A Model to Determine the Optimal Parameters for Sustainable Energy Machining in a Multi Pass Turning Operation

7/23/2019 A Model to Determine the Optimal Parameters for Sustainable Energy Machining in a Multi Pass Turning Operation

1/12

Original Article

Proc IMechE Part B:

J Engineering Manufacture

2014, Vol. 228(6) 866877

IMechE 2013

Reprints and permissions:

sagepub.co.uk/journalsPermissions.nav

DOI: 10.1177/0954405413508945

pib.sagepub.com

A model to determine the optimalparameters for sustainable-energy

machining in a multi-pass turningoperation

Muhammad Arif, Ian A Stroud and Olcay Akten

Abstract

This study presents a model for the optimization of machining parameters for the minimum energy consumption in a

multi-pass turning operation. The model takes into account finishing and roughing passes separately for the energy opti-mization followed by the dual optimization of the energy functions for a combination of one finishing pass and multipleroughing passes to finish a desired diameter on a cylindrical workpiece. The parametric constraints, tool-life constraintsand operational constraints are enforced in the model before optimizing the energy function using non-linear program-ming. The model is applied to an example case for the optimization. The effects of total-depth-to-be-removed, materialremoval rate and tool replacement time are evaluated on the optimal parameters for sustainable machining.

Keywords

Sustainable energy, sustainable manufacturing, sustainable machining, multi-pass turning, optimization model, greenmachining

Date received: 21 May 2013; accepted: 23 September 2013

Introduction

Manufacturing is the key engineering sector to build

stronger economies and improve human living stan-

dards. Manufacturing processes utilize energy, often

the electrical energy, to transform work materials into

products, and the energy supplied to a manufacturing

process is only partly embodied into the product. The

balance of energy is inevitable wasted in the form of

heat generated and waste produced. A considerableproportion of the electrical energy available is utilized

in the industries of which manufacturing is an impor-

tant sector. Sustainable manufacturing has become a

growing area of interest for manufacturing industries

due to the environment conscious regulations imposed

by the governments and the environmental protection

agencies.

The US Department of Commerce defined sustain-

able manufacturing as the creation of manufactured

products that use processes that are non-polluting, con-

serve energy and natural resources and are economi-

cally sound and safer for employees, communities and

consumers.1 A common definition of sustainability andsustainability development is passing on to the future

generations a stock of capital that is at least as big as

the one that our own generation inherited from the pre-

vious generations.2

Energy consumption causes carbon emissions with

part of the emissions occurring during manufacturing.

In the manufacture of a product, the energy consumed

is directly linked to the carbon emission in producing

electrical energy for running the manufacturing pro-

cess.3 This means the reduction in the energy consump-

tion leads to the reduction in the carbon emission and

hence mitigation of the greenhouse effect. Carbon emis-sion is often represented by carbon footprint (CF).

Although CF is a decent step towards the environmen-

tal consciousness, it is not a sufficient criterion to com-

prehend the overall environmental impact. This is

because CF is related to greenhouse gas emission,

mainly carbon dioxide, and there are practical cases

Laboratory for Computer-Aided Design and Production (LICP), Swiss

Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland

Corresponding author:

Muhammad Arif, Laboratory for Computer-Aided Design and Production(LICP), Swiss Federal Institute of Technology Lausanne (EPFL), 1015

Lausanne, Switzerland.

Email: [email protected]

at WEST VIRGINA UNIV on November 17, 2015pib.sagepub.comDownloaded from
http://pib.sagepub.com/http://pib.sagepub.com/http://pib.sagepub.com/http://pib.sagepub.com/


2/12

where greenhouse gas emission is negligible but still the

process leaves a significant negative impact on the

environment.2

According to a survey conducted by US Energy

Information Administration (EIA) in year 2011, 31%

of the total energy consumption was consumed in

industrial sector.4

Manufacturing comprises a signifi-cant proportion of the total industrial sector and is

believed to be the area where sustainable energy

approach can bring about propitious results.

Sustainable machining

Sustainability in manufacturing is the optimization of

the overall efficiency of the company, technologies, pro-

cesses and products.5 In its broader sense, the sustain-

ability in manufacturing brings about every element of a

manufacturing system under investigation for resource

efficiency. The optimization of energy and environmen-tally associated resources contribute to the ecological

and economical effectiveness. Machining is considered as

key technology in the manufacture of products, believed

to be the most widely applied technology among all the

manufacturing technologies and has a significant impact

on the growth of global economy.

Machining process is particularly useful due to high-

dimensional accuracy achievable on the parts, flexibility

of its application and cost-effectiveness in producing

limited quantities of the parts. Among manufacturing

processes, machining is considered as unique in that it

can be used to not only create the new products but alsoto finish them to final shape. In a typical machining

process, the unwanted portion of the workpiece is

removed in the form of chips to transform the starting

workpiece into the desired shaped product. Machining

is classified as subtractive manufacturing process. Being

inherently a material removal process, machining can

be wasteful in its use of both energy and material.6

Furthermore, due to coolant employment and waste

creation, machining can potentially leave adverse

impact on the environment. The waste of energy occur-

ring in machining process can have a considerable

impact on the economic orientation of the society. With

limited capacity to generate energy against the ever

mounting demand for energy consumption in human

society, economization of energy use has become an

important pillar of sustainability paradigm. Hence,

reducing energy in manufacturing is perceived as one of

the pronounced leaps towards achieving the sustainabil-

ity. This approach calls for a profound analysis of the

key manufacturing technologies such as machining

from energy consideration viewpoint.

