contents lists available at growingscience management ... · process parameter work material...

* Corresponding author. Tel.: +9831568294 (M), +33-2548-2655 (R) E-mail address: [email protected] (S. Chakraborty) © 2019 by the authors; licensee Growing Science, Canada doi: 10.5267/j.msl.2018.12.004

Management Science Letters 9 (2019) 467–494

Contents lists available at GrowingScience

Management Science Letters

homepage: www.GrowingScience.com/msl

Applications of optimization techniques for parametric analysis of non-traditional machining processes: A Review

Shankar Chakrabortya*, Bijoy Bhattacharyyaa and Sunny Diyaleyb

aDepartment of Production Engineering, Jadavpur University, Kolkata, West Bengal, India bDepartment of Mechanical Engineering, Sikkim Manipal Institute of Technology, Majitar, Sikkim, India C H R O N I C L E A B S T R A C T

Article history: Received: October 27, 2018 Received in revised format: No-vember 28, 2018 Accepted: December 7, 2018 Available online: December 7, 2018

The constrained applications of conventional machining processes in generating complex shape geometries with the desired degree of tolerance and surface finish in various advanced engineering materials are being gradually compensated by the non-traditional machining (NTM) processes. These NTM processes usually have higher procurement, maintenance, operating and tooling cost. Hence, in order to attain their maximum machining performance, they are usually operated at their optimal or near optimal parametric settings which can easily be determined by the application of different optimization techniques. In this paper, 133 international research papers published during 2012-16 on parametric optimization of NTM processes are extensively reviewed to have an idea on the selected process parameters, observed responses, work materials machined and optimization techniques employed in those processes while generating varying part geometries for their indus-trial use. It is observed that electro discharge machining is the mostly employed NTM process, applied voltage is the identified process parameter with maximum importance, surface roughness and material removal rate are the two maximally preferred responses, different steel grades are the mostly machined work materials and grey relational analysis is the most popular tool utilized for parametric optimization of NTM processes. These observations would help the process engineers to attain the machining performance of the NTM processes at their fullest extents for different work material and shape feature combinations.

© 2019 by the authors; licensee Growing Science, Canada

Keywords: Non-traditional machining pro-cess Response Process parameter Work material Optimization

1. Introduction Difficulties in machining complicated and intricate shape features in varied hard-to-machine, high-strength-temperature-resistant materials, superalloys, metal matrix composites (MMCs) and other ad-vanced engineering materials for aviation, nuclear power, wafer fabrication and automobile applica-tions using conventional machining processes have caused to the evolution of an array of non-tradi-tional machining (NTM) processes. In conventional material removal processes, like turning, milling, shaping, drilling etc., forces are applied on the workpiece with the help of a cutting tool to remove excess material in the form of chips. It induces plastic deformation within the workpiece leading to material removal due to shear action. On the other hand, in NTM processes, instead of employing sharp

468

cutting tools, materials are removed using mechanical, thermal, electrical or chemical energy or com-binations of them. In some of the NTM processes, the tool does not even make any contact with the workpiece, and it is also not mandatory that the tool material should have higher hardness than the work material. Basically, in NTM processes, material removal takes place from the workpiece surface in the form of minute particles while attaining superior surface smoothness and dimensional accuracy. These processes are generally classified based on the form of energy deployed for removal of material, i.e.: a) Mechanical processes: Water jet machining (WJM), ultrasonic machining (USM), abrasive jet ma-

chining (AJM) etc. b) Electrochemical processes: Electrochemical machining (ECM), electro jet drilling (EJD), electro-

chemical deburring (ECD) etc. c) Electrical thermal processes: Electrical discharge machining (EDM), wire electrical discharge ma-

chining (WEDM), laser beam machining (LBM), electron beam machining (EBM) etc. d) Chemical processes: Chemical machining (CHM), photochemical machining (PCM) etc. e) Hybrid machining processes: Electrochemical grinding (ECG), electrochemical discharge machin-

ing (ECDM), electrochemical honing (ECH), abrasive water jet machining (AWJM), travelling wire electrochemical spark machining (TW-ECSM) etc.

These NTM processes have exceptionally low material removal rate (MRR) and consume excessive specific energy. Most of them have comparatively high procurement cost, tooling and fixture cost, power consumption and operating cost, and maintenance cost. Therefore, for productive and economic exploration of the capacities of NTM processes, their different machining/process parameters need to be optimally selected. Identification of the most relevant process parameters and their settings for any NTM process mainly depend on the expert knowledge and skill of the concerned operator. Sometimes, manufacturer’s catalogues may also help in setting these parametric combinations to achieve the best machining performance. But, in most of the cases, these parametric combinations are conservative and depart from their optimal settings which cause hindrance in full exploitation of the capabilities of NTM processes. Selection of the optimal or near optimal parametric mix for different NTM processes thus becomes a vital decision making task. A variety of analytical techniques in the form of mathematical algorithms has been fruitfully employed for parametric optimization of NTM processes in order to fully explore their machining potentials and capabilities. At this very time, it now becomes essential to have an exhaustive investigation on the application and efficacy of various optimization techniques in deter-mining the best parametric settings for different NTM processes. In this paper, altogether 133 research papers published in different international journals during 2012-16 dealing with the applications of ECM, EDM, WEDM, LBM, USM and hybrid machining (HM) processes in real time manufacturing environment are extensively reviewed. This review mainly fo-cuses on the identification of the type of work material used for the machining operation, machining parameters and responses chosen for the considered NTM processes, nature of the optimization prob-lem developed and optimization technique(s) employed for achieving the best machining performance. The details of this analysis for the six above-mentioned NTM processes are presented here-in-under. 1.1 ECM process The first machining process similar to ECM was patented by Gusseff in 1929, and its successful com-mercial application was started in late 1950s and early 1960s in aerospace and other manufacturing industries to perform shaping and finishing operations. It is a carefully regulated anodic dissolution process to shape the workpiece (anode) using a tool (cathode), approaching towards the workpiece with a constant feed rate. The electrolyte (NaCl, NaNO3) flowing at high speed through the inter-electrode gap usually removes the dissolved metal from the machining zone. Between the pre-shaped cathode tool and the anode workpiece, a DC voltage (10-25 V) is applied, causing atomic level reactions to take

S. Chakraborty et al. / Management Science Letters 9 (2019) 469

place within the electrolytic medium which is mainly responsible for removal of metal from the work-piece to achieve its desired shape. The final workpiece shape thus becomes an approximate negative mirror copy of the tool electrode (Rajurkar et al., 1999). In ECM process, as the mechanical or physical properties of the work material do not influence the material removal mechanism, it can machine different electrically conductive materials regardless of their hardness, toughness or thermal characteristics. Almost all kinds of metal, including high-alloyed nickel, titanium-based alloys, superalloys and MMCs, can be efficiently machined using this process. It has several advantages, like no tool wear, high MRR, good surface finish, no need for deburring operation, and capability of generating complex shape geometries (contours, ring ducts, grooves etc.) for subsequent use in aerospace, automotive, defense and medical industries. Nowadays, it is also being successfully utilized in micro-machining and fabrication (dimensions within 1-999 μm) of engineering components (Bhattacharyya et al., 2002). For achieving maximum benefits from this machining sys-tem, its different process parameters can be selected as applied voltage, current, electrolyte temperature, electrolyte flow rate, electrolyte concentration, inter-electrode gap and tool material (copper, brass or bronze) (Senthilkumar et al., 2013). In view of difficulties encountered from the conventional machin-ing processes, like high tool wear and high tooling cost, this process now proffers an effective alterna-tive to fulfill the requirements of the machining personnel. Table 1 exhibits the details of the research works executed during 2012-16 on parametric optimization of ECM processes. It mainly includes various control parameters and responses selected for ECM op-eration, type of the work material considered for this process and optimization technique(s) adopted for its parametric optimization. The analysis of this information is graphically represented in Fig. 1. From this figure, it can be revealed that among various machining parameters of ECM process, applied volt-age is the most important one, followed by tool feed rate, electrolyte flow rate, electrolyte concentration and inter-electrode gap. With respect to the responses, MRR is provided with the maximum importance, followed by surface roughness and radial overcut. Among the chosen work materials, various grades of steel are maximally machined using ECM processes, followed by different MMCs, and titanium and its alloys. From the parametric optimization point of view, various advanced optimization techniques, like biogeography-based optimization, cuckoo search optimization, artificial bee colony optimization etc. are identified as the most popular methods, followed by grey relational analysis (GRA) and genetic algorithm (GA). In single objective optimization, among all the considered responses, each of them is separately optimized and different parametric combinations are derived for each of the responses which are quite difficult to maintain from the machining point of view. Rather, multi-objective optimization is more practical because in this approach, a unique parametric setting is obtained while simultaneously optimizing all the conflicting responses. Among the 14 research papers identified dealing with para-metric optimization of ECM processes, ten papers considered only multi-objective optimization of the responses, while the remaining papers provided emphasis on both single and multi-objective optimiza-tion of the responses. 1.2 EDM process Although, Joseph Preistly first observed the principles of the EDM process in 1770, two Soviet re-searchers, the Lazarenkos’, succeeded in the development of a machining process in the 1940’s that formed the foundation for the present EDM system. In this process, electrical energy is utilized to originate electrical spark between an electrode and a workpiece, and material removal principally takes place due to electro-discharge erosion. An intense heat with temperature between 8000o-12000o C is generated by this electric spark, which when carefully controlled and localized, can only affect the workpiece surface. The metal removal mechanism is based on the application of a pulsating (on/off) electrical charge carrying high frequency current through the electrode to the workpiece, which causes controlled erosion of minute particles of metal from the workpiece. Instant vapourization and melting of the material are thus responsible for material removal. The tool and the work material are submerged

