Optimization of modern machining processes using ... - Springer Link

Int J Adv Manuf Technol (2014) 73:1159–1188 DOI 10.1007/s00170-014-5894-4

ORIGINAL ARTICLE

Optimization of modern machining processes using advanced optimization techniques: a review R. Venkata Rao & V. D. Kalyankar

Received: 22 October 2012 / Accepted: 23 April 2014 / Published online: 15 May 2014 # Springer-Verlag London 2014

Abstract Thorough literature review of various modern machining processes is presented in this paper. The main focus is kept on the optimization aspects of various parameters of the modern machining processes and hence only such research works are included in this work in which the use of advanced optimization techniques were involved. The review period considered is from the year 2006 to 2012. Various modern machining processes considered in this work are electric discharge machining, abrasive jet machining, ultrasonic machining, electrochemical machining, laser beam machining, micro-machining, nano-finishing and various hybrid and modified versions of these processes. The review work on such a large scale was not attempted earlier by considering many processes at a time, and hence, this review work may become the ready information at one place and it may be very useful to the subsequent researchers to decide their direction of research. Keywords Electric discharge machining . Abrasive jet machining . Ultrasonic machining . Electrochemical machining . Laser beam machining . Micro-machining . Nano-finishing . Parameters optimization . Advanced optimization techniques

1 Introduction The modern machining processes are now replacing the conventional machining processes rapidly for many applications due to their significant advantages which are proving beneficial to a greater extent to the present industrial scenario. The R. V. Rao (*) : V. D. Kalyankar Department of Mechanical Engineering, S.V. National Institute of Technology, Surat, Gujarat 395007, India e-mail: [email protected]

requirements of industrial products have started increasing and new materials are getting introduced which are very hard in nature and difficult to cut by conventional machining processes. Successful machining of such materials by the modern machining processes have added significant lifeline to the industrial growth and given new dimensions to the quality of components. The various modern machining processes getting widely used in the industries are: electric discharge machining (EDM), abrasive jet machining (AJM), ultrasonic machining (USM), electrochemical machining (ECM) and laser beam machining (LBM) including various modified versions of these processes. These processes work on a particular principle by making use of certain properties of materials which makes them most suitable for some applications and at the same time put some limitations on their use. These processes involve large number of respective process variables (also called as process parameters) and selection of exact parameters setting is very crucial for these highly advanced machining processes which may affect the performance of any process considerably. Due to involvement of large number of process parameters, random selection of these process parameters within the range will not serve the purpose. The situation becomes more severe in case if more number of objectives are involved in the process. Such situations can be tackled conveniently by making use of optimization techniques for the parameters optimization of these processes. During the past two decades, few researchers had developed some good quality advanced optimization techniques such as genetic algorithm (GA), simulated annealing (SA), artificial bee colony (ABC), ant colony optimization (ACO), particle swarm optimization (PSO), teaching-learning-based optimization (TLBO), etc. which had already proved their significance in the field of parameters optimization of various manufacturing processes. Modelling and optimization of parameters of a modern machining process through advanced optimization techniques is now proving as a milestone for the

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manufacturing industries and hence various researchers are trying to make use of these advanced optimization techniques for different processes. In this paper, efforts are made to identify all such works in which the use of various advanced optimization techniques are involved during years 2006–2012 for the parameters optimization of modern machining processes. In the next section, importance of each process is described separately including the important input and output parameters involved. It is then followed by the thorough literature review based on parameters optimization of respective processes.

2 Review of modern machining processes The modern machining processes considered in this work are categorised in the following subsections separately as EDM, AJM, USM, ECM, LBM, micro-machining and nanofinishing processes, respectively. 2.1 Review of electric discharge machining process parameters optimization The concept of EDM process was found long back to 100 years but its real practical use was implemented for industries during 1950s and since then it is slowly progressing. However, in the 1980s, it was coupled with the computer numerical control approach and that brought significant developments in EDM process and made it suitable for machining of complex shapes and hard to cut materials. Presently, the modern industries started using the EDM process for machining of various materials such as alumina particle reinforced material, high-speed tool steel, titanium aluminide alloy, nickel-based super alloy, various metal matrix composites (MMC) and ceramics, etc. which cannot be machined easily by the conventional machining processes. In recent years, some modifications took place in the basic EDM process and various versions of EDM process were developed such as WEDM, powder-mixed EDM, micro-EDM, diesinking EDM, etc. in order to make the process more efficient and also to meet the varying needs of modern industries. The EDM process involves a large number of input parameters such as open circuit voltage, pulse-on time, pulse-off time, discharge current, duty cycle, die-electric flushing pressure, electrode polarity, electrode material, pulse wave form and frequency, inter-electrode gap, dielectric fluid, powder concentration, powder size, wire feed rate, wire tension, noload voltage and servo voltage, capacitance, etc. Kumar et al. [1] had given the historical background of EDM process with the importance of various input parameters of EDM process. These process parameters can affect the various responses of the process like MRR, surface roughness, tool wear rate, radial over cut, cutting width, cutting speed, crater size, corner

Int J Adv Manuf Technol (2014) 73:1159–1188

deviation, etc. Close control over the setting of these parameters is very much essential for the success of this process. The process has to maximise the material removal rate (MRR) and at the same time the tool wear rate is to be minimised. Shifting the current from low level to high level can significantly increase the MRR but it can also increase the surface roughness. The electrodes with different surface areas have more electrode corner wastage and less MRR. Settings of these parameters through trail attempts may satisfy only one objective at a time and may lead to either reduced production rate or poor quality level. Hence, an optimum parameters setting is the requirement for the process which can satisfy all the conflicting objectives. This can be achieved by making use of suitable optimization technique, however, efforts should be such that to obtain the global optimum solution. In the past, similar efforts were carried out by several researchers and the same is presented in Table 1 in the chronological order from 2006 onwards and only that research is highlighted in this work in which the use of suitable optimization technique was adopted for the EDM process and its versions. From the past research, it is observed that a lot of research was carried out recently, related to the parameters optimization of EDM process. Most of the available research reveals that Taguchi method was used by the large community of researchers to get the optimum combination of parameters and to study their effects on the process responses. However, different materials were used for machining in most of those cases and also different parameters were considered for investigations. Attempts were also observed in the past to use some advanced optimization techniques such as GA and its versions, SA, PSO, ABC, TLBO, BBO, etc. The approach of hybridisation of two algorithms was also used by few researchers such as ANN integrated with Taguchi method or GA. Research on various versions of EDM process such as WEDM, micro-EDM, powder-mixed EDM, etc. was also reported in the past. However, it is observed that the process constraints were not taken into account in almost all those cases discussed above. There is a need to independently study the effect of each input parameter on various responses of the process and thereby developing a mathematical model for each response considering all the parameters along with the process constraints. Combined effect of various input parameters on the process response is also required to be studied simultaneously. More and consistent use of recent optimization techniques is also required especially to attempt multiobjective problems of EDM process. 2.2 Review of abrasive jet machining process parameters optimization AJM is one of the most versatile modern machining processes and is equally suitable for machining of ductile as well as

Process version

WEDM

EDM

EDM

CNC-based WEDM

EDM

Powder-mixed EDM

EDM

EDM

Year/author(s)

2006/Chiang and Chang [69]

2006/Keskin et al. [70]

2006/Chang et al. [71]

2006/Manna and Bhattacharyya [72]

2006/Lin et al. [73]

2007/Kansal et al. [74]

2007/Dhar et al. [75]

2007/Tzeng and Chen [76]

MRR and gap current was mostly affected by the open gap voltage

Taguchi method and Gauss elimination method

Tool steel SKD11

Aluminium alloy and SiCP composite

AISI-D2 die steel

SKH-57 high-speed steel

Al/SiC-MMC

SKD–61 hot-working mold steel

Taguchi method

Linear programming, DOE

– Machining rate

– MRR Tool wear rate – Radial over cut – Precision and accuracy

Taguchi-fuzzy-based approach

Taguchi method

– MRR – Electrode wear rate – Surface roughness

– Machining and reaming amount – Surface roughness – Electrode corner loss – MRR – MRR – Surface roughness – Gap current – Spark gap

Surface roughness had shown increasing trend with increase in discharge duration Electrodes with different surface areas had shown big electrode corner wastage and less MRR

Multiple regression and design of experiments (DOE) Taguchi method and data mining approach

– Surface roughness

Steel

Mathematical model in terms of process variables was not given

Optimum conditions for the objectives were not given

The machining rate was mostly influenced by peak current and concentration of silicon powder

MRR and electrode wear rate was significantly affected by machining polarity and peak current