Until recently, most of the research study in machin-

ing has focused on the innovation and improvement of

process capability for short-term profitability. With

world now entering an era of energy starvation andenvironmental consciousness, sustainable manufactur-

ing or more specifically sustainable machining

technology is emphasized to be adapted as long-term

technological strategy for sustainable development and

ultimately survival.

Several initial studies have been reported, which ana-

lyse the machining processes from energy viewpoint.

Gutowski et al.7 reported a generalized electrical energy

requirement analysis for a variety of manufacturingprocesses and concluded that energy requirements of a

manufacturing process are not constant but variable

depending upon the rate of processing. Jawahir and

Jayal8 presented an overview of product and process

sustainability evaluation methods and modelling tech-

niques to predict the performance of sustainable

machining processes. An important measure in evaluat-

ing the performance of manufacturing process is setting

the system boundaries so as to include not only the

manufacturing process itself but also the production of

material and impact on environment for a more com-

pact evaluation of sustainability.6,7

In machining processes, it is possible to model only

those sustainability elements, which are deterministic in

nature using analytical and numerical techniques.9 The

other sustainability elements are modelled using non-

deterministic techniques.8

The environmental impact of machining process is

very high due to creation of hazards through the chip

removal and the coolant incineration.10 The recycling of

machining waste is an important measure to mitigate the

environmental impact of machining.11 For some energy-

intensive materials, the energy involved in material pro-

duction can exceed the energy required for machine tool

operation.5 The environmental hazard can be mitigatedconsiderably by employing cryogenic cooling in machin-

ing. Cryogenic cooling uses liquid nitrogen, which eva-

porates during machining and improves the tool life by

reducing the coefficient of friction between the tool and

chip.12 Another development in machining is the near-

dry machining, which significantly increases the machin-

ing performance by reducing the cutting forces, improv-

ing the surface finish and tool life.13

In machining, tool characteristics are vital consider-

ation to determine the sustainability of both the prod-

uct and process. Marksberry and Jawahir14 proposed a

method to predict tool-life performance for sustainabil-ity in near-dry machining by extending a Taylor speed-

based dry machining equation. It was reported that the

edge radius of the tool leaves a significant effect on sur-

face integrity of the workpiece and hence on the sus-

tainability of the resulting product.15 Guodong et al.16

proposed a virtual machining model to quantitatively

analyse the sustainability impacts of machining process

and determine a better sustainable machining plan in a

virtual environment before the actual machining is

performed.

The material selection is also notable consideration

in reducing the energy consumption in machining.

However, the choice for material is dictated by theproperties desired by the product, and hence, there is

usually a very limited option for using alternative

Arif et al. 867



3/12

material for improved sustainability. The harder mate-

rials offer greater resistance to machining, and hence,

energy consumption is inevitably higher.17

The selection of tool material is also important in

sustainable machining. As discussed earlier, the dry

machining and higher material removal rates (MRRs)

favour the energy sustainability of a machining process;the cutting tool is preferred to be made of a material

allowing higher cutting speeds and heat resistance. It

has been established that coefficient of friction between

the tool and chip is reduced considerably if a coated

cutting tool is used. Consequently, energy consumed in

overcoming friction is reduced and the machining pro-

cess is more energy efficient.18,19 In high-speed machin-

ing, the cutting temperature does not increase beyond a

certain limit even if the cutting speed continues to

increase but the cutting force is reduced due to soften-

ing of the work material.20 Hence, high-speed machin-

ing with coated cutting tool under dry or near-dryconditions is considered as key approach to reduce the

specific cutting energy requirements in machining pro-

cess accompanied by a benign effect on the

environment.

It was also established that only a small fraction of

total energy requirement of a machining system is

accounted for actual machining, and the dominant

share of energy consumed is used in the start-up and

running the supporting equipment.7,21 The fraction of

energy consumed in actual machining becomes even

smaller at lower MRRs. These analyses suggest that

the energy required in a machining process can be

reduced by designing energy efficient support equip-

ment and removing the material at high rate.

Energy optimization of machining

operations

In most of the study reported in the literature, the

major emphasis has been on highlighting the more gen-

eral overview of the energy in machining processes.

Some studies have focused on developing cryogenic

coolant system in machining processes. Furthermore,

these studies are based on energy analysis of general

turning or milling process or a single-pass turning pro-

cess.17 In practical situation, it is often not feasible to

finish a desired shape on the workpiece in one pass. In

most cases in industrial sector, the workpiece is desired

to be machined to the final shape in multiple passes. In

such practical cases, one or more roughing passes are

performed first to remove the bulk of material from the

workpiece. The roughing pass(es) is often followed by

one finishing pass to achieve the desired level of surface

finish on the final shape of the workpiece. To the

knowledge of the authors, there is no study reported in

the literature until now, which provides a comprehen-

sive optimization model for sustainable energy consid-ering the multi-pass turning process to offer a complete

solution to the practical cases of machining.

This study presents a comprehensive model to opti-

mize machining parameters for minimum energy con-

sumption in a multi-pass turning process taking into

account the practical constraints encompassing

machine tool capability, tool replacement time and fea-

sible range of parametric values. In this way, a com-

plete solution from sustainable energy viewpoint isproposed for a multi-pass turning process under practi-

cal constraints.

Energy-based model for turning

In the literature reviewed in the aforementioned sec-

tions, there are several approaches proposed to improve

the sustainability of machining processes. However,

most of these approaches revolve around minimization

of coolant use or using innovative coolant systems. But

for a given machine tool system, where redesign of sup-

porting equipment for low-energy consumption is notpossible due to restriction on machine tool design modi-

fications. Furthermore, it is not always possible to elim-

inate the use of coolant especially in cutting of difficult-

to-machine alloys where the frequency of cutting tool

failure is likely to rise significantly if the use of coolant

is eliminated. Also, the use of cryogenic liquids is not

always feasible on a large scale as liquefaction process

of the nitrogen or other non-reacting gases itself is an

energy-driven process.

