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Modern Optimization Techniques for Advanced Machining Processes – A Review
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ScienceDirect Materials Today: Proceedings 18 (2019) 3034–3042
www.materialstoday.com/proceedings
ICMPC-2019
Modern Optimization Techniques for Advanced Machining Processes – A Review Rohit Surebana*, Vinayak N Kulkarnib, V. N. Gaitondec a,b,c
School of Mechanical Engineering, KLE. Technological University, Hubballi
Abstract A review about the optimization techniques used for modern machining techniques are analyzed in this paper. The chief focus is kept on the modern optimization techniques employed to obtain the optimal machining parameters and during both nonconventional machining processes. The various Non-traditional machining techniques analyzed for this study are the Wire Electro Discharge Machining (WEDM), Laser Beam Machining (LBM), Abrasive Water Jet Machining (AWJM) and Electro Discharge Machining (EDM). These machining processes were analyzed with respect to different modern optimization techniques. The most widely used, frequently used and rarely used optimization techniques for the mentioned non-conventional machining processes are identified. The paper provides an overall idea about the optimization techniques and helps to choose the suitable technique for the researchers. © 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the 9th International Conference of Materials Processing and Characterization, ICMPC-2019 Keywords: optimization: Optimization Techniques, Wire Electro Discharge Machining (WEDM), Laser Beam Machining (LBM), Abrasive Water Jet Machining (AWJM) and Electro Discharge Machining (EDM)
1.
Introduction
With the advent of new and smart materials, a question of machining such materials with precision and efficiency are being raised in the industries. These materials are used widely in biomedical applications, automobile, aerospace, defence, and various other sectors. The machining of such materials with conventional technique does not meet the customer requirement because the finishing quality of the component is very minimal. This is because
* Corresponding author. Tel.:+919481050491 E-mail address: [email protected] 2214-7853© 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the 9th International Conference of Materials Processing and Characterization, ICMPC-2019
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of the difficulties in machining such materials; certain materials like NiTi work harden during machining via conventional machining method this result in accelerated flank wear, notching and cratering. Hence certain special and advanced machining processes are to be adopted for efficient machining. Industries adopting such machining practices have an added lifeline for its growth as they have the better quality of components. The various advanced (non- conventional) machining processes are Laser Beam Machining (LBM), Wire Electrode Discharge Machining (WEDM), Electrode Discharge Machining (EDM), Abrasive Water Jet Machining (AWJM), Water Jet Machining (WJM), Electromechanical Machining (ECM) and Ultrasonic Machining (USM) and also various custom-made versions of these processes. All the above processes work according to their own principle which makes them suitable for machining certain materials and may also have a certain limitation of their use. All these processes have various input parameters and the selection of these parameters plays a crucial role as it affects the characteristics and performance of the machined component. Vinayak N Kulkarni et al [1] analysed the effects of WEDM process parameters on NiTi alloys. Random selection of such input parameters will not serve the purpose and might worsen the situation. To avoid such unconventional way of selecting the input parameters certain optimization techniques are used. Many optimization techniques have been developed for good quality of advanced machined components; some of them are Taguchi's optimization Techniques, Genetic Algorithm (GA), Simulated Annealing (SA), Fuzzy logic, etc. these techniques have already proven their significance in other parameter optimization of the various manufacturing process. Industries have been adopted certain of these techniques for their manufacturing optimization. In this paper, an effort is made to identify all such advanced machining process and the optimization techniques in recent years. 2.