Mathematical models were not produced in their work

Grey relational analysis

– Surface removal rate – Surface roughness

– Cutting radius of work piece – On-time of discharging – Off-time of discharging – Arc-on time – Arc-off time – Voltage – Wire feed – Water flow – Power – Pulse time – Spark time – Pulse-on time – Pulse-off time – Open discharge voltage – Electrodes of different shapes – Pulse peak voltage – Pulse-on time – Pulse-off time – Peak current – Wire feed rate – Wire tension – Spark gap set voltage – Polarity – Peak current – Auxiliary current – Pulse duration – No-load voltage – Servo voltage – Peak current – Pulse-on time – Pulse-off time – Concentration of powder – Gain – Nozzle flushing – Current – Pulse-on time – Gap voltage – Open circuit voltage – Pulse time – Duty cycle – Discharge current – Powder concentration

Remarks

Al2O3 particle-reinforced material

Optimization technique(s) involved

Objective(s)

Important input variables considered

Work material

Table 1 Summary of EDM and allied processes parameters optimization review

Int J Adv Manuf Technol (2014) 73:1159–1188 1161

Process version

WEDM

Micro-Wire-EDM

EDM

EDM

Micro-EDM

EDM

EDM

Year/author(s)

2007/Mahapatra and Patnaik [77]

2008/Yan and Fang [78]

2008/Tzeng [79]

2008/Salman and Kayacan [80]

2008/Sundaram et al. [81]

2008/ Markopoulos et al. [82]

2008/Chiang [83]

Table 1 (continued)

Mild steel, alloyed steels (C45 and 100Cr6), micro-alloyed steel and dual-phase steel Al2O3 + TiC mixed ceramic

A2 tool steel

DIN 1.2379 grade cold work steel

Tool steel SKD11

–

D2 tool steel

Work material

– Discharge current – Pulse-on time – Duty factor – Open discharge voltage

– Regular distance for electrode lift – Time interval for electrode lift – Powder size – Discharge current – Pulse duration – Pulse frequency – Wire speed – Wire tension – Dielectric flow – Servo motor torque – Inertia – Radius of the feeding roller – Armature voltage, current, resistance and inductance of the wire feed motor – Circuit voltage – Pulse duration – Duty cycle – Pulsed peak current – Powder concentration – Regular distance for electrode lift – Time interval for electrode lift – Powder size – Current – Pulse-on time – Pulse-off time – Arc voltage – Capacitance – % of peak power used for ultrasonic vibration – Feed rate – Machining time – Pulse current – Pulse duration


Genetic expression programming (GEP), Taguchi method Taguchi method

Artificial neural network (ANN)


– MRR – Tool wear


Response surface methodology (RSM)

Taguchi method

– Surface roughness – Geometrical accuracy

– MRR – Electrode wear ratio – Surface roughness

GA-based fuzzy logic controller

GA


– Wire tension – Wire feed

– MRR – Surface finish – Cutting width

Objective(s)

Discharge current and duty factor were the significant factors affecting the MRR

Research was carried out on five specific grades of steels

Capacitance and ultrasonic vibration was shown as the significant variables affecting the MRR

Different materials were used as electrodes for investigation

Optimised results obtained in the work had reduced the geometrical variation of the machined product by about 28 %

Faster transient response with less error was achieved by the proposed fuzzy logic controller

Attempt was made for multiobjective, multi-variable nonlinear optimization problem

Remarks

1162 Int J Adv Manuf Technol (2014) 73:1159–1188

EDM

WEDM

WEDM

WEDM

Rotary EDM

EDM

Dry EDM

Die-sinking EDM

EDM

2008/Saha et al. [87]

2008/Kung and Chiang [88]

2009/Rao and Pawar [85]

2009/ Chattopadhyay et al. [89]

2009/Rao et al. [90]

2009/Saha and Choudhury [91]

2009/Yang et al. [92]

2009/Habib [93]

Al/SiC MMC

Steel

EN32 Mild steel

Ti6Al4V, HE15, 15CDV6 and M-250

EN-8 carbon steel

Oil hardened and nitrided steel (OHNS)

Aluminium oxide-based ceramic

Tungsten carbide–cobalt composite

Tungsten carbide and cobalt composites

WC/Co cemented carbide

Die-sinking EDM

2008/Kanagarajan et al. [86]

BD3 steel

Die-sinking EDM

2008/Assarzadeh and Ghoreishi [84] 2008/Kanagarajan et al. [85]

Work material

Process version

Year/author(s)

Table 1 (continued)

– Gap voltage – Discharge current – Pulse-on time – Duty factor – Air pressure – Spindle speed – Discharge current – Source voltage – Pulse-on time – Pulse-off time – Pulse-on time – Pulse peak current – Average gap voltage

MRR

– Current – Period of pulses – Source voltage – Pulse current – Pulse-on time – Electrode rotation – Flushing pressure – Pulse current – Pulse-on time – Electrode rotation – Flushing pressure – Pulse-on time – Pulse-off time – Peak current – Capacitance – Peak current – Pulse-on time – Duty factor – Wire speed – Pulse-on time – Pulse-off time – Peak current – Servo feed setting – Peak current – Pulse-on time – Rotational speed of tool electrode – Peak current – Voltage

RSM

ANN and GA


– MRR Tool wear rate – Response gap size

Taguchi method and linear regression analysis

– MRR – Electrode wear ratio – Surface roughness

ANN and SA algorithm

ABC

– Cutting speed

– MRR – Surface roughness

RSM


RSM

ANN

– Cutting speed – Surface roughness

– MRR Surface roughness – Tool wear rate

Non dominated sorting genetic algorithm-II (NSGA-II)

ANN and augmentedLagrange multiplier algorithm RSM




Objective(s)


All the objectives were not attempted simultaneously

Individual models for MRR and surface roughness were not presented

Performance of the process was influenced by the type of material Improvement of upto 350 % was shown in case of MRR by shifting the current from low level to high level

Empirical models were developed for prediction of output process parameters

MRR was affected by pulse-on time and duty factor whereas surface roughness was affected by peak current Surface roughness was considered as constraint

Peak current and capacitance had significantly increased both the objectives

Non-dominated Pareto set of solution was produced

Statistical models in terms of process characteristics were developed

Surface roughness was kept as the process constraint

Remarks


Die-sinking EDM

EDM

2010/Pradhan and Biswas [102]

2010/Patel et al. [103]

Die-sinking EDM

Die-sinking EDM

Micro-EDM

2009/Pradhan and Bhattacharyya [98] 2010/Maji and Pratihar [99]

2010/Patowari et al. [101]

EDM

2009/Patel et al. [97]

WEDM

Mild steel

Die-sinking EDM

2009/Taweel [96]

2010/Chen et al. [100]

Titanium super alloy Ti6Al-4V

Powder-mixed EDM

2009/Kung et al. [95]

Al2O3–SiCw–TiC

AISI D2 steel

W-Cu P/M sintered electrodes to apply coating on workpiece

Pure tungsten

Al2O3/SiCw/TiC ceramic composite

CK45Steel

Cobalt-bonded tungsten carbide (94WC-6Co)

Medium carbon steel

EDM

2009/Sohani et al. [94]

Work material

Process version

Year/author(s)

Table 1 (continued)

ANN and RSM

Adaptive network-based fuzzy inference system ANN integrated with SA approach

ANN

ANN and neuro-fuzzy approach

– MRR – Tool wear rate – Overcut – MRR – Surface roughness – Surface roughness – Cutting velocity

– Material transfer rate – Average layer thickness

– MRR – Tool wear rate – Radial overcut

Taguchi method and grey relation analysis

RSM and trust region method



RSM

– MRR – Tool wear rate

RSM

RSM

– Surface roughness – MRR – Tool wear rate

– % volume fraction of SiC – Discharge current – Pulse-on time – Pulse-off time – Tool area – Various shapes of copper tools – Discharge current – Pulse-on time – Grain size – Concentration of aluminium powder particles – % of TiC – Peak current – Dielectric flushing pressure – Pulse-on time – Discharge current – Pulse-on time – Duty cycle – Gap voltage – Peak current – Pulse-on time – Dielectric flushing pressure – Peak current – Pulse-on time – Pulse-duty factor – Pulse-on time – Pulse-off time – Arc-off time – Servo voltage – Wire feed rate – Wire tension – Water pressure – Compaction pressure – Sintering temperature – Peak current – Pulse-on time – Pulse-off time – Discharge current – Pulse duration – Duty cycle – Voltage – Discharge current – Pulse-on time


– MRR – Electrode wear ratio

Objective(s)


Mathematical models of the objectives were not presented

Discharge current was the most significant factor affecting the MRR and radial overcut

Effect of process variables on the responses was clearly presented through several plots