It is therefore of utmost importance to optimize the

machining process under given conditions and con-

straints for the minimization of the energy consump-

tion by operating the process at optimal parameters.Currently, the turning process has not been optimized

in broader sense for energy consumption. The available

optimization approaches revolve around more general

aspect of optimization and cover single-pass turning

operation only. However, it is impractical in most of

the cases to finish the desired shape on a workpiece in

one pass. Furthermore, there are different constraints

on the surface roughness requirement in finishing and

roughing pass in addition to the operational and para-

metric constraints arising from the machine tool capa-

bility and stiffness.

It has already been established that in order to opti-mize the machining parameters for minimum cost, the

total cost of machining is differentiated with respect to

most dominant parameter for tool life, that is, velocity,

and then the optimal tool life is calculated for the mini-

mum cost followed by the determination of optimal

parametric values.22,23 A similar approach has been

proposed for minimum energy approach.17 The model

proposed here takes into account practical constraints

dictated by the operation, tool-life and parametric lim-

itations for a turning process in a broader sense.

The overall energy consumed in a machining process

can be categorized into four constituent energies in a

single pass operation17

E= Ec+ EI+ ER+ ET 1

868 Proc IMechE Part B: J Engineering Manufacture 228(6)



4/12

Machining energy

This is the energy consumed in the real machining pro-

cess per unit piece and is the energy consumed in

powering the machine modules and actual energy con-

sumed in material removal7

Ec= (p0+ k _v)tm 2

where p0 (J/min) is the power consumed in powering

the machine modules without performing the machin-

ing and with spindle stationary, _v =fdV(m3/min) is the

volume rate of material removal andk(J/m3) is the spe-

cific cutting energy of the material. Here, tm(min) is the

actual time of material removal, Vis cutting velocity, d

is depth of cut andfis the feedrate.

The machining time is the time consumed in one fin-

ishing pass andn roughing passes, that is23

tm= tms+ tmr 3

wheretmsis the time in one finishing pass and tmris the

total time consumed in n roughing passes.

In turning operation, the machining time can be cal-

culated as24

tms= pDL

1000Vsfs4

tmr=Xi= ni= 1

pDL

1000Vrifri5

where D is the diameter (mm) of the workpiece, L is

the machining length (mm) and Vs (m/min) and Vri

(m/min) are the cutting speeds in finishing and ithroughing pass, respectively,

And hence, equation (3) can be written as follows

tm= pDL

1000Vsfs+

Xi= ni= 1

pDL

1000Vrfr6

Hence

Ec= p0+ k _v pDL

1000Vsfs+

Xi= ni= 1

pDL

1000Vrifri

" # 7

Machine idle energy per unit piece

As mentioned earlier, in idle state, the energy spent is

equal to the energy required to power the machine

modules and in this state machine spindle is assumed

to be stationary

EI=p0tl 8

wheretlis the machine idle time, which can be further

subdivided into tp for workpiece loading and unload-

ing, and t iis the tool idle motion time when the tool is

approaching/departing the edge of the workpiece.

Again it can be subdivided into one finishing and nroughing passes. In the case ofn roughing passes25

ti= n h1Lt+ h2 + h1Lt+ h2 9

where h1 (min/mm) and h2 (min) are constants related

to tool approach/departure time.

Hence

EI=p0 tp+ n h1Lt+ h2 + h1Lt+ h2 10

Energy for tool replacement per unit piece

The tool is replaced when the machine tool modules are

on but spindle is switched off. For turning process,

Shaw26 reported that the tool replacement time per

piece is the product of tool replacement time per edgeteand total number of edges consumed per piece, (tm=T).

Hence, energy consumed during tool replacement,

ER, in turning operation is given as

ER=p0tetm

T 11

Tool energy per unit piece

Here, Et is defined as the energy footprint of the tool

per edge per piece and represents the energy embodied

into the tool, the energy consumed in the manufacture

of the tool and the energy consumed in any secondary

operation such as coating17

Et= Pttm

T

12

wherePt is the tool energy per cutting edge and (tm=T)represents number of cutting edges consumed per piece.

Total energy consumed

The total energy represented by equation (1) consumed

in machining per piece is given by combining the four

aforementioned energy equations (7) and (10)(12)

E= p0+ k _v pDL

1000Vsfs+

Xi= ni= 1

pDL

1000Vrifri

" #

+p0 tp+ n h1Lt+ h2 + (h1Lt+ h2) +p0te

tm

T

+ Pt

tm

T

13

E= Es+Xi= ni= 1

Eri+p0tp 14

where

Es= p0+ k _v +Pt

Ts+

p0te

Ts

pDL

1000fsVs+p0 h1L + h2

15

Eri= p0+ k _v + Pt

Tri

+p0te

Tri

pDL

1000friVri

+p0 h1L + h2

16

Arif et al. 869



5/12

Optimization approach

The objective is to minimize the energy consumed in

the machining process. To achieve this objective, first,

an optimal tool life is for the minimum energy con-

sumption determined by differentiating the total energy

consumption equation followed by the total energy

consumed and the parametric values corresponding to

the minimum energy consumption.

The tool life in a turning operation can be given as a

function of cutting speed, feedrate and depth of cut as27

VTafbdg= C 17

Here, we assume that the tool life in roughing and fin-

ishing are the same for simplification of calculation as

well as this is more practical approach. Practically, the

same tool is used for finishing and roughing with only

process parameters being different. This means as soon

as the tool reaches its life limit for any of the two pro-cesses (finishing or roughing), the tool must be replaced

with a new one, and hence, the assumption of having

identical tool life in finishing and roughing is valid.