Advanced machining process parameters optimization
The advanced machining processes considered in the present work are Wire Electric Discharge Machining (WEDM), Electric Discharge Machining (EDM), Laser Beam Machining (LBM), and Water Jet Machining. 2.1. Optimization of Wire Electric Discharge Machining (WEDM) process parameters Wire Electric Discharge Machining (WEDM) is one of the advanced machining processes in which highly intricate shapes can be machined. It is one of a non-contact type of machining process. The WED machine uses wire of predefined sizes as the electrode and die-electric fluid for machining purpose. A very minute gap is maintained between the wire electrode and the work piece, a high voltage is passed through the wire, a dielectric field is generated between the electrode and the work piece which is greater than the dielectric constant of the workpiece to be machined, thus eroding the material. The dielectric fluid used acts a coolant and also it washes away the debris formed on the machined surface. Various types of electrode wires can be used for the machining of the WEDM process like copper, zinc coated copper, brass, and zinc coated brass wires. The output response for the WEDM process varies with the type of wire used in the process. Hence the optimal input parameters such as pulse on/off time, servo voltage and wire feed also change with the type of wire being used. The input parameters that are studied for this process are Wire Feed (WF), pulse off time (Toff), Pulse on time (Ton) and the Servo Voltage (SV) and the most studied output responses are Material Removal Rate (MRR) and Surface Roughness (SR). Himadri Majumder and Kalipada Maity [2] used a multivariate hybrid approach known as the Multivariate VIKOR-Fuzzy approach to predict and optimize WEDM machining responses for machining of Nitinol alloy. The study was designed using Design of Experiments and Taguchi's L28 orthogonal array. Five controlled parameters were considered for high, medium and low levels. A General Regression Neural Network (GRNN) Model was developed in order to control and forecast the insufficient data. It is a one-pass learning algorithm. The data set is preprocessed to predict the responses which involve smoothing. The input parameters Discharge current (I), Pulse on time (Ton), Wire Tension (WT), Wire-speed (WS) and Flushing Pressure are considered as the input layer for the GRNN model and the output responses were Mean Roughness (Ra), Root Mean Square roughness (Rq), Maximum
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peak to valley height (Rz) and Micro Hardness (MH). The optimal input parameters were obtained by the VIKORFuzzy method and a confirmation test was carried out, the error obtained were well below 12% with the GRNN model having ±5% error was a very well fitted model. This optimization method yielded the result under the conflicting response criteria. The ANOVA results show that the pulse-on time was found to be affecting the most. A. Conde et.al [3] studied the optimization of machining of Nitinol by WEDM using Simulated Annealing (SA), the machining predictions were carried out using Elman-based Layer Recurrent Neural Network (LRNN) which predicted with an average deviation of 6µm for the wire path radius which implies its good performance. The network was trained using industrial information, the hidden layer had the Bayesian regularization with tangent hyperbolic activation function and the weights were applied according to the Nguyen-Widrow algorithm. The deviation between the machined and the predicted values were well below 5.6µm. Thus by amalgamating the predictions of the developed LRNN with the Simulated Annealing optimization technique, the wire paths of the variable radius can be generated thus the radial deviations due to the wire deformations can be minimized with the present method as it follows an iterative process that minimizes the error of LRNN solution. An optimization technique known as Modified Flower Pollination Algorithm (FPA) which is known to outperform the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) was studied by Sreenivasa Rao M et.al [4] for WEDM parameters for the machining of Inconel-690 alloy. The effect of input parameters on the responses such as Material Removal Rate (MRR) and Surface Roughnes (SR) was studied. The experiments were carried out according to face-centered Central Composite Design and the percentage contribution of the process parameters on the responses have been estimated using ANOVA. It was found that the Modified FPA was faster in comparison with the present FPA. It had a better accuracy and speed due to its novel two-stage initialization concept. It's a multi-objective optimization technique and gives us a privilege to adjust the weights for the responses and get the result as per our requirement. However, the comparison of the results with the results of GA and PSO were not compared by the author. An alloy that is widely