Ain Shams Engineering Journal (2015) xxx, xxx–xxx

Ain Shams University

Ain Shams Engineering Journal

www.elsevier.com/locate/asejwww.sciencedirect.com

MECHANICAL ENGINEERING

A decision guidance framework for non-traditional

machining processes selection

* Corresponding author. Tel./fax: +91 033 2414 6153.

E-mail address: s_chakraborty00@yahoo.co.in (S. Chakraborty).

Peer review under responsibility of Ain Shams University.

Production and hosting by Elsevier

http://dx.doi.org/10.1016/j.asej.2015.10.0132090-4479 � 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article in press as: Prasad K, Chakraborty S, A decision guidance framework for non-traditional machining processes selection, Ain Sham(2015), http://dx.doi.org/10.1016/j.asej.2015.10.013

Kanika Prasad, Shankar Chakraborty *

Department of Production Engineering, Jadavpur University, Kolkata 700 032, West Bengal, India

Received 13 June 2015; revised 11 September 2015; accepted 14 October 2015

KEYWORDS

Non-traditional machining

process;

Decision making;

Decision guidance frame-

work;

Process selection

Abstract In order to realize the manufacturing/machining demands thrived by newer, hard and

difficult-to-machine materials being utilized in the present day industries, an assortment of non-

traditional machining (NTM) processes has been developed over the past few decades. These pro-

cesses are capable of generating intricate and complex shapes with high degree of accuracy, close

dimensional tolerance and better surface finish. In this paper, a decision guidance framework is

developed in Visual BASIC 6.0 to help the process engineers in selecting the most appropriate

NTM process for a specific work material and shape feature combination. It also assists in identi-

fying the ideal process parameter combinations for the most suitable NTM process. The derived

results highly corroborate with the opinions of the experts in the related field, demonstrating the

acceptability of the developed system.� 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an

open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

Components/parts manufactured by casting, forming and var-

ious other traditional manufacturing processes often requireadditional advanced machining operations before they aremade ready for use or assembly. In many engineering applica-

tions, parts need to be interchangeable in order to performmulti-functional tasks suitably and reliably during theirexpected service life. Therefore, control over dimensional accu-

racy and surface finish of those components is of critical

importance during any machining operation [1]. In spite oftechnological advancements, the conventional machining pro-

cesses are inadequate to generate complex geometrical shapesin harder, tougher, stronger and temperature-resistant materi-als, such as tungsten carbides, alloy steels, high speed steels,

fiber reinforced composites, ceramics and ceramic-based tools,and diamonds, as being frequently utilized in aerospace, elec-tronics, nuclear, missile and automotive industries. It has ledto the development of advanced machining processes where

the physical properties of the workpiece material do notimpose any restriction on the material removal procedure. Adiverse range of machining processes involving application of

mechanical, thermal, electrical, and chemical energies havebeen evolved out over the past few decades. These modernmachining methods are also known as non-traditional machin-

ing (NTM) processes, harness unconventional sources of ener-gies for material removal, like electrochemical reaction, hightemperature plasma, high velocity jet of liquids or abrasives,

s Eng J

2 K. Prasad, S. Chakraborty

high frequency sound waves and coherent lights, instead ofelectric motors and hard tool materials employed in conven-tional machining processes [2]. These NTM processes are cap-

able of generating complex shape geometries, such as internaland external profiles, or small diameter holes with better sur-face finish, close dimensional tolerance, high accuracy, greater

surface integrity and required miniaturization. Moreover, insome cases, the workpieces are too flexible or slender to with-stand the cutting or grinding forces generated during the con-

ventional machining processes. The NTM processes are alsocapable of performing machining operations in those caseswhere there is no direct contact between the tool and the work-piece material. Technological improvements of the NTM pro-

cesses can further be achieved through innovative techniquesor modification of the existing method, while combiningtogether two or more machining actions or phases of the mate-

rial removal process. These hybrid machining techniques thusdeveloped can exploit the combined advantages of the con-stituent NTM processes, and avoid the adverse effects of those

processes when they are individually applied for carrying outthe required machining operation.

Over the years, more than 20 different NTM processes have

been successfully developed and implemented in diverse rangeof manufacturing industries. To exploit the full potential ofthose NTM processes, it becomes important to understandthe exact nature of the machining problem because a NTM

process considered suitable for a particular application maynot be equally efficient for another application. A NTM setupis usually complex, which demands skilled personnel for its

proper operation and maintenance. It is also observed thatmost of the NTM processes have relatively high capital invest-ment cost, power consumption and operating cost, tooling and

fixture cost, and maintenance cost which can easily offset thebenefits elicited from the enhanced machining capabilities ofthose processes. Therefore, selection of an appropriate NTM

process from a pool of modern machining methods is a keydecision making problem for justification and implementationof the same for performing a given machining operation. Theprocess engineers entrusted with the responsibility of suggest-

ing and identifying the most suitable/feasible NTM methodmust have an in-depth technological knowledge regarding itsapplicability and characteristics. But, only a few process engi-

neers are able to keep track with the ever increasing and morespecialized technological enhancements in the area of NTMprocesses. So, the need of a decision guidance framework (in

the form of a prototype expert system) is eminently felt toaid the process engineers in making NTM process selectiondecision. Therefore, in this paper, a database is first createdthat contains all the information pertaining to various machin-

ing characteristics and applicability of 25 NTM processes. It isthen integrated with a decision guidance framework to assistthe process engineers in selecting the most suitable NTM pro-

cess for a given machining application.

