CHAPTER 3

Optimizatior~ of Process Parameters

3.1 Introduction

The liberalization of ninrket economics and suhscquent global competition has

made tlie entsepreneiirs to realize tlie impel-tancc of cjuality goods at affordable price.

One of the kcy areas iticntifird to rcaiize this objecti\.e is parameter optimization.

In\estigatio~is indicate that in the grii~ciing process dcptli o f cut, numhcr ofpnsses, \vheel

spccd and \ ~ o r l i speed ni-e the iiiqjor influential pal.al11ett.r~ \ihich affect the clualiry o f

the part grounded. In this cliaptzl thc studies contiuctcil to e\ aluate tlie effect of various

process parametci-s on tlie attainable surt'ace rougiint.:;~ an t i surt'zice hard~lcss are

presented. Tlie primary ol?iecri\,c of this study is to implement Taguchi 's Quality

engineering concept for the process variables optilnizatioli of grinding process.

Experiments are coiiducted according to Taguchi's LC) orthogonal array. Optirnum

grinding co~lditioils are a l ~ i v e d by einploying lower surface rouglzness c u ~ u higher

surface hardness are better as the strategy. The Secondary objective is to develop an

ANN systen: to integrate the ATN model with Taguchi 's model.

3.2 Selection of grinding wheel

Two problems, ~ilheel glazing and wheel loading occur in grinding process due to

wrorig selectio~i of grinding ~&i l~ee l or improper cutting conditions. Wheel glazing refers

to the co1:ditiol:s when the grains are worn dowil to the level of bond and held for too

long for grinding the matel-ial. The problem can be remedied either by changing the

wheel or by c l ~ a n g i ~ l g the cutting conditions. Wheel loading occurs urhen uol-l< piece

chips are embedded in the cutting Face of t l ~ e wheel, wl?icli reduces the effective depth of

penetration of the wheel abrasive into the work surface and thereby reducing the rate o f

cutting. Wheel loading is aggravated b y the presence of small voids o n the face of the

g~iniiing \i.lic.cl .-l'ki> < > i i 'ilc ciircil i>> :!>ci.c;ising the \\~hceI speeti or usiiig different wheel

e \ 211.

T i~us , the seiection of' grinding \i.ileei f :. correct, cor~tinuous efficient cutting

dcii~ands the correct se1cc:ioi: 01' tlic ty?e of abrasi\.e. the size 01' grains. and the type o f

boniiing agents and its s~rt.ngth nilti the size of \.(>ids. FLII-tiler, the behaviors of the

grinding v:liczls are ai'fcctccl by tilo work piece materials, cutting speed, depth o f cut and

the feed rate.

A1203 and SIC have high hardness in comparison to ha]-dcned steel and thus can

be ilsed for metal remoi a1 by plastic defoimatioii. The cutting tool Inaterials have to b e

harder for material removal by plastic deformation and also to maintain its shape and for

less wear. To analyze the per fo~mancr and to study the othzl- aspects like loading, life.

rtc.. o f 4 1 2 0 ; and SIC abrasive \vheels grinding tests are conducted as per the conditions

given in Table 3 . I and the results arc reportcd in Table 3.2 and 3.3.

Table 3.1 Grinding conditions (selection of wheel) --

Ivlacliine used : Uiliversal Cylindrical Grinding Machille of .Model U 127' 500

Abrasive wheel : (i) S i c - C3615V - 9 trials , (ii) A1203 - .r\46L5V - 9 trials

Coolant used : Water based emulsion. Design of experiment : Taguchi's L9 01-thogonal array

Material : AISI 33 10 (Low Carbon structural steel)

Diameter ; 35 inn1 , Length ; 150 111nl

Depth of cut (Dc) : 300.400,and 500pm

Work speed (Nw) : 65,140,and 300 i -p~~l

Number of passes ( Xp) : 1.3,and 5

Wheel speed (Ks) : 1750 and 2000 iyrn

Table 3.2 Orthogonal at-ray of grinding parameters and test results

--- (.AIISI 3310 \\ith SiC \ ~ h e e l ) I Ra in p111 1 Average

I DCII: Nn ~ i i hs 111 1 Ra I S NO. 1 Np -- 1 1 kin1 ~ I I I I 1;)m , 1 T---, I 11 1 I I I I value I

I I I I trial tnnl ~, trial 111 pn1

I 1750 I 0 10 0 18 0 17 0 17 - - - _ _ _ --

07 , 3 00 1 40 3 7000 0 15 0 14 -------

03 i 300 300 5 7000 O i l 000 01-3 0 1 1

Table 3.3 Orthogonal array of grinding parameters and test results (AISI 3310 \tit11 A1203 wheel)

I

Rn in pln Average

Ti12 a\cl.:igt ei'fci-!s ~ i ' ~ i l ; \ i i : !'i!ctors oil ~ L I ~ I ~ I C C ~.oiiglilicss are gi\-eil in Table 3.4

snc! 3.5 ii)r the c o ~ l ~ h i ~ i a t i i ~ ~ ; 0 1 ' . i lSl 7.2iO \ii!Il SIC an(! .%IS1 3310 wit11 A1203

respecti\ el!.

ANOVA ana!ysis is carried oLii i~ driznnii~t ' the influrncc of 111ain factors and to

dctcrmine the percentage contribution of each t'actol.. Tabie 3.6 sho\i~s the results of

percentage contributiixl ofeacll factor.

The clerails of caiculation to find tile aicrttge cffect of main factors on surface

~.oughnt.ss at three difii.1-cnt Ic\,cls. percentage contriburiou of' tach facial- on surface

1.oughness. and optimum surface roug1111ess estiliiation tbr the ~vliezl \vol.l< combination of

AISI 3310 with AI2o3 wheel a r c given beion'.

Average surface roughness value f o r the parameter dep th of cu t a t each level

Low level (300 pm) Ra =(0.14+ 0.12+ 0.09)i 3 = 0.1 167

Medium level (400 pm) Ra = (0. !2+ 0. I5+ 0.1 X).' 3 = 0.1467

High level (500pm) R a = (0.12+ 0.15+ 0.18)i 3 = 0.15

Similarly, for Number of passes, Wliesl speed, and work speed the average effect

on surface roughness is calculated.

ANOVA analysis to find the percentage contr ibut ion of each fac tor

Correctioll Factor,

CF=(0.14+0.12-0.09-0.12+0.15+ 0.18 +0.12+ 0.15+ 0.18) ' i 9

= 0.1708

Sum of squares,

S T = (0.14'+ 0.12'+ 0.09' -0.12~+ 0.15'+ 0.18 '+0 .12~+ 0.1j2+ 0.18') - CF

ST = ~),00005~>

SLII :~ O ~ ' S L ~ U ~ I I . C S liw tii'j>til ~ ~ ' C L I I .

SDc = -?((I. 1 1672-*- 0 . 1 - 0. ! 5 ' ) 0. 170s

SDc = 0.002022

Similarly for i7tht.1- pai.amctcrs [hi. suiil of sclu:jl-e ia ~aiculnted.

S N w = 0.001)8272, S N 17 = ij.002132, S N s = 0.OCI I089

En-or sun? ofsquai-es is :ilso c:ilculated.