used other than iron is Aluminum. An aluminum-based composite was machined using WEDM and the responses were studied. To get the optimal input parameters Titus Thankachan et.al [5] used Taguchi coupled Grey Relational Analysis (GRA). Responses such as MRR and SR were studied for various Input parameters and the prediction was made by using Machine learning and neural network models. The experiments were carried out according to Taguchi's L32 Orthogonal Array as the study included 6 factors that prompt to be expensive if Full Factorial Design was followed. As Taguchi's optimization method is a single objective optimization technique, GRA method was used. The Sound to Noise ratio (SN ratio) was obtained for MRR based on the objective- higher the better, and the SN ratio for SR was decided based on the performance characteristicslower the better. ANN model predicted the responses close to the actual values and the for the multi-objective study i.e. for maximum MRR and Minimum SR the optimum process parameters obtained from GRA are Ton=125µs, Toff = 40µs and WF= 7m/min. Bikash Choudhuri et.al [6] studied optimization of Surface Roughness (SR) and Wire Consumption (WC) for machining of AISI H21 grade steel using Fuzzy-logic and Particle Swarm Optimization (PSO) through RSM Modeling. Through experimental results, it was found that the Ton is a significant parameter as with its increase leads to an increase in SR and decreased in WC respectively. To optimize this conflicting objective problem, the multi-performance criteria index (MPCI) is to be maximized by Combining RSM fitness function with Fuzzy-logic and PSO algorithm for optimization. The results yielded the Ton to be 0.5µs, Toff 9.5 µs, the current of 160A, SV to be 50V and wire tension of 1.4kg for which the machining yielded higher quality with lower cost. The optimization of machining of Aluminum 7075 with activated charcoal as reinforcement to fabricate Metal matrix composites using Grey-Fuzzy technique was studied by G. Ramanan et.al [7]. The Grey-Fuzzy Reasoning Grade was found out to be 0.761 which means it’s the optimize setting that gives good quality of machined product with low SR and high MRR. Ko-Ta Chiang et.al [8] studied the multi perform characteristics of WED machining on 6061 alloy reinforced with AL2O3 using Grey Relational Analysis. The MRR and SR were optimized considering the parameters such as WF, Ton, Toff and water flow. Machining of D2 tool was studied by S.S. Mahapatra et.al [9] using the Taguchi method. The MRR, SR, and kerf were optimized independently. It was found that Ton, I, Dielectric flow rate play a significant role in machining. Using multi-objective, multivariable, and as a non-linear problem the maximization of MRR and minimization of SR and kerf width was also studied and the confirmation tests were carried out in which the error was found to be 3.14%, 1.95% and 3.725 for MRR, SR and kerf width respectively. R.V. Rao et.al [10]
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used an Artificial Bee Colony (ABC) method to find the optimum parameter by modeling by RSM method. The ABC algorithm combines the source position gathered by onlooker bees. The ABC has a good convergence rate and accuracy. Vinayak N Kulkarni et.al [11] studied the optimization of multi-performance characteristics of NiTi alloys using Taguchi's utility and Quality loss function. It was found that both the methods provided the same optimized results for maximized MRR and minimized SR. The optimized values were Ton=115µs, Toff=25µs, WF= 6m/min and SV=40V. 2.2. Optimization of Electric Discharge Machining (EDM) process parameters Electric Discharge Machining (EDM) is an advanced machining process where the desired shapes are machined by electrical discharges (sparks), hence the name Spark Machining. The principle is the same as that of WEDM except it does not have wire as the electrode. Also, the input process parameters and the responses are the same as the WEDM process. The optimization of multiple responses for EDM was studied by Nakka Nagraju et.al [12] through Taguchi Methods and Fuzzy-logic for machining of AISI Stainless Steel with the Cylindrical copper electrode. In this study, the author also included the study of electrode gap from the workpiece. For ease of calculations, the multiple responses were converted into single response index by using Fuzzy logic called as Multiple Performance Characteristic Index (MPCI) which was further optimized using Taguchi optimization Technique. The responses MRR, TWR, and SR were optimized through MPCI. The optimum values were found out to be Ton=500µs, SV=45V, Discharge Current =10A and the electrode gap of 150µ-*m. To improve the machining efficiency of dry EDM and to avoid short circuit at lower boundary, a new technique of using two-phase dielectric medium i.e. liquid and gas has been studied by Nimo Singh Khundrakpam et.al [13]. The machining was carried out by using liquid dielectric medium and compressed