2. Literature review

The past researchers already applied different mathematicalmodels and developed some distinct expert systems to selectsuitable NTM processes from the existing alternatives for

varying machining applications. Yurdakul and Cogun [3]proposed a multi-attribute selection procedure to help the

Please cite this article in press as: Prasad K, Chakraborty S, A decision guidance f(2015), http://dx.doi.org/10.1016/j.asej.2015.10.013

manufacturing personnel in identifying suitable NTM pro-cesses for the given application requirements. Chakrabortyand Dey [4] presented a systematic methodology for selecting

the best NTM process under constrained material and machin-ing conditions. Chakrabarti et al. [5] developed a managementinformation system for selection of complex machining pro-

cesses in modern manufacturing organizations. Chakrabortyand Dey [6] presented a quality function deployment (QFD)-based methodology to ease out the optimal NTM process

selection procedure. Chakladar and Chakraborty [7] employedtwo multi-criteria decision making (MCDM) methods, i.e.technique for order performance by similarity to ideal solution(TOPSIS) and analytic hierarchy process (AHP) to select the

most suitable NTM process for a given machining application.Edison et al. [8] developed a web-based knowledge base systemfor identifying the most appropriate NTM process to suit some

specific requirements on the basis of input parameters, such asmaterial type, shape applications, and process economy, andsome of the process capabilities, such as surface finish, corner

radii, width of cut, length to diameter ratio, and tolerance.Chakladar et al. [9] designed a digraph-based expert systemto aid the decision maker in selecting the most suitable NTM

process for a given work material and shape feature combina-tion. Sugumaran et al. [10] investigated the use of artificial neu-ral networks in NTM process selection decision. Chakraborty[11] explored the application of multi-objective optimization

on the basis of ratio analysis (MOORA) method to solveNTM process selection problems in real time manufacturingenvironment. Sadhu and Chakraborty [12] proposed a two-

phase decision model for NTM process selection consideringten alternative NTM processes and ten performance criteria.Ic [13] explored the applicability of TOPSIS and design of

experiment methods to solve various computer-integratedmanufacturing process selection problems for different indus-trial applications. Karande and Chakraborty [14] combined

preference ranking organization method for enrichment evalu-ation (PROMETHEE) and geometrical analysis for interactiveaid (GAIA) techniques to guide the process engineers in arriv-ing at the best NTM process selection decision. Chatterjee and

Chakraborty [15] applied Organization Rangement Et Syn-these De Donnes Relationnelles (ORESTE) method to solveNTM process selection problems. Chatterjee and Chakraborty

[16] explored the applicability, suitability and potentiality ofevaluation of mixed data (EVAMIX) method for solvingNTM process selection problems. Based on TOPSIS method

and using distinct systematic approaches both in fuzzy and

in crisp environments, Temucin et al. [17] proposed a decisionsupport model to deal with the selection problems of appropri-ate NTM processes. Prasad and Chakraborty [18] developed a

decision making model based on QFD technique to automatethe NTM process selection procedure with the help of graphi-cal user interface and visual decision aids. Roy et al. [19]

employed fuzzy AHP method to calculate the performancescores of various NTM processes taking into considerationproduct and process characteristics as the basis of comparison.

Temucin et al. [20] identified the most appropriate NTM pro-cess for cutting of a specific work material while utilizing dif-ferent fuzzy MCDM methods. Madic et al. [21] employed

operational competitiveness rating analysis (OCRA) methodfor solving NTM process selection problems.

It becomes evident from the review of past researches thatmost of the NTM process selection procedures are either

ramework for non-traditional machining processes selection, Ain Shams Eng J

A decision guidance framework 3

problem specific and limited in their scope or based on a speci-fic manufacturing organization’s product line. Moreover,those works assumed that the workpiece material and the cor-

responding part geometry were predetermined. Almost all theprevious research works except one [18] failed to provide anyinformation about the settings of the optimal process parame-

ters, which have a direct influence on the economy and effi-ciency of the entire machining operation. Therefore, in thispaper, a decision guidance framework with an exhaustive data-

base for different workpiece materials and shape features isdesigned and developed in Visual BASIC 6.0 to aid the processengineers in identifying the most appropriate NTM process fora given machining application and provide the ideal paramet-

ric settings of the identified process. A conscious effort is alsomade to incorporate all the existing NTM processes along withtheir hybrid forms into the developed database.

3. Decision guidance framework for NTM process selection

The need for quick and better results is adherently felt in the

present day global competitive environment, where the manu-facturing organizations are characterized by rapid technologi-cal development and advanced manufacturing technology.