Error sum of scjuarcs SE ST- ( S DciS N\\.- S K p- S N S ) = 0.0006

Percentage error = SE .' ST' = S.626?6

Percentage influence of ciepth of cut = SDc, ST * I00 = 29.07U/b

Similarly for other parameters thc percentage conti.ibution is calculated.

Work speed - 11.82%. Numl~ei- of passes - 34.82"/0. Whcel speed - 15.651/0

Optimum condition for surface I-ough~less

Lotver surface ro~~gliness is better as the strategy is ibllowed. 111 table 3.5 and 3.6

the lo~vtfs values of surface roughiicts!: are liigliligl~ted ancl thc co~.rcsponding Levels of the

parameiers constitute the Optin~uni treatment cornhination.

Optimr~m surface I-oughness

Ra mean = (0.14+ 0.12- 0.09 -0.12+ 0.15+ 0.18 +0.12+ 0.15+ 0.18) / 9

= 0.1378

Optimum conlbination - Average surface roughness values

Depth of cut (Low level) - 0.1 167

Workspeed (Low level) - 0.1267

Number of passes (High level) - 0.11 67

Wheel speed (High level) - 0.17 17

Ra optimum = Ra mean + i(0.1167-0.1378) - (0.1267-0.1378) t (0.1 167- 0.1378)

- (0.17 17-0.1378)) = 0.07667

T:~blc 3.4 .\\ el-age effcct of main factors on surface I-oughness (.%IS1 3310 nit11 Sic n heel) ---

.A\ c ~ , ~ g i . Ra \ a lu r for maln factors at ,

Table 3.5 Average effect of main factors on surface roughness (AISI 3310 with AlzOz wheel)

Xael-age Ra value for maiil factors at

I 1 Lr\.el 2 I Level 3

1 - Number of Passes 1

I 0.1567 ~ 4 v l

Work speed

Table 3.6 Percentage contl.ibution of each factor on surface roughness (AISI 3310 with A1203 1 S i c wheel)

Parameters AISI 33 10 1 AISI 33 10

with SIC with A1203

I I

Number of Passes

Wheel speed

En-or

44.34% , 34.82%

14.48% 15.65%

06.1 00/0 08.63% I I I

Optii11un-i conciition si~l.fjcc ~-c~ugIrness for boih tile \vIleel anti work

combination is foiind b adopting thc lo\i.er ss!ri.ice roughness is better as the sti.atcgy

nnci results are gi\en i!nde~..

( i ) Depth of' cut at Lel.el 1 (3OOum)

(ii) Work speed at Level I (65 ipm)

(iii) Nunibcr of passes at Level 3 ( 5 passes)

t i \ : ) Wheel s p e d ai Le~.el 2 (2i)OO I-pm)

Opt~mum \usi'Llce rougJi~i~ss nluc is

( i j For AISI 33 10 \\.it11 S iC - 0.098~m

( i i ) For AiSI 33 10 with .4i2@ - 0.077~irn

This study shows that the SIC gives a pool- susfiice kinis11 on steel work

materials like the ones used in this woi-k because of its high hardness( Table 3.2 and 3.3).

Further, it is observed that, with SIC wheel there is a loss of cutting efticieilcy

accompanied by the appearance of chatter marks on the work surface after grinding a few

pieces. And also, the work ~uaterials used in this research \vosk are of high tensile in

nature. These materials offer inore resistance to machining preceded by plastic

deforillation with SiC wheel. More over. SIC abrasi1.e~ being more brittle in nature. they

fall of rapidly due to quick fi-actures resulting in frequent wileel dl-essing. Because of the

above mentioned reasons, A120; grinding wheel oi'specificntio~l A46L5V is selected and

used for all rile expesimeilts in tiis study.

3.3 Comparison of design of experiments

Changing denlands of dynamic inarket place ha1.e improved and increased the

commitment to quality consciousness. All over the world, companies are developing

quality management systeills like IS0 9001-2000 and investing in total qualit>. One of

. ,

ijie ;!-iti~ai rCi!~ii:~c!i;c~;r.; lo? ? I I C IS0 Ol!Ol-2!jOO is ntlcc~ilnrr control ot.c.1' process

I L ' ~ I L C t i i i i ! I i s .-in auditing l.el)ort of

ijie [SO indicates tilax :he ~:~;ijol.it~ O I ' I ~ ~ L ' i ~ l a ~ ~ ~ i f . i ~ ~ t i i r i ~ ~ g ili(iusti->. pl-esents the improper

c~ppiication of proccss 1-ai.inb!cs an<! inadzcjuate control o\.er t i i t : process parametel-s.

Co:i!sc>i of process paralnete~. is possibIc ti~l.ough <,ptimization 01' process vat-iables.

Dctzrn?ination of' opt imum p:il-;i~i:t.tcrs lies in tile proper selection and intsociuction of

suitable design ni'cspcriment at tile cai.licst strife ot'the process aild product developlnent

c\.,cies so as to restilt in the quality and p~.oducti\,ity impro\.eineni.

This part of the study cornpal-es and contrasts the factorial design of experiment

\vith Taguchi's design 01' experiment ~ised in the detei-mination of optimum grinding

conditions. GI-inding test is conducted as per the conciitioils gi\,en in Table -3.7 and the

tcst res~111s are rcported in Table -3.8 and 3.9. Tile avzragc cffects of main factors on

surface rouglincss are gi\.en in Table 3.10 for the Taguclli's method of DOE Ibr AISI

4340 with Ai20; coml?i~iation.

Table 3.7 Grinding conditions (cornpal-ison of DOE)

Machine used : Universal Cylindrical Grinding Macl~i r~c of Model U 127.' 500

Abrasive wheel : Al7Oj - A46L5V

Coolant used : Water based elnulsion

Design of experiment : (i) Taguchi's L9 orthogonal al-1-ay

(ii) Factorial Method 2'( 2Levels with 4 Parameters)

Depth of cut : 300.400 ,and 50011111

Work speed : 65.140,and 300 l-pm

Nuniber of passes : 1,3. a11d.5

Wheel speed : 1750 and 2000 1l3m

Material : AISI 4340 (Medium Carbon structural steel)

Dialneter : 35 111m Length ; 150 mm

Tallle 3.8 Classical design arm! of grinding paranleters arid test results

(&IS1 1310 \\it11 .4120; - Z~ Factorial n3ethod)

I Ra in kin1 1 .A\.e~.;~gt. : SDS APD DL i XI\, Ss

I . , Ra i~~c t i iod method S N O i i \ p i i l -___- 7---

I I I l l i l l i a i u e R a i n I R a i n hiin 1 rpm ~ p m ,

I I I , trial trial trial i i pm 1 urn

AKOVA ailalysis is carried out to detellniile the influence of main factors and to

detelnline the percentage contribution of each factol-. Table 3.1 1 sho~bs the results of

percentage contributioll of each factor.

Table 3.9 0rthogom:~l arm! of grinding parameters and test results

I Average I

8 Dcin hiiirl S No

, j i l l l Ipnl I !

-+

Table 3.10 Average effect of main factors on surface roughness

01 1 300 65 I 7 0 , 0 i 5 ' 0 13 0 14

(AISI 4340 with ,4I2o3 - L9 Tsguchi's method) Average Ra value for iliaill i'acrors at three

0 14 ~

Parameters I ie\/els i

00 140 3 0 I . 0 2 0 1 0 0 1 2 1

I ____I

Depth of cut / 0.1 167 0.1367 1 0.1533

Work speed

Number of Passes

Wheel speed

0.1267

0.16

0.1533

0.14 0.15

0.14 0.1 167

!