Hence

VsTas f

bs d

gs = VriT

ar f

brid

gri= C 18

To optimize the turning process for minimum energy

consumption, the optimal tool life is determined by dif-

ferentiating equation (13) with respect to cutting velo-

city and equating the resultant to 0, that is, E=V= 0.For finishing pass

Es= p0+ k _v +Pt

Ts+

p0te

Ts

pDL

1000fsVs

+p0 h1L + h2

19

By substituting the value of _v, the energy equation

becomes

Es= p0+ kfsdsVs + Pt

Ts+

p0te

Ts

pDL

1000fsVs

+p0 h1L + h2

20

Now differentiating equation (20) with respect to cut-

ting velocity and equating to 0

Es

Vs= p0f

1s V

2s

+ Pt+p0te 1

a1

V

1a

1 s f

b

a1

s dg

as

C = 0

21

By solving further, equation (21) reduces to

C

V1a

s fb

a s d

g

as

= 1

a1

Pt+p0te

p0

22

The left-hand side of equation (22) is equal to tool-lifeequation and hence

Ts= 1

a1

Pt+p0te

p0

23

Similarly, for roughing pass (as the tool life is

identical)

Tr=

1

a 1 Pt+p0te

p0

24

It follows from equation (24) that the optimum tool

life depends only on the velocity exponent in tool-life

equation, machine idle power, tool energy footprint

and tool change time. This equation for an individual

cut is identical to the equation for single pass turning

operation for a given depth of cut.17 However, in multi-

pass turning operation, the number of passes required

to finish a certain diameter on a cylindrical workpiece

makes the difference in the parametric optimization

scheme, which is addressed in this study.

Constraints

The typical constraints in a machining process are pre-

sented in the reported literatures, which are widely

acceptable.23 The same are applied here and the details

are mentioned in the following.

Finishing pass constraints

The constraints applied on finishing pass are described

in the following.

Parametric constraints. The parametric constraints for fin-

ishing pass are given as follows

Vmin4Vs4Vmax 25a

fmin4fs4fmax 25b

dmin4ds4dmax 25c

Tool-life constraints.

C

VmaxTas4fbs d

gs 4

C

VminTas26

Surface finish constraints. IfRs, maxis in micro-meter

fs4

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffireRs, max

32:1

r 27

Cutting force constraints.

F= k1fms d

ns4Fmax 28

Cutting power constraints.

P = FVs60000h

= k1fm

s dn

sVs60000h

4Pmax 29




6/12

Roughing pass constraints

The constraints applied on roughing pass are described

in the following.

Parametric constraints.

Vmin4Vri4Vmax 30a

fmin4fri4fmax 30b

dmin4dri4dmax 30c

Tool-life constraints.

C

VmaxTar4f

brid

gri4

C

VminTar31

Surface finish constraints. IfRr, maxis in micro-meter

fri4

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffireRr, max

32:1

r 32

Cutting force constraints.

F= k1fmrid

nri4Fmax 33

Cutting power constraints.

P = FVri

60000h

=k1f

mrid

nriVri

60000h

4Pmax 34

Roughing and finishing pass mutual relationship

The sum of depth of cut for one finishing pass and all

roughing passes (nr) must be equal to the total-depth-

to-be-removed,dt, that is

dt= ds+Xi= nri= 1

dri 35

Model solution

Based on the nature of problem stated, non-linear pro-

gramming is used for solving this model. The objective

function is to minimize the dual energy function repre-

sented by equation (14) and find the optimum values of

Vs,fs, ds, Vri,fri, driand nrfor a multi-pass turning oper-

ation. Subscripts s and r denote finishing and roughing

pass respectively.

The data and parametric constraints considered in

this study are widely valid for a variety of turning pro-

cesses.23 Some other authors, in the past, have used

these data and parametric constraints to validate their

machining cost models.25,28 The same applicable dataand parametric constraints are considered in our study

for validation of presented model as the major

emphasis is on providing the methodology and

approach. Gutowski et al.7 have reported the typical

electrical requirements of a turning machine when not

in a cutting state, that is, p0. The specific cutting energy

of alloy steel is available in the literature, and its values

remain reasonable constant within the operating win-dow of the range of feedrate considered in this study.

The specific cutting energy is reported for a variety of

practical materials in Geoffrey and Winston.29 The

data considered are depicted in Table 1. The non-linear

programming software LINGO is used for generating

the solutions by solving the individual and dual energy

functions.

Using the known values of all factors in the optimal

tool-life equation (24), the optimal tool life, for both

finishing and roughing pass, comes out to be

T= 30:5779 minIt is important to mention here that optimum tool

life for minimum cost criteria using the same

Table 1. Parametric values and constraints.