470

in a dielectric medium (kerosene or deionized water which also acts as a coolant and washes out the eroded metal particles), and a gap is steadily maintained between the tool and the workpiece. In EDM process, both the tool and the workpiece material must be good conductor of electricity. Thus, any material that is electrically conductive (steel, titanium, superalloys, brass etc.) can easily be machined using this process (Ho and Newman, 2003). Its major advantages include limited heat affected zone (HAZ), surface hardening, no burr formation, generation of complex part geometries and faster ma-chining operation. It has huge applications in die and mold making industries. Its various control pa-rameters are open circuit voltage, spark gap, pulse-on time, pulse-off time, maximum (peak) current, polarity, dielectric medium etc.

(a)

(b)

(c) (d)

Fig. 1 Analysis on the parametric optimization of ECM processes A list of the reviewed research papers on parametric optimization of EDM processes is provided in Table 2. On the other hand, the pertinent information regarding the process parameters and responses selected, work materials machined and optimization techniques adopted are provided in Fig. 2. It can be revealed from this figure that supply current, pulse-on time and pulse-off time are the three most predominant parameters for EDM process. They are subsequently followed by other parameters, like gap voltage, duty factor/duty cycle, applied voltage, tool rotational speed, pulse duration, tool electrode lift time, flushing pressure etc. according to their preference. Some least important process parameters, like feed rate, work time, capacitance, machining polarity, dielectric level, tool material, dielectric flow rate, aspect ratio of the tool, no load voltage, inter-electrode gap etc. are merged together into a single group (treated as others). These parameters are not so common in EDM processes, and their availability and settings mainly count on the type of EDM machine employed for material removal and part gener-ation. Among the responses, MRR, surface roughness and electrode wear rate (EWR) have the maxi-mum importance, followed by overcut, taper ratio, white layer thickness, surface crack density etc. Circularity, process energy, residual stress, process time, dielectric consumption etc. are identified as the least important responses. It is interestingly noted that different grades of steel (mainly AISI varie-ties) and alloys (Inconel 718, Invar, aluminum alloys and Rene 80) are the widely machined work

02468

101214

0

2

4

6

8

10

12

14

MRR Surfaceroughness

Overcut Cylindricity

0

1

2

3

4

5

Steels MMCs Ti and itsalloys

Tungstencarbide

Otheralloys

0

1

2

3

4


materials using EDM process. Different MMCs, titanium and its alloys, and ceramics (aluminum oxide) are also machined by this process. For parametric optimization of EDM process, GRA is the most popular technique, followed by the other advanced optimization methods (like bio-geography based optimization, teaching learning-based optimization, particle swarm optimization etc.), principal com-ponent analysis (PCA), non-sorting genetic algorithm (NSGA-II), desirability function, neural net-works (NN), simulated annealing (SA) etc. For this process, only a single paper considered single ob-jective optimization, 31 papers attempted multi-objective optimization and the remaining four papers took into account both single and multi-objective optimization of the responses. 1.3 WEDM process The principle of material removal in WEDM process is quite identical to that of EDM process. In this process, material is gradually worn out from the workpiece using a series of sparks generating between the workpiece and the wire isolated by a flow of dielectric fluid. This fluid is continuously supplied into the machining zone, enabling complex shapes being generated with high accuracy. The wire is made of thin copper, tungsten or brass having diameter 0.05-0.3 mm. Because of its versatility, it is used in several areas, like aviation, medical, electronics and semiconductor applications, tool and die making industries, manufacturing of fixtures, gauges, cams, gears, strippers, punches, electrodes etc. As this process employs no force and does not form burrs, it can be effectively applied for machining of delicate parts (Ho et al., 2004). Its different process parameters include peak current, supply voltage, pulse-on time, pulse-off time, polarity, work material, size and speed of wire, feed rate, gain, rate of flushing, type of the dielectric medium etc. Table 1 Parameters, responses, work materials and optimization techniques considered in ECM processes

Sl. No.

Name of the authors Work material machined

Single/ Multi-

objective

Optimization tool(s) adopted

Process parameters Responses

1. Abuzied et al. (2012) - Multiple Artificial neural network

Electrolyte flow rate, applied volt-age, tool feed rate

MRR, surface roughness

2. Dhobe et al. (2014)

Titanium Both Quality loss function Electrolyte concentration, electrolyte flow rate, tool feed rate, inter-elec-trode gap, applied voltage


3. Gopal and Chakrachar (2012)

EN31 steel Multiple Grey relational analysis

Electrolyte concentration, applied voltage, tool feed rate

MRR, overcut, cylindricity

4. Jegan et al. (2013) Metal matrix composites

Multiple Weighted sum genetic algorithm

Current, applied voltage, tool feed rate, electrolyte concentration


5. Kalaimathi et al. (2014)

Monel 400 alloy Multiple Desirability function Inter-electrode gap, applied voltage, electrolyte concentration


6. Manikandan et al. (2015)

Ti-6Al-4V titanium alloy

Both Grey relational analysis

Tool feed rate, electrolyte concentration, electrolyte flow rate

MRR, overcut

7. Mukherjee and Chakraborty (2013)

EN8 steel

Both

Biogeography-based optimization

Electrolyte concentration, applied voltage, electrolyte flow rate, inter-electrode gap

MRR, overcut

8. Rao and Pad-manabham (2015)

Aluminium matrix and boron carbide metal matrix com-

posite

Both Utility concept Applied voltage, electrolyte flow rate, tool feed rate, reinforcement content

MRR, surface roughness, radial overcut

9. Sathiyamoorthy et al. (2015)

High carbon high chromium die tool

steel

Multiple Genetic algorithm Applied voltage, inter-electrode gap, tool feed rate, electrolyte discharge rate


10. Sathiyamoorthy and Sekar (2016)

AISI 202 stainless steel

Multiple Genetic algorithm Applied voltage, tool feed rate, elec-trolyte discharge rate


11. Sohrabpoor et al. (2016)

Cemented tungsten carbide

Multiple Cuckoo optimization algorithm

Electrolyte concentration, applied voltage, electrolyte flow rate, tool feed rate

MRR, surface roughness, radial overcut

12. Solaiyappan et al. (2014)

Silicon carbide composite

Multiple Hybrid fuzzy-artificial bee colony algorithm

Applied voltage, electrolyte flow rate, current, inter-electrode gap, tool feed rate, electrolyte concentration

MRR, surface roughness, overcut

13. Tang and Yang (2013) Special stainless steel

00Cr12Ni9Mo4Cu2

Multiple Grey relational analy-sis

Applied voltage, electrolyte pressure, electrolyte concentration, tool feed rate

MRR, side gap, surface roughness

14. Teimouri and Shor-abpoor (2013)

Cemented tungsten carbide

Multiple Adaptive neuro-fuzzy inference system,

cuckoo optimization algorithm

Electrolytic concentration, applied voltage, electrolyte flow rate, tool feed rate


47

2

Tab

le 2

Pa

ram

eter

s, re

spon

ses,

wor

k m

ater

ials

and

opt

imiz

atio

n te

chni

ques

con

side

red

in E

DM

pro

cess

es

Sl.N

o.

Nam

e of

aut

hor(

s)

Wor

k m

ater

ial

mac

hine

d Si

ngle

/ M

ulti-

obje

ctiv

e O

ptim

izat

ion

tool

(s) a

dopt

ed

Proc

ess p

aram

eter

s R

espo

nse(

s)

1.

Aic

h an

d B

aner

jee

(201

6)

Hig

h sp

eed

stee

l M

ultip

le

Teac

hing

lear

ning

-bas

ed o

ptim

iza-

tion

algo

rithm

Su

pply

cur

rent

, pul

se-o

n tim

e, p

ulse

-off

tim

e M

RR

, sur

face

roug

hnes

s

2.