0.1317 1 I

Tahle 3.1 1 Pcrceritagc cont r i l~~r t ion of each factor on surf'ace roughness (-IISI 4310 v i t i l 111:03 - Factorial !'s Tagucl~i 's n~ethod) ---

I I Par3meti.l.s Taguchi's metiloci Factorial method I

i

Work speed 11.28O0 ~ 1 1.82% 1

I Errol- 0572 ' !h 08.63% I ,

Optilnurn condition for surface roughr~ess obtained il l the Taguchi's method

adopting the lo& er is better strategy and thc results arc gi\.en ul~tier.

( i j Depth oi'cut at Lc\.rl i j3OOpm)

(ii) Work specti at i-c\,el I (65 i p ~ n )

( i i i j S~111117er OS~I:ISS~S at Le\.el 3 ( 5 passcs)

( i ) Wheei speed at Lew! 2 (1000 11-1rn)

Optimu~ll surface roughness ~ a l u e for AISI 4340 is 0.075pm 111 Taguchi's

i ~ ~ e t h o d .

In factorial method, regression analysis IS done uslng EES software by applying

Sum of Deviation Square inethod (SDS) and Average Percentage Deviation method

(APD). The regression model (equation) so obtained to predict surface roughness is

A = 0.000021871, B = -- 0.002753973, C = -- 0.000078756, D = 0.000074365 (SDS)

A = 0.000028024, B = -- 0.003117953, C = -- 0.000089156, D = 0.000071569 (APD)

In the above equation the values of input parameters call directly be substituted

and the surface roughness can be deteinlined.

Both of t!le desip:) nf espc.riinc~~t stuciics re\-eal that thc depth of cut and number

01 ' passus are ha! ing more ii:t!ucncc i r i l tl1.t. surihce t inis l~. Tnguclii's ~net l iod g k e s the

op:irnum gl-inciing contiitiol~s tiircc!l~- r~nci inturli the optimum grinding conditions can be

utilized t i7 ca1c~:latc the gi.intiing q .c le t in~e . Tliis iiifcrence is not possible lvith Factorial

~:~i.tliotl and i t is lilso to he r:oti.d ti lnt t ! ~ c i'rictorial nit.tIioi1 titkcs 11i0i.e trials to give the

r.'sulrs. Fu~.tlirr. F2ictorial 11ic.tllcttl lint! Taguciii's nietlioti gi\-c the saliie results on the

pcrcenragc int!uen.cs ol'e;1c11 paramcirr oil surf~icc roug!~i:css. Hence. Taguchi's method

i>i ' design of ctxperimciit can bc applieci to optimize tile process par~umeters \\.it11 added

ad\,nntnge of sa\,iilg t!~c tiine.

3.4 Grinding performance

Grinding performance differ from machine to macl~ine even when same

~i;oricmateriai and saine v,.heel ~naterial are uscd. In order to fix the type of 111achine best

suited for this complete research war!<. a perfolmance comparison is made between

universal cylindrical grinding n~achine and high precision plunge type cylindrical

grinding macliine. The grinding conditions and the lzst results a]-e given in Table 3.12.

3.13 and 3.14 respecti\rely. The n\.erage effects of main factors on surface ~ O U ~ I I I I C S S are

gi\,en in Table 3.15 and 3.16 for rhe blacl~ine ( i ) and Machine ( i i ) for the combination of

AISI 1040 ~vorkmaterial lvith A1:0: !vl~eel abrasivct material.

Tahle 3.12 Grinding conditions (Selection of machine)

Machine used : ( i ) Universal Cylindrical Grinding Machine 01' Model U 127: 500

(ii) High precision plunge type cylindrical Grinding Machine of

Model UCG 2601350 unit 500

Abrasive wheel : AlzOj - A46L5V ,Coolant used: Water based ernulsion

Material : AISI 1040 , Dia meter ; 35mm , Length ; 150 1i1m

Design of experilllent : Taguchi's L9 01-thogonal array

Depth of cut : 200, 400 and 600yin, Number of passes: 2,4,and 6

Machine (i) Machine (ii)

Work speed : 65,140,and 300 ipm Worli speed : 56,225,and 450 i-pm

Wheel speed : 1750 and 2000 rpln Wheel speed : 1550 and 1740 l-pm

'Table 3.13 Orthogonal :lt-ra? of grillding pal-anieters and test results (.%IS1 1040 -- nith -\IZ03 - llacktir~e ( i ) / Uni\ cl-sal)

I I ICa in ,ULII I Aberagz I

I Dciii S i i . i i 3 i Ns ill : value 1 S.No. 1 -

p In q,rn

I 1 I I

I Dcin Nw in i N s i n 1 value S.No. 1 I

11p1n I 11 I11 Ra in

I trial trial trial p111 ----

2 1550 0.15 0.16 0.14 0.15

Table 3.14 Orthogonal array of grinding parameters and test results (AISI 1040 with Ai2Q3 - Machine (ii) / Plunge)

1 I

I I

Ra in urn 1 Avciage

Tahlc 3.15 ;i\cl-agc cfl'cct of taaai11 factors on surface roughness (,-lISl 1040 ~ j i t h ..i1203 - J l ; ~ c I ~ i r ~ c ( i ) i L-rii\.crsal)

-- -- - -. - i ic ragi ' Ra i niilc k)l- lnain factors a1 i

- -- .- ---- Lei ti I ~ c . l e l 2 T T e T

\\heel speed 0 190 0 1717

Table 3.16 A ~ e r a g e effect of main factors on surface roughness (AISI 1010 nit11 A1201 - RIachine (ii) I Plunge) --

I : . \si.rageRa la luc ji71 llialli ractot~ at

I Parameters I i ! tl~ree Ie\,els I 1

I I Lcscl 1 ~ c ~ c l 2 ~ e v e l 3

-- . - -?-

~ o r ~ < speed 0. 1367 0.15 0 16 ,

XNOVA analysis is carried out to determine the influence of main factors and to

dete~mine the percentage contribution of each factor. Table 3.17 shows the results of

percentage contribution of each fact01

I Work speed ' 09.49% 09.92" 1

Table 3.17Percentage contribution of each factor on surface roughness (AISI 1040 with A1203 - Universal 1 Plunge)

Para~neters I Machine(1) I

I

Machine(il)

Number of Passes 43.05% 1 39.68% I

Depth of cut I

30 85% , 37.27%

Wheel speed 10.25% 11.33% ,

Optimum conilition fbr juriiici. rougi~ness for both tiic muchines ( i and i i ) is found

bx atiapting tile loncr is lxtter straicgL. and given under the Tabit. 3.18.

Table 3.18 O p t i n ~ u n l conditions f o r sur face roughness

(:\ISI 104O \\ it11 .4lzO3 - Ul~ir,crsal / Plunge)

I Pal ameten hlachine(i) Mncli~ne(ii) I - I Depth of cut 100pm 100pn1

1 humber of. Passcs 6 pa\ws 6 passes I

1 Wheel speed 2000 ~ p m 1 1740 i-pm

Optimum surface roughness value for AISI 1010 with h1203 in

Machine (i) universal - 0.1 183um and

Machine (ii) plunge -- 0.08 17pm.