Parameter/constant Symbol (units) Value

Idle power p0(kWh) 3.594Specific cutting energy K(MJ/m3) 5250Tool replacement time T(min) As calculated

Tool energy Pt(MJ/insert) 5.3Nose radius of tool re(mm) 1.2Workpiece diameter D(mm) 50Workpiece cutting length L(mm) 300Tool change time te(min/edge) 1.5Preparation time tp(min/piece) 0.75Tool return time h1(min/mm) 0.0007Tool advance/return time h2(min) 0.3Maximum cutting speed Vmax(m/min) 500Minimum cutting speed Vmin(m/min) 5Maximum feedrate fmax(mm/rev) 0.9Minimum feedrate fmin(mm/rev) 0.1Maximum depth of cutfor finishing

ds,max(mm) 2.0

Minimum depth of cut for

finishing

ds,min(mm) 0.5

Maximum depth of cutfor roughing

dr,max(mm) 4.0

Minimum depth of cut forroughing

dr,max(mm) 1.0

Surface roughnessrequirementfor finishing

Rs,max(mm) 2.5

Surface finishingrequirementfor roughing

Rr,max(mm) 25

Maximum cutting force Fmax(N) 1960Maximum cutting power Pmax(kW) 5Machine tool efficiency h 0.85Tool-life equation

constantand exponents

C 227

a 0.2b 0.35g 0.15

Constants and exponentsin cutting forceand power equations

k1 1058m 0.75n 0.95

Arif et al. 871



7/12

constraints comes out to be T=26min. This showsthat there is significant difference in both approaches,

that is, minimum cost machining and minimum energy

machining.

For finishing pass

Having applied all the relevant constraints, the optimal

values of parameters for finishing pass alone are

obtained by solving equation (15) using LINGO soft-

ware. The optimal values of the parameters obtained

are given in Table 2.

For roughing pass

Having applied all the relevant constraints, the optimal

values of parameters for roughing pass alone are calcu-

lated by solving equation (16) using the software

LINGO, and the values are given in Table 3.

For multi-passes

The complete solution giving the optimal values of all

the turning parameters including number of rough

passes required and the optimal value of energy con-

sumed for different total-depth-to-be-removed dt isshown in Table 4: in this case, LINGO generates simul-

taneous dual optimization of the overall energy func-

tion represented by equation (14).

Discussion on results

Finishing pass

The model for finishing pass is solved by non-linear

programming using software LINGO. The optimum

values of energy consumed within the allowed range of

depth of cut are given in Table 2. This table also shows

the optimal values of cutting velocity Vs-opt andfeedratefs-optat each instant of depth of cut. An incre-

ment of 0.1 mm has been used in the depth of cut values

for the illustration purpose. The non-linear program-ming model can be solved for any small increment or

decimal points in depth of cut value, which is program-

mable on the computer numerical control (CNC)

machine tool depending upon the resolution and posi-

tioning accuracy of the machine tool. It follows from

this table that optimal value of feedrate is governed by

the constraint arising from surface roughness require-

ment and hence it stays constant. A relax constraint for

surface roughness would also allow variation in the

optimal value of the feedrate as we see in roughing pass

case. It follows from plot in Figure 1 that Vs-opt

decreases as a power function of depth of cut withincrease in depth of cut in finishing pass. This is

because the feedrate is fixed due to surface roughness

as discussed earlier in addition to all other factors,

which remain constant in optimal energy equation.

Hence, for example, under consideration under the

given set of constraints and conditions, the optimal

velocity is the decreasing power function of depth of

cut with exponent equal to the depth of cut exponent

considered in tool-life equation. The plot in Figure 1

also shows the variation in optimal energy, Es-optwith

depth of cut in a finishing pass and it is noted that

Es-opt increases with increase in depth of cut due to

increase in MRR enabled by increasing depth of cut.Another interesting scenario is the analysis of overall

specific energy (OSE) in finishing, Eos-s, variation with

Table 3. Optimal parametric values for roughing pass.

dr(mm) Vr-opt(m/min) fr-opt(mm/rev) Er-opt(MJ/piece)

1.0 118.8390 0.9000000 0.4802551.1 117.1521 0.9000000 0.5069691.2 115.6330 0.9000000 0.533555

1.3 114.2530 0.9000000 0.5600321.4 112.9900 0.9000000 0.5864141.5 111.8267 0.9000000 0.6127141.6 110.7493 0.9000000 0.6389411.7 109.7468 0.9000000 0.6651051.8 108.8099 0.9000000 0.6912111.9 107.9310 0.9000000 0.7172652.0 107.1037 0.9000000 0.7432722.1 106.7830 0.8889611 0.7703172.2 108.2501 0.8380922 0.8015652.3 109.6709 0.7922069 0.8328052.4 111.0486 0.7506307 0.8640382.5 112.3864 0.7128036 0.8952642.6 113.6868 0.6782570 0.9264832.7 114.9524 0.6465961 0.957696

2.8 116.1852 0.6174859 0.9889032.9 117.3874 0.5906403 1.0201043.0 118.5605 0.5658139 1.0512993.1 119.7064 0.5427949 1.0824883.2 120.8264 0.5213994 1.1136733.3 121.9219 0.5014676 1.1448523.4 122.9943 0.4828593 1.1760263.5 124.0446 0.4654514 1.2071953.6 125.0739 0.4491355 1.2383603.7 126.0831 0.4338155 1.2695203.8 127.0733 0.4194060 1.3006753.9 128.0452 0.4058311 1.3318264.0 128.9997 0.3930229 1.362973

Table 2. Optimal parametric values for finishing pass.

ds(mm) Vs-opt(m/min) fs-opt(mm/rev) Es-opt(MJ/piece)

0.5 192.4159 0.3057 0.45346590.6 187.2254 0.3057 0.48450040.7 182.9454 0.3057 0.5147311

0.8 179.3175 0.3057 0.54435950.9 176.1772 0.3057 0.57351851.0 173.4148 0.3057 0.60230051.1 170.9532 0.3057 0.63077251.2 168.7365 0.3057 0.65898501.3 166.7227 0.3057 0.68697651.4 164.8796 0.3057 0.71477771.5 163.1821 0.3057 0.74241291.6 161.6100 0.3057 0.76990211.7 160.1470 0.3057 0.79726181.8 158.7798 0.3057 0.82450561.9 157.4973 0.3057 0.85164542.0 156.2902 0.3057 0.8786909