air near the copper electrode and EN-8 workpiece and the surface roughness was studied as the response. The Multi-response optimal process parameter was found by using Taguchi orthogonal array with Grey relational analysis. Through ANOVA for grey relation grade, it was found that discharge current influences the output responses and also the S/N ratio provided the necessary optimal setting of process parameter for which the response is minimum which was also very near to the predicted optimal result and hence the study is validated. For the biomedical purpose the Mg-ZN-Mn alloy was to be coated with Hydroxyapatite (HA) [14] which is a bio-ceramic. A progressive dispersed smart model that understands restricted and un-restricted optimization conditions is Swarm Intelligence (SI) that is an inspiration if organic examples by shoaling singularities, shoaling and swarming. One of the optimization techniques which work on SI is the Particle Swarm Optimization (PSO). In PSO all the particles (Input parameters) in the swarm (various values) are inclined towards improved position; therefore the optimized value can be attained through mutual efforts of sample size. The objective was to maximize Micro Hardness (MH) and to minimize SR and Recast layer Thickness (RLT). Through the PSO method the low values of SR=0.70µm, RLT =11.85µm and high MH of 246HV were obtained through the optimized values of concentration of hydroxyapatite powder =5.28g/l, discharge current of 3.48A, Ton value of 40.33µs and Toff value of 109.29µs was obtained that was confirmed through the XRD pattern for MH. Another material widely used in aerospace is Aluminum Silicon Carbide (AlSiC) as it has a high strength to weight ratio. Aharwal K. R. et.al [15] studied the EDM process of AlSiC for higher MRR and lower SR by using Genetic Algorithm for optimization of input parameters. A linear regression equation was developed to predict the responses. To optimize MRR and SR single objective optimization method was adopted and a genetic algorithm was developed which yielded the optimum values for both the responses. The error percent when the value from GA and predicted value was compared was found out to be less than 7% and the SR value was found to be stable. B.P. Mishra et.al [16] studied the optimization of MRR and TWR both as sing objective and as a multi-objective function using Taguchi's optimization technique and Grey Relation Analysis respectively on machining of EN-24 alloy steel. The input current (I) and Ton have a significant contribution to MRR and TWR. The multi-objective functions for MRR and TWR were converted into single optimization problem by Grey Relational Analysis and were found out that at Ton=100µs, Toff=30µs, I=15A, flushing pressure=0.25kg/cm2 the responses were optimized. Vikram Reddy et.al
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[17] studied the optimization using the S/N ratio on the stainless steel 304 alloy. The relationship between the process parameters was established using non-linear regression equation. It was found that MRR and SR were decreased with increase in Toff time. The experimental MRR and SR were in comparison with predicted value at the optimum setting with error accounting to 2.93% and 3.14% respectively, whereas TWR had an error of 18.18%. Optimization of AA6061 with 10% Al2O3 was studied by Bhaskar Chandra Kandpal et.al [18], the experiment design was carried out as per Taguchi’s L9 orthogonal array and it was found that pulse current had the significant influence in MRR and through S/N ratio it was found that the pulse current of 14A, Ton=200µs and duty factor of 50% provided maximum MRR. The parameters for machining Inconel 718 alloy using Satisfaction Function Approach combined with Taguchi Philosophy was carried out by Rahul et.al [19] and the L25 orthogonal array was referred for experimentation. Through this method, the limitations of Multiresponse optimization techniques were overcome and the optimized process parameters were open circuit voltage of 80V, a current of 5A, Ton=100µs, flush pressure of 0.6 bar provided the optimized EWR, SR, Surface Crack Density and white Layer Thickness. 2.3 Optimization of Laser Beam Machining (LBM) process parameters Laser Beam Machining (LBM) process is one of the non-traditional machining processes involved in machining of metallic and non-metallic components in the desired shape. The laser is made up of high-frequency monochromatic light which when directed on the surface produce high temperature due to which melting of material takes place and later the material is evaporated, it is used for precision machining of complicated shapes. The various input parameters involved for machining with LBM are the cutting speed (mm/s), pulse frequency (kHz), pulse power (W). The effect of laser process parameters on final geometry and surface roughness of micro-channel on Hastelloy C276 was studied my V. Chengal Reddy et.al [20]. Genetic Algorithm has been