'4 propel- selection of grinding machine could result in tlie in~provement of surface

quality. retained strength. tolerances /finish. production rate. cost per part, and product

performance of grounded components. Tilest results arc nlostly affected by the macl~ine

tool factors such as rigidity. precision, dynamic stability, controls, powerlspeed, slide

moven~ents/axles, truing and dressing equipment, coolant type. pressure, flow ztc.

Table 3.13 and 3.14 coinpare the surface finish results obtained fiom universal

and plunge type cylindrical grinding machine. They show that machine (ii) performs

better. Further, the optiinum surface roughness value is lesser in the case of Machine (ii)

and also the optimu~n condition obtained for machine (ii) indicates that the grinding cycle

time is lesser. Hence, considering the facility of Multi diameter grinding and other

advantages of Plunge type Cylindrical Grinding n~achine over the Universal Cylindrical

grinding machine. Pluilge type is prefened and selected to conduct experiments in this

study.

3.5 Opt imi~at ion using l'nguchi's Slethotl

The idea oi'statisticaliy ilesigneti ~xpcri inents to investigate pi-ocess variables and

their effects 011 p1.oduct q u a l i r characteristics has been around since the 1920's.

promoted largely by the great Sir Ronalti A. Fisher. who \<,as knighted for his creative

accomplishments in rhis iield. Taguchi's method emphasizes the use o f statistically

designed zxperim~'nts as a n~cans of tnhilncing tile quality of design o f products a i d the

pt.rii,~.mailce of procluc~ioii PI-occsses. >lost process control techniques lxeasure one or

inore output qua!ity cl~asactcristics. and if'these quality zhnl.ncteristics arc satisfactory. no

modification of the process is matic. I-io\vc\.er. in soi-i~i. situations \\rhei-c there is a strong

reln~ionship betn,ccli one 01. more controllable intiepcndcnt \.arinbles, other process

contsoi methods can so~notimes be employeci.

In this study Taguchi's 01-thogonal array of design of experiment is introduced in

a cylindrical grinding process for the optimization of process variables to improve the

quality of a ground surface. The experiments are conducted to study the influence of'

process parameters on surface roughness anci surface hardness as per Taguchi 's DOE.

Three different optimization analyses are done for different matel-ials. The detailed

procedures are given under.

Three different carbon percentage AISI steel materials are taken for this analysis

namely AISI 8620, .&IS1 H 1 1 . and AISI T I . AISI 8620 (low carbon) is subjected to fine

grinding with depths of cut 100,200 and 300 pi11 following the LO experimentation.

Optimizatioil is done using the Response graph method. The optirliizatioll results

obtained are coinpared and verified with Six ratio analysis and grey relational analysis.

AISI H I I (medium carbon) is subjected for nlediuin fine griilding test with depths of cut

200,300, and 400 urn. A detailed signal to noise ratio analysis is carried out to predict the

best treatment coillbination out of the nine trials conducted. Similarly, for AISI T 1

material with higher depth of cut (300,400 and 500pm) the grinding results are analyzed

using grey relational technique.

3.5.1 Optimization using Taguchi's Method b~ith Response Graph analysis

Tapch i essentially utilizes the conventional statistical tools, but he simplifies

them by identifying a set of stringent guide!ines for experiment layout and analysis of

results. Response graph analysis gives the output of interest to be optimized i.e.

minimized, maximized, targeted, etc.The output can be more than one and also it can be

quantitative or qualitative. The grinding conditions adopted in the experimentation are

given in Table 3.19, and the test results are reported in Table 3.20

Table 3.19 Grinding conditions for AISI 8620 (Response graph analysis)

/ Machine used : High precision plunge type cylindrical Grinding Machine of I 1 Model UCG 2601350 unit500 I Abrasive wheel : A1203 - A46L5V, Coolant used : Water based emulsion

Design of experiment : Taguchi's L9 orthogonal array with Response Graph method

analysis, Material : AISI 8620 (Low Carbon case carburising steel)

Depth of cut : 100, 200,and 300 pm , Number of passes : 1,2,and 3 , Length : l5Omrn

I Work speed : 56,225,and 450 rpm , Wheel speed : 1550and1740 rpm ,Dia : 35 mm I

Table 3.20 Orthogonal array of grinding parameters and test results (AISI 8620 with A12O3 - Response graph analysis)

S.No.

01

Dc

in

Pm

100

Nw

in

rPm

56

Np

1

Ns in

Surface roughness Ra in prn

Surface hardness in

HRA iI

trial

0.14

rpm I I

Average

Ra

I

trial

32 1550

Average

HRA

11

trial

3 3

value 1 trial

0.13

in pm

0.14 32

The a\.eragc c i t c t s of'~l:;iiii !'nctol.s 1711 susi'ai'e I - O U ~ I ~ I ~ ~ J S S a i d surface liarciness are

gi\.en in Table 3.?! and Table 3.12 Ibi- the combination of .4ISI 8610 \i>ith AI20:,

Table 3.21 Average effect of main factors 011 surface roughness

(AISII 8620 \ \ i rh A I Z 0 3 - Respor~sc graph analysis)

: Parameters ; .A\ erngi. Ra \ nlue Ibr ~na in fBctors at

1 Wheel speed 0.1167 1 0.1281 1 - 1

Table 3.22 .Average effect of main factors on surface hardness

(.4ISI 8620 with A1203- Respor~sc graph analysis)

Parameters Av?rage H RA \,slue Sor main factors I

at thl-ee le\ els I

Lc! el I Level 2 1 Level 3

I I I

Work speed 1 30.67 1 31.33 1 32.00 1 I I I

Wheel speed 32.33 30.83 I

1 Number of Passes I 1 I 1

Response graphs are drawn using the Table 3.21 and 3.22. Response graphs are

used to find out the optimum treatment coillbination (Ramamoorthy et al., 2000) .Figures

3.1 and 3.2 (Response Graph) show the influence of main parameters on the surface

3 1.67 34.33

roughness and surface hardness.

30.00

Depth of cut in prn

100 3.00 300 400 500

Work speed in rpm

Number of Passes

Wheel speed in rpm

Figure 3.1 Influence of main parameters on surface roughness

Depth of cut in pm

Work speed in rpm

Number of Passes

Wheel speed in rpm

Figure 3.2 Influence of main parameters on surface hardness

ANOVA analysis is carried out to determine the influence of main factors on

surface roughness and surface hardness and also to determine the percentage contribution

of each factor. Table 3.23 shows the results of percentage contribution of each factor.

Optimum condition for surface roughness and surface hardness are found

adopting the lower is better strategy and higher is better strategy respectively. The results

are given in Table 3.24.