Therefore, this paper proposes the application of a decisionguidance framework in the form of a prototype expert systemto process huge amount of data quickly and efficiently in order

to automate the entire decision making procedure for NTMprocess selection. The designed software prototype works onan Intel� CoreTM i5-2034M CPU with 2.50 GHz, 4.00 GBRAM operating platform. It is often noticed that efficiency

of a NTM process is considerably affected by any change inthe type of application and required attributes of the end prod-uct. Hence, it is quite important for a process engineer to start

the selection procedure with a clear understanding of the prob-lem, and subsequently, provide the required information interms of workpiece material, machining operation and finally,

process characteristics, such as material removal rate (MRR),and surface finish. The framework is designed in such a waythat it discards those NTM processes which are not suitable

with respect to work material limitation, machining operationdescription and process characteristic requirements, and grad-ually narrows down the list of feasible processes to arrive at thebest possible decision. The architecture of this decision guid-

ance framework for NTM process selection is shown in Fig. 1.The first step associated with this decision guidance frame-

work is to define a NTM process selection problem with

respect to three parameters, i.e. workpiece material, machiningoperation and process characteristics. A detailed database ofNTM processes is already developed and organized in three

different modules in accordance with the predefined processrequirements. The relevant information for this database isaccumulated from various documented sources [2,3,22]. TheNTM processes can be classified into various categories based

on different forms of energy, such as mechanical, chemical,electrical, and thermal as utilized for material removal fromthe workpiece. In mechanical type of NTM processes, material

removal takes place due to erosion mechanism while employ-ing high velocity particles or liquids as the transfer media.Hydraulic pressure acts as a source of energy in these pro-

cesses. Ultrasonic machining (USM), abrasive flow machining(AFM), magnetic abrasive finishing (MAF), abrasive jet

Please cite this article in press as: Prasad K, Chakraborty S, A decision guidance f(2015), http://dx.doi.org/10.1016/j.asej.2015.10.013

machining (AJM), water jet machining (WJM), and abrasivewater jet machining (AWJM) are some of the examples in thiscategory. In chemical machining (CHM) process, material is

removed through chemical dissolution of the workpiece, whichis in contact with a chemically active reagent. Additionally,two or more than two forms of energy are combined together

to develop hybrid NTM processes in order to enhance themachining capabilities of individual processes. Materialremoval in electrochemical type of NTM processes is carried

out through ion displacement mechanism, which requires highcurrent as the source of energy and electrolyte as the transfermedium. Those processes include electrochemical machining(ECM), electrochemical grinding (ECG), and electrochemical

honing (ECH). For thermo-electrical type of NTM processes,fusion or vaporization of the small areas at the surface of theworkpiece removes the material. The source of energy utilized

can be high voltage, amplified light or ionized material.Thermo-electrical processes comprise of electrical dischargemachining (EDM), wire EDM (WEDM), laser beam machin-

ing (LBM), electron beam machining (EBM), plasma arcmachining (PAM), etc. EDM and WEDM processes requiredielectric medium for transfer of energy. Some of the variants

of these NTM processes are micro-EDM, computer numericalcontrol (CNC) EDM, die-sinking EDM, rotary EDM, dryEDM, CNC WEDM and micro-WEDM. EBM process doesnot require any medium for energy transfer. LBM process uti-

lizes highly collimated, monochromatic and coherent lightbeam to melt and evaporate the work material. Laser micro-machining and laser milling are two different categories of

LBM process. High temperature plasma arc is generated toremove material in PAM process. Electrochemical dischargemachining (ECDM) process combines features of both electro-

chemical erosion and electrochemical dissolution. It employselectrical discharges in electrolytes for removal of material,which aids in achieving higher MRR. A detailed listing of all

the existing NTM processes is shown in Fig. 2. In this frame-work, altogether 25 NTM processes are considered rangingfrom the naıve to the hybrid processes.

The workpiece material module consists of an exhaustive

list of materials that can be processed employing the consid-ered NTM processes [22]. They can be broadly classified as(a) alloys, (b) ceramics, (c) composites, (d) glass, (e) metals,

(f) plastics, (g) refractories, (h) steel and (i) others.A diverse range of shape features can be generated on the

workpiece materials employing different NTM processes.

The following machining operations are taken into considera-tion in the present decision guidance framework:

(a) Deep cutting – This machining operation is carried out

to generate the desired shape feature on a workpiecematerial with higher depth of cut (greater than 40 lm).

(b) Shallow cutting – In this machining operation, the depth

of cut is comparatively low (less than 40 lm).(c) Drilling – It is to cut or machine a hole of circular cross-

section in a solid workpiece material.

(d) Precision cavity – In this machining operation, cavitywith close dimensional tolerance (±50 lm) is generatedfor intrinsic applications.

(e) Standard cavity – Cavity with explicit set of dimensionsis generated, but cannot be employed for intrinsicapplications.

ramework for non-traditional machining processes selection, Ain Shams Eng J

Docum

entsS

preadsheetsW

ebpage

Process

characteris�cs

Machining

opera�on

material

Workpiece

Database

Ideal

parametric se�ng

Workpiece material type

Machining opera�on descrip�on

Process characteris�c requirements

Maximization of MRR

Maximization of safety

Minimization of cost

Minimization of toxicity

- - - - - - - - - - - - - - - - -

Det

ails

of N

TM p

roce

ss s

elec

tion

prob

lem

Shortlisting of feasible NTM processes

Best NTM process with the optimal parametric mix

Processengineer

Decision guidance framework for NTM process selection

Figure 1 Architecture of the decision guidance framework for NTM process selection.