8/12

total material removed at corresponding depth of cut in

finishing pass. The term Eos-s is different from optimal

energy in way that it is obtained by dividing the optimal

energy at each depth of cut by the total amount of

material removed at the corresponding depth of cut in

finishing pass. Hence, Eos-s also involves the energy

consumed by non-cutting modules of the machine tool

and is, therefore, also different from specific energy of

the material, which takes into account purely the rate

of energy consumed in the cutting operation divided by

the MRR. It is significant to note that Eos-s is decreas-ing with rate of material removal enabled by increase in

depth of cut in finishing pass, as shown in Figure 2,

which plots both Eos-s and MRR at different values of

depth of cut in finishing pass. This is because removing

material at a higher rate decreasing the machining time,which means the non-cutting modules (which makes a

significant proportion of total energy consumed) will

be accounted for a shorter time, and the overall energy

consumed in the process is lower than that occurring at

lower MRR. This explains although variable part of

energy consumption increases with depth of cut, the

dividend arising from reducing the constant part of the

overall energy consumption by completing the machin-

ing in shorter times is more dominant.

Roughing pass

Besides for finishing pass, the roughing pass problem is

also solved by non-linear programming using the soft-

ware LINGO, and the optimal values of cutting velo-

city and feedrate are depicted in Table 4 within the

permissible range of depth of cut and other constraints.

It follows from the plot that the optimal cutting velocity

first decreases as a power function of depth of cut in

roughing with the same exponent used in tool-life equa-

tion. This decreasing relationship exists only within a

certain bound of depth of cut ranging from 1.0 to 2.0

mm. From depth of cut value of 2.1 mm onwards, the

optimal cutting velocity, Vr-opt, increases sharply. The

first part of optimal velocity and depth of cut relation-ship is similar to finishing pass due to a constant opti-

mal feedrate fr-opt, existing within this range of depth

Table 4. Optimal parameters for multi-pass turning.

dt(mm)Parameters

6.0 8.0 10.0 12.0 15.0 20.0

ds-opt 2.0 0.5 2.0 2.0 2.0 2.0

fs-opt 0.3057 0.3057 0.3057 0.3057 0.3057 0.3057

Vs-opt 156.2902 192.4158 156.2902 156.2902 156.2902 156.2902

nr 1 2 2 3 4 5

dr1-opt 4.0 4.0 4.0 3.96 4.0 4.0

fr1-opt 0.3930 0.3930 0.3930 0.3980 0.3930 0.3930

Vr1-opt 128.997 128.9997 128.9997 128.6217 128.9997 128.9997

dr2-opt 3.5 4.0 3.96 4.0 4.0

fr2-opt 0.4655 0.3930 0.3980 0.3930 0.3930

Vr2-opt 124.0446 128.9997 128.6217 128.9997 128.9997

dr3-opt 2.08 2.92 4.0

fr3-opt 0.9 0.5854 0.3930

Vr3-opt 106.4783 117.6285 128.9997

dr4-opt 2.08 3.92Fr4-opt 0.9000 0.4031

Vr4-opt 106.4783 128.2410

dr5-opt 2.08

Fr5-opt 0.9000

Vr5-opt 106.4783

Et-opt(MJ/piece) 2.40339 3.185364 3.766368 4.50552 5.556777 7.231464

Figure 1. Variation of optimal parameters with depth of cut in

finishing pass.

Arif et al. 873



9/12

of cut, which renders the optimal cutting velocity varia-

tion governed by depth of cut in roughing, and hence,

the power exponent is equal to the exponent for depth

in tool-life equation, as plotted and shown in Figure 3.

This region is also explained in another way. That is,

within a given bound of MRR at a given depth of cut,

the increase in MRR is achieved by maximizing the fee-

drate rather than cutting velocity as tool life is more

sensitive to the cutting velocity than the feedrate.Hence, in this way, the model tends to economize the

tool life. This maximization of feedrate is determined

by the most dominant constraint on the feedrate, which

is the surface roughness in our case. Once, the feedrate

is increased beyond a certain limit, increase in MRR is

not permissible by increasing feedrate as feedrate has

already been increased to the value allowed by the most

dominant constraint on feedrate. Hence, further

increase in MRR in roughing pass must be achieved by

increasing the cutting velocity alone, which means fee-

drate is to be adjusted to a new value (lower than the

maximum permissible) to get the optimal tool life with

respect to the minimum energy consumption and so asto get the optimal energy consumed. That is why when

cutting velocity initiates an upward surge in the plot,

the feedrate starts decreasing from the same point

onwards. Since now both optimal cutting velocity and

optimal feedrate are being varied iteratively at a given

depth of cut in roughing pass, the further variation or

trend of plot is no more governed by the cutting depth

exponent in tool-life equation rather it is governed byan iteratively optimized function. Furthermore, the

optimal energy consumed Er-opt increases linearly with

Figure 2. Variation of optimal specific energy and optimal material removal rate with depth of cut in finishing pass.MRR: material removal rate.

fr-opt(mm/rev)

Vr-opt(m/min)

fr-opt= 0.9 (1 fr-opt 2)

fr-opt= 2.2762dr-1.267

(2.1 fr-opt 4.0)

Vr-opt= 85.942dr0.293

(1Vr-opt2)

Vr-opt = 118.84dr-0.15

(1 Vr-opt 2)

dr (mm)

Figure 3. Variation of optimal parameters with depth of cut in roughing pass.

Figure 4. Variation of optimal specific energy with depth of cut

in roughing pass.




10/12

increase in depth of cut in roughing pass similar to that

in finishing pass and this is depicted in Figure 4.

