium-silicon alloys(A356)rdquoWear vol 155 no 1 pp 83ndash104 1992

[8] O Yilmaz and S Buytoz ldquoAbrasive wear of Al2O3-reinforcedaluminium-based MMCsrdquo Composites Science and Technologyvol 61 no 16 pp 2381ndash2392 2001

[9] D Mandal B K Dutta and S C Panigrahi ldquoDry sliding wearbehavior of stir cast aluminium base short steel ber reinforcedcompositesrdquo Journal of Materials Science vol 42 no 7 pp2417ndash2425 2007

[10] P K Rohatgi B F Schultz A Daoud and W W ZhangldquoTribological performance of A206 aluminum alloy containingsilica sand particlesrdquoTribology International vol 43 no 1-2 pp455ndash466 2010

[11] B K Prasad S V Prasad and A A Das ldquoAbrasion-inducedmicrostructural changes and material removal mechanismsin squeeze-cast aluminium alloy-silicon carbide compositesrdquoJournal of Materials Science vol 27 no 16 pp 4489ndash44941992

[12] B N P Bai B S Ramasesh and M K Surappa ldquoDry slidingwear of A356-Al-SiCp compositesrdquo Wear vol 157 no 2 pp295ndash304 1992

[13] K J Bhansali and R Mehrabian ldquoAbrasive wear of aluminum-matrix compositesrdquo Journal of Metals vol 34 no 9 pp 30ndash341982

[14] C S Lee Y H Kim K S Han and T Lim ldquoWear behaviourof aluminiummatrix composite materialsrdquo Journal of MaterialsScience vol 27 no 3 pp 793ndash800 1992

[15] A P Sannino and H J Rack ldquoDry sliding wear of discontinu-ously reinforced aluminum composites review and discussionrdquoWear vol 189 no 1-2 pp 1ndash19 1995

[16] Sudarshan and M K Surappa ldquoDry sliding wear of y ashparticle reinforced A356 Al compositesrdquoWear vol 265 no 3-4pp 349ndash360 2008

[17] Sudarshan and M K Surappa ldquoSynthesis of y ash particlereinforced A356 Al composites and their characterizationrdquoMaterials Science and Engineering A vol 480 no 1-2 pp117ndash124 2008

[18] H K Durmuş E Oumlzkaya and C Mericcedil ldquoe use of neuralnetworks for the prediction of wear loss and surface roughnessof AA 6351 aluminium alloyrdquoMaterials and Design vol 27 no2 pp 156ndash159 2006

[19] W Zhou and Z M Xu ldquoCasting of SiC reinforced metal matrixcompositesrdquo Journal of Materials Processing Technology vol 63no 1ndash3 pp 358ndash363 1997

[20] A R Ahamed P Asokan and S Aravindan ldquoEDM of hybridAl-SiCp-B4Cp andAl-SiC p-GlasspMMCsrdquo International Jour-nal of Advanced Manufacturing Technology vol 44 no 5-6 pp520ndash528 2009

[21] M Ramachandra and K Radhakrishna ldquoEffect of reinforce-ment of yash on sliding wear slurry erosive wear and corrosivebehavior of aluminium matrix compositerdquo Wear vol 262 no11-12 pp 1450ndash1462 2007

[22] P Shanmughasundaram R Subramanian and G PrabhuldquoSome studies on aluminiumyash composites fabricated bytwo step stir casting methodrdquo European Journal of ScienticResearch vol 63 pp 204ndash218 2011

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Submit your manuscripts athttpwwwhindawicom

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Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



T 1 Elements of AA-6351 and its weight ()

Elements Al Si Mn MgWeight () 978 10 06 06

F 1 Micrograph of y ash

2 Materials and Experimental Procedures

21 Raw Material Description and Its Composition Alu-minum alloy 6351 is used in heavy engineering applicationsdue to its strength bearing capacity ease of workability andweldability It is also used in building ship column chimneyrod mould pipe tube vehicle bridge crane and roof [18]e elements of aluminum alloy 6351 and the weight in aretabulated in Table 1