Table 3.23 Percentage contribution of each factor on surface r o u g l l ~ ~ e s s and surface hardtless

(AISI 8620 ~ i t h A1203 - Resporlse graph analysis) I I 1 P i i ~ a ~ i ~ e t e ~ ~ Surface ~oughness Surface Hardness I

1 Depth of cut 27.117% I 40.43% 1 I

Work speed 10.59S6 I 02.83%

10.7 I?;I 04.78% 1 _* ..-___- _ -__ -11 Error 03 . ? j0 ;~ OS . 6 r I

Table 3.24 Optimum conditions for surface roughness and surface hardness

(AISI 8620 with A1203 - Response gl-aph analysis) I I I

I

Parameters i Surface roughness Surface hardness : I I

Depth of cut 1 0Opm

1 h'umber of Passes 3 passes I 1 passes I

3OOum I

1 Wheel speed 1 1710 lpm I 1550 ipm 1

Work speed 56 1p1n I

Optimum surface I-oughness and surface hardness value,

For AISI 8620 with A1203- O.OS8~tm and 38.33HRA.

450 ~ p m ,

To verify and compare the results obtained in the above analysis. SAT analysis and

grey relational analysis are made for surface roughness alone with the same test results of

table 3.20 (with an additional trial) and the inferences are given in table 3.25J.26, 3.27,

3.28:3.29,3.30; and 3.31. However, the detailed procedures are presented in the

succeeding chapters.

l'atsle 3.15 Orthogonal array of grinding parameters and test results ~ v i t h SIN ratio

(AISI 8620 v i t h .A12Q3- S/N ratio analysis)

07 3 0 0 ( 5 6 3 1 7 1 0 0.12 0.1 I O i l 0.11 1 8 . 9 0 5 3 1

Table 3.26 Average effect of main factors on SIN ratio of surface roughness

(AISI 8620 with 41203 - SIN ratio analysis) 1 Average S,N value for main factors at

1 Parameters I three levels ~ I I

Depth of cut 1 18.46 16.70 17.28 I 1

I

LVork speed 1 17.89 17.50 17.05 1

Level 1 Level 2 Level 3 1

I L\'heel speed 1 16.66 17.89 1 I

I I -1 h'umber of Passes 1 6.49 17.32 18.62 I

Table 3.27 Optimum condition for surface roughness

(.4ISI 8620 with .A1203 - S/?i ratio analysis) I

I

I Parameter:, Surface snugl~ness

Wol-k speed

I Number of Passes I 3 passes

I I Wlicsl speed I

I 1740 1pi1-i

Even though thc third set of operating condition is the best combination out o f t h e

nine trials, the optimum condition result obtained in S i r j method il-iatches n(it11 the

optilnum result obtained fiorn the response graph analysis.

Table 3.28 Orthogonal array of grinding parameters and test results j Xii balues)

(AISI 8620 with A1203- Grey relational analysis)

Table 3.29 Gre! relational anal) \is for wrface roughness ( x," -Xii values) (.4ISI 8620 nit11 -\ilO;- Grey relational anaks is )

I 7

Table 3.30 Grey relational analysis for surface roughness -Grey relational coefficient < Values (AISI 8620 u i t h A1203- Grey relational analysis)

Nw

in

113 Ill

5 Values

Average 1 I I

Raiik ~ I I

Values I I

Table 3.31 Best trc;iitment com!,in:~tion for l o n e r surface roughness

(.I\ISI 8020 \\it11 A1203- G r e y retational analysis)

I

- -- -- - - - - - - -- - - - -- -- Depth O I ' C L I I I 0 0 1.i 111

t

I \+ ol h speed 150 1p1n I

Niunber 01' Passe& 3 passes I I

~ h e e E ~ e e d 1710 I-pm , .--

In Grey relational analysis also the best results are obtained with the same

combination offactors as obtained Srom SiN method. Ho\i,e~zer. grey relational method is

lia\.ing the limitation that optilnum condition coulti not b? Sound.

3.5.2 Optimization using Taguchi's S l c t l ~ o d nit11 Signal to 'Voisc rat io analysis

SIN ratio analysis estilnates the c i l c r of iioise facrol-s on the perfonnance

characteristics. It was developed as a proactive equivalent to the reactive loss function.

Signal factors (Y) are set by the designer to obtaiil tlie intended value of the response

variable. Noise factors (s') are not controlled or are very expensive or difficult to control.

Both the average and variance needs to be controlled to the single merit (Harisingh and

Pradeep Kurnar, 2004; Davillzar el al., 2003).The grinding conditions adopted in the

experimentation are given in Table 3.32, and tile test results are repolTed in Table 3.33.

Fomn~ula to determine SIN ratio for minimizing the response factor as the

oljective (Minimizing the surface roughness) is

Six = -1 0 Loglo [ ~ ~ i ' i n ]

Similarly for detenllini~lg S / N ratio for inaxirnizillg the response factor as the

objective (Maximizing the surface hardness) is

SM = -10 Loglo [ l i ~ ~ i ' * n ]

Where, yi - is the cxperime~ltal response values for tlie trials, 11 - lumber o f trials

The n\.i.i-ngc rlt;.cts oi'1naii1 I;iitor~ o n S,Y ratio oi'suri'ace I-oughness and surface

linrc!ness are g i w n in Tabii. 3.34 anti Table 3.35 fix the combination of AISI H 11 with

.l.I:0; respecti\ cl!..

Table 3.32 Grinding conditions for AISI H I I(SW ratio analysis) - - -- -- - - - - - -- - -

I

Llac!?ine used : High psecisiol: plu~lgc t1 .p~ c);iindric:tl Ciri!itiiiig bliichine of I

Model UCG 260'350 unit 500 i I

1 .-\brasi\.e wheel : AlQ; - A4hL5V . Coolant ~iscd : L\;att.r based emulsion. I I Design of expel-i~nent : Taguchi's LO ortl~ogonal array \\it11 SIN ratio analysis

Materials : AISI H 11 (Mcdium Carbon liot-ivoi.1.: stecl)

Diameter : 35 inm Lzngtli : 150 111ni

Depth of cut : 200, 300, and 400 pm. h'umbes of passes : 2,3,and4

Work speed : 56.225,and 450 ipm . Wheel speed : 1550 and 1740 lpnl

Table 3.33 Orthogonal arra? of griildii~g parameters and test results with SIN ratio (AIS1 H I 1 wi th AlzO, - SIN ratio analysis)

i- I S ho

- - 7 r - -

Dc 1 Nix

111 in N p I I

p111 ipin , I

1 - 0 1

-r--- - - - ---- -7

I sm 1 St\ Na Ra ~n urn

02 I

1 03

04

01;

06

07

08 - 09

111

9 200

300

300

300

400

400

' 4 0 0 4 5 0

I f01- fol 1

1 ip 1-11

tilal

225

56

221;

450

56

225 --

11 111 I I1 I11

tiial trial t1ia1 t~ l a l tiial j I

3 1740 0 14 0.13 0 1.5 29 30 31 1 17.1 I 39 I

x?&T-%-n%%- 0 15

0 1 8 ' 0 1 6 j

0 2 1

2

31 30

33 34 I

38 3 9

32 17 1

32 1 5 8 I

40 1 3 9 I

0 14

0 2 0

1740 0.17 0.18 0.19 39 41 40 14 9 41.6 1

3

4

2

35

3 9 4 1

3 9 9

4 1 4 )

3 v 0 2 0 0 2 0 37 7 39 38 1 4 4 4 1 2 ' 1 I I I

1740 0 13

1 5 5 0 0 . 1 4

1 7 4 0 ' 0 1 9 I

34 33 17 1 4 0 2 01.5 0 1 5 / 1

4 1740 0 I2

Table 3.34 Average effect of main factors on SIN ratio of surface roughness (AISI H I 1 with A1.03- SIN ratio analysis)

/ Average SO! value for main factors at ; I / Parameters 1 !

three levels I I I I 1 Depth of cut 1 17.37 1 15.64 15.44

1

/ Work speed 1 16.67 1 15.94 1 15.84

Level 1 I ~ e v e l 2 1 Level 3 1

I I 1

Number of Passes 14.92 16.18 17.35 1

Table 3.35 Average effect of main factors on SIN ratio of surface hardness (AISI B11 with A1203 - S/N ratio analysis)

Wheel speed

Average S I N value for main factors at

Parameters

15.39

1 three levels I

16.53

Depth of cut

Work speed

Optimum condition for surface roughness and surface hardness were found

adopting the higher the S I N ratio is better as the strategy and results are given in Table

3.36. However, the best treatment combinations out of the nine trials are third and eighth

for the low surface roughness and high surface hardness respectively.