Figure 2 NTM processes considered in decision guidance framework.

4 K. Prasad, S. Chakraborty

(f) Double contouring – The shape feature generated by

this machining operation is defined by two separateand different, top and bottom contours of theworkpiece.

Please cite this article in press as: Prasad K, Chakraborty S, A decision guidance f(2015), http://dx.doi.org/10.1016/j.asej.2015.10.013

(g) Surface of revolution – This machining operation pro-

duces a surface on the workpiece while rotating a two-dimensional curve about an axis.

ramework for non-traditional machining processes selection, Ain Shams Eng J

Table 1 Scale indicating total costs for NTM processes.

Cost (in USD) Scale Interpretation

1000–7000 1 Lowest

7001–15,000 2 Very low

15,001–22,000 3 Low

22,001–30,000 4 Medium

30,001–37,000 5 High

37,001–50,000 6 Very high

50,001–80,000 7 Highest

A decision guidance framework 5

(h) Finishing – This machining operation is performed to

achieve mirror-like finish with very high dimensionalaccuracy and superior surface finish.

Finally, the process capability attributes need to be identi-fied to measure the suitability of a NTM process for a givenmachining application. This module includes the related infor-mation about various process characteristics to be taken into

consideration while performing the desired machining opera-tion. The following 12 important process characteristics havingdirect influence on the productivity and effectiveness of a

NTM process are recognized in this framework:

(a) Applied voltage (in V) – potential difference applied to

operate a NTM setup/equipment.(b) Corner radius (in mm) – It refers to the radius of a circle

that is generated if the corner arc of a rectangle isextended to form a complete circle.

(c) Total cost (in relative (R) scale) – This encompasses ini-tial acquisition, tooling and fixture, power consumptionand tool wear cost of a NTM setup/equipment.

(d) Current (in A) – flow of electrons or ions inside an elec-tric circuit to initiate the material removal process.

(e) Volumetric MRR (in mm3/min) – volume of material

removed from the workpiece per unit of machining time.(f) Power requirement (in W) – power rating of a NTM

setup/equipment.

(g) Safety (in R scale) – It deals with the safety of the oper-ators while performing a machining operation employ-ing a NTM setup/equipment.

(h) Surface damage (in lm) – damage caused on the work-

piece surface due to destructive machining forces or ionbeam bombardment.

(i) Surface finish (in lm) – It is the allowable deviation

from an absolutely flat surface which can be attainedthrough the machining action and is measured in termsof center line average or roughness average value.

(j) Taper (in mm/mm) – a gradual thinning or narrowing ofthe workpiece toward its one end.

(k) Tolerance (in lm) – dimensional closeness of an endproduct to its required specifications.

(l) Toxicity (in R scale) – environmental and other hazardscaused due to machining medium contamination.

All of the abovementioned process characteristics, exceptMRR and safety are identified as non-beneficial attributesfor which lower values are preferred. On the other hand,

MRR and safety representing beneficial attributes alwaysrequire their higher values. The total cost for all the consideredNTM processes comprises of the procurement, tooling and fix-

ture, power consumption and tool wear cost, which isexpressed using a R scale of 1–7, where the scale values 1and 7 signify the lowest and the highest costs respectively. Adetailed range of total cost and the corresponding R scale

are given in Table 1.The decision making process begins with identifying the

type of work material to be machined, followed by specifying

the shape feature to be generated, and finally, providing thevalues for process characteristics based on which the mostappropriate NTM process is selected. The database for this

decision guidance framework contains all the crucial/relevantinformation required for choosing the most suitable NTM

Please cite this article in press as: Prasad K, Chakraborty S, A decision guidance f(2015), http://dx.doi.org/10.1016/j.asej.2015.10.013

process in accordance with the given input parameters, as dis-cussed above.

If at the end, more than one NTM processes are shortlistedas the feasible choices for a specific work material and shapefeature combination, those shortlisted NTM processes then

need to be further evaluated and ranked with a view to identifythe most appropriate one. A decision matrix is thus requiredfor evaluating and ranking the feasible NTM processes with

respect to the already given process characteristic values. Thisdecision matrix is developed while retrieving the relevant pro-cess characteristics for the feasible NTM processes from thedatabase of the decision guidance framework. In this paper,

the priority weight for each criterion (process characteristic)as considered for evaluation is calculated using Shannon’sentropy method [23]. The performance scores of the feasible

NTM processes are then calculated applying the followingequation:

Performance score ðPSiÞ ¼Xn

j¼1

wj � Normalized valueð Þij

ði ¼ 1; 2; . . . ;m; j ¼ 1; 2; . . . ; nÞð1Þ

where wj is the weight for jth process characteristic, m is the

number of feasible NTM processes and n is the number ofthe considered process characteristics. The normalized valueshowing the normalized performance of ith NTM process with

respect to jth process characteristic is derived from the decisionmatrix of a given NTM process selection problem using linearnormalization technique. The ranking of the feasible NTM

processes is then achieved based on their descending perfor-mance scores, and subsequently, the most appropriate NTMprocess for the given machining application is identified alongwith its ideal parametric settings.