Fly ash is an industrial by-product recovered from the uegas of coal burning electric power plants Fly ash particlesare mostly spherical in shape e y ash is collected fromTuticorin thermal power station India with an averageparticle size of 2ndash10 microns and its typical micrograph ispresented in Figure 1

22 Fabrication of Composite AA-6351 with 5 10 and15wt y ash reinforced MMCs are prepared by stir castingprocess e melting was carried out in a resistance furnacee y ash particles were preheated on an open hearthfurnace for one hour to remove the moisture Scraps of AA6351 were preheated at 450∘C for 3 to 4 h before meltinge preheated aluminum scraps were rst heated above theliquidus temperature to melt them completely ey werethen slightly cooled below the liquidus to maintain the slurryin the semisolid stateis procedure has been adopted whilestir-casting aluminum composites [19 20] Typical specimenis shown in Figure 2

23 Wear Tests e pin-on-disc machine was used to eval-uate the friction and wear response to the sliding contactsurface of the specimens as shown in Figure 3 Tests wereconducted under dry conditions as per ASTM G99-95standards e pin was initially cleaned with acetone andweighed accurately using a digital electronic balancee testwas carried out by applying load (981 1962 and 2943N)and run for a constant sliding distance (3000m) at differentsliding velocities (1 2 and 3ms)ediscmaterial wasmade

F 2 Typical specimen of AA 6351-y ash reinforced MMC

F 3 Pin-on-disc machine

of EN-32 steel with the hardness of 65 HRC At the endof each test the weight loss of the specimen was noticede results obtained during this work have been presentedin terms of sliding wear specic wear rate and frictioncoefficient Sliding wear is related to interactions betweensurfaces and more specically the removal and deformationof material on a surface as a result of mechanical action ofthe opposite surface Specic wear rate is the volume loss persliding distance and load Friction coefficient is the ratio ofthe force of friction between twobodies and the force pressingthem together

24 Selection of Testing Parameters and eir Levels eapplication of design-of-experiments (DoE) requires carefulplanning prudent layout of the experiment and expertanalysis of results Taguchi has standardizedmethods for eachof theseDoE application stepse experiment species threeprinciple wear testing conditions including applied load (A)sliding speed (B) and percentage of y ash (C) as the processparameters e experiments were carried out to analyzethe inuence of sliding wear specic wear rate and frictioncoefficient on the MMCs Control factors and their levels areshown in Table 2is table shows that the experimental planhad three levels

e standard Taguchi experimental plan with notationL9(3)

4 was chosen based upon the degree of freedom edegrees of freedom for the orthogonal array should be greater







4 orthogonal array













119909119909119894119894119894119894 =1007650100765011991011991011989411989411989411989410076661007666max

minus 1199101199101198941198941198941198941007650100765011991011991011989411989411989411989410076661007666max

minus 1007650100765011991011991011989411989411989411989410076661007666min

(1)




120574120574 100765010076501199091199090119894119894 11990911990911989411989411989411989410076661007666 =Δmin + 120577120577ΔmaxΔ119894119894119894119894 + 120577120577Δmax

(2)

for 119894119894 = 119894 119894119894 119894119894 119894119894 = 119894 119894119894 119894119894 where

Δ119894119894119894119894 = 100925010092501199091199090119894119894 minus 11990911990911989411989411989411989410092501009250

Δmin = Min 10076821007682Δ119894119894119894119894 119894119894 = 119894 119894119894 119894119894119894 119894119894 = 119894 119894119894 11989411989410076981007698

Δmax = Max 10076821007682Δ119894119894119894119894 119894119894 = 119894 119894119894 119894119894119894 119894119894 = 119894 119894119894 11989411989410076981007698

(3)




120572120572119894119894 =119894119894119894

11989411989410055761005576119894119894=119894

120575120575119894119894119894119894 (4)














1 0807 0521 07412 0934 0607 00003 1000 0979 07654 0461 0607 08775 0349 0734 08406 0765 1000 10007 0136 0665 04288 0090 0233 08319 0000 0000 0815