Ani

tha

et a

l. (2

016)

A

ISI D

2 to

ol st

eel

M

ultip

le

Arti

ficia

l neu

ral n

etw

ork

Puls

e cu

rren

t, pu

lse-

on t

ime,

dut

y cy

cle,

ap-

plie

d vo

ltage

M

RR

, sur

face

roug

hnes

s

3.

Ay

et a

l. (2

013)

In

cone

l 718

M

ultip

le

Gre

y re

latio

nal a

naly

sis

Dis

char

ge c

urre

nt, p

ulse

dur

atio

n

Tape

r rat

io, h

ole

dila

tion

4.

Bar

aska

r et a

l. (2

013)

EN

8 ca

rbon

stee

l M

ultip

le

Non

-dom

inat

ed so

rting

gen

etic

al

gorit

hm

Dis

char

ge c

urre

nt,

pul

se-o

n tim

e, p

ulse

-off

tim

e M

RR

, sur

face

roug

hnes

s

5.

Beh

era

et a

l. (2

015)

ZA

27/S

iC

met

al m

atrix

co

mpo

site

Mul

tiple

G

rey

rela

tiona

l ana

lysi

s Po

wde

r con

cent

ratio

n in

die

lect

ric, S

iC%

in d

i-el

ectri

c, p

ulse

d cu

rren

t, du

ty c

ycle

M

RR

, su

rfac

e ro

ughn

ess,

tool

wea

r rat

e

6.

Bha

rti e

t al.

(201

2)

Inco

nel 7

18

Mul

tiple

N

on-d

omin

ated

sorti

ng g

enet

ic a

l-go

rithm

Sh

ape

fact

or, d

isch

arge

cur

rent

, pul

se-o

n tim

e,

gap

volta

ge, d

uty

cycl

e, fl

ushi

ng p

ress

ure,

tool

el

ectro

de li

ft tim

e

MR

R, s

urfa

ce ro

ughn

ess

7.

Chak

ravo

rty e

t al.

(201

2)

Cer

amic

s (A

l 2O3 +

30

vol%

Ti

C),

stai

nles

s ste

el 3

04

Mul

tiple

PC

A-b

ased

gre

y re

latio

nal a

naly

-si

s, PC

A-b

ased

pro

porti

on o

f qua

l-ity

loss

redu

ctio

n, P

CA

-bas

ed

TOPS

IS, w

eigh

ted

prin

cipa

l com

-po

nent

ana

lysi

s

Mac

hini

ng p

olar

ity, p

eak

curr

ent,

appl

ied

volt-

age,

no

load

vol

tage

, pu

lse

dura

tion,

se

rvo

vo

ltage

, cap

acita

nce,

resi

stan

ce, f

eed

rate

, too

l ro

tatio

nal s

peed

MR

R,

surf

ace

roug

hnes

s, el

ectro

de w

ear r

ate,

ent

ranc

e cl

eara

nce,

exi

t cle

aran

ce

8.

Dav

e et

al.

(201

2)

Inco

nel 7

18

Mul

tiple

Ta

guch

i los

s fun

ctio

n O

rbita

l rad

ius,

orbi

tal s

peed

, gap

vol

tage

, cur

-re

nt, p

ulse

-on

time,

dut

y fa

ctor

M

RR

, too

l wea

r rat

e, su

rfac

e ro

ughn

ess

9.

Dew

anga

n an

d B

isw

as (2

013)

A

ISI P

20 to

ol st

eel

Mul

tiple

G

rey

rela

tiona

l ana

lysi

s D

isch

arge

cur

rent

, wor

k tim

e, li

ft tim

e, p

ulse

-on

tim

e, in

ter-

ele

ctro

de g

ap

MR

R, t

ool w

ear r

ate

10.

Dew

anga

n et

al.

(201

5)

AIS

I P20

tool

stee

l M

ultip

le

Gre

y-fu

zzy

logi

c D

isch

arge

cur

rent

, t

ool

wor

k tim

e, t

ool

lift

time,

pul

se-o

n tim

e

Whi

te l

ayer

thi

ckne

ss,

sur-

face

cr

ack

dens

ity, s

urfa

ce ro

ugh-

ness

11

. G

olsh

an e

t al.

(201

2)

Al/S

iC c

ompo

site

M

ultip

le

Non

-dom

inat

ed so

rting

gen

etic

al-

gorit

hm

Puls

e-on

tim

e, a

vera

ge g

ap v

olta

ge, p

ulse

pea

k cu

rren

t, pe

rcen

t vol

ume

frac

tion

of S

iC

MR

R, s

urfa

ce ro

ughn

ess

12.

Jaga

dish

and

Ray

(201

5)

AIS

I D2

tool

stee

l M

ultip

le

Gre

y re

latio

nal a

naly

sis

Puls

e du

ratio

n, p

eak

curr

ent,

diel

ectri

c le

vel,

flush

ing

pres

sure

Pr

oces

s tim

e,

proc

ess

en-

ergy

, too

l wea

r rat

io, c

once

n-tra

tion

of a

eros

ol,

diel

ectri

c co

nsum

ptio

n

13.

Maj

umda

r (20

12)

Mild

stee

l Si

ngle

G

enet

ic a

lgor

ithm

C

urre

nt, p

ulse

-on

time,

pul

se-o

ff ti

me

Elec

trode

wea

r rat

e 14

. M

ajum

der (

2013

) A

ISI 3

16 L

N st

ainl

ess s

teel

M

ultip

le

Fuzz

y-ba

sed

parti

cle

swar

m o

pti-

miz

atio

n Su

pply

cur

rent

, pul

se-o

n tim

e, p

ulse

-off

tim

e M

RR

, ele

ctro

de w

ear r

ate

15.

Maj

umde

r et a

l. (2

014)

A

ISI 3

16 L

N st

ainl

ess s

teel

M

ultip

le

Des

irabi

lity-

base

d pa

rticl

e sw

arm

i

ii

Supp

ly c

urre

nt, p

ulse

-on

time,

pul

se-o

ff ti

me

MR

R, e

lect

rode

wea

r rat

e 16

. M

ajum

dar

(201

5)

Mild

stee

l M

ultip

le

Gen

etic

alg

orith

m, s

imul

ated

an-

neal

ing,

par

ticle

swar

m o

ptim

iza-

tion,

arti

ficia

l neu

ral n

etw

ork

Dis

char

ge c

urre

nt,

puls

e-on

tim

e,

puls

e-of

f tim

e M

RR

, to

ol w

ear r

atio

S. C

hakr

abor

ty e

t al.

/ M

anag

emen

t Sci

ence

Let

ters

9 (2

019)

47

3

Tab

le 2

Pa

ram

eter

s, re

spon

ses,

wor

k m

ater

ials

and

opt

imiz

atio

n te

chni

ques

con

side

red

in E

DM

pro

cess

es (C

ontin

ued)

Sl

.No.

N

ame

of a

utho

r(s)

W

ork

mat

eria

l m

achi

ned

Sing

le/

Mul

ti-ob

ject

ive

Opt

imiz

atio

n to

ol(s

) ado

pted

Pr

oces

s par

amet

ers

Res

pons

e(s)

17.

Min

g et

al.

(201

6)

SiC

/Al

com

posi

te

Mul

tiple

G

enet

ic a

lgor

ithm

with

de

sira

bilit

y fu

nctio

n Pe

ak c

urre

nt, p

ulse

-on

time,

pul

se-o

ff ti

me,

ap-

plie

d vo

ltage

M

RR

, sur

face

roug

hnes

s

18.

Mog

hadd

am a

nd K

olah

an

(201

5)

AIS

I 231

2 ho

t wor

ked

stee

l M

ultip

le

Sim

ulat

ed a

nnea

ling

Peak

cur

rent

, pul

se-o

n tim

e, p

ulse

-off

tim

e, a

p-pl

ied

volta

ge,

duty

fact

or

MR

R, t

ool w

ear r

ate,

surf

ace

roug

hnes

s 19

. M

ohan

ty e

t al.

(201

6a)

AIS

I D2

stee

l M

ultip

le

Non

-dom

inat

ed so

rting

gen

etic

al-

gorit

hm

Dis

char

ge c

urre

nt,

puls

e-on

tim

e, d

uty

fact

or

MR

R, t

ool

wea

r ra

te, r

esid

-ua

l stre

ss

20.

Moh

anty

et a

l. (2

016b

) In

cone

l 718

M

ultip

le

Parti

cle

swar

m o

ptim

izat

ion

Dis

char

ge c

urre

nt,

open

circ

uit

volta

ge,

duty

fa

ctor

, pu

lse-

on t

ime,

flu

shin

g pr

essu

re,

tool

m

ater

ial

MR

R,

surf

ace

roug

hnes

s, el

ectro

de w

ear r

ate,

ove

rcut

21.