The plot in Figure 5 shows the variation of OSE for

roughing pass Eos-r. It follows that Eos-r decreases

sharply with MRR up to certain level, and then it

almost flattens with very little further decrease. Thisrefers to the fact that above a certain critical depth of

cut in roughing, both MRR and Eos-r settle to the rea-

sonably stable values.

Overall turning operation

The overall turning operation is based on the hypoth-

esis of one finishing pass and one or more roughing

pass(es). The non-linear programming gives a combina-

tion of one finishing pass and one or more roughing

passes for optimum energy consumption in the overall

operation to remove a given depth of material.In Figure 6, OSE for the overall turning process,

Eos-t, is plotted for different values of material depth to

be removed (dt). The graph shows that Eos-t increases

with increase in dt. This is because the number of

roughing passes or number of overall passes is also

increasing as the dt increases, which means after per-

forming first roughing pass, the diameter of the work-

piece reduces and an equivalent depth of cut in

roughing will now remove less material from a reduced

diameter workpiece. Hence, Eos-t will increase as the

same amount of energy consumed is accounted for less

material removal. Hence, the Eos-t is always like to rise

with increase in number of overall passes.

An interesting observation is that as dt = 10.0 mm,

Eos-tis less than Eos-t at dt = 8.0 mm. This is because,

in both cases, the total number of passes required to fin-

ish the workpiece is the same. However, in the case of

dt= 10.0 mm, based on plot in Figure 6, the combina-

tion of three overall passes is such that it accounts for

the minimum energy consumed in each of these passes.

But in the case ofdt= 8.0 mm, due to less material to

be removed, the combination of three passes cannot be

the same as in dt = 8.0 mm and hence Eos-t for dt =

10.0 mm is slightly less than that for dt= 8.0 mm. It is

concluded from this observation that the total-depth-

to-be-removed has a dominant effect on Eos-tin turningwhere equal numbers of total passes are required to

complete the machining process.

Effect of tool replacement time

Since the model presented here is based upon the opti-

mal tool-life criteria for the minimum energy consump-

tion, the determination of the effect of tool replacement

time serves two purposes; it verifies the best tool

replacement time for the minimum energy consumption

in the turning process, and it also provides an insight

into the variation in the minimum energy consumed ifa different tool replacement time constraints were to be

implemented. Due to practical constraint such as

Figure 5. Variation of optimal specific energy and optimal material removal rate with depth of cut in roughing.MRR: material removal rate.

Figure 6. Variation of overall optimal specific energy with

total-depth-to-be-removed in roughing pass.MRR: material removal rate.

Arif et al. 875



11/12

inability of the cutting tool to produce a desired mini-

mum level of surface roughness after a certain machin-

ing time despite the flank wear is less than the standard

value adopted for the determination of the cutting tool

life. This is a very practical consideration when a very

low value of surface roughness is desired in the finish-

ing pass. Depending upon the surface finish require-

ment in the finishing pass, a certain tool replacement

time can be enforced as a constraint on the standard

tool life calculated by the minimum energy criteria. In

some cases, a preventive tool replacement strategy is

implemented where a tool is planned to be replaced

before the optimal tool life to avoid any risk of part

rejection due to failure to produce the desired finish onthe part by the tool having reached very close to its life.

The plot in Figure 7 shows the effect of tool

replacement time on the optimal energy Es-opt in fin-

ishing pass at two given depth of cuts. The plot veri-

fies that minima of the curves occur at Ts = 30.779

min, which is the unconstrained tool life obtained

from the minimum energy criteria. It follows from the

plot that the optimal energy increases on both ends of

the unconstrained tool life. However, the optimal

energy increases much steeper if the tool is replaced

earlier than the unconstrained tool replacement time

compared to that if the tool is replaced after theunconstrained too-life. This refers to the fact that

super finishing process tends to consume more unit

energy than the coarser or rougher process.

Conclusion

In this study, a comprehensive model has been pre-

sented to optimize the machining parameters in a multi-

pass turning operation for minimal energy consumption

considering the practical constraints. The following

conclusions are drawn from this study:

In finishing pass, the optimal cutting velocitydecreases as a power function of depth of cut.

For finishing process, the OSEs decreases and theoptimal MRR increases with increase in depth of

cut. In finishing pass, the removal of material at a

higher rate decreases the energy consumed by the

machine modules (the constant part of overall

energy) due to decreased machining time, which, inturn, decreases the overall specific cutting energy.

In roughing process, the optimal cutting velocitydecreases up to a certain threshold value of depth

of cut and then increases. The threshold value is

governed by the most dominant parametric con-

straint imposed. In roughing process, the optimal feedrate remains

constant up to a certain threshold value of depth of

cut and then decreases. In roughing process, the optimal specific cutting

energy decreases up to a certain threshold value of

depth of cut and then tends to remain reasonably

constant with further increase in depth of cut. In roughing process, the optimal MRR increases

up to a certain threshold value of depth of cut and

then tends to remain reasonably constant with fur-

ther increase in depth of cut. In multi-pass turning operation, the overall specific

cutting energy is less for higher total-depth-to-be-

removed if the optimal number of passes is equal.

Declaration of conflicting interests

The authors declare that there is no conflict of interest.

Funding

This research received no specific grant from any fund-

ing agency in the public, commercial or not-for-profit

sectors.

References

1. Westkamper E and Alting A. Life cycle management and

assessment: approaches and visions towards sustainable

manufacturing. CIRP Ann: Manuf Techn 2000; 49:

501526.

2. Gaussin M, Hu G, Abolghasem S, et al. Assessing the

environmental footprint of manufactured products: asurvey of current literature. Int J Prod Econ 2013; 46:

515523.