4. Illustrative examples

Fig. 3 summarizes the procedural steps involved in selecting

the most appropriate NTM process for a specific machiningapplication while employing the developed decision guidanceframework. Three examples are considered here to demon-

strate its applicability and potentiality in solving NTM processselection problems.

4.1. Example 1

The aim of this selection problem is to identify the most suit-able NTM process that can drill a hole of 0.75 mm diameter

with a slenderness ratio of less than equal to 20 on Porcelain.

ramework for non-traditional machining processes selection, Ain Shams Eng J

Figure 3 Flowchart exhibiting procedural steps for NTM process selection.

6 K. Prasad, S. Chakraborty

It is a ceramic material produced by heating kaolin(Al2O3_s2SiO2_s2H2O) in a kiln at temperature between 1200and 1400 �C. It has wide applications in ceramic engineering,

such as manufacturing of heat-resistant tiles for spacecraft,components of nuclear fuel, pollution control devices, andfiber optic cables. It also acts as an excellent insulator for

use at high voltage, especially in outdoor applications.The first step for solving this NTM process selection prob-

lem using the decision guidance framework is to select the typeof workpiece material and sub-workpiece material as ‘Ceram-

ics’ and ‘Porcelain ceramic’ respectively from the ‘Work mate-rial’ module, as shown in Fig. 4. Based on this initial workmaterial choice, some of the NTM processes, such as PAM,

EDM, ECM, and ECDM are straightforwardly eliminatedfrom further consideration due to their inability to machine

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Porcelain material. ‘Drilling’ is then chosen as the requiredmachining operation to be performed from the drop-downmenu in the ‘Machining operation’ module. Subsequently,

‘Details of machining operation’ functional key is pressed toelucidate the distinct features of the chosen machining opera-tion. In this problem, the required hole diameter is 0.75 mm

with a slenderness ratio of less than equal to 20. Once theabovementioned details pertaining to hole diameter and slen-derness ratio are provided in the framework, a set of NTMprocesses, such as AWJM, ECG, and WJM which are inca-

pable of performing the desired machining operation on thesaid work material are discarded from the list of feasible pro-cesses. The process engineer is finally required to identify some

of the important evaluation criteria from the ‘Process charac-teristics’ module to further justify the feasibility of the NTM

ramework for non-traditional machining processes selection, Ain Shams Eng J

Figure 4 NTM process selection window for hole generation on Porcelain.

A decision guidance framework 7

processes for the given machining application. Based on the

end product requirements, cost, MRR, surface damage, sur-face finish, taper and tolerance are recognized as the six mostimportant criteria. Among these, MRR is the sole beneficialattribute requiring its higher value, whereas, the remaining five

attributes are non-beneficial in nature preferred with theirlower possible values. Next, the desired values for the selectedcriteria are entered into the corresponding empty cells, which

can be generated on pressing the ‘Input value’ key. Again,some of the NTM processes, already identified as feasible withrespect to work material and shape feature combination, are

discarded based on their incompatibility to meet the stringentprocess characteristic requirements. At the end, a final nar-rowed down list of NTM processes according to their suitabil-

ity for the considered machining application with respect to thegiven process characteristics is obtained on pressing the ‘Fea-sible NTM process(es)’ functional key while removing otherincompatible NTM processes. For this selection problem,

AJM, CHM, EBM, LBM and USM emerge out as the mostfeasible NTM processes. Since, there are more than oneNTM processes which can perform the desired machining

operation on Porcelain, the finally selected NTM processesneed to be again critically examined and ranked in order toidentify the best choice.

The best performing NTM process can now be ascertainedon pressing the ‘Best NTM process’ functional key. The rank-ing of the finally selected NTM processes is obtained as USM–AJM–LBM–EBM–CHM indicating USM as the best choice

for generation of a hole of diameter 0.75 mm and slenderness

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ratio of less than or equal to 20 on Porcelain work material.

The derived results also guide the concerned process engineerin deciding the ideal parametric settings of the finally selectedUSM process, as shown in Fig. 5. These ideal process param-eter settings need to be fine-tuned depending on the end pro-

duct requirements and technical specifications of the USMsetup.

4.2. Example 2

In this problem, the most suitable NTM process from a widerange of available alternatives needs to be chosen that is cap-

able of generating standard cavity on Zirconia (a refractorymaterial). Zirconia is a white crystalline oxide of zirconium.Cubic zirconia can be synthesized in various colors for use

as gem stone or diamond stimulant. It can also be appliedfor insulation, as abrasives and enamels. Stabilized zirconiais used in oxygen sensors and fuel cell membranes due to itsability to allow oxygen ions to move freely through the crystal

structure at high temperatures.The initial step in identifying the best NTM process for this

machining application is to select the workpiece material as

‘Refractories’ from the ‘Work material’ module. ‘Zirconia’ isthen chosen as the sub-workpiece material in the same module.The next step of this NTM process selection procedure com-

prises of clearly detailing out the required machining opera-tion. Thus, ‘Cavity’ is selected as the machining operationfrom the drop-down menu provided in the ‘Machining opera-

tion’ module of Fig. 6. The generated cavity may not have the

ramework for non-traditional machining processes selection, Ain Shams Eng J

Figure 5 Identification of the best NTM process for drilling hole on Porcelain.