Muk

herje

e an

d C

hakr

abor

ty

(201

2)

Die

stee

l

Bot

h B

ioge

ogra

phy-

base

d op

timiz

atio

n al

gorit

hm

Peak

cur

rent

, av

erag

e ga

p vo

ltage

, pu

lse-

on

time,

per

cent

vol

ume

frac

tion

of S

iC p

rese

nt in

al

umin

um m

atrix

Surf

ace

roug

hnes

s, w

hite

la

yer t

hick

ness

, sur

face

crac

k de

nsity

, M

RR

, to

ol

wea

r ra

te, g

ap si

ze

22.

Padh

ee e

t al.

(201

2)

EN31

stee

l M

ultip

le

Non

-dom

inat

ed so

rting

gen

etic

al-

gorit

hm

Puls

e-on

tim

e, p

eak

curr

ent,

duty

fact

or,

con-

cent

ratio

n of

the

abra

sive

M

RR

, sur

face

roug

hnes

s

23.

Pand

a et

al.

(201

5)

Stai

nles

s ste

el (S

304)

M

ultip

le

Gre

y re

latio

nal a

naly

sis,

parti

cle

swar

m o

ptim

izat

ion

Dis

char

ge cu

rren

t, di

elec

tric f

low

rate

, pul

se-o

n tim

e, p

ulse

-off

tim

e

MR

R, t

ool w

ear r

ate,

surf

ace

roug

hnes

s 24

. Po

rwal

et a

l. (2

012)

In

var

Mul

tiple

A

rtific

ial n

eura

l net

wor

k G

ap v

olta

ge, c

apac

itanc

e, sp

indl

e sp

eed

MR

R, t

ool w

ear r

ate,

hol

e ta

-pe

r 25

. Pr

adha

n (2

012)

A

ISI D

2 st

eel

Mul

tiple

Pr

inci

pal c

ompo

nent

ana

lysi

s, gr

ey

rela

tiona

l ana

lysi

s D

isch

arge

cur

rent

, pul

se-o

n tim

e, a

pplie

d vo

lt-ag

e, d

uty

cycl

e

MR

R, s

urfa

ce ro

ughn

ess

26.

Priy

adar

shin

i and

Pal

(201

6)

Tita

nium

allo

y (T

i-6A

l-4V

) B

oth

Prin

cipa

l com

pone

nt a

naly

sis,

grey

re

latio

nal a

naly

sis

Puls

e du

ratio

n, d

uty

fact

or, d

isch

arge

cur

rent

, ga

p vo

ltage

M

RR

, too

l wea

r rat

e, su

rfac

e ro

ughn

ess

27.

Rad

hika

et a

l. (2

015)

A

lum

iniu

m a

lloy

(Al-S

i10M

g)

Mul

tiple

G

rey

rela

tiona

l ana

lysi

s Pe

ak c

urre

nt, f

lush

ing

pres

sure

, pul

se-o

n tim

e M

RR

, su

rfac

e ro

ughn

ess,

tool

wea

r rat

e 28

. R

aja

et a

l. (2

015)

D

ie st

eel

Mul

tiple

Fi

refly

alg

orith

m

Supp

ly c

urre

nt, p

ulse

-on

time

Surf

ace

roug

hnes

s, m

achi

n-in

g tim

e 29

. Sa

hu a

nd N

ayak

(201

5)

AIS

I P20

tool

stee

l M

ultip

le

Gen

etic

alg

orith

m

Puls

e-on

tim

e, d

isch

arge

cur

rent

M

RR

, ove

rcut

, too

l wea

r rat

e 30

. Si

ngh

(201

2)

Alu

min

ium

met

al m

atrix

com

po-

site

s M

ultip

le

Gre

y re

latio

nal a

naly

sis

Asp

ect r

atio

, dut

y cy

cle,

gap

vol

tage

, pul

se cu

r-re

nt, p

ulse

-on

time,

tool

ele

ctro

de li

ft tim

e M

RR

, su

rfac

e ro

ughn

ess,

tool

wea

r rat

e 31

. Ta

ng a

nd D

u (2

014)

Sp

ecia

l sta

inle

ss st

eel

00C

r12N

i9M

o4C

u2

Mul

tiple

G

rey

rela

tiona

l ana

lysi

s To

ol f

eed

rat

e, a

pplie

d vo

ltage

, el

ectro

lyte

pr

essu

re, e

lect

roly

te c

once

ntra

tion

MR

R,

side

ga

p,

surf

ace

roug

hnes

s 32

. Te

imou

ri an

d B

aser

i (20

12)

EN 3

2 m

ild st

eel

Bot

h A

rtific

ial b

ee c

olon

y a

lgor

ithm

Pu

lse

curr

ent,

gap

volta

ge, p

ulse

-on

time,

dut

y fa

ctor

, air

inta

ke p

ress

ure,

spin

dle

spee

d M

RR

, sur

face

roug

hnes

s

33.

Teim

ouri

and

Bas

eri

(201

4)

SPK

(X21

0Cr1

2) c

old

wor

k st

eel

Mul

tiple

A

dapt

ive

neur

o-fu

zzy

infe

renc

e sy

stem

, con

tinuo

us a

nt c

olon

y op

-tim

izat

ion

algo

rithm

Mag

netic

fie

ld

inte

nsity

, ro

tatio

nal

spee

d,

prod

uct o

f cur

rent

and

pul

se-o

n tim

e M

RR

, sur

face

roug

hnes

s

34.

Than

gadu

rai a

nd A

sha

(201

4)

Alu

min

ium

bor

on c

arbi

de c

om-

posi

te

Mul

tiple

D

esira

bilit

y fu

nctio

n, g

enet

ic a

lgo-

rithm

Pu

lse-

on ti

me,

pul

se-o

ff ti

me,

cur

rent

M

RR

, su

rfac

e ro

ughn

ess,

to

ol w

ear r

ate

35.

Uyy

ala

and

Kum

ar (2

014)

R

ENE

80 n

icke

l sup

er a

lloy

Bot

h G

rey

rela

tiona

l ana

lysi

s Pe

ak c

urre

nt, p

ulse

-on

time,

pul

se-o

ff ti

me

MR

R, s

urfa

ce ro

ughn

ess,

ra-

dial

ove

rcut

36

. Y

adav

and

Yad

ava

(201

5)

Tita

nium

allo

y (T

i-6A

l-4V

) M

ultip

le

Gre

y re

latio

nal a

naly

sis,

prin

cipa

l co

mpo

nent

ana

lysi

s To

ol e

lect

rode

spe

ed, p

ulse

-on

time,

dut

y fa

c-to

r, ga

p cu

rren

t M

RR

, su

rfac

e ro

ughn

ess,

circ

ular

ity

474

(a)

(b)

(c) (d)

Fig. 2 Analysis on the parametric optimization of EDM processes

In Table 3, the parameters and responses considered, work materials machined and optimization tech-niques adopted for parametric optimization of WEDM processes are listed based on the reviewed re-search papers. Figure 3 presents the related detailed analyses. It can be noticed that for WEDM process, wire feed rate, pulse-off time and pulse-on time are the most frequently set process parameters, followed by wire tension, current, gap voltage, flushing pressure, pulse duration, servo feed and workpiece thick-ness. There are also some insignificant process parameters, like spark gap, capacitance, duty factor, pulse frequency, taper angle, power, dielectric flow rate, rotational speed, corner servo etc. as set by different researchers depending on their availability and settings in the WEDM set-ups. Among the responses, surface roughness is provided with the maximum priority, followed by MRR, cutting rate, overcut, wire wear rate (WWR), kerf width and angular error. Spark gap, machining time and white layer thickness are also the other responses which are less frequently considered by the researchers. In WEDM processes, different grades of steel and alloys (specially Inconel) are the most commonly machined work materials, followed by titanium and its alloys, MMCs, aluminum and its alloys, and tungsten carbide. For paramet-ric optimization of WEDM processes, different advanced optimization methods along with NSGA are the most popular approaches among the researchers, followed by GA, GRA, utility concept, desirability function, SA, NN and PCA. Out of 28 research papers surveyed, 17 of them dealt with multi-objective optimization, and the remaining papers considered both single and multi-objective optimization of WEDM responses. 1.4 LBM process

In LBM process, a laser beam with high energy density is made to focus on the workpiece surface. The work volume is heated by the absorbed thermal energy and transformed into a molten, vaporized or modified into another chemical state, and is subsequently removed from the machining zone using a high pressure assist gas jet (Meijer, 2004). Thus, melting, vaporization and chemical degradation (chemical bonds are disintegrated causing the material to dissipate) are the three stages of the material removal mechanism in LBM process (Dubey & Yadava, 2008). Ruby (chromium alumina alloy), Nd-glass laser,