Pulse-on time was shown as the most significant factor

Optimum parameter setting was suggested to satisfy all the objectives simultaneously Final optimised result was not produced

Surface roughness was dominated by pulse-on time

Peak current was reported as the most significant factor affecting both the objectives

Showed that both the objectives were increased due to increase in grain size of powder particles

Circular tool shape was shown as the best tool shape for higher MRR and lower tool wear rate

Remarks


EDM

WEDM

EDM

2011/ Prabhu and Vinayagam [110] 2011/Sanchez et al. [111]

2011/Amini et al. [114]

Die-sinking EDM

2011/Joshi and Pande [109]

WEDM

EDM

2010/Chen et al. [108]

2011/Kondayya and Krishna [113]

Micro-EDM

2010/ Somashekhar et al. [107]

Die-sinking EDM

AISI-1045 steel

Abrasive-mixed EDM process

2010/Kumar et al. [106]

2011/Maji and Pratihar [112]

Inconel-825 material

EDM

2010/Ponappa et al. [105]

TiB2 nano-composite ceramic

Hard metal alloys and MMC

Mild steel

AISI P20 mold steel

ZrO2 ceramic

Aluminium

EN-24 tool steel

Microwave-sintered magnesium nano compoite

Ti–6Al–4V alloy

EDM

2010/Kao et al. [104]

Work material

Process version

Year/author(s)

Table 1 (continued)

– Peak current level – Pulse-on time – Pulse-off time – Peak current – Pulse-on time – Pulse-duty factor – Pulse-on time – Pulse-of time – Wire feed rate – Wire tension – Power – Time off – Voltage

– Current – Discharge voltage – Duty cycle – Discharge duration – Machining parameters

– Duty cycle – Gap voltage – Discharge current – Open voltage – Pulse duration – Duty factor – Pulse-on time – Pulse-off time – Voltage gap – Servo speed – Concentration of abrasive powder in dielectric fluid – Peak current – Pulse-on time – Duty factor – Gap voltage – Capacitance – Feed rate – Speed – Peak current – Pulse duration



ANN and GA

Taguchi method

Integrated approach of finite element method (FEM), ANN and GA Taguchi method


– MRR

– MRR – Electrode wear rate – Surface roughness – Crater size – MRR – Tool wear rate – Surface roughness

Genetic programming and NSGA-II

Combination of Taguchi method, ANN and GA

– MRR – Surface roughness – MRR – Surface roughness

GA, NSGA-II

RSM

Taguchi method

– Surface finish – Hole taper

– MRR – Electrode wear rate – Surface roughness – MRR – Surface roughness

Taguchi method and grey relation analysis


– Electrode wear ratio – MRR – Surface roughness

Objective(s)

Shown that the achieved optimization results were in

Pareto optimal solution was presented

Simple and more flexible inversion models were developed Pareto-optimal front of solutions was produced

Attempt was made to optimise the surface roughness upto nano level

Both input variables had significantly affected the MRR and surface roughness Mathematical models in terms of input–output variables were not presented

More variation in MRR was observed due to capacitance compared to others

Effect of abrasive particles was very significant compared to other variables

Both objectives were mainly affected by servo speed and pulse-on time

Improvement of result in the range of 12–19 % for the various responses was shown

Remarks


WEDM

WEDM

EDM

Micro-EDM

WEDM

WEDM

Micro-EDM

Micro-EDM

2011/Tzeng et al. [115]

2012/Rao and Kalyankar [116]

2012/Singh [117]

2012/Ay et al. [118]

2012/Yang et al. [119]

2012/Lingadurai et al. [120]

2012/Azad and Puri [121]

2012/Mahardika et al. [122]

WEDM

Process version

Year/author(s)

Table 1 (continued)

Polycrystalline diamond

Titanium alloy

AISI 304 stainless steel

Tungsten

Nickel-based Inconel 718 super alloy

6061Al/Al2O3p/20P aluminium MMC

Oil hardened and nitrided steel (OHNS)

Pure tungsten

Work material

– Pulse-on time – Frequency – Voltage – Current – Charge voltage – Capacitance – Vibration of tool electrode – Discharge-off time

– Pulse-on time – Pulse-off time – Arc-off time – Servo voltage – Wire feed rate – Wire tension – Water pressure – Voltage – Pulse-on time – Pulse-off time – Wire feed

– Servo – Wire feed rate – Pulse-on time – Pulse-off time – Arc-off time – Servo voltage – Wire feed rate – Wire tension – Water pressure – Pulse-on time – Pulse-off time – Peak current – Servo feed setting – Pulse current – Pulse-on time – Duty cycle – Gap voltage – Tool electrode lift time – Discharge current – Pulse duration


Taguchi method

Taguchi method

– MRR – Tool wear rate – Overcut – MRR – Tool electrode wear – Surface roughness – Productivity

DOE

DOE


– Hole taper ratio – Hole dilation

– MRR – Kerf width – Surface roughness

Taguchi method and grey relational analysis

– MRR – Tool wear rate – Surface roughness

Combination of RSM, ANN and SA algorithm

TLBO

– Cutting speed

– MRR – Average roughness – Corner deviation

RSM, back-propagation neural network and GA



Objective(s)

Voltage had mainly affected the MRR, pulse-on time had affected the kerf width whereas wire feed rate had affected the surface roughness. Voltage and current was shown as the significant variables while attempting the individual objectives Increased the MRR upto 66 % by applying vibration on the tool electrode

Discharge current was shown as more efficient on the performance than pulse duration Optimised results were implemented in a production line of industry producing the semiconductor components

Among all the process parameters, pulse current had shown the strongest effect on all the objectives

Surface roughness was considered as constraint

good agreement with the experimental result The integrated approach was shown as the effective tool for optimization of WEDM process parameters

Remarks


Dry micro-EDM, Oil microEDM

Die-sinking EDM

WEDM

Powder-mixed EDM

Die-sinking EDM

2012/Bharti et al. [127]

2012/Kumar and Agarwal [128]

2012/ Bhattacharya et al. [129]

2012/Puertas and Luis [130]

Copper–iron–graphite MMC

Hot-pressed B4C, cobaltbonded tungsten carbide ceramic

EN31, H11, and high carbon high chromium (HCHCr) die steel

High-speed steel (M2, SKH9)

Nickel-based Inconel 718 alloy

γ-titanium aluminide alloy

SK3 carbon tool steel

Micro-EDM

2012/Paul et al. [126]

Aluminium

Micro-WEDM

2012/ Somashekhar et al. [124] 2012/Lin et al. [125]

Work material

Polycrystalline diamond microtools

Process version

2012/Fonda et al. [123]

Year/author(s)

Table 1 (continued)

Taguchi method

ANN and NSGA

Taguchi method and NSGAII

Taguchi method

DOE and multiple linear regression analysis

ANN, GA and grey relational analysis

– Overcut



– MRR – Tool wear rate – Surface finish

– Surface roughness – Volumetric electrode wear – MRR – MRR – Wheel wear rate

RSM

A combined parameters setting was not produced to satisfy all the objectives simultaneously

Under the similar process conditions. MRR for H11 was lower than EN31 but significantly higher than HCHCr steel

Pareto optimal solution was presented

Mathematical models in terms of input–output variables were not presented

Capacitance was shown as the most influencing variable affecting the performance Peak current was shown as the most significant variable affecting the process performance Overcut was significantly affected by discharge capacitance

– MRR – Overcut – Surface roughness – Electrode wear – MRR – Overcut SA algorithm

Discharge-off time and arcing sensitivity had shown the largest effect on machining speed


Remarks

– Arcing sensitivity – Discharge voltage – Servo voltage – Discharge current – Wire speed – Gap voltage – Capacitance – Feed rate – Peak current – Pulse-on time – Pulse-off time – Electrode rotation speed – Open circuit voltage – Discharge capacitance – Pulse frequency – Pulse-on time – Shape factor – Pulse-on time – Discharge current – Duty cycle – Gap voltage – Flushing pressure – Electrode lift time – Pulse peak current – Pulse-on time – Pulse-off time – Wire feed – Wire tension – Flushing pressure – Workpiece material – Dielectric fluid – Electrode material – Pulse-off time – Pulse-on time – Current – Type of suspended powder – Intensity – Pulse duration – Duty cycle Open-circuit voltage – Dielectric flushing pressure – Peak current – Pulse-on time


Objective(s)



Particle reinforced aluminium alloy matrix composite

WEDM 2012/Shahali et al. [134]

DIN 1.4542 stainless steel alloy

– Pulse-on time – Peak current – Gap voltage – % volume of SiC – Power – Time-off – Voltage – Number of finish passes

Die Steel EDM

2012/Mukherjee and Chakraborty [133]