employed to optimize the process parameters i.e. scanning speed, pulse power, pulse frequency for milling of the workpiece and to lower its surface roughness. Full factorial design of experiments was adopted for the complete investigation and the Surface roughness was measured using Talysurf surface roughness tester and the milling depth was measured using an optical microscope. The GA algorithm was developed in MATLAB, the calculation run till 40 generation of chromosomes, which yielded the optimum response of surface roughness to me 1.386µm for the input parameters of scanning speed of 60mm/sec, pulse power of 60W and pulse frequency of 30 kHz and the milling depth to be 105.08µm for the setting parameters of speed of 50mm/sec, pulse power of 40W and pulse frequency of 20 kHz. As discussed earlier. Laser machining is not only restricted for metallic but also non-metallic components can be machined, G. Kibria et.al [21] studied the turning of Alumina (Al2O3) ceramic using pulsed Nd: YAG laser. Here the workpiece rotational speed also plays an important role, hence another parameter to optimize beside the pulse frequency, beam power, feed, and air pressure. The mathematical model to predict the response was developed by RSM method and was justified by regression analysis and Multi-objective optimization technique was adopted to optimize the input variables using MINITAB software. It was found that Surface roughness of 5.63µm and depth deviation of -0.0002mm was observed for 7.81 W of power, 5601.59 Hz of pulse frequency, workpiece rotating at 435.60rpm speed, 0.30 kgf/cm2 of air pressure and 0.443 mm/s of Y feed rate and the confirmation experiments were carried out which yielded an error of 4.78% and 4.76% for SR and depth which are acceptable results with Multi-objective optimized results. The need for the high rate of productivity and precision ask for the automated processes. One of the machining methods that help in increasing productivity is laser drilling, which can be automated and is also precise. The common defects of machining such as spatter, recast, Heat-Affected Zone (HAZ) and the taper are the limitations. For minimizing the HAZ, hole circularity and to maximize MRR the process parameters need to be optimized, Sumanta Panda et.al [22] used Grey Relational Analysis to optimize the process parameters i.e. pulse width, pulse frequency, assist gas flow rate, and its supply pressure. GRA is an effective way of analyzing the relationship between the variables when the data is less. This study decreased the try-error time and also consuming cost by recognizing the affecting variables. In order to reduce the HAZ Shashi Prakash et.al [23] studied the machining of polymethyl methacrylate under water. The adequacy of the model was checked by ANOVA, and the responses microchannel width, depth, burr width, and height were considered. The parameters were optimized individually by a single objective optimization process and then the responses were optimized at the same time using multiobjective optimization process. The multi-objective optimization process yielded the results as current=14.2142A,
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pulse frequency=5kHz, pulse width=15% and cutting speed=0.2175mm/s, and the responses were found to be channel width=15.2601mm, channel depth=155.1885mm, burr height=11.7782 mm and burr width=36.5329mm, the confirmation test was carried out and the error was below 5% for burr width and height and 3.05% for channel width. The channel depth yielded an error of 9.07%. Normally the laser machining process uses gas to assist the machining process, Suvradip Mullick et.al [24] studied the laser machining of AISI304 Stainless steel plate by a water-jet assist. The experiments were carried out according to the Box-Benken design of experiments, the process efficiency was to be maximized and thus the dependency of cut quality was also studied around those values of input parameters. The study does not include any optimization technique but only include the investigation about the maximum efficiency. Chih-Wei Chang et.al [25] studied the laser-assisted machining for the precision manufacturing of aluminum oxide ceramic parts and the optimum setting was estimated using the Taguchi method. The primary objective of the study was to find an optimal setting of input parameters to reduce surface roughness hence the experiment was carried out as per Taguchi's L9 Orthogonal array and the S/N ratio was calculated. It was found that the surface roughness is minimized at the optimum machining conditions of Rotational speed=1500rpm, feed=0.013mm/rev, depth of cut=0.2mm and frequency=40 kHz. As laser machining is tool-free and high precision machining process with good MRR, J. Ciurana et.al [26] studied the process parameters optimization using PSO for 3-D geometrical machining of AISI H13 hardened tool steel. The modelling was carried out using Neural Network. The experimental results had large variations in dimensional quality; ANN was used to predict the parameters and their output. The multi-objective optimization PSO was efficient enough to provide parameters that give minimal SR and better dimensional quality. 