048

12162024283236

048121620242832

0

4

8

12

16

20

24

Steels Alloys MMCs Ti and itsalloys

Ceramics

02468

1012


Nd-YAG laser etc. are the examples of solid state laser, whereas, Helium-Neon, Argon, CO2 etc. are the gas lasers. The LBM process has several advantages, such as no built-up edge formation, low operating cost, no tool wear, rapid machining ability, capability of generating very tiny holes at difficult entrance angles etc. It is most suitably deployed for welding of non-conductive and refractory materials, and also for drilling, cutting, grooving, scribing, trimming and patterning operations. Its main process parameters include pulse shape, pulse frequency, wave length, duration, laser energy, assist gas type and pressure, focal length and position etc. A list of the reviewed research papers published during 2012-16 on para-metric optimization of LBM processes is provided in Table 4, and Fig. 4 exhibits the parameters and responses selected, work materials machined and optimization techniques deployed in those processes. Pulse frequency, assist gas pressure, cutting speed and pulse width are identified as the most significant process parameters, followed by laser power, lamp current and focus position. There are also some other insignificant parameters, e.g. Y feed rate, workpiece thickness, arc radius etc., which are less frequently chosen by the past researchers. Surface roughness, HAZ, hole taper and kerf taper are the maximally preferred responses for their optimization; although, other responses, like top kerf width, upper deviation, channel width, burr height, depth deviation, MRR, lower deviation are also given due importance. In these processes, some unimportant responses, like burr width, depth of separation line, drag line separa-tion and channel width are also noticed to exist. Among the work materials, aluminium and its different alloys are primarily machined using LBM process, followed by various ceramics (alumina and zirconia) and thermoplastic polymers. Other materials, like different grades of steel, Inconel 718, and titanium and its alloys are also machined using LBM process, but their occurrences are observed to be quite less as compared to other work materials. For parametric optimization of LBM processes, GRA is identified as the most effective method, followed by the application of different advanced optimization techniques. Other optimization methods, like GA, PCA, NSGA etc. are also occasionally adopted by the past re-searchers for the said purpose. Among 18 research papers reviewed, 16 papers considered multi-objective optimization, while one paper dealt with single objective optimization of the responses. There is only a single paper where both single and multi-objective optimization of the responses were considered.

(a)

(b)

(c)

(d)

Fig. 3 Analysis on the parametric optimization of WEDM processes

04812162024

048

1216202428

0

2

4

6

8

10

12

Steels Alloys Ti and itsalloys

MMCs Al and itsalloys

Tungstencarbide

02468

1012

47

6 Tab

le 3

Pa

ram

eter

s, re

spon

ses,

wor

k m

ater

ials

and

opt

imiz

atio

n te

chni

ques

con

side

red

in W

EDM

pro

cess

es

Sl.N o.

Nam

e of

aut

hors

W

ork

mat

eria

l ma-

chin

ed

Sing

le/

Mul

ti-

obje

ctiv

e

Opt

imiz

atio

n to

ol(s

) ad

opte

d Pr

oces

s par

amet

ers

Res

pons

es

1.

Agg

arw

al e

t al.

(201

5)

Inco

nel 7

18

Bot

h D

esira

bilit

y fu

nctio

n W

ire f

eed

rate

, pu

lse-

on t

ime,

pul

se-o

ff t

ime,

w

ire te

nsio

n, g

ap v

olta

ge

Cut

ting

rate

, su

rfac

e ro

ughn

ess

2.

Azh

iri e

t al.

(201

4)

Alu

min

ium

silic

on c

ar-

bide

com

posi

te

Mul

tiple

A

dapt

ive

neur

o-fu

zzy

infe

r-en

ce s

yste

m, g

rey

rela

tiona

l an

alys

is

Puls

e-on

tim

e, p

ulse

-off

tim

e, g

ap v

olta

ge, d

is-

char

ge c

urre

nt, w

ire fe

ed, w

ire te

nsio

n C

uttin

g ve

loci

ty, s

urfa

ce

roug

hnes

s

3.

Boo

path

i and

Siv

akum

ar

(201

3)

Hig

h sp

eed

stee

l M

ultip

le

Epsi

lon

dom

inan

ce

ap-

proa

ch o

f gen

etic

alg

orith

m

Dis

char

ge cu

rren

t, pu

lse-

on ti

me,

pul

se-o

ff ti

me,

ga

p vo

ltage

, air-

mis

t pre

ssur

e M

RR

, sur

face

roug

hnes

s

4.

Bho

opat

hi a

nd S

ivak

umar

(2

016)

H

SS-M

42 to

ol st

eel

Mul

tiple

A

rtific

ial

bee

colo

ny a

lgo-

rithm

Sp

ark

curr

ent,

oxyg

en-m

ist i

nlet

pre

ssur

e, m

ix-

ing

flow

rate

, pul

se-o

n tim

e

MR

R, s

urfa

ce ro

ughn

ess

5.

Cha

lisga

onka

r and

Kum

ar

(201

3)

Tita

nium

B

oth

Util

ity c

once

pt

Wire

feed

, wire

tens

ion,

pul

se-o

n tim

e, p

ulse

-off

tim

e, g

ap v

olta

ge, p

eak

curr

ent

Cut

ting

spee

d,

surf

ace

roug

hnes

s 6.

G

arg

et a

l. (2

012)

Ti

tani

um 6

-2-4

-2

Mul

tiple

N

on-d

omin

ated

sor

ting

ge-

netic

alg

orith

m

Puls

e-on

tim

e, p

ulse

-off

tim

e, g

ap v

olta

ge, w

ire

feed

, wire

tens

ion,

pea

k cu

rren

t C

uttin

g sp

eed,

su

rfac

e ro

ughn

ess

7.

Jaya

dith

ya e

t al.

(201

4)

Inco

nel 8

25

Mul

tiple

U

tility

con

cept

W

ire f

eed,

wor

kpie

ce th

ickn

ess,

puls

e-on

tim

e,

puls

e-of

f tim

e

Cut

ting

spee

d,

surf

ace

roug

hnes

s, di

men

sion

al

devi

atio

n 8.

K

ovač

ević

et a

l. (2

014)

N

itrid

ed st

eel,

AIS

I D

5/D

IN 1

.260

1 st

eel,

alum

iniu

m, t

itani

um a

l-lo

y

Mul

tiple

Ex

haus

tive

itera

tive

sear

ch

proc

edur

e Pu

lse-

on ti

me,

pul

se-o

ff ti

me,

ser

vo f

eed,

pea

k cu

rren

t M

RR

, cut

ting

spee

d, su

r-fa

ce ro

ughn

ess,

over

cut

9.

Kris

han

and

Sam

uel (

2013

) A

ISI D

3 to

ol st

eel

Mul

tiple

N

on-d

omin

ated

sor

ting

ge-

netic

alg

orith

m

Puls

e-of

f tim

e, s

park

gap

, ser

vo fe

ed, r

otat

iona

l sp

eed,

flus

hing

pre

ssur

e M

RR

, sur

face

roug

hnes

s

10.

Kum

ar a

nd A

garw

al (2

012)

H

igh

spee

d st

eel

Bot

h N

on-d

omin

ated

sor

ting

ge-

netic

alg

orith

m

Puls

e pe

ak c

urre

nt,

wire

ten

sion,

wire

fee

d,

flush

ing

pres

sure

, pul

se d

urat

ion,

pul

se-o

ff ti

me

MR

R, s

urfa

ce ro

ughn

ess

11.

Kur

iach

en e

t al.

(201

5)

Tita

nium

allo

y

(Ti-6

Al-4

V)

Mul

tiple

Pa

rticl

e sw

arm

opt

imiz

atio

n G

ap v

olta

ge, c

apac

itanc

e, fe

ed ra

te, w

ire te

nsio

n M

RR

, sur

face

roug

hnes

s

12.

Min

g et

al.

(201

4)

Tung

sten

stee

l (Y

G15

) M

ultip

le

Non

-dom

inat

ed s

ortin

g ge

-ne

tic a

lgor

ithm

W

ire te

nsio

n, p

ulse

-on

time,

pul

se-o

ff ti

me,

wire

sp

eed,

wat

er p

ress

ure,

cut

ting

feed

rate

M

RR

, sur

face

roug

hnes

s

13.

Muk

herje

e et

al.

(201

2)

Inco

nel 6

01

Bot

h G

enet

ic a

lgor

ithm

, pa

rticl

e sw

arm

opt

imiz

atio

n, s

heep

flo

ck a

lgor

ithm

, an

t co

lony

op

timiz

atio

n,

artif

icia

l be

e co

lony

, bio

geog

raph

y-ba

sed

optim

izat

ion

Peak

cur

rent

, pu

lse

dura

tion,

pul

se f

requ

ency

, w

ire sp

eed,

die

lect

ric fl

ow ra

te, d

uty

fact

or, w

ire

tens

ion,

wat

er p

ress

ure,

MR

R, w

ear r

atio

, sur

face

ro

ughn

ess,

kerf

wid

th

14.