Number of Passes

Wheel speed

Model calculation

(i) S/N ratio for minimizing the surface roughness (1 st Experimental run)

S/N = - 10 loglo {(0.16~+ 0.15~+0.17*)/3)

= 15.9063

39.38

39.93

41.07

40.52

40.22

40.2

41.04

40.5 1

39.94

40.06

39.63

( i i ) SIN ratio for maximizing the surface hardness (1st Experimental run)

Table 3.36 Optimum conditions for surface roughness and surface hardness (AISI H l l with AlzQ3- SIN ratio analysis)

Parameters / Surface roughness Surface hardness

Depth of cut 200pm 400pm

I Work speed / 56 rpm / 450 Ipm / 1 I Number of Passes / 4 passes 1 2 passes i

3.53 Optimization using Taguchi's Method with Grey relational analysis

I

Grey relational analysis combines all the responses into a single number. For this,

experimental data are first nornialized in the range between zero and one, which is also

called the grey relational generation. Grey relational coefficient is calculated from the

normalized experimental data and then g e y relational grade is computed (Lin, J.L. and

Lin, C.L., 2002). Grinding conditions adopted in the experimentation are given in Table

3.37, and the test results are reported in Table 3.38.

Wheel speed

Table 3.37 Grinding conditions for AISI T1 (Grey relational analysis)

1740 rpm

Machine used : High precision plunge type cylindrical Grinding Machine of

Model UCG 260/350 unit 500

Abrasive wheel : Alz03 - A46L5V, Coolant used : Water based emulsion

Design of experiment: Taguchi's L9 orthogonal array with Grey relational analysis

Materials : AISI T1 (High Carbon high speed steel)

Diameter : 35 mm Length : 150 mm

Depth of cut : 300,400 and 500 pm, Number of passes : 3,4 ,and 5

Work speed : 56,225, and 450 rpm, Wheel speed : 1550 and 1740 rpm

Table 3.38 Orthogonal array of grinding paranletel-s and test results

(.AlSI T1 \ \ i th .1i203 - Grey relational anal! sis) I / DC / '\J\v ' IS I R 3 in 111

I H R A I

I

1 pll, , l , ~ l l l I , 1-1x11 1 I ~ trial lriiii ~ s i s l a 1 trial 1 ~ I I 1

In the grey relational arlalysis, no]-malizing is tione and the grey I-elational

coefficient is derern~ined as per tlie follo~ving PI-ocetiuses.

Model cslculation

(i) Sn~aller the better

Smaller surface roughness is better (calculation for 1" set of data)

Nornialized value, Xii = (maxiYii - Yi,)/ (111ax,~Y~.~ -mii~,~Y ii)

X I 1=(0.24-0.17)i(0.24-0.13) = 0.63636

Grey relational coefficient <;(I<) = ( h min + 5 A ~nax)/(A ,i(k) + 5 h max)

0 (1 A min =mini 1 Xi - X i , = 0 , A max = maxi maxi Xi - Xi,/ = 1

A = 1 Xi (I -

X, " = Reference value = 1

j = Distinguishing coefficient = 0.25 (0< < c: 1 )

i(k) = (0+0.2 5" 1)/(0.36364+0.25) = 0.4074

( i i ) Largcl- the better

Lai-ger SUI-facc hardness is better (calettlation for 1"set of data)

Nai-maiizecI mluc. S, , = O',, - ~ l i i n ~ \ r ' , ~ ) , (max,Y, , -~nin,Y, , )

S ! (39-29) , (45-29) = 0.625

Cr1.t.y i-elational coef ic ient < , ( I < ) = ( A mi11 - < S max)/( A , , ,(k) + A max)

1 lnin = min, m i i ~ . I S, ' ' - - X,, = 0 . 1 m;lu = :n:ls, maxi , X, " - S,, = 1

A < , , ( I < ) = 1 Xi " - Xlil

S , " - Ret'erzi~ce \.niue = I

< = Distinguisliii~g cocfliciei~t = 0 . 2 5 (0.c < I )

< i ( l<) = (OLO.75* I ) : (0 .375~0 .25) = 0.40

Table 3.39 Grey relational analysis for surface roughness ( Xii Values)

(AISI TI with A1203 - Grey relational analysis)

Tal~ l e 3.110 Gre!. I-el;~tional an:11>-sis for surface rorigl~ncss (Xi " - Xil )Values

-- 1 I (.&IS1 TI \\.it11 .&1203 - Crcy relational analysis) 1

Table 3.41 Grey relational analysis for surface roughness - Grey relational coefficient < Values - (AISI T1 with A1203 - Gre? relational analysis)

S No 111

pm

1 1 01 j 300

02 300

Average ~ 5 Rank ,

I I Values I I

~n 3 ~n (X, " - X,, ) Values I

Iplll 1 11111 I 11 / 111

I I I t11a1 t~ la1 tl-ial I I

S No.

56 1 3 I 5 5 0 0 1 7 018 0 1 9 0.3636 I I

225 1 4 1 7 4 0 0 1 4 0.16 0 1 5 00909

1ym

Dc 1 Nw

ill 1 111 Np

0.6666

04444

Ns 111 i; Values

071421

014281

SC

m < - E

e S P 0 V)

ffi N (/I ffi -4 a

Table 3.44 Grey relational analysis fo r surface hardness -Grey relational

coefficient < \'slues - (AISI T1 \\-it11 A12Q3 - G r e y relational analysis)

i Average I

I ,i Values I 5 Rank /

Values 1 1 - -,

0 1 0 3672 ; 4 1

From the table 3 41 and 3.44. the best treatment comb~natlnl~ is found according

to the Rank for the Response factors sui-face roughness (lolver 1s better) and surface

hardness (higher 1s better) and the ~esults arz tabulated In the table -3.45.