2.3. Optimization of Abrasive Water Jet Machining (AWJM) process parameters Abrasive waterjet machining is a modification of waterjet machining (WJM) process. It uses a nozzle of diameter 0.05mm to 1mm with pressure as high as 1400MPa, AWJM uses abrasive particles such as silicon carbide or aluminum oxide, abrasive particles along with water rush on to the surface of the workpiece to be machined cutting it to the desired shape. These abrasive particles help in higher Material Removal Rate than WJM. This process can machine metallic, non-metallic, and composite materials particularly heat sensitive materials of various thicknesses, with cutting speed as high as 7.5m/min. in this process the burr produced is very minimal and also no heat is generated while machining. K. Ravi Kumar et.al [27] studied the machining of 2, 4, 6, 8 and 10% tungsten carbide reinforced composite specimen using multi-response optimization technique form the model developed using the RSM method. The responses that were a target are the MRR to be maximized and to minimize the SR; these were optimized simultaneously through a set of 20 process parameters that were derived by RSM. Not just the input parameters were studied but also the percentage of Tungsten carbide in the composite also affects the machinability. Both the MRR and SR were given equal weights in order to have equal importance. The optimum values were found to be standoff distance=4.22mm, transverse speed-223.28mm/min, and percentage of tungsten carbide=2.10%. As AWJM can be employed in machining non-metallic components, it is used in the marble industry for cutting purpose. But the drawback is that it leaves striation marks, and also difficulty related to dimensional inaccuracy. Padmakar J Pawar et.al [28] studied the parameter optimization of AWJM on marble using Multi-objective Artificial Bee colony (ABC) algorithm. The responses kerf width and kerf taper need to be minimized were as the striation free surface need to be maximized. The ABC method uses the greedy selection characteristics of onlooker bees to select the best source and also the random selection of scout bees to maintain diversity in the solution. The Multi-objective optimization approach gave 14 non-dominant solutions thus providing a ready reference for best-operating parameters. The objective was obtained at stand-off distance 0.75 to 1 mm and 1.75 to 2.5 mm, traverse speed = 50 to 95 mm/min, water jet pressure of 140 to 230 MPa, Abrasive flow rate: 200 to 900 g/sec. Rajkamal Shukla et.al [29] studied the best optimization technique out of seven techniques i.e. particle swarm optimization (PSO), firefly algorithm (FA), Artificial Bee Colony (ABC), Simulated Annealing (SA), Black Hole (BH), biogeography based (BBO) and non-dominated sorting genetic algorithm (NSGA), for machining of AA631-T6 material. The process parameters considered were transverse speed, standoff distance and mass flow rate with kerf top width and taper angle as the response. Taguchi's experimental design was applied to get the best combination of parameters. After examining results from all the optimization techniques, it was found that the NSGA method had good agreement with the experimental result. Farhad Kolahan et.al [30] studied the optimization of AWJ cutting parameters using
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Simulated Annealing algorithm on machining of Aluminium 6063-T6 alloy through Taguchi method and regression modelling. The values attained from the SA algorithm were efficient and accurate to determine cutting parameters to have the desired depth of cut. The surface finish of AWJ cut Kevlar composites was studied by T.U. Siddiqui et.al [31] using a Hybrid Taguchi and response surface method (HTRSM), and a second-order Response surface model was developed using Central Composite Rotatable Design (CCRD). The HTRSM model showed that low values of surface finish were obtained at high water jet pressure, low abrasive flow rate and low traverse speed is desirable for optimum surface finish as shown in below Table 1. Table 1. Other works on Advanced Machining Process Parameter Optimization Author Year Process
Work Material
Optimization Technique
Kung and Chiang [32]
2008
WEDM
Al2O3-based ceramic
RSM
Rao and Pawar [33]
2009
WEDM
Oil hardened and nitrided steel (OHNS)
ABC
Chen et.al. [34]
2010
WEDM
Tungsten
ANN integrated with SA approach
Amini et.al [35]
2011
WEDM
TiB2 nano-composite ceramic
Kondayya and Krishna [36]
2011
WEDM
Hard metal alloys and MMC
amalgamation of Taguchi method, ANN and Genetic algoritm Genetic programming and NSGA-II
Manna and Bhattacharyya [37]
2006
CNC-based WEDM
Al/SiC-MMC
Keskin et.al [38]
2006
EDM
Steel
Kansal et.al [39]
2007
Powder-mixed EDM
AISI-D2 die steel
Tzeng and Chen [40]