Figure 6 NTM process selection window for generating standard cavity on Zirconia.

8 K. Prasad, S. Chakraborty

exact dimensions, without any tight tolerance requirement. So,

‘Standard’ is opted as the sub-machining operation to be per-formed. Moreover, the desired feature in the final product can

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be associated with different important process characteristics,

such as corner radii, cost, MRR and surface finish. Hence,the process engineer selects the abovementioned four criteria

ramework for non-traditional machining processes selection, Ain Shams Eng J

A decision guidance framework 9

from the list of process characteristics for evaluating the alter-native NTM processes. Pressing of the ‘Input value’ key allowsthe process engineer to provide the desired values for the pro-

cess characteristics in accordance with the demands of thespecified machining application.

Finally, pressing of the ‘Feasible NTM process(es)’ key

enables the developed framework to eliminate all the incom-patible NTM processes and derives a trimmed down list ofthe feasible NTM processes comprising of AJM, AWJM,

CHM, die-sinking EDM, dry EDM, EBM, ECM, EDM,USM and WJM for generating standard cavity on Zirconia.These feasible NTM processes again need to be evaluated withrespect to the preset process characteristics to identify the most

appropriate process for the given application. On pressing ofthe ‘Best NTM process’ key, a decision matrix consisting ofall the feasible NTM processes along with their corresponding

machining characteristics is developed at the backend of theframework. The priority weights for the considered four eval-uation criteria are then calculated and the performance score

for each NTM process alternative is subsequently computedto rank all the feasible processes. The developed frameworkdetermines the ranking of feasible NTM processes such as A

JM–USM–AWJM–WJM–ECM-dry EDM–EDM-die sinkingEDM–CHM–EBM. It is observed that AJM is the bestmachining process for generating standard cavity on Zirconia.The details of the best performing NTM process along with its

photograph and ideal parametric settings are shown in Fig. 7.

4.3. Example 3

Here, themost suitable NTMprocess is intended to be identifiedfor deep cutting operation of Germanium. Germanium is agrayish-white, lustrous, hard metal of carbon group, chemically

similar to tin and silicon. It is primarily used as semiconductorin transistors and other electronic devices having micro-level

Figure 7 Selection of NTM process for g

Please cite this article in press as: Prasad K, Chakraborty S, A decision guidance f(2015), http://dx.doi.org/10.1016/j.asej.2015.10.013

dimensions. The important issues that need to be consideredwhile selecting a NTM process for this application are surfacefinish and surface damage caused on the workpiece surface, as

the end components are often miniaturized.The selection procedure starts with choosing the workpiece

material as ‘Metals’ from the ‘Work material’ module. In this

module, ‘Germanium’ is selected as the sub-workpiece materialon which cutting operation is to be performed. Next, ‘Cutting’is chosen as the machining operation, followed by ‘Deep cut-

ting’ option in Fig. 8. It is important to note that all the 25NTM processes considered here are capable of cutting metals.Therefore, none of the processes is eliminated from the initiallist of alternatives for the given work material and shape fea-

ture combination. In this example, MRR, surface damage, sur-face finish and tolerance are the four important evaluationcriteria which may affect the final NTM process selection deci-

sion. In the ‘Input value’ module, the desired values of all theseprocess characteristics are provided. Subsequently, some of theNTM processes, such as EDM, ECH, and PAM are automat-

ically discarded from the list of feasible alternatives based onthe process characteristic requirements.

It is noticed that AJM, AWJM, CHM, CNC EDM, CNC

WEDM, ECM, LBM, micro-EDM, micro-WEDM, USMand WJM evolve out as the feasible NTM processes capableof performing the required deep cutting operation on Germa-nium. ECM–AWJM–USM–WJM-micro-WEDM–micro-ED

M–CNC WEDM–AJM–CNC EDM–CHM–LBM is thederived ranking of the feasible NTM processes for the givenapplication. ECM emerges out as the most appropriate

NTM process for the considered application. Selection ofECM process over the other NTM processes is justified fromthe fact that it is one of the few processes capable of producing

parts/components with higher surface finish. The technicaldetails, ideal parametric settings and an actual photographof the ECM setup are provided in Fig. 9.

enerating standard cavity on Zirconia.

ramework for non-traditional machining processes selection, Ain Shams Eng J

Figure 8 NTM process selection window for deep cutting operation of Germanium.

Figure 9 Identification of ECM as the best NTM process for deep cutting of Germanium.