Micro-GA

Biogeography-based optimization (BBO) algorithm

– Surface roughness – Surface crack density – White layer thickness – MRR – Tool wear rate – Gap size – Surface finish – Surface roughness – Thickness of white layer

Surface roughness and thickness of white layer was reduced by 52 and 67 %, respectively

Comparision of BBO algorithm was made with GA, ACO and ABC algorithms

RSM and NSGA-II MRR – Surface – roughness

– Pulse-on time – Pulse-off time – Discharge current – Peak current – Pulse-on duration EN-8 carbon steel

– Pulse-off time – Grit size

Electric discharge diamond grinding die-sinking EDM 2012/Shrivastava and Dubey [131] 2012/Baraskar et al. [132]

Table 1 (continued)

Work material

Important input variables considered Process version Year/author(s)

Objective(s)


Improved the MRR by about 76 % and wheel wear rate by about 31 % Results were produced directly using the optimization tool box


Remarks

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brittle materials including metals and non-metals with material thickness ranging from 1 to 100 mm. Nowadays, it is getting widely used for the machining of some important materials such as titanium, Inconel, brass, aluminium, rubber, glass and several types of composites such as fibre reinforced composites, particle-reinforced aluminium alloy (MMC), etc. In order to meet the varying demands of industries, several modified versions of AJM process are also available such as AWJM, WJM, etc. in which either abrasive particles are mixed with compressed air or mixed with water or even only water is impacted as per the requirements. Based on the type of process the large number of input parameters of the process are categorised as hydraulic parameters, abrasive parameters, cutting parameters, mixing and acceleration parameters, etc. [2]. The hydraulic parameters include pump pressure, air/ water flow rate, orifice diameter, etc. whereas the abrasive parameters include abrasive particle diameter and shape, size distribution, type of abrasive, abrasive mass flow-rate, etc. Similarly, the cutting parameters includes the jet traverse speed, impact angle, number of passes, stand-off distance, etc. and the mixing parameters include focus diameter and length. The efficiency of the process depends on the selection of optimum parameter setting and hence each of these input parameters may affect outputs of the process such as MRR, surface roughness, depth of cut, kerf width, etc. To avoid the complexity of the process due to large number of process parameters, there is a strong need to develop the rigorous mathematical models to form the relation between the input and output process parameters. These models can be effectively used to obtain the optimum parameters setting by using advanced optimization techniques. In the present work, all such efforts carried out recently by various researchers are summarised in Table 2 by keeping main focus on the development of process models and optimization of process parameters. It is observed that various versions of GA and ANN were used by various researchers in most of the cases for the parameters optimization of AJM and its allied processes. Use of few other algorithms such as SA, TLBO and Taguchi method was also observed. However, overall use of advanced optimization techniques in the field of AJM process was very less and still some software-based approaches and traditional techniques are getting used for parameters optimization of AJM process. Moreover, the development and use of mathematical models was also less, hence, in future, researchers will find lot of scope to carry out research on various aspects of parameters optimization of AJM process. 2.3 Review of ultrasonic machining process parameters optimization USM process is finding its wide applications in the production of various automotive and electronics components due to its

Glass, Al-6061-T6

–

AWJM

Abrasive jet polishing

AWJM

AJM

WJM

2008/Caydas and Hascalik [138]

2008/Tsai et al. [139]

2009/Parikh and Lam [2]

2010/Rao et al. [140]

–

SKD61 mold steel

AA 7075 aluminium alloy

6063-T6 aluminium alloy

AWJ cutting

2008/Srinivasu and Babu [137]


Titanium

AWJM

AWJ cutting

–

WJM

2007/Jegaraj and Babu [136]

Glass, Al-6061-T6

AJM

2007/Jain et al. [135]

Work material

Process version

Author(s)

– Mass flow rate of abrasive particles – Mean radius of abrasive particles – Velocity of abrasive particles – Water jet pressure at the nozzle exit

Neuro-genetic approach

Taguchi method and ANN

Taguchi method

– Depth of cut – Surface roughness

– Surface finish

– MRR Specific energy

SA algorithm

SA algorithm

ANN

Neuro-fuzzy approach

– Depth of cut – Kerf width – Surface roughness

– Abrasive mass flow rate – Focus diameter – Traverse rate – Pump pressure – MRR

GA

– MRR

GA

GA

– MRR

– Mass flow rate of abrasive particles – Mean radius of abrasive particles – Velocity of abrasive particles – Water jet pressure at the nozzle exit – Diameter of nozzle – Traverse rate of nozzle – Stand-off distance – Water jet pressure at the nozzle exit – Diameter of nozzle – Feed rate of nozzle – Mass flow rate of water – Mass flow rate of abrasives – Water jet pressure – Abrasive flow rate – Traverse rate – Orifice size – Focusing tube size – Water pressure – Abrasive flow rate – Jet traverse rate – Traverse speed – Water jet pressure – Stand-off distance – Abrasive grit size – Abrasive flow rate – Additive type – Abrasive particle material – Abrasive particle diameter – Orifice diameter – Depth of cut – Workpiece-abrasive material combination factor


– MRR – Specific energy

Objective(s)


Table 2 Summary of AJM and allied processes parameters optimization review

Power consumption was considered as a process constraint

Surface roughness was considered as a process constraint. Objectives for brittle and ductile materials were attempted separately

Results of neural network was shown as better than other technique

Improved the surface finish approximately by 87 %

Water jet pressure was shown as the dominant variable affecting the surface roughness to greater extent

Optimised depth of cut was confirmed almost nearer to the desired value

Suggested minimum experiments for developing the models



Surface roughness was considered as a process constraint. Objectives for brittle and ductile materials were attempted separately

Remarks


C-17 steel Transformation induced plasticity sheet steels

AJM

AWJM

AWJM

2011/Ke et al. [145]

2011/Wenjun et al. [146] 2011/Kechagias et al. [147]

Abrasive water jet cutting

Silicon wafers

AWJM

2011/Kok et al. [144]

2011/Iqbal et al. [148]

7075 aluminium alloy composite

AWJM

2011/Zain et al. [143]

AISI 4340 and aluminium 2219



AWJM

2011/Zain et al. [142]


Titanium

AWJM

AWJM

Work material

Process version

2011/Zain et al. [141]

Author(s)

Table 2 (continued)

– Diameter of nozzle – Traverse rate of nozzle – Stand-off distance – Water jet pressure at the nozzle exit – Diameter of nozzle – Feed rate of nozzle – Mass flow rate of water – Mass flow rate of abrasives – Traverse speed – Water jet pressure – Stand-off distance – Abrasive grit size – Abrasive flow rate – Traverse speed – Water jet pressure – Stand-off distance – Abrasive grit size – Abrasive flow rate – Traverse speed – Water jet pressure – Stand-off distance – Abrasive grit size – Abrasive flow rate – Depth of cut – Particle size and particle weight fraction – Mesh size – Impact angle – Stand-off distance – Jet pressure – Platform revolution – Jet pressure – Traverse speed – Sheet thickness – Nozzle diameter – Stand-off distance – Traverse speed – Jet pressure – Cutting feed rate – Abrasive mixing rate – Sheet thickness


Integrated approach of SA and GA

GA and SA algorithms

GEP

Taguchi method

Arbitrary Lagrange–Euler algorithm Taguchi method





– Cutting depth

– Surface roughness – % proportion of striation free area – Width of cut – Production rate

ANOVA and DerringerSuich multi-criteria decision modelling approach

Integrated approach of ANN and SA


– Kerf geometry – Surface roughness

SA algorithm


– MRR

Objective(s)

Cutting feed and thickness were shown as the highly influential parameters

Work was focused more on the software-based approach Nozzle diameter was shown as the most influencing variable affecting both the objectives

Results obtained by using GEP were compared with the experimental results Optimum variable setting was produced to reduce the surface roughness to 0.118 μm

Same model was attempted by GA and SA algorithms. Number of iterations taken by their approach was comparatively more than the other advanced optimization techniques.