Nay

ak a

nd M

ahap

atra

(201

4)

Stai

nles

s ste

el

Mul

tiple

U

tility

con

cept

D

isch

arge

cur

rent

, pa

rt th

ickn

ess,

wire

spe

ed,

wire

tens

ion,

tape

r ang

le, p

ulse

dur

atio

n

Ang

ular

er

ror,

surf

ace

roug

hnes

s, cu

tting

spee

d

S. C

hakr

abor

ty e

t al.

/ M

anag

emen

t Sci

ence

Let

ters

9 (2

019)

47

7

Tab

le 3

Pa

ram

eter

s, re

spon

ses,

wor

k m

ater

ials

and

opt

imiz

atio

n te

chni

ques

con

side

red

in W

EDM

pro

cess

es (C

ontin

ued)

Sl

.N o.

Nam

e of

aut

hors

W

ork

mat

eria

l ma-

chin

ed

Sing

le/

Mul

ti-ob

-je

ctiv

e

Opt

imiz

atio

n to

ol(s

) ad

opte

d Pr

oces

s par

amet

ers

Res

pons

es

15.

Nay

ak a

nd M

ahap

atra

(201

6)

In

cone

l 718

B

oth

Bat

alg

orith

m

Part

thic

knes

s, di

scha

rge

curr

ent,

wire

spe

ed,

wire

tens

ion,

tape

r ang

le, p

ulse

dur

atio

n

Ang

ular

er

ror,

surf

ace

roug

hnes

s, cu

tting

spee

d

16.

Pras

ad a

nd K

rishn

a (2

015)

A

ISI D

3 di

e st

eel

Mul

tiple

H

arm

ony

sear

ch a

lgor

ithm

Puls

e-on

tim

e, p

ulse

-off

tim

e, d

iele

ctric

flo

w

rate

, wire

feed

, wire

tens

ion

K

erf,

wire

wea

r rat

io

17.

Raj

yala

kshm

i and

Ram

aiah

(2

013)

In

cone

l 825

B

oth

Gre

y re

latio

nal a

naly

sis

Puls

e-on

tim

e, p

ulse

-off

tim

e, c

orne

r ser

vo, w

ire

tens

ion,

gap

vol

tage

, ser

vo f

eed,

flu

shin

g pr

es-

sure

of d

iele

ctric

flui

d, w

ire fe

ed ra

te

MR

R,

surf

ace

roug

h-ne

ss, s

park

gap

18.

Rao

et a

l. (2

014)

A

l707

5/Si

Cp

met

al m

a-tri

x co

mpo

site

s B

oth

Hyb

rid g

enet

ic a

lgor

ithm

Pu

lse-

on t

ime,

pul

se-o

ff t

ime,

pea

k cu

rren

t, sp

ark

gap

volta

ge, s

ervo

feed

rate

, flu

shin

g pr

es-

sure

, wire

feed

rate

, wire

tens

ion

MR

R,

surf

ace

roug

h-ne

ss, w

ire w

ear r

ate

19.

Rao

and

Kris

hna

(201

4)

Al7

075/

SiC

p m

etal

ma-

trix

com

posi

tes

Mul

tiple

N

on-d

omin

ated

sor

ting

ge-

netic

Alg

orith

m

Parti

cula

te s

ize,

vol

ume

of S

iCp,

pul

se-o

n tim

e,

puls

e-of

f tim

e, w

ire te

nsio

n M

RR

, su

rfac

e ro

ugh-

ness

, wire

wea

r rat

io

20.

Saha

and

Mon

dal (

2016

) N

ano

stru

ctur

ed h

ard

faci

ng m

ater

ial

Mul

tiple

G

rey

rela

tiona

l an

alys

is,

prin

cipa

l co

mpo

nent

ana

ly-

sis

Serv

o vo

ltage

, w

ire t

ensi

on,

wire

fee

d ra

te,

puls

e-on

tim

e, p

ulse

-off

tim

e

MR

R,

surf

ace

roug

h-ne

ss, m

achi

ning

tim

e

21.

Saha

et a

l. (2

013)

Ti

tani

um c

arbi

de

Bot

h N

euro

-gen

etic

tech

niqu

e Pu

lse o

n-tim

e, p

ulse

off

-tim

e, w

ire fe

ed ra

te, g

ap

volta

ge

Cut

ting

spee

d,

kerf

w

idth

22

. Sh

ahal

i et a

l. (2

012)

D

IN 1

.454

2 st

ainl

ess

stee

l B

oth

Mic

ro-g

enet

ic a

lgor

ithm

Po

wer

, tim

e-of

f tim

e, g

ap v

olta

ge, s

ervo

vol

tage

, in

vers

, num

ber o

f fin

ish

pass

es

Surf

ace

roug

hnes

s, w

hite

laye

r thi

ckne

ss

23.

Shay

an e

t al.

(201

3)

Cem

ente

d tu

ngst

en c

ar-

bide

B

oth

Des

irabi

lity

appr

oach

, par

ti-cl

e sw

arm

opt

imiz

atio

n Pu

lse-

on ti

me,

pul

se-o

ff ti

me,

dis

char

ge c

urre

nt,

wire

te

nsio

n,

gap

volta

ge

Cut

ting

velo

city

, sur

face

ro

ughn

ess,

over

size

24.

Som

ashe

kar e

t al.

(201

2)

Alu

min

ium

M

ultip

le

Sim

ulat

ed a

nnea

ling

Gap

vol

tage

, cap

acita

nce,

feed

rate

M

RR

, su

rfac

e ro

ugh-

ness

, ove

rcut

25

. Su

brah

man

yam

and

Sar

car

(201

3)

Die

stee

l M

ultip

le

Gre

y re

latio

nal a

naly

sis

Puls

e-on

tim

e, p

ulse

-off

tim

e, p

eak

curr

ent,

gap

volta

ge, w

ire te

nsio

n, w

ire fe

ed ra

te, s

ervo

feed

, flu

shin

g pr

essu

re

MR

R, s

urfa

ce ro

ughn

ess

26.

Var

un e

t al.

(201

6)

M

onel

400

B

oth

Des

irabi

lity

func

tion,

par

ti-cl

e sw

arm

opt

imiz

atio

n Pu

lse-

on ti

me,

pul

se-o

ff ti

me,

pea

k cu

rren

t, w

ire

feed

M

RR

, sur

face

roug

hnes

s

27.

Yan

g et

al.

(201

2)

Tung

sten

M

ultip

le

Sim

ulat

ed a

nnea

ling

Pu

lse-

on ti

me,

pul

se-o

ff ti

me,

arc

-off

tim

e, w

ire

tens

ion,

wat

er p

ress

ure,

serv

o vo

ltage

, wire

feed

ra

te

MR

R,

surf

ace

roug

h-ne

ss, c

orne

r dev

iatio

n

28.

Zhan

g et

al.

(201

4)

Hig

h ca

rbon

, hig

h

chro

miu

m a

lloy

tool

st

eel

Mul

tiple

N

on-d

omin

ated

sor

ting

ge-

netic

alg

orith

m

Puls

e-on

tim

e, p

ulse

-off

tim

e, p

ulse

curr

ent,

wire

tra

velli

ng sp

eed,

trac

king

coe

ffic

ient

M

RR

, sur

face

roug

hnes

s

478

1.5. USM process

In USM process, a tool having the appropriate shape geometry oscillates over the workpiece at an ultra-sonic frequency of 19~25 kHz and amplitude of 15-50 μm. Between the tool and the workpiece, the machining zone is deluged with abrasive particles (Al2O3, SiC, B4C, diamond etc.) mixed with water to form of a water-based slurry. When the tool oscillates over the workpiece, the abrasive particles make indentations to remove material from the workpiece. Crack initiation, propagation and brittle fracture are the three phases causing removal of material in USM process. It can be effectively employed for gener-ating square, round, irregular shaped holes and surface impressions on hard and brittle materials, like glass, ceramics, stones, carbides, silicon nitride, nickel/titanium alloys etc. (Thoe et al., 1998). Amplitude and frequency of vibration, feed force and pressure, abrasive size and material, contact area of the tool, volume concentration of abrasive in slurry etc. are the different parameters influencing the machining performance of USM process.