1 0722 0511 06592 0883 0560 03333 1000 0960 06814 0481 0560 08025 0435 0653 07576 0680 1000 10007 0366 0599 04668 0355 0395 07489 0333 0333 0730

4 WearMechanism






A 0701 0708 0481 0227B 0574 0569 0746 0178C 0674 0557 0657 0117Error 0570 0654 0664 0094








(a) (b)





07010708

04810574

0569

07460674

0557

0657

0

01

02

03

04

05

06

07

08

A1 A2 A3 B1 B2 B3 C1 C2 C3

Ava

rage

SN

rat

io

Parameter

SN ratio chart






Conion ss


7 Conclusions











References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation















4 orthogonal array













119909119909119894119894119894119894 =1007650100765011991011991011989411989411989411989410076661007666max

minus 1199101199101198941198941198941198941007650100765011991011991011989411989411989411989410076661007666max

minus 1007650100765011991011991011989411989411989411989410076661007666min

(1)




120574120574 100765010076501199091199090119894119894 11990911990911989411989411989411989410076661007666 =Δmin + 120577120577ΔmaxΔ119894119894119894119894 + 120577120577Δmax

(2)

for 119894119894 = 119894 119894119894 119894119894 119894119894 = 119894 119894119894 119894119894 where

Δ119894119894119894119894 = 100925010092501199091199090119894119894 minus 11990911990911989411989411989411989410092501009250

Δmin = Min 10076821007682Δ119894119894119894119894 119894119894 = 119894 119894119894 119894119894119894 119894119894 = 119894 119894119894 11989411989410076981007698

Δmax = Max 10076821007682Δ119894119894119894119894 119894119894 = 119894 119894119894 119894119894119894 119894119894 = 119894 119894119894 11989411989410076981007698

(3)




120572120572119894119894 =119894119894119894

11989411989410055761005576119894119894=119894

120575120575119894119894119894119894 (4)














1 0807 0521 07412 0934 0607 00003 1000 0979 07654 0461 0607 08775 0349 0734 08406 0765 1000 10007 0136 0665 04288 0090 0233 08319 0000 0000 0815




1 0722 0511 06592 0883 0560 03333 1000 0960 06814 0481 0560 08025 0435 0653 07576 0680 1000 10007 0366 0599 04668 0355 0395 07489 0333 0333 0730

4 WearMechanism






A 0701 0708 0481 0227B 0574 0569 0746 0178C 0674 0557 0657 0117Error 0570 0654 0664 0094








(a) (b)





07010708

04810574

0569

07460674

0557

0657

0

01

02

03

04

05

06

07

08

A1 A2 A3 B1 B2 B3 C1 C2 C3

Ava

rage

SN

rat

io

Parameter

SN ratio chart






Conion ss


7 Conclusions











References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation




















1 0807 0521 07412 0934 0607 00003 1000 0979 07654 0461 0607 08775 0349 0734 08406 0765 1000 10007 0136 0665 04288 0090 0233 08319 0000 0000 0815




1 0722 0511 06592 0883 0560 03333 1000 0960 06814 0481 0560 08025 0435 0653 07576 0680 1000 10007 0366 0599 04668 0355 0395 07489 0333 0333 0730

4 WearMechanism






A 0701 0708 0481 0227B 0574 0569 0746 0178C 0674 0557 0657 0117Error 0570 0654 0664 0094








(a) (b)





07010708

04810574

0569

07460674

0557

0657

0

01

02

03

04

05

06

07

08

A1 A2 A3 B1 B2 B3 C1 C2 C3

Ava

rage

SN

rat

io

Parameter

SN ratio chart






Conion ss


7 Conclusions











References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












(a) (b)





07010708

04810574

0569

07460674

0557

0657

0

01

02

03

04

05

06

07

08

A1 A2 A3 B1 B2 B3 C1 C2 C3

Ava

rage

SN

rat

io

Parameter

SN ratio chart






Conion ss


7 Conclusions











References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation















References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









dry sliding friction and wear studies of fly ash reinforced aa

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