Remarks


Suggested the improvement in results by replacing the membership function distributions Fuzzy logic combined with GA – Depth of cut

Surface roughness was considered as a process constraint. Objectives for brittle and ductile materials were attempted separately TLBO algorithm – MRR

Power consumption was considered as a process constraint TLBO – MRR

– Water jet pressure at the nozzle exit – Diameter of nozzle – Feed rate of nozzle – Mass flow rate of water – Mass flow rate of abrasives – Mass flow rate of abrasive particles – Mean radius of abrasive particles – Velocity of abrasive particles – Nozzle diameter – Jet pressure – Abrasive mass flow rate – Jet traverse speed Titanium

Glass, Al-6061-T6


AWJM

AJM

AWJM

2012/Pawar and Rao [149]


2012/Vundavilli et al. [150]

Optimization technique(s) involved Objective(s) Work material Process version Author(s)

Table 2 (continued)

1171


Remarks


several advantages like distortion free operation, no thermal and chemical effects on the product and can be reasonably used for machining of non-conductive material. The concept of USM was introduced before 1950s but it was getting used for a particular few assignments. However, subsequently it was proved a good process for machining of various aluminabased ceramic composites, titanium alloys, cobalt-based super alloys and other similar ceramics and carbides. Ultrasonic drilling, ultrasonic abrasive machining, ultrasonic cutting, USM-assisted turning, rotary-USM are some of the modified USM-based processes used for special purpose operation. Singh and Khamba [3] had presented the overall review of USM process and discussed the importance of each input parameter and its effect on performance of the process. The input parameters of the process are associated to the properties of tool, properties of abrasives, ratings of the machine, etc. Some of the important parameters are amplitude of vibration, frequency of vibration, abrasive type, grit size of the abrasive, slurry concentration, slurry temperature, static feed force, work material, tool material and power. These parameters need to be carefully handled in order to control various conflicting responses of the process namely MRR, tool wear rate, surface roughness, production accuracy such as hole oversize, out-of-roundness, etc. Use of optimization techniques for this purpose can be proved as a useful tool which can definitely improve the performance of the process by satisfying various conflicting objectives of the process. In the present work, efforts are made to identify the use of advanced optimization techniques for the parameters optimization of USM process in the past, which will help to decide the future scope of the process. Table 3 presents the summary of review of such research works. From the above survey, it is observed that only few researchers had attempted the use of optimization techniques for the parameters optimization of USM process. Very few mathematical models were observed in the literature describing the relation between input and output parameters. Also, the work was carried on very less number of materials like titanium alloy with involvement of few input parameters. Hence, there is a scope for the use of advanced optimization techniques to improve the performance of USM process by considering various input parameters and attempting multi-objective task of the process. Attempt can also be made to obtain optimum parameters setting by using different work materials and abrasive types. 2.4 Review of electrochemical machining process parameters optimization Several sophisticated parts including the micro-components were manufactured with the help of ECM process since 1950 and its potential was proved time to time through several researches. Burger et al. [4] quoted that more than 1,200

Process version

Ultrasonic drilling

Ultrasonic drilling

Ultrasonic drilling

Ultrasonic drilling

USM-assisted drilling

USM

USM

USM

Author(s)

2006/Jadoun et al. [151]




2007/Dvivedi and Kumar [155]

2007/Jain et al. [135]

2007/Singh and Khamba [156]

2009/Singh and Khamba [157]

Titanium and its alloys

Titanium and its alloys

Tungsten carbide

Titanium alloy

Alumina-based ceramics

Hot-pressed aluminabased ceramic composites



Work material

Taguchi method

Taguchi method

Taguchi method

Taguchi method

Taguchi method

GA

Taguchi method

Combined approach of Taguchi method, dimensional analysis and Buckingham’s pie theorem

– Cutting ratio

– Tool wear rate

– MRR

– Hole oversize – Out-of roundness – Conicity – Surface roughness

– MRR

– MRR

– Tool wear rate

– Work material – Tool material – Grit size of the abrasive – Power rating – Slurry concentration – Work material – Tool material – Grit size of the abrasive – Power rating – Slurry concentration – Work material – Tool material – Grit size of the abrasive – Power rating – Slurry concentration – Work material – Tool material – Grit size of the abrasive – Power rating – Slurry concentration – Work piece – Grit size – Slurry concentration – Power rating – Tool material – Amplitude of vibration – Frequency of vibration – Mean diameter of abrasive grains – Concentration of abrasive particles in slurry – Static feed force – Power rating – Tool type – Slurry concentration – Slurry type – Slurry temperature – Slurry size – Tool material – Power rating – Slurry type – Slurry temperature – Slurry concentration


Objective(s)


Table 3 Summary of USM and allied processes parameters optimization review

About 7 % improvement in result was shown

Power rating had contributed more compared to others for improvement of MRR

Surface roughness was considered as a process constraint

Surface roughness was significantly affected slurry concentration and grit size compared to other parameters

All the individual factors except work material and power rating had significant effect on hole oversize

Similar approach with different objectives

The tool materials were ranked in order of increasing cutting ratio as: HSS < HCS < TC

Remarks


USM

USM

USM

Ultrasonic drilling

2009/Kumar and Khamba [158]

2009/Kumar et al. [159]

2010/Kumar and Khamba [160]

2009/Singh and Gill [161] 2009/Gill and Singh [162] 2010/Gill and Singh [163]

USM

Ultrasonic vibration cutting

2010/Rao et al. [140]

2011/Liu et al. [165]

2011/Gauri et al. [166] USM

USM

2010/Rao et al. [164]

Ultrasonic drilling

Ultrasonic drilling

Process version

Author(s)

Table 3 (continued) Important input variables considered

Cobalt-based super alloy

– Slurry grit size – Type of abrasive slurry – Abrasive size and concentration – Tool material – Power rating of the machine Titanium – Tool material – Abrasive material – Slurry concentration – Abrasive grit size – Power rating of the ultrasonic machine Pure titanium – Tool material – Abrasive type – Grit size – Power rating – Slurry concentration Porcelain ceramic – Depth of penetration – Time for penetration Alumina ceramic – Depth of penetration – Time for penetration Sillimanite ceramic – Depth of penetration – Time for penetration – Penetration rate Tungsten carbide – Amplitude of vibration – Frequency of vibration – Mean diameter of – abrasive grains – Concentration of abrasive particles in slurry – Static feed force Tungsten carbide – Amplitude of vibration – Frequency of vibration – Mean diameter of abrasive grains – Concentration of abrasive particles in slurry – Static feed force SiC monocrystal material – Cutting speed – Feed rate – Work piece rotational speed – Wave amplitude Titanium – Tool material – Grit size of the abrasive slurry

Work material

Taguchi method

Taguchi method and dimensional analysis

Adaptive neuro-fuzzy inference system Adaptive neuro-fuzzy inference system Adaptive neuro-fuzzy inference system

– Tool wear rate

– MRR

– MRR

ABC, HS and PSO algorithm

SA algorithm

GA

Taguchi method

– MRR

– MRR

– Tangential force


– MRR

– MRR

Taguchi method



Objective(s)

The models and variables involved were not explained clearly

Optimization aspects of the example and the results were not illustrated in detail

Obtained improved result over GA

Improved MRR was given by all the three optimization techniques over GA

Fuzzy logic-based models were designed to simulate the MRR Similar approach with different work material

A micro-model was developed for prediction of MRR

Tool material and power rating were identified as the important contributing factors mostly affecting the tool wear rate

Power rating was shown as the most contributing factor with about 30 % contribution

Remarks



Taguchi method

TLBO algorithm


– MRR USM 2012/Rao and Kalyankar [116]

Tungsten carbide

– Power rating of machine – Tool material – Abrasive type – Grit size – Power rating – Slurry concentration – Amplitude of vibration – Frequency of vibration – Mean diameter of abrasive grains – Concentration of abrasive particles in slurry – Static feed force Titanium

Process version Author(s)

Table 3 (continued)

Work material


Objective(s)


Remarks

Improved result was given by TLBO algorithm over other six advanced optimization algorithms

1174

scientific papers were published related to ECM process since 1970. Such successful use of ECM process is possible due to its various advantages out of which the important is that any conductive material can be machined effectively irrespective of its mechanical properties. The major application of the process includes turbine blades, rifle bores, aeronautical parts and such similar components. Some of the important input parameters involved in the process is: electrolyte concentration, electrolyte flow rate, applied voltage, inter-electrode gap, pulse on/off ratio, voltage frequency, tool vibration frequency, etc. These input parameters can significantly affect several performances of the process such as MRR, surface roughness, radial overcut, tool wear rate, heat affected zone (HAZ), cylindricity error, etc. Various hybrid versions of the process are ECDM, ECMM, electrochemical polishing, electrochemical honing, electrochemical deburring, etc. Various models were developed by the researchers which were helpful to the industries to make appropriate decisions. Summary of all such works related to optimization aspects of ECM process from year 2006 onwards is included in this paper and presented in Table 4. Works related to ECM and ECDM processes are included in Table 4, whereas works related to other hybrid processes of ECM are summarised in short at the end of this subsection. Research on optimization aspects of some other variants of ECM process was also carried out by several researchers in the past. Electro-chemical honing process parameters optimization was carried out by Dubey [5–7] and Dubey et al. [8]. Parameters optimization of electrochemical polishing process was carried out by Lee and Shin [9], whereas, effort was made by Lee et al. [10] and Hung et al. [11] for the parameters optimization of electrochemical micro-deburring process. Similarly, ECMM process parameters optimization was attempted by Munda and Bhattacharyya [12], Munda et al. [13] and Malapati and Bhattacharyya [14]. From the above review, it is observed that most of the research was carried out on the machining of various composites of aluminium silicon carbides, various grades of steels, etc. using ECM and its hybrid processes. Some researchers had also developed the mathematical models in the form of relations between input output parameters. In most of the cases, Taguchi method and RSM was observed as the common method, whereas use of some advanced optimization techniques was also observed such as GA, PSO, BBO, ABC, TLBO, etc. Use of ANN was also made by some researchers in the past for modelling and optimization of the parameters of ECM process. 2.5 Review of laser beam machining process parameters optimization Past few decades had witnessed the wide use of laser technology in various engineering and medical fields. Large number