(a)

(b)

(c)

(d)

Fig. 4 Analysis on the parametric optimization of LBM processes

Table 5 presents a list of the reviewed research papers published during 2012-16 on parametric optimi-zation of USM processes, and Fig. 5 provides an idea on the process parameters and responses, work materials and optimization techniques selected in those considered processes. It can be easily revealed that grit size and power rating are the two important control parameters mostly preferred by the past researchers, followed by the type of tool material, slurry concentration and tool feed rate. Tool profile, and type and thickness of the work material are some of the least preferred machining parameters for USM process. In this process, maximum importance is allocated to the optimization of surface roughness and MRR, followed by tool wear rate (TWR) and overcut. Titanium is identified as the most widely machined work material in USM process. It is also employed for machining of different ceramics (mainly zirconia) and MMCs. Among the optimization tools, GRA, utility concept and different advanced opti-mization techniques are mainly utilized for parametric optimization of USM processes. Other tools, such as GA, NN and Taguchi loss function are also applied for the same purpose. There are applications of weighted signal-to-noise ratio and multi-response signal-to-noise ratio for simultaneous optimization of USM responses. From Table 5, it can be noticed that four research papers dealt with multi-objective optimization, while three papers considered both single as well as multi-objective optimization of the responses for USM process.

02468

10121416

012345678

0

1

2

3

4

5

6

Al and itsalloys

CeramicsPolymers Steels Inconel718

Ti and itsalloys

0

2

4

6

8

10

GRA Adv. opt.methods

GA PCA Fuzzylogic

NSGA

S. C

hakr

abor

ty e

t al.

/ M

anag

emen

t Sci

ence

Let

ters

9 (2

019)

47

9

Tabl

e 4

Pa

ram

eter

s, re

spon

ses,

wor

k m

ater

ials

and

opt

imiz

atio

n te

chni

ques

con

side

red

in L

BM

pro

cess

es

Sl.

No.

N

ame

of a

utho

rs

Wor

k m

ater

ial m

a-ch

ined

Si

ngle

/Mul

ti-ob

-je

ctiv

e O

ptim

izat

ion

tool

(s) a

dopt

ed

Proc

ess p

aram

eter

s R

espo

nses

1.

Ach

erje

e et

al.

(201

4)

Poly

met

hyl-

met

h-ac

ryla

te

Mul

tiple

G

rey

rela

tiona

l ana

lysi

s La

mp

curr

ent,

scan

ning

spe

ed,

puls

e fr

eque

ncy,

pul

se w

idth

C

hann

el d

epth

, bur

r hei

ght,

burr

wid

th

2.

Gan

guly

et a

l. (2

012)

Zi

rcon

ium

oxi

de

Mul

tiple

G

rey

rela

tiona

l ana

lysi

s La

mp

curr

ent,

puls

e fr

eque

ncy,

ai

r pr

essu

re, p

ulse

wid

th

Hol

e ta

per,

heat

aff

ecte

d zo

ne

3.

Kib

ria e

t al.

(201

3)

Alu

min

a ce

ram

ics

Mul

tiple

G

rey

rela

tiona

l ana

lysi

s A

vera

ge p

ower

, pul

se fr

eque

ncy,

rota

-tio

nal s

peed

, air

pres

sure

, Y fe

ed ra

te

Surf

ace

roug

hnes

s, de

pth

devi

atio

n

4.

Kua

r et a

l. (2

012)

A

lum

iniu

m o

xide

M

ultip

le

Gre

y re

latio

nal a

naly

sis

Lam

p cu

rren

t, pu

lse

freq

uenc

y,

air

pres

sure

, pul

se w

idth

H

ole

tape

r, he

at a

ffec

ted

zone

5.

Mad

ic e

t al.

(201

4)

Stai

nles

s ste

el

Mul

tiple

G

rey

rela

tiona

l ana

lysi

s La

ser

pow

er, c

uttin

g sp

eed,

ass

ist g

as

pres

sure

, foc

us p

ositi

on

Bur

r hei

ght,

drag

line

sepa

ratio

n, d

epth

of

sepa

ratio

n lin

e 6.

M

adic

et a

l. (2

015)

St

ainl

ess s

teel

M

ultip

le

Cuc

koo

sear

ch a

lgor

ithm

La

ser

pow

er, c

uttin

g sp

eed,

ass

ist g

as

pres

sure

, foc

us p

ositi

on

Surf

ace

roug

hnes

s, he

at a

ffec

ted

zone

, to

p ke

rf w

idth

7.

M

ishr

a an

d Y

adav

a (2

013a

) In

cone

l 718

M

ultip

le

Gre

y re

latio

nal

anal

ysis

, pr

inci

pal

com

pone

nt a

naly

sis

Puls

e w

idth

, pu

lse

freq

uenc

y, p

eak

pow

er, w

orkp

iece

thic

knes

s M

RR

, hol

e ta

per,

heat

eff

ecte

d zo

ne

8.

Mis

hra

and

Yad

ava

(201

3b)

Alu

min

ium

M

ultip

le

Gre

y re

latio

nal

anal

ysis

, pr

inci

pal

com

pone

nt a

naly

sis

Ave

rage

pow

er, p

ulse

wid

th, p

ulse

fre-

quen

cy, n

ozzl

e st

and-

off d

ista

nce

MR

R, h

ole

tape

r, he

at a

ffec

ted

zone

9.

Muk

herje

e et

al.

(201

3)

Zirc

oniu

m o

xide

, al-

umin

ium

oxi

de

Bot

h A

rtific

ial b

ee c

olon

y al

gorit

hm

Lam

p cu

rren

t, pu

lse

freq

uenc

y,

air

pres

sure

, pul

se w

idth

, cut

ting

spee

d H

eat a

ffec

ted

zone

, tap

er, u

pper

dev

ia-

tion,

low

er d

evia

tion,

dep

th d

evia

tion

10.

Pand

ey a

nd D

ubey

(201

2a)

Tita

nium

allo

y M

ultip

le

Gen

etic

alg

orith

m

Gas

pre

ssur

e, p

ulse

wid

th,

puls

e fr

e-qu

ency

, cut

ting

spee

d Su

rfac

e ro

ughn

ess,

kerf

tape

r

11.

Pand

ey a

nd D

ubey

(201

2b)

Dur

alum

in

Mul

tiple

Ta

guch

i-bas

ed fu

zzy

logi

c G

as p

ress

ure,

pul

se w

idth

, pu

lse

fre-

quen

cy, c

uttin

g sp

eed

Ker

f wid

th, t

op k

erf d

evia

tion,

bot

tom

ke

rf d

evia

tion

12

. Pa

ndey

and

Dub

ey (2

013)

D

ural

umin

M

ultip

le

Gre

y-fu

zzy

logi

c

Gas

pre

ssur

e, p

ulse

wid

th,

puls

e fr

e-qu

ency

, cut

ting

spee

d Su

rfac

e ro

ughn

ess,

kerf

ta

per,

kerf

w

idth

13

. Pa

war

and

Ray

ete

(201

4)

Stai

nles

s ste

el

Mul

tiple

N

on-d

omin

ated

sorti

ng g

enet

ic a

l-go

rithm

G

as

pres

sure

, cu

tting

sp

eed,

la

ser

pow

er, p

ulse

freq

uenc

y K

erf w

idth

, tap

er a

ngle

, sur

face

roug

h-ne

ss

14.

Phip

on a

nd P

radh

an (2

012a

) A

lum

iniu

m a

lloy

Sing

le

Gen

etic

alg

orith

m

Oxy

gen

pres

sure

, pu

lse

wid

th,

puls

e fr

eque

ncy,

cut

ting

spee

d K

erf t

aper

, sur

face

roug

hnes

s

15.

Shar

ma

and

Yad

ava

(201

2)

Alu

min

ium

allo

y M

ultip

le

Gre

y re

latio

nal a

naly

sis

Oxy

gen

pres

sure

, pu

lse

wid

th,

puls

e fr

eque

ncy,

cut

ting

spee

d Su

rfac

e ro

ughn

ess,

kerf

tape

r

16.

Shar

ma

and

Yad

ava

(201

3)

Alu

min

ium

allo

y M

ultip

le

Gre

y re

latio

nal a

naly

sis

Arc

ra

dius

, ox

ygen

pr

essu

re,

puls

e w

idth

, pul

se fr

eque

ncy,

cut

ting

spee

d K

erf d

evia

tion,

ker

f tap

er

17.

Tam

rin e

t al.

(201

5)

Poly

met

hyl m

etha

c-ry

late

, pol

ycar

-bo

nate

, p

olyp

ropy

lene

Mul

tiple

G

rey

rela

tiona

l ana

lysi

s Po

wer

, cut

ting

spee

d, c

ompr

esse

d ai

r pr

essu

re

Hea

t aff

ecte

d zo

ne, d

iam

eter

of t

he c

ut

18.

Texi

dor e

t al.