10 K. Prasad, S. Chakraborty

Please cite this article in press as: Prasad K, Chakraborty S, A decision guidance framework for non-traditional machining processes selection, Ain Shams Eng J(2015), http://dx.doi.org/10.1016/j.asej.2015.10.013

A decision guidance framework 11

5. Discussions

Although AHP is one of the most popular MCDM methodsadopted for solving selection and evaluation problems, it

involves lengthy computational steps requiring comparativelymore time to arrive at the final solution due to its pairwisecomparison approach. Additionally, it suffers from the rank

reversal problem, i.e. changing the set of alternatives mayaffect the decision scores of all the alternatives. This impliesthat AHP method is occasionally subjected to inconsistenciesin judgment and ranking. In this paper, three other widely

accepted MCDM methods, i.e. Vlse Kriterijumska Opti-mizacija Kompromisno Resenje (VIKOR), PROMETHEEand TOPSIS are employed to rank the final list of NTM pro-

cess alternatives in order to validate the applicability androbustness of the developed decision guidance framework forNTM process selection. The degree of agreement between

the rankings of the alternative NTM processes as derived bythe developed approach and other three MCDM methods ismeasured applying the Spearman’s rank correlation coefficient

(rs) values. In the first NTM process selection problem, it isobserved that the ranking orders of NTM process alternativesas obtained using these three MCDM methods exactly corrob-orate with those derived by the decision guidance framework.

For all the three methods, rs values of exactly 1 are obtainedfor a perfect match. For the second problem, rs values of 1,0.9605 and 0.71 are computed for VIKOR, PROMETHEE

and TOPSIS methods respectively when the rankings fromthe decision framework are compared with those fromMCDMmethods. There exists an excellent agreement between the

derived rankings of NTM processes which proves the robust-ness of the developed approach. In the third problem, it is alsonoticed that very good rank correlations exist for the three

MCDM methods (1 between VIKOR and decision framework,0.9181 between PROMETHEE and decision framework, and0.6272 between TOPSIS and decision framework). Higher rsvalues indicate strong rank agreements between the considered

approaches showing applicability and potentiality of thedesigned framework. It is quite interesting to observe thatfor all the three NTM process selection problems, the rankings

derived from the adopted framework perfectly match withthose obtained employing VIKOR method.

Most of the decision making tasks as conventionally carried

out often require retraining of the skilled human resources. Nomatter how good the training is, employees make mistakes.Additionally, human resources are limited in efficiency, whichnecessitates new hiring when scalability is needed. A large pro-

portion of time and infrastructure is also required to create,maintain and process the database, which consequentlydemands huge monetary investment. Application of the devel-

oped decision guidance framework on a job floor for NTMprocess selection now overcomes those shortcomings of theconventional decision making methods through reduced man-

power, enhanced personnel efficiency and better utilization ofinformation. Additionally, the time required for selecting anappropriate NTM process for a given machining application

is drastically trimmed down with minimal scope of any humanerror. All these expected benefits combine together to ulti-mately reduce the total cost of decision making process. Thedesigned framework has also some distinct advantages over

the existing decision making tools. It can now emphasize on

Please cite this article in press as: Prasad K, Chakraborty S, A decision guidance f(2015), http://dx.doi.org/10.1016/j.asej.2015.10.013

achieving manufacturing excellence through continuousimprovement while regularly updating its database accordingto the changing business environment. Additionally, it pos-

sesses scalability and flexibility features, i.e. its database canaccommodate numerous NTM processes to fulfill the require-ments of diverse machining applications. It also facilitates

inception of quality assurance concept in a manufacturingorganization through ensuring effectiveness and efficiency ofmachining operations while providing the optimal settings of

various process parameters, which majority of the existingdecision making techniques fail to provide.

6. Conclusions

With the advancement of technology, newer materials, energysources, manufacturing strategies, decision making procedures

and management techniques are also being concurrently devel-oped. The developed decision making framework providesconsistent guidance to the process engineer facing problemwith NTM process selection decisions for varying machining

applications. It incorporates an exhaustive database of 25NTM processes ranging from the basic to hybrid methods. Itassists the process engineer while providing an easy access to

choose various work materials, machining operations and pro-cess characteristics to finally select the best feasible NTM pro-cess along with its ideal parametric settings on a single

platform. The process engineer can obtain almost accurateresults while processing a large amount of data in less time.The graphical user interface facilitates a trouble-free interac-tion with the process engineer. It provides flexibility to shortlist

any number of process characteristics for evaluation of the fea-sible NTM processes. Thus, it can identify the best NTM pro-cess for a given machining application as all the considered

NTM processes pass through a three-stage screening process.The priority weights for various process characteristics are cal-culated using entropy method to avoid subjectivity in judg-

ments, thus making the evaluation procedure entirelyunbiased. It not only selects the best NTM process for a givenmachining operation, but also provides the ideal parametric

settings of the finally selected process. However, a fine-tuning of those parametric settings is often recommendeddepending on the end product requirements and technicalspecifications of the NTM setup. The future scope may include

development of a web-based NTM process selection system forglobal access.

References

[1] El-Hofy H. Advanced machining processes: nontraditional and

hybrid machining processes. New York: The McGraw-Hill

Companies; 2005.

[2] Pandey PC, Shan HS. Modern machining processes. New Delhi:

Tata McGraw-Hill Publishing Com., Ltd.; 1981.

[3] Yurdakul M, Cogun C. Development of a multi-attribute

selection procedure for non-traditional machining processes. Proc

Inst Mech Eng, Part B: J Eng Manuf 2003;217:993–1009.

[4] Chakraborty S, Dey S. Design of an analytic-hierarchy-process-

based expert system for non-traditional machining process selec-

tion. Int J Adv Manuf Technol 2006;31(5–6):490–500.