Process version

ECDM

ECDM

ECM

ECM

ECM

ECDM

ECM

ECM

ECM

ECM

ECM

Author(s)

2006/Sarkar et al. [167]

2006/Mediliyegedara et al. [168]

2007/Jain and Jain [169]

2008/Rao et al. [170]

2008/Asokan et al. [171]

2008/Chak and Rao [172]

2009/Senthilkumar et al. [173]



2009/Ramarao et al. [176]

2010/Datta and Das [177]

– Tool feed rate – Electrolyte flow velocity – Applied voltage – Tool feed rate – Electrolyte flow velocity – Applied voltage – Current – Voltage – Flow rate – Gap – Supply – voltage – Duty – factor – Electrolyte conductivity – Applied voltage – Electrolyte concentration – Electrolyte flow rate – Tool feed rate – Applied voltage – Electrolyte concentration – Electrolyte flow rate – Tool feed rate – Applied voltage – Electrolyte concentration – Electrolyte flow rate – Tool feed rate – Current – Voltage – Flow rate – Gap between work piece and tool – Current – Voltage – Electrolyte flow rate

–

Hardened steel

Aluminium silicon carbide

Al/15 % SiCp composites

Al/20 %SiCp composites

LM25 Al/10 %SiC composites

Sintered aluminium oxide

Hardened steel

–

RSM

RSM

RSM and NSGA

NSGA

Genetic-fuzzy approach

GA

– MRR – Surface roughness – MRR – Surface roughness – MRR – Surface roughness – MRR – Surface finish


ANN and grey relational analysis

PSO

GA

ANN

RSM


– Volume of – material removed

– Dimensional accuracy – Tool life – MRR – MRR – Surface roughness

– MRR – Radial overcut – HAZ thickness – Electro chemical pulse – Electro chemical discharge pulse – Spark pulse – Arc pulse – Short circuit pulse Geometrical inaccuracy

– Applied voltage – Electrolyte concentration – Inter-electrode gap – Peak voltage – Average voltage – Peak current – Average current

Silicon nitride ceramics

Mild steel

Objective(s)


Work material

Table 4 Summary of ECM and allied processes parameters optimization review

The accuracy level obtained in the model was comparatively less

Accuracy in prediction of responses was tested for ten different test cases

Almost similar approach with variation of SiC % in work material and slight variation in optimization approach

Process constraints on temperature, choking and passivity were considered Process constraints on temperature, choking and passivity were considered Proposed to integrate the ANN models with some advanced techniques for further improvement Effect of stationary electrode and a rotary electrode was investigated

Applied voltage was shown as more significant parameter affecting all the objectives Five different types of pulses in the ECDM process were identified and attempted

Remarks


EN-8 steel


Copper

ECM

ECDM

ECMM

2011/Samanta and Chakraborty [179]

ECM

ECM

2012/Abuzied et al. [183]

2012/Mukherjee and Chakraborty [184]

EN-8 steel

–

Silicon nitride


ECDM

Hybrid ECM and EDM

EN-8 steel

ECM,


2012/Panda and Yadava [182]

EN-31 steel

ECM

2011/Chakradhar and Venugopal [180]

2Cr13 steel

ECM

2010/Li and Niu [178]

Work material

Process version

Author(s)

Table 4 (continued)

– Electrolyte concentration – Electrolyte flow rate – Applied voltage – Inter-electrode gap – Applied voltage – Electrolyte concentration – Inter-electrode gap – Supply voltage – Spark on-time – Electrolyte concentration – Applied voltage – Feed rate – Electrolyte flow rate – Electrolyte concentration – Electrolyte flow rate – Applied voltage – Inter-electrode gap

– Inter-electrode gap – Applied voltage – Initial machining gap – Cathode feed rate – Electrolyte temperature – Pressure difference – Electrolyte concentration – Electrolyte flow rate – Applied voltage – Inter-electrode gap – Applied voltage – Electrolyte concentration – Inter-electrode gap – Pulse on/off ratio – Machining voltage – Electrolyte concentration – Voltage frequency – Tool vibration frequency – Electrolyte concentration – Feed rate – Applied voltage


ABC algorithm

– MRR – Radial overcut – HAZ thickness – MRR – Radial overcut

ANN

Biogeography-based optimization algorithm

– MRR – Overcut

ANN, grey relational analysis and FEM

TLBO algorithm

TLBO algorithm

Grey relation analysis


– MRR – Radial overcut – HAZ thickness – MRR – Surface roughness

– MRR – Overcut – Cylindricity error – Surface roughness – MRR – Overcut

ABC algorithm

– MRR – Overcut

ABC algorithm

Neural network


– Machining accuracy

Objective(s)

Over 500 % improvement was shown in MRR over the initial parameters setting Most of the work is carried out directly by using MATLAB toolbox Results of BBO algorithm were compared with GA and ABC algorithm

Obtained better results over ABC algorithm

Feed rate was shown as the most significant parameter affecting the performance

Compared the results of ABC algorithm with other methods such as RSM, steepest ascent method, etc.

From 3125 possible machining parameter combinations, 657 optimised parameter combinations were discovered

Remarks



of operations can be performed on the materials using laser beam such as cutting, machining, welding, cladding, etc. In the present work, the focus is kept mainly on the use of laser beam for machining operations such as LBM, laser milling, laser turning, laser drilling, etc. Various micro-machining operations are also performed with the help of laser beam such as laser micro-machining, laser micro-drilling, laser micro-milling, laser micro-grooving, etc. The laser machining processes involves large number of important input parameters, each of which plays very important role on the performance of the process. Some of these parameters are: laser power, pulse frequency, pulse intensity, lamp current, air pressure, pulse width, workpiece rotational speed, overlapping, scanning speed of the laser source and scanning modality, etc. Most of the performance measures of the process are mainly related to the dimensional accuracy and quality of the product. Hence, the manufacturers are now in the extreme need of proper parameters setting of the process which can satisfy their conflicting objectives of the process. This can be achieved by optimising the parameters of the process with the help of advanced optimization techniques. In the literature, it is observed that various researchers tried to achieve the optimum parameters setting for the chosen process associated with laser machining. All such important works are summarised in this paper which will be very useful to the subsequent researchers working on the optimization aspects of laser machining processes. Earlier, Dubey and Yadava [15] had carried out the review work on LBM process, but it was the overall review of all the works associated with LBM process up to year 2008 with thorough description of the process. However, in the present paper, the work is restricted only to the research involving optimization aspects in the field of laser machining processes and all the recent literatures are included in this paper as explained in Table 5. The literature summarised in Table 5 is related to the various laser machining and laser micro-machining processes. However, it is also observed that considerable work was done on laser beam cutting in the past and hence that work also is included in this paper. Some of the important works related to optimization aspects of laser beam cutting are carried out by Almeida et al. [16], Li et al. [17], Dubey and Yadava [18–20], Tsai et al. [21], Caydas and Hascalik [22], Rao and Yadava [23], Li and Tsai [24], Tsai and Li [25], Sharma et al. [26], Syn et al. [27], Adelmann and Hellmann [28], Chen et al. [29], Sharma and Yadava [30, 31], Pandey and Dubey [32–34], etc. From the above literature survey, it is observed that researchers had worked on various advanced materials such as titanium aluminide, AISI H13 steel, zirconium oxide, aluminium titanate, aluminium oxide ceramic, aluminium–magnesium alloy, tungsten-molybdenum high-speed steel, etc. using different versions of LBM process. It is also observed that in most of the cases, the optimum process parameters were obtained using RSM. However, in few cases use of GA,