Table 3.45 Best t rea tment combitlation for l o l ~ e r surface roughness a n d higher surface l l a r d ~ ~ e s s

(AISI T l with A1203- Grey relational analysis)

I parameters

I I

Depth of cut 1 300pm

Surface

roughness

5OOprn

Number of Passes

Wheel speed

Surface I

haidi~ess 1

Work speed 450 ipm

5 passes

1740 ipm

450 l-p~n I

3 passes

1740 ipm 1

Unlike tllc coil~~enrional surfiicu grin~liiip n.liicii ~-eiluil.es inore nuinber o f passes

to ren1oi.e the desired amount oi' t11c n.orkmaterial. the plunge cut cylindrical grinding

process can be completed in o i i l~ . a t'en,ei- passes. Because of large ciepth o f cut, specific

energy in pluilge cylindrical grinding is higher than nol.~nal grinding process. This

necessitates a special care to avoid thei-mnl darnage to the ivork piece. The production

rate achievable by grinding is often liinited by grinding temperatures and rhe resultant

hai-iniul effects on worl; piece quality. In-process energy generated being used for

strcngtllening the \i~orlz piece is considel-ed to he beneficial effect on the mechanical

properties whereas more amount of heat generation has adverse effect. So it can be

expected that by having proper control on tile grinding process. thc wear resistance o f the

\\,orl< surface can be cilhi~nced. For il~aiiy practical designs. ~vhich impose a limit on

n?asimum allowable surface roilghness. a proper operation o f many devices also,

necessities sn~ooth surfaces. The reliability of mechanical components. especially for

high strength application. often critically depends upon the sui-face and sub-sui-face

quality produced by grinding.

Analytical lllodels that explain the highly non-linear relationship with interactions

among grinding variables such as hardness value, carbon percentage, work speed, wheel

speed, depth o f cut, temperature, and surface roughness are difficult to obtain. Moreover,

at present no analytical inodels that capture the dynamics of entire grinding process

exists. Statistical models such as ~nul t i response graph/ S-N ratio model i Liner regression

iGray relational requires assumptioils about the parametric and functional nature of the

factors, which inay or nlay not be true. Also, this reduced linear o r lower power inodel is

not enough to describe the coinplicated input / o~ltput relations i11 the grinding process.

-4rtificial intelligent Techniques such as neural network and expert systenls have

been increasingly used to successfully inodel complex process behaviour in areas where

analytical inodels are unavailable or difficult to implement. The use of neural network is

motivated because of their accon~modation of non-linearities, interactions and nlultiple

1,ariabIes. Neural networks are also tolerant of noise data anci respond to the operating

cilndition fairly quiclily so that the hardi\.are part of i t can be integrated with the

system/machine for real rinis control. Neural net\vorks do not ~-equire such assumptions,

\\-Iiich are esseiitial for tlie analysis using other con~.entional models or tools. Neural

nt.tn.o~.k models are data driven nloclels. The neural nt.ti\.oi.ks have strong abilities to

learn and self organizc infol-mation ant! need only a specific requireme~lt and prior

different set of assunlptions for modeling (\Jijayaraha\.an er al.. 2003). These advantages

hate attracted much interest in combini~-ig ANN lvith Taguchi's Design of Experiments.

The objecti1;e of this study is to model and forecast the grinding process by

applying Back Propagation Algorithnl (BPA). Tlie experimental data using Taguchi's

design of experiment technique is used in the training of the BP network. The .ANN

results are compared n.ith the experimental results.

3.6.1 Neural Ketwork used

In the present ivol-k, a multilayer perceptrons with each layer consisting of

number of computing neurons l i a t t been used. The algorithm used in this \vori< is BPA.

The BPA uses the steepest descenl method lo reach a global minimurn. The number of

layers and number of nodes in the hidden layers are decided. Tlie connections between

nodes are initialized n;ith ral~dolil weights. A pattern from tlie training set is presented in

the input layer of the network and the error at the output layer is calculated. The ei-ror is

propagated backwards towards the input layer and the weights are updated. This

procedure is repeated for all the training patterns. At the end o f each iteration. test

pattei-ns are presented to ANN and the classification performance o f ANN is evaluated.

Further training of AKN is continued till the desired classification p e r f o ~ l l ~ a ~ ~ c e is

reached. The weights "MT" and the threshold values "0" are adjusted until the e n o r

comes within the limit (Fengguo Cao and Qinjian Zang, 2004).The steps involved in

training ANN by using BPA are.

Step I The \\,eights ancl tl?resiioltis arc ra~~dorn ly initializeti bctureen layers by impoiTing

the Esperimzniai input arlti output to the k1.ATLhB.

Step 2 The inpui oi'a piirtcrli is pl.csctltcd to tlie input Inyzi- :untl the outputs o f t h e

neuron a!-r computcd as l i j i l ~ \ \ ' ~ :

The ac~i\.ation ti~nction ';I' ~isccl 1ie1.z is the PURELIN i i~~?ct ioi l and is given by:

a = w " p - i b

between the iiipuis. iiitliirn and o ~ ~ t p ~ i t l a y r s .

Where. \v is tile \vcigi~t. p is thc input neuron anti b is tile bias values.

Step 3

The training of ANN is done \vitli number of itesatiolis.

Step 4

The ell-or pattern is calculated.

Step 5

The en-or is minimized by ~.aryiiig tile ANN configuration.

Step 6

For each training pattern. steps 3 and 4 are repeated tiil the goal is reaci2ed.

Step 7

The itera~ion process contii~ues until the desired goal is rcached.

When the .4KN training is over, the network can predict tlie surface roughness!

surface hardness using the feed forward mechanism. Tbe ,ANN model of configuration

4-9-9-2 used in this study for training and testing the data is sllown in figure 3.3. The NN

architecture is of Back Propagation Algorith~n with Feed F o ~ w a r d type composed of four

layers .There are four neurons in the input layer for the four input variables naintly depth

of cut, number of passes, work speed and wheel speed. I11 tlie output layer, one neuron is

used for surface roughness (R,) and the other one for surface hardness (HRA). T w o

hidden layers with nine (9) nodes each are selected.

At the main paif of the network architecture, the number of hidden layers ,nodes

is the key problem to be solved. More hidden layers and nodes will lead to long training

time, whereas less hidden layers and nodes may deteriorate the training effect. I n many

cases. the detei?~~inatioi; oS the !:urnbi.~ of hidden layers anti 11odes depends heavily on the

r s p a i e n c e or expesims~:ts. Tilo Rcnson lhs selecting TWO hidden layers in th is

co~itiguration is to reclucr ossos ant! time factor also. The .4KN is trained wit11 different

number of nodes in the llidden i~iyer. Tile val.>,ing plot o f the training time and t ra in ing

ssroi. \.cssus the number of hiddtr: 1;odcs is si:o\\.ii in figure 3.4. I t is seen t h a t if the

rruiniiig time a~ltI 11,aining errol- art. co!~sitic:-i.ti simuirant.ousiy the optimal nu l l lbz r of

hidtien nodes shoult! b s nine (0).

Figure 3.3 A Typical \lultilayered Feed forward back Propaga t ion 1 N Model of co~ifiguration 4-9-9-2

0 3 6 9 1 2 \ o . o f V o d e s i n H ~ d e e n l a y e r s

Figure 3.4 Relationship between Training time a n d Training e r r o r and the Number of hidden nodes in the hidden layers

3.7 Results and discussion

The productivity. accuracy. and cost of grinding process depend to a considerable

extent on the correct choice of'gi-inding wheel, as the ad\.nntages of a good machine and

optirnunl-grinding conditions can be lost by operating then1 under unsuitable/

unfa\,ourable grinding wheel.