3. Jeswiet J and Kara S. Carbon emissions and CES in

manufacturing. CIRP Ann: Manuf Techn 2008; 57:

1720.

4. US Energy Information Administration. Annual energy

review 2011. DOE/EIA-0384(2011), September 2012.

Washington, DC: US Energy Information Administration.

5. Pusavec F and Kopac J. Achieving and implementation

of sustainability principles in machining processes. Adv

Prod Eng Manag2009; 4: 151160.

6. Dahmus JB and Gutowski TG. An environmental analy-

sis of machining. In:Proceedings of IMECE 2004, ASME

international mechanical engineering congress and RD&D

expo 2004, Anaheim, CA, 1319 November 2004, pp.1

10. New York: ASME.

0.452

0.454

0.456

0.458

0.46

0.462

0.464

0.466

0.873

0.875

0.877

0.879

0.881

0.883

0.885

0.887

0.889

0.891

0.893

5 15 25 35 45 55 65

d s= 2. 0m m d s= 0. 5m m

Es-opt

fords=0.5

mm,

(MJ/piece)

Es-opt

fords=2.0

mm,

(MJ/piece)

Tool replacement me (min)

Figure 7. Effect of tool replacement time on the optimal

specific energy in finishing pass.MRR: material removal rate.




12/12

7. Gutowski T, Jeffrey D and Thiriez A. Electrical energy

requirements for manufacturing processes. In:13th CIRP

international conference of life cycle engineering, Lueven,

31 May2 June 2006. CIRP International, 2006.

8. Jawahir IS and Jayal AD. Product and process innova-

tion for modelling of sustainable machining processes.

In: Seliger G (ed.) Advances in sustainable manufacturing:

proceedings of the 8th global conference on sustainable

manufacturing. Berlin: Springer, 2011, pp.301307.

9. Granados S, Jawahir IS and Fernandez J. A comprehen-

sive criterion for sustainability evaluation of machining

processes. In: 7th Global conference of sustainable manu-

facturing, IIT Madras, Chennai, India, 24 December

2009, pp.385391.

10. Baniszewski B.An environmental impact analysis of grind-

ing. BSc Thesis, Massachusetts Institute of Technology,

Cambridge, MA, 2005.

11. Kurd M.The material and energy flow through the abrasive

water-jet machining and recycling processes. BSc Thesis, Mas-

sachusetts Institute of Technology, Cambridge, MA, 2004.

12. Hong SY, Markus I and Jeong W-c. New coolingapproach and tool life improvement in cryogenic machin-

ing of titanium alloy Ti-6Al-4V. Int J Mach Tool Manu

2001; 41: 22452260.

13. Um J, Chow LC and Jawahir IS. An experimental inves-

tigation of the application of the spray cooling method in

stainless steel machining. In: ASME IMECE MED, 1995,

vol. 2, pp.165178. New York: ASME.

14. Marksberry P and Jawahir IS. A comprehensive tool-life

performance model in near-dry machining for sustainable

manufacturing.Int J Mach Tool Manu 2008; 48: 878886.

15. Outeiro JC, Kandibanda R, Pina JC, et al. Size-effects and

surface integrity in machining and their influence on prod-

uct sustainability.Int J Sustain Manuf2010; 2: 112126.

16. Guodong S, Deogratias K and Kevin L. A virtual

machining model for sustainability analysis. In: Proceed-

ings of the ASME 2010 international design engineering

technical conferences & computers and information in

engineering conference, Montreal, QC, Canada, 1518

August 2010, IDETC/CIE 2010, pp.875883. New York:

ASME.

17. Rajemi MF.Energy analysis in turning and milling. PhD

Thesis, University of Manchester, Manchester, 2010.

18. Derflinger V, Bra ndle H and Zimmermann H. New hard/

lubricant coating for dry machining. Surf Coat Tech

1999; 113: 286292.

19. Grzesik W. Friction behaviour of heat isolating coatings

in machining: mechanical, thermal and energy-based con-

siderations. Int J Mach Tool Manu2003; 43: 145150.

20. Schulz H and Moriwaki T. High-speed machining.CIRP

Ann: J Manuf Techn1992; 41: 637643.

21. Gutowski T, Murphy C, Allen D, et al. Environmentally

benign manufacturing: observations from Japan, Europe

and the United States.J Clean Prod2005; 13: 117.

22. Gilbert WW. Economics of machining. In: American

Society for Metals (ed.) Machining theory and practice.

Cleveland, OH: American Society for Metals, 1950,

pp.465485.

23. Shint YC and Roor YS. Optimization of machining con-ditions with practical constraints.Int J Prod Res1992; 30:

29072919.

24. Hitomi K. Manufacturing systems engineering. London:

Taylor & Francis, 1979.

25. Libao A. Optimization of machining parameters in multi-

pass turning and milling operations. MSc Thesis, Concor-

dia University, Montreal, QC, Canada, 2003.

26. Shaw MC. Metal cutting principle. Oxford: Clarendon

Press, 1984.

27. Armarego EJA and Brown RH.The machining of metals.

Englewood Cliffs, NJ: Prentice Hall, 1969.

28. Gupta R, Batra JL and Lal GK. Profit rate maximization

in multi-pass turning with constraints: a geometric pro-

gramming approach.Int J Prod Res1994; 32: 15571569.

29. Geoffrey B and Winston A. Fundamentals of machining

and machine tools. Boca Raton, FL: CRC Press, Taylor

& Francis Group, 2006.

Arif et al. 877

at WEST VIRGINA UNIV on November 17 2015pib sagepub comDownloaded from

Documents

A Model to Determine the Optimal Parameters for Sustainable Energy Machining in a Multi Pass Turning Operation