1177

PSO was also reported. Effect of various input parameters on the process performance was also discussed, however, a clear relation between the input and output parameters was not confirmed almost in all the cases. Different materials were processed with different approaches. 2.6 Review of micro-machining process parameters optimization Micro-machining processes are also finding their use in large number of applications, especially in high quality requirements during the production of micro-sized parts. The major application areas include products related to medical fields and aerospace components. In such cases, the MRR will be comparatively less but more consideration will be given to surface roughness and dimensional accuracy. Two important micro-machining processes which are getting widely used in industries are micro-drilling and micro-milling. Some research work was observed in the literature related to parameters optimization of these processes and the same is summarised below for each process. 2.6.1 Micro-drilling process Sen and Shan [35] used the hybrid approach of ANN, desirability function and GA for modelling and optimising the micro-drilling operation performed by electrojet drilling process on the nickel base super alloy and attempts were made to optimise the MRR and roundness error. Yoon et al. [36] used Taguchi methods and RSM to analyze the performance of micro drill-bits during drilling on printed circuit boards. Guu et al. [37] used the Taguchi-based experimental design coupled with FEM for determining the machining parameters in order to optimise the stress concentration during the micro-drilling of titanium alloy. Chen et al. [38] optimised the tool life and surface roughness, during micro-drilling of polymethyl methacrylate polymer, using Taguchi method. The important process parameters considered were: coated deposition, spindle speed and feed rate and the work had shown that the least tool wear and best holes quality was obtained by using TiAlN-coating drills. It is observed that comparatively less work was carried out in the past for the micro-drilling process parameters optimization and most of them were based on the Taguchi method. Hence, attempt can be made in future to carry out research on micro-drilling process. 2.6.2 Micro-milling process Chern and Chang [39] carried out the investigation on micromilling assisted with vibration cutting and studied the geometrical shape and machining accuracy of aluminium alloy components. Cardoso and Davim [40] investigated the

Laser-based laser milling process

2007/Campanelli et al. [187]

Laser drilling

2008/Ghoreishi and Nakhjavani [192]

2008/Samant et al. [193] Laser surface processing

Laser micromachining

2008/Dhara et al. [191]

2009/Dhupal et al. [190] LBM



Laser-assisted turning Aluminium oxide ceramic

2007/Chang and Kuo [186]

Porous alumina ceramic

Stainless steel sheets

– Peak power – Pulse time – Pulse frequency – Number of pulses – Gas pressure – Focal plane position – Pulse width – Repetition rate – Scanning speed

Taguchi method

– Inter dendritic porosity – Grain size

Neural network

– Depth of groove – Height of recast layer

Neural network and GA

Optimised parameter setting was produced to satisfy all the objectives simultaneously

ANN coupled with GA

– Deviation of taper – Deviation of depth

– Hole entrance diameter – Circularity of entrance and exit holes – Hole exit diameter – Taper angle of the hole

Recommended the lower cutting speed for achieving smaller deviation in both taper angle and depth

– Deviation of taper angle RSM – Deviation of depth

Scanning speed had played a vital role in increasing the grain size whereas the pulse repetition rate

Attempt was also made for multiobjective optimization

A graphical method was used for searching the optimal combination of parameters from the set of all possible combinations

Optimised parameter setting was produced to satisfy all the objectives simultaneously

A set of optimum parameters setting was not produced in their work

Identified the rotational speed as the most significant parameter affecting both the objectives

Lamp current had very significant effect on both the responses compared to other controlling parameter

Remarks

– Upper width ANN and RSM – Lower width – Depth of the trapezoidal microgroove

DOE

– Depth of removed material – Surface roughness

RSM

Taguchi method

– HAZ thickness – Micro-hole taper

– Pulse frequency – Pulse width – Lamp current – Assist air pressure


– Surface roughness – MRR

Objective(s)


– Rotational speed – Feed – Depth of cut – Pulsed frequency Aluminium–magnesium – Laser power alloy – Frequency – Overlapping – Pulse width – Scanning speed of laser source – Scanning modality Aluminium titanate – Lamp current – Pulse frequency – Pulse width – Assist air pressure – Cutting speed Aluminium titanate – Lamp current – Pulse frequency – Pulse width – Assist air pressure – Cutting speed Aluminium titanate – Lamp current – Pulse frequency – Pulse width – Assist air pressure – Cutting speed Tungsten-molybdenum high- – Lamp current speed steel – Frequency – Pulse width – Air pressure

Zirconium oxide

Laser micro-drilling

2006/Kuar et al. [185]

Work material

Process version

Author(s)

Table 5 Summary of LBM and allied processes parameters optimization review


LBM

2009/Karazi et al. [194]

Laser micro-milling


Laser micro-turning

2012/Kasman and Saklakoglu [200]

2012/Ganguly et al. [201]

2012/Kibria et al. [202]

Alumina ceramic

Zirconium oxide

AISI H13 steel

High carbon steel

Titanium aluminide

2010/Biswas et al. [198] Laser micro-drilling


Titanium aluminide


2011/Panda et al. [199]

Titanium aluminide

Objective(s)

ANN


Taguchi method

Taguchi method and grey relational analysis

RSM

– Hole circularity at exit – Hole taper

– HAZ – Hole circularity – MRR – Surface roughness – Milling depth – Hole taper – HAZ width

– Surface roughness – Deviation in depth – Laser beam power – Pulse frequency – Workpiece rotational speed – Assist air pressure – Feed rate

RSM


– Pulse width and focal length – Lamp current – Pulse frequency – Air pressure – Thickness of the job – Pulse width and focal length – Lamp current – Pulse frequency – Air pressure – Thickness of the job – Width – Number of pulses – Assist gas flow rate – Supply pressure – Scan speed – Scan direction – Frequency – Fill spacing – Lamp current – Pulse frequency – Air pressure – Pulse width

RSM


ANN and PSO

ANN


– Lamp current – Pulse frequency – Air pressure – Thickness of the job

– Surface roughness – Volume error

– Power – Dimensional accuracy – Pulse repetition frequency – Traverse speed


AISI H13 hardened tool steel – Pulse intensity – Scanning speed – Pulse frequency

Micro-channels of glass

Work material


2009/Ciurana et al. [195] Laser micromachining

Process version

Author(s)

Table 5 (continued)

Results can be further improved by making use of suitable advanced optimization technique

Shown the improvement of about 16 and 8 % from the initial condition to optimal condition in case of hole taper and HAZ width respectively

Shown that scan speed plays a very significant role on the process performance


The work carried earlier was again attempted using ANN

Extended their earlier work by considering pulse width and focal length also as the process variables

Pulse frequency and air pressure was shown as the most significant parameters affecting the hole taper and hole circularity respectively


ANN modelling technique was shown as more accurate than that of DOE technique

was most significant factor for inter dendritic porosity

Remarks


Mathematical models in terms of input–output variables were not presented Taguchi method and grey relational analysis

RSM, grey relational grades Attempt was also made for multiand desirability functions objective optimization

– Taper – Spatter – HAZ – Hole taper – HAZ width

Wait time and modulation frequency were identified as the most significant and significant parameters for MRR RSM – MRR – Taper

Aluminium matrix/silicon carbide particulate MMC

Alumina

2012/Padhee et al. [206] Laser drilling

2012/Kuar et al. [207]

– Laser power – Modulation frequency – Gas pressure – Wait time – Pulse width – Pulse width – Number of pulses – Concentration of SiCp – Lamp current – Pulse frequency – Air pressure – Pulse width Al/Al2O3 MMC

machining of aluminium alloy using micro-milling operation in order to optimise the surface roughness. However, no advanced optimization technique was used by them to optimise the results. Chiu and Weigh [41] used GA to optimise the MRR and tool life during the investigation on micro-milling process. Prediction models were developed in their work and a set of non-dominated solutions was produced in order to satisfy both the objectives. Periyanan et al. [42] used Taguchi method to maximise the MRR of micro-end milling operation by optimising spindle speed, feed rate and depth of cut. Mian et al. [43] explored the significant cutting parameters of micro-milling process by experimenting on Inconel 718 nickel alloy. Optimization of the parameters was carried out using Taguchi method. Natarajan et al. [44] developed the models for MRR and surface roughness of a micro-end milling operation using RSM and obtained the optimal parameter setting of spindle speed, feed rate and depth of cut using desirability function approach in order to achieve multipleresponse optimization. RSM was used by Saedon et al. [45] to develop the tool life model of micro-milling hardened tool steel. Thepsonthi and Ozel [46] used RSM and PSO technique to minimise the surface roughness and burr formation during machining of titanium alloy by micro-end milling process. The non-dominated Pareto set of solutions was produced by them in order to satisfy both the objectives under consideration. Some research works were reported in the literature related to micro-milling process, but most of those were attempted using Taguchi method and RSM. It is observed that use of the advanced optimization techniques were not involved in the past, except GA and PSO, for the parameters optimization of micro-milling process.