A variety of Iiigh precision machinvs ha\^ been in use and new surface generation

n ~ ~ ~ c h i n e s are constantly being de\,eIoprd to achii.\,c estl-emely close geometric tolerances

or to improve surface tinish. The objccti\.e of all high preoisii~n grinding machines is to

acliieve geometrically precise co~npo~icn ts or surlkce of' conirolled texture or surface

finish.

Rased 011 the results o f tlie pi-eliminary eupesiments conducted, plunge type

cylindrical grinding machine \v~tll Al? 0; is selected and used for all the grinding tests

involved in this work.

Not only the machine and cutting tool, but the operational factors are also having

a greater influence on the quality and productivity impro\!ernent. Quality and productivity

iinprovement is most effective when it is an integral part of the product and process

developmellt cycle. The introduction of proper design of experiment at the earliest stage

of development cycle where new products are designed , existing products design

iniproved, and nianufacturiii process optinlized is often key to overall product success.

In order to coilduct study on this. a comparative study between Taguchi's DOE and

Factorial DOE is made. The test results obtained from both of these methods are listed in

table 3.8 and 3.9.

Both the case studies show that depth of cut and number of passes are having

more influence i.e.: 31.40% and 38.72% respectively under Taguchi's method and

29.07% and 35.83% respectively under Factorial method on surface roughness of the

workpiece. Optimal conditions are directly obtained from Taguchl's method. whereas.

Factorial method gives a regression model to predict the surface roughness for ally input

\aiue. Factorial l l ~ t . t h ~ d is not a simple analysis. But Taguchi's inethod is a si~iil?le

method of DOE. efficient. systematic. small number of tests and large number o f

info]-mation. Hence. Taguchi's n:ctliod of DOE is followed for all the grinding tl-ials

involved in this work.

The traditional factorial espe~~itnenral tiesign pl.oct'du~.c ti)ci~ses on the average

product or process pel-hi-manct. cllat-acreristics. But the Tagucl~i 's metl~od concentrates

on the efl'ect of variation on tllc prociucl or process quality chor;lcttrristics rather than on

its averages (Phadkc. M. S.. 1989; Rosb ..[01in. P, 1980). T11;1[ is. tlie Taguchi's approach

makes the product or proccss perfhrmance inseiisiti\,e (robust) to variations in

uncontrolled (or) noise 1.actors. To achieve the required cluality in a specific situation.

operating parameters are ofien detel-mined with the aid of grintiing tests. I f nlorc nui~iber

of parameters are there, the conventional testing methods are time consu~ning and

expensive. Here lies the importance of the Taguchi's method for the design o f

experiment.

AISI 8620, H l I , and T 1 carbon steels are subjected for grinding test. The surface

roughness of the ground pieces which is one of the n u i n quality requirements and the

surface hardness which is the secondary response variable are selected as quality

characteristics for the study and their results are presented in tables 3.20, 3.33 and 3.38.

The operating conditions such a s Depth of cut. Nulnber of passes. Wheel speed and

Workspeed which are generally controllable in any grinding situation are selected as

factors for study. Three levels having equal spacing within the operating range of the

iilachine are selected for each of the factors. By selecting three levels, the nonlinearity

effects could be studied. The data obtained are analyzed by three different optimization

methods for different nlaterials and details are given below.

(i) AISI 8620 -- Response graph method and vei-lfication with SIX ratio

method and Grey relational method.

(ii) AISI Hl 1 -- Signal to Noise ratio method.

(iii) AISI T1 -- Grey relational method.

Analysis oi ' \ariance ( .INO\''-\) is tione for XIS1 33 10. 1330. 1040, and 8620. The

.4kOVA results (Tables 3.6. 3.1 i . 3.17 and 3.23) indicate that depth o f cut is having

more influence on the surface roughness, surface hartlness. If the depth o f cut is low the

surface finish is good. Howe\,er. if the depth of cut is r;loi-e the hardness improve (Table

3.21).

Figure 3 . I and 3.2 s h o ~ i . the response graph obtained for the material AISI 8620.

They show that a nlodernte feed spceti is reci~~il-cd to attain n good si~rface finish grounded

components with liigii su1.111ce 1;al.dncss. For the material AISI 8670 .Response graph

inethod. Signal to Noise ratio metl~od and Grey relational a11pl.oacii have given the same

optimal i best treatment combination results (Tables 3.21, 3.27. and 3.3 1 j.

Similarly. Signal to Noise ratio method and Grey relational approach h a w given

the same optinla1 parameters level I best treatment conlbination results for the materials

AIS1 H 11 and T I(Tab1es 3.36, and 3.45).

For all the optiillu~n results obtained. the confirmation trials are carried out in

grinding machine. The confimlation of experiment sho\\ls that tlie experimental

observations (surface roughness and surface hardness) and estimated results are very

close and the percentage deviation is in tlie accepted level of 2-3 percentages.

The optinlization studies on different nlaterials at different conditions by different

techniques reveal that the depth of cut and nulnber of passes are having greater effect on

surface integrity of the workpiece. In industries mostly. in order to remove the blackened

surfaces of surface hardened components, finish grinding is done. During this material

removal, the hardness of the hardened components decreases. In order to avoid this

reduction in hardness level and to have less grinding cycle time, t l ~ e maximum nletal

removal depth to be kept, as far as possible, as the minimal one.

Nowadays, for high volume o f metal removal also grinding process is used. In

such a case, the intense heat generated in grinding due to relatively high fi-ictional effect

impairs xvorl;piecc ciuality 1,. incii~ciiig tliermnl damage. Therefore, cooiirig and

lubrication play n tlecisi\.e role i l l grinding. E1,i.n thougii. many limitations 11ax.e been

observed in the usage of coolnnrs, liquid coolants in flood form have been the

choice to deal wit11 the thermal damages.

Simple. quick and economic parameters selection methods are very much needed

by the industry. In the present \yolk. it is accomplished through integrating Taguchi's

DOE with ANN.

With nine (9) data sets for AISI 8620 ~iiaterial (Table 3.20) thz YN is instructed to

SUII for 5000 iterations with tile prograni developed in MATLAB. The average percentage

training error is 8.22 ?/o and 3.87% i'or Ra and HR.4 I-espectiveiy (Table 3.46, figure

3.jand figure 3.6). As can be see11 from the figures 3.5 and 3.6 the training of the model

is successfully accomplished. The nlodel is x'ery closc to the actual data and is able to

follotv the trend. The value of the ANN trials and experimental trials correspond closely.

Table 3.46 Esperimental results Vs ANN predicted results with training error at 5000 iterations ai

Experilllent al -

I 1

9

Expt. ANN / Expt. AKIi

0.16

order values

I % elms , I, % ell-or values i i values values !

i

0.1534

Average error

4 125 34 35.3393 3 9391 18 I

8.223354 Average error 3.876667

Concluding remarks

, Figure 3.5 Experimental Surface Roughness Figure 3.6 Elperimental Surface Hardness 1 1

O Depth of cut is having inore influence on the surface integrity of the grounded

parts.

~ a l u e s Vs ANS predicted Surface Roughness L alues

I

Q A1203 is better suiled for grinding carbon steels than SIC since S i c gives a pool

surface finish.

~ a l u c s Vs ANN predicted Surface Hardness Yalues

*:* Optimization of the process parameters in cylindrical grinding process was done

by integrating Taguchi's parameter design and AUK\'. This approach has been

found to be very effecrive in optimization.