[5] Chakrabarti S, Mitra S, Bhattacharyya B. Development of a

management information system as knowledge base model for

ramework for non-traditional machining processes selection, Ain Shams Eng J

12 K. Prasad, S. Chakraborty

machining process characterization. Int J Adv Manuf Technol

2007;34(11–12):1088–97.

[6] Chakraborty N, Dey S. QFD-based expert system for non-

traditional machining processes selection. Exp Syst Appl 2007;32

(4):1208–17.

[7] Chakladar ND, Chakraborty S. A combined TOPSIS-AHP-

method-based approach for non-traditional machining processes

selection. Proc Inst Mech Eng, Part B: J Eng Manuf 2008;222

(12):1613–23.

[8] Edison RC, Jehadeesan R, Raajenthiren M. Web-based knowl-

edge base system for selection of non-traditional machining

processes. Malaysian J Comput Sci 2008;21(1):45–56.

[9] Chakladar ND, Das R, Chakraborty S. A digraph-based expert

system for non-traditional machining processes selection. Int J

Adv Manuf Technol 2009;43(3–4):226–37.

[10] Sugumaran V, Muralidharan V, Hegde BK. C RT Intelligent

process selection for NTM – a neural network approach. Int J Ind

Eng Res 2010;1(1):87–96.

[11] Chakraborty S. Applications of the MOORA method for decision

making in manufacturing environment. Int J Adv Manuf Technol

2011;54(9–12):1155–66.

[12] Sadhu A, Chakraborty S. Non-traditional machining processes

selection using data envelopment analysis (DEA). Exp Syst Appl

2011;38(7):8770–81.

[13] Ic YT. An experimental design approach using TOPSIS method

for the selection of computer-integrated manufacturing technolo-

gies. Rob Comput Integr Manuf 2012;28(2):245–56.

[14] Karande P, Chakraborty S. Application of PROMETHEE–

GAIA method for non-traditional machining processes selection.

Manage Sci Lett 2012;2:2049–60.

[15] Chatterjee P, Chakraborty S. Advanced manufacturing systems

selection using ORESTE method. Int J Adv Oper Manage 2013;5

(4):337–61.

[16] Chatterjee P, Chakraborty S. Non-traditional machining pro-

cesses selection using evaluation of mixed data method. Int J Adv

Manuf Technol 2013;68(5–8):1613–26.

[17] Temucin T, Tozan H, Valıcek J, Harnicarova M. A fuzzy based

decision support model for non-traditional machining process

selection. Tech Gaz 2013;20:787–93.

[18] Prasad K, Chakraborty S. A decision-making model for non-

traditional machining processes selection. Decision Sci Lett

2014;3:467–78.

[19] Roy MK, Ray A, Pradhan BB. Non-traditional machining

process selection using integrated fuzzy AHP and QFD tech-

niques: a customer perspective. Prod Manuf Res 2014;2

(1):530–49.

Please cite this article in press as: Prasad K, Chakraborty S, A decision guidance f(2015), http://dx.doi.org/10.1016/j.asej.2015.10.013

[20] Temucin T, Tozan H, Vayvay O, Harnicarova M, Valıcek J. A

fuzzy based decision model for non-traditional machining process

selection. Int J Adv Manuf Technol 2014;70:2275–82.

[21] Madic M, Petkovic D, Radovanovic M. Selection of non-

conventional machining processes using the OCRA method.

Serbian J Manage 2015;10(1):61–73.

[22] Rao RV, Kalyankar VD. Optimization of modern machining

processes using advanced optimization techniques: a review. Int J

Adv Manuf Technol 2014;73:1159–88.

[23] Rao RV. Decision making in the manufacturing environment

using graph theory and fuzzy multiple attribute decision making

methods. London: Springer-Verlag; 2007.

Kanika Prasad is working as a DST research

fellow in Production Engineering Depart-

ment, Jadavpur University, Kolkata. She

graduated in Mechanical Engineering from

Sikkim Manipal Institute of Technology,

Sikkim in 2010. She completed her Master of

Engineering in Production Engineering from

Jadavpur University, Kolkata in 2013. She

has worked with Sikkim Manipal Institute of

Technology as an Assistant Professor for

almost a year and taught various undergrad-

uate subjects, like Operation Research, The-

ory of Machines, Manufacturing Process etc. At present, her research

interests include applications of quality function-based method for

manufacturing decision-making.

Shankar Chakraborty is a Professor in Pro-

duction Engineering Department of Jadavpur

University. He had been graduated in 1986

from University of Calcutta and had obtained

his postgraduate degree from Jadavpur

University in 1989. He had been awarded with

Ph.D. (Engg.) degree from Jadavpur Univer-

sity in 1994. His research interests include

applications of different multi-criteria

decision-making methods in manufacturing

environment, control chart pattern recogni-

tion, and development of MIS and ERP sys-

tems for diverse engineering applications. He has guided several M.E.

and Ph.D. (Engg.) theses, and published numerous papers in interna-

tional journals. He is also a regular reviewer of several journals of

international repute.

ramework for non-traditional machining processes selection, Ain Shams Eng J