2.7 Review of nano-finishing process parameters optimization


Taguchi method – Aspect ratio – HAZ – Circularity 2012/Canel et al. [204]

Laser milling process AISI H13 hardened tool steel – Scanning speed – Pulse intensity – Pulse frequency Laser drilling PVC – Laser frequency – Laser fluence – Wavelength 2012/Teixidor et al. [203]

2012/Ghosal and Manna LBM [205]

PSO – Surface roughness – Dimensional accuracy

Comparatively low R2 value was obtained for the aspect ratio model

Optimization technique(s) involved Objective(s) Important input variables considered Work material Process version Author(s)

Table 5 (continued)

A non-dominated sets of solution was produced


Remarks

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In some critical applications, the surface finish in the range of nano level is desired. Due to lot of technological developments, this could be achieved by high-end finishing processes which are referred as nano-finishing processes. There are various nano-finishing processes available out of which few are finding regular use in the industries. Jain [47] had given the overview of various abrasive-based nano-finishing processes by involving various hybrid processes related to nanofinishing. The working principle of these processes was described along with the importance of various parameters of respective processes. In another work, Jain [48] described various magnetic field assisted abrasive-based nano-finishing processes with analysis of the surface texture and surface roughness plots obtained for each processes under consideration.


Parameters optimization of these processes is very essential as the cost associated to achieve such a nano level finish is very high and the trial-based parameters settings may increase the manufacturing cost of the product to a greater extent. In the present work, it is observed that some researchers had carried out their work related to the parameters optimization of nanofinishing processes. Some of those important nano-finishing processes considered in this work are: AFM, MAF, MRAFF and ELID process. In the following subsections, the review on parameters optimization of each of these processes is covered. 2.7.1 Abrasive flow machining process Mali and Manna [49] had given the review of research related to application of AFM processes. A combination of ANN and GA was used by Tavoli et al. [50] for modelling and optimising the parameters of AFM process. Walia et al. [51] used the hybrid version of AFM process referred as centrifugal force assisted abrasive flow machining (CFAAFM) for machining of brass material and attempted to optimise MRR and scatter of surface roughness using Taguchi method whereas the input parameters considered in their work were: rotational speed of rectangular rod, extrusion pressure, and grit size. In another work, Walia et al. [52] extend their work to carry out multi-objective optimization of the process by optimising MRR, % improvement of surface finish and scatter of surface roughness simultaneously using the combined approach of utility theorem and Taguchi method. Singh et al. [53] studied the effect of various input parameters of AFM process, including the effect of magnetic field, on the surface roughness using Taguchi method. A surface roughness model was developed by Jain et al. [54] for the AFM process and used GA for the parameters optimization of the process. Reddy et al. [55] used RSM for developing the relationship between the input parameters of CFAAFM process in terms of MRR and % improvement in surface finish during machining of cast aluminium alloy components. Sankar et al. [56] carried out the experimental investigation on AISI 1040 and AISI 4340 materials using the hybrid process referred as drill bit-guided abrasive flow finishing process and compared the results with abrasive flow finishing process. Pawar et al. [57] used the PSO and SA algorithms for the parameters optimization of AFM process. Mali and Manna [58] investigated the finishing of Al/15 wt% SiCp-MMC workpiece using AFM process and used Taguchi method for developing mathematical models and optimization of MRR and surface finish. 2.7.2 Magnetic abrasive finishing process Jain et al. [54] used GA for optimising the surface roughness model of a MAF process. The input parameters involved in their work were: mean diameter of the magnetic abrasive

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particles, relative velocity between magnetic abrasive particles workpiece, volume ratio of ferromagnetic material in the magnetic abrasive powder, input current and finishing time whereas the aim of the work includes the difference between initial and final surface roughness values. Taweel [59] carried out the research on 6061 Al/Al2O3 composite using the hybrid process by combining the electrochemical turning process and MAF process. The modelling and analysis of MRR and surface roughness was carried out using RSM and the input process parameters considered were: magnetic flux density, applied voltage, tool feed rate and workpiece rotational speed. Yang et al. [60] carried out the research on AISI304 stainless steel using MAF process in order to optimise surface roughness and material removal weight. Mulik and Pandey [61] used RSM and Taguchi method for optimising the surface roughness and MRR during machining of AISI 52100 hardened steel using ultrasonic-assisted MAF process. The authors had shown that the MRR was significantly affected by weight of the abrasives whereas the surface roughness was influenced by the abrasive mesh number. 2.7.3 Magnetorheological abrasive flow finishing process Jung et al. [62] used the concept of penalized multi-response Taguchi method to optimise the parameters of a wheel-type magneto rheological finishing process used for machining of Al2O3–TiC made hard-disk slider surface. Two objectives were involved in their work related to MRR and surface roughness and the response weights and constraint conditions were considered while attempting the multi-objective task by including the weighting loss factor and the severity factor in the Taguchi method. RSM was used by Das et al. [63] to study the effects of process parameters of MRAFF process on its finishing performance such as surface finish and % improvement in the MRR. Das et al. [64] extended their work to investigate the out-of-roundness of the internal surface of the tube using RSM. In another work, Das et al. [65] attempted the similar work for nano-finishing of flat workpiece by considering four important process parameters as: hydraulic extrusion pressure, number of finishing cycles, rotational speed of the magnet and volume ratio of CIP/SiC and considered RSM for the process parameters optimization. 2.7.4 Electrolytic in-process dressing process A neuro-fuzzy network was used by Babu et al. [66] for the parameters optimization of ELID process. Experimental investigation on grinding of Al/SiC composite was carried out with a particular aim of achieving higher MRR and surface quality. Various input parameters considered were: number of pass, work speed, depth of cut, current duty ratio and voltage; however, the mathematical models in terms of input output parameters were not presented in their work. Subsequently,

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adaptive neuro-fuzzy interference system was used by Babu et al. [67] for the optimization of surface roughness. In another work, grey relation analysis was used by Babu et al. [68] to carry out optimization aspect of the similar work. It is observed from the above research review of various nano-finishing processes that comparatively less research was carried out in the past related to parameters optimization of nano-finishing processes. In most of those cases, RSM and Taguchi method were preferred by the researchers. However, use of some advanced optimization techniques such as GA, SA and PSO was observed in case of AFM process and use of only GA was reported in case of MAF process. Also the research was restricted to very few materials. Hence, there is a considerable scope in the field of nano-finishing processes to carry out research on various materials and also to make use of advanced optimization techniques for the optimization of parameters of these processes.


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& 3 Conclusions In this work, the optimization aspects of the widely used modern machining processes such as EDM, AJM, USM, ECM, LBM, micro-machining processes, nano-finishing processes and the allied versions are considered. The thorough literature review related to parameters optimization of these processes from year 2006 to 2012 is made and summarised. The work materials used by several researchers are also highlighted including the various input and output parameters. A critical remark on various research works is also presented and following observations are made based on this review work. & &

&

&

&

Lot of research work was conducted on EDM process as compared to other modern machining processes. In case of EDM and its allied processes, majority of the optimization related works were based on RSM and Taguchi methods. Some works were also attempted using GA, ANN and their modified versions. However, use of some advanced optimization techniques such as BBO and TLBO was also observed in few research works. Research on variety of materials was made using EDM process which includes large number of ceramics, composites, tool steels and various alloy steels including aluminium. In case of AJM and its allied processes, use of GA, ANN and their hybrid approaches were used considerably. However, integration of SA with other techniques was also observed in few cases. Majority of the research works related to this process group was focused on aluminium and its alloys. Maximum work related to parameters optimization of USM and its allied processes was attempted in the

&

literature using Taguchi method. In some cases, use of adaptive neuro-fuzzy inference system and GA was observed whereas in few cases use of other advanced optimization techniques such as SA, ABC, HS, PSO and TLBO was also observed. Research on titanium and its alloys and some alumina-based ceramics was observed in most of the cases. A consistent research on any particular material was not observed in case ECM and its allied processes. Also most of the optimization works were based on RSM, ANN and grey relational analysis with few cases of some other advanced optimization techniques. In case of LBM and its allied processes, research on several high quality materials was observed that include aluminium titanate, titanium aluminide, zirconium oxide and several alumina-based ceramics. But in many of the cases, optimization aspects were attempted using only RSM and ANN. Comparatively very less research work was conducted in the case of micro-machining and nano-finishing processes. Some important micro-machining processes are covered in this review work which includes micro-drilling and micro-milling. Optimization aspects of selected nanofinishing processes are also presented. The modern machining processes described in this paper have many industrial applications. Due to the potential advantages, these processes are now preferred in various leading industrial sectors such as automobile, electronic, aeronautical, aerospace, nuclear engineering, medical and biomedical fields, etc. for production of various complicated parts.

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