(201

3)

AIS

I H13

tool

stee

l M

ultip

le

Parti

cle

swar

m o

ptim

izat

ion

Scan

ning

spe

ed, p

ulse

inte

nsity

, pul

se

freq

uenc

y C

hann

el d

epth

, cha

nnel

wid

th, s

urfa

ce

roug

hnes

s

480

(a)

(b)

(c) (d)

Fig. 5 Analysis on the parametric optimization of USM processes

1.6 HM processes

Technological improvement of NTM processes can be efficiently attained when different machining ac-tions or phases are combined together for material removal. This combination of individual processes leads to the subsequent development of a HM process where the combined advantages of the constituent processes can be achieved while avoiding or reducing some of their adverse effects when they are applied individually (El-Hofy, 2005). The performance characteristics of a HM process are expected to be better than those of the single-phase processes with respect to productivity, accuracy and surface quality (Sundaram, 2014). In AJWM process, the mechanical energy of water and abrasive particles is utilized to remove material from the workpiece. In this process, abrasive particles, like sand (SiO2), glass beads etc. are mixed with the water jet to enhance its machining capability by many folds (Janković et al., 2012). The ECDM is also a hybrid NTM process combining the features of ECM and EDM processes. It consists of a tool (cathode), an auxiliary electrode (anode) and a workpiece, which are isolated by a gap filled with electrolyte, and a pulsed DC is applied between them. This causes generation of electrical discharges between the tool and electrolyte where the workpiece is placed, thus attaining both electro-chemical dissolution and electro discharge erosion of the workpiece (Mediliyegedara et al., 2005). The TW-ECSM process combines the material removal mechanisms of ECM and WEDM processes (Malik and Manna, 2017). It can be efficaciously employed for machining of hard-to-machine non-conductive materials that cannot be machined by the other NTM processes, like EDM, ECM, WEDM etc. Electrical discharge diamond grinding (EDDG) is a hybridized NTM process, consisting of EDM with rotary disc electrode and grinding using diamond abrasives (Yadav et al., 2012). During EDDG, the electrically non-conducting reinforcements that hinder the generation of sparks can be removed by the abrasion action of diamond abrasives. On the other hand, the wheel loading and clogging problems can be avoided due to electrical sparks, and dressing of diamond wheel can also take place. In magnetic abrasive finishing (MAF) process, the workpiece is kept between the two poles of a magnet, and the machining gap between the workpiece and the magnet is flooded with magnetic abrasive particles. A magnetic abrasive flexible brush is thus formed, which acts as a multipoint cutting tool, due to the effect of magnetic field in the

01234567

0123456

0

1

2

3

4

Titanium Ceramics MMCs

0

1

2

3

Others GRA Adv.opt.

methods

Utilityconcept

GA Lossfunction

NN


machining gap (Kumar et al., 2013). The ECG combines electrochemical dissolution and mechanical grinding processes (Goswami et al., 2009). The workpiece is connected to the positive electrode and an electrically conductive grinding wheel is made as the negative electrode. As the electric current flows between the workpiece and wheel through the electrolyte, electrochemical dissolution takes places caus-ing material to be removed from the workpiece. Mechanical abrasion is also responsible for material removal to some extent.

Table 5 Parameters, responses, work materials and optimization techniques considered in USM processes

Sl.No. Name of author(s)

Work material

machined

Single/ Multi-

objective

Optimization tool(s) adopted

Process parameters Responses

1. Chakravorty et al. (2013)

Cobalt, tungsten carbide

Multiple

Weighted signal-to-noise ratio, utility theory, grey relational analysis, multi-response signal-to-noise ratio

Tool material, abrasive slurry material, slurry concentration, grit size of slurry material, power rating

MRR, tool wear rate, surface roughness

2. Cheema et al. (2013)

Titanium (ASTM Gr 2),

Titanium (ASTM Gr 5)

Both Utility concept Workpiece material, abrasive grit size, abrasive slurry concen-tration, power rating, tool mate-rial

Surface roughness, tool wear ratio, hole oversize

3. Das et al. (2013)

Zirconia Multiple Genetic algorithm Abrasive grit size, abrasive slurry concentration, power rating, tool feed rate


4. Goswami and Chakraborty

(2015)

Zirconia Both Gravitational search algo-rithm, fireworks algorithm

Grit size, slurry concentration, power rating, tool feed rate


5. Kataria et al. (2016)

WC-Co composite

Multiple Grey relational analysis Cobalt content, workpiece thickness, tool profile, grit size, power rating

MRR, tool wear rate

6. Kumar (2014) Titanium (ASTM Gr 1)

Both Taguchi loss function Tool material, abrasive type, grit size, power rating

Surface roughness, micro-hardness

7.

Teimouri et al. (2015)

Titanium (ASTM Gr 1)

Multiple Adaptive neuro-fuzzy in-ference system, imperialist competitive algorithm

Tool material, grit size, power rating

MRR, tool wear rate, surface roughness

In Table 6, a list of research papers published during 2012-16 on parametric optimization of HM pro-cesses (i.e. AJWM, ECDM, TW-ECSM, EDDG, MAF and ECG) is provided. The responses set in these six HM processes, work materials machined and optimization techniques employed for deriving the best parametric mix are shown in Fig. 6. As the control parameters in the considered HM processes are widely varying and entirely depend on the type of the machining set-up employed, it is not at all a wise decision to make an exhaustive list of those parameters. It can be perceived from Fig. 6 that among the responses, MRR and surface roughness are allotted with the maximum importance, followed by kerf width, HAZ, overcut, TWR, current density and depth of cut. In these processes, glass, MMCs, different grades of steel, and aluminium and its alloys are mostly machined for generation of different shape features for industrial use, followed by ceramics, brass, Inconel, tungsten carbide and silicon. With respect to the techniques adopted for parametric optimization of HM processes, GRA and advanced optimization meth-ods become quite popular among the researchers. Other techniques, like NSGA. GA, NN, loss function, PCA, utility concept and desirability function are also applied for the same purpose. Among 30 papers surveyed on HM processes, 18 of them considered multi-objective optimization, while only four dealt with both single as well as multi-objective optimization of the responses. There are also eight research papers where the authors optimized only a single response.

2. Discussions When all the reviewed papers are classified based on their year of publication, as depicted in Fig. 7, it can be clearly revealed that the earlier researchers maximally considered EDM process for its parametric optimization, followed by HM and WEDM processes. It is also quite interesting to notice that in this direction, maximum research works were mainly carried out in 2012 and 2014 for almost all the consid-ered NTM processes. When all the responses, as considered by the past researchers for deriving their

482

optimal values, are analyzed in Fig. 8, it can be observed that surface roughness has the top priority, followed by MRR. Other responses, like TWR, overcut, cutting rate, HAZ, taper, kerf width etc. have also moderate importance. There are several less significant responses, like cylindricity, white layer thickness, surface crack density, channel depth, burr height, depth deviation, lower deviation, current density etc. which are occasionally considered by the past researchers in order to satisfy varying end product requirements. It is a well known fact that NTM processes are mainly employed for generation of complex shape geometries on varying hard-to-machine materials which cannot be machined by the con-ventional material removal methods. From the reviewed papers, it can be clearly visualized that these processes were mainly utilized for machining of different grades of steel, followed by MMCs and various metal alloys to meet their high demands for diverse industrial applications. A list of different work ma-terials machined by the considered NTM processes is graphically presented in Fig. 9. The application of various tools and techniques for parametric optimization of the considered NTM processes is exhibited in Fig. 10. It is quite interesting to observe that among all these applied methods, GRA supersedes the others due to its mathematical simplicity, comprehensiveness and capability for performing multi-objec-tive optimization of the responses quite easily. But when GA, NSGA and SA are coupled together with the other advanced optimization methods, they become the most popular techniques due to their ability to solve both single and multi-objective optimization problems. These techniques are also capable of finding out the global optimal solutions for parametric optimization problems. Among the employed advanced optimization methods, particle swarm optimization algorithm is mostly preferred by the re-searchers, followed by artificial bee colony optimization and cuckoo optimization techniques, as shown in Fig. 11. Biogeography-based optimization, teaching learning-based optimization, ant colony optimi-zation, firefly algorithm, bat algorithm, sheep flock algorithm, harmony search algorithm, gravitational search algorithm and fireworks algorithm are the other techniques applied for parametric optimization of the considered NTM processes according to their preference. It is observed that amongst 133 research papers surveyed, in only 10 papers (7.52%), a single response was optimized; in 96 papers (72.18%), multiple responses were optimized simultaneously; and 27 papers (20.30%) dealt with both single as well as multi-objective optimization of the responses.

(a)

(b)

(c)

Fig. 6 Analysis on the parametric optimization of HM processes

05

10152025

0123456

012345678

S. C

hakr

abor

ty e

t al.

/ M

contents lists available at growingscience management ... · process parameter work material...

Documents