2007
EDM
Tool steel SKD11
Taguchi-fuzzy-based approach
Lin et.al [41]
2006
EDM
SKH-57 high-speed steel
Taguchi method
Salman and Kayacan [42]
2008
EDM
DIN1.2379gradecold work steel
Karazi et.al [43]
2009
LBM
Micro-channels of glass
Genetic expression programming (GEP), Taguchi method ANN
Ghosal and Manna [44]
2012
LBM
Al/Al2O3 MMC
RSM
Jegaraj and Babu [45]
2007
AWJ cutting
6063-T6aluminium alloy
Neuro-fuzzy approach
Srinivasu and Babu [46]
2008
AWJ cutting
6063-T6aluminium alloy
Neuro-genetic approach
Caydas and Hascalik [47]
2008
AWJM
AA7075aluminium alloy
Taguchi method and ANN
Zain et.al [48]
2011
AWJM
AA7075aluminium alloy
Wenjun et.al [49]
2011
AWJM
C-17 steel
incorporated approach of ANN and SA Arbitrary Lagrange–Euler algorithm
Taguchi method and Gauss elimination method Multiple regression and design of experiments (DOE Taguchi method
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Conclusions In this review article, the optimization aspects and methods adopted for various advanced machining processes like WEDM, EDM, LBM, and AWJM are taken into account. A thorough literature review about the parameter optimization of these processes has been summarised. The various processes and the work material used by the researcher have been presented. The following observation has been made based on the review work. Lot of work has been conducted on WEDM and EDM processes when compared with other processes. The most widely used modelling technique was found to be the RSM technique. The most commonly used optimization technique was Taguchi’s optimization. Some researchers combined Taguchi’s method with other methods like SA, GRA to obtain multiple optimizations at a single time. Very few works have been carried out on the Artificial Bee Colony optimization technique. Very less work on Water jet machining process parameters optimization have been carried out. References 1) 2) 3) 4) 5)
6) 7) 8) 9) 10) 11) 12) 13) 14) 15) 16) 17) 18) 19)
Vinayak N Kulkarni , V,N,Gaitonde, Viranna Hadimani, Vasant Aiholi, Analysis of Wire EDM Process Parameters in Machining of NiTi Superelastic Alloy, Materials Today: Proceedings 5 (2018) 19303-19312 Himadri Majumder, Kalipada Maity, Application of GRNN and multivariate hybrid approach to predict and optimize WEDM responses for Ni-Ti shape memory alloy, Applied Soft Computing 70 (2018) 665–679 A. Conde, A. Arriandiaga, J.A. Sanchez, E. Portillo, S. Plaza, I. Cabanes, High-accuracy wire electrical discharge machining using artificial neural networks and optimization techniques, Robotics and Computer–Integrated Manufacturing 49 (2018) 24–38 Sreenivasa Rao M, Venkata Naresh Babu A, Venkaiah N, Modified Flower Pollination Algorithm to Optimize WEDM parameters while Machining Inconel-690 alloy, Materials Today: Proceedings 5 (2018) 7864–7872 Titus Thankachan, K. Soorya Prakash, R Malini, S Ramu, Prabhu Sundararaj, Sivakumar Rajandran, Devaraj Rammasamy, Sathiskumar Jothi, Prediction of Surface roughness and Material removal rate in Wire Electrical Discharge Machining on Aluminum Based Alloys/Composites using Taguchi Coupled Grey Relational Analysis and Artificial Neural Networks, Applied Surface Science 2018. Bikash Choudhuri, Ruma Sen, Subrata Kumar Ghosh, S. C. Saha, Modelling of Surface Roughness and Tool Consumption of WEDM a. and Optimization of Process Parameters Based on Fuzzy-PSO, Materials Today: Proceedings 5 (2018) 7505–7514 G. Ramanan, J. Edwin Raja Dhas, Multi objective Optimization of Wire EDM Machining Parameters for AA7075-PAC Composite Using Gey-Fuzzy Technique, Materials today: Proceedings 5(2018) 8280-8289 Ko-Ta Chiang, Fu-Ping Chang, Optimization of the WEDM process of particle-reinforced material with multiple performance characteristics using grey relational analysis, Journal of Materials Processing Technology 180 (2006) 96–101. S. S. Mahapatra, Amar Patnaik, Optimization of wire electrical discharge machining (WEDM) process parameters using the Taguchi method, Int J Adv Manuf Technol (2007) 34:911–925. R V Rao, P J Pawar, Modelling and optimization of process parameters of wire electrical discharge machining, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture (2009) Vinayak N Kulkarni, V. N. Gaitonde, Vasant Aiholi, Viranna Hadimani, Multi Performance Characteristics Optimization in Wire Electric Discharge Machining of Nitinol Superelastic Alloy, Materials Today: Proceedings 5(2018) 18857-18866 Nakka Nagaraju, Sadu Venkatesu, N.G. Ujwala, Optimization of Process Parameters of EDM Process Using Fuzzy Logic and Taguchi Methods for Improving Material Removal Rate and Surface Finish, Materials Today: Proceedings 5 (2018) 7420–7428 Nimo Singh Khundrakpam, Gurinder Singh Brar, Dharmpal Deepak, Grey-Taguchi Optimization of Near Dry EDM Process Parameters on the Surface Roughness, Materials Today: Proceedings 5 (2018) 4445–4451. Chander Prakash, Sunpreet Singh, Manjeet Singh, Kartikey Verma, Babulal Chaudhary, Subhash Singh, Multi-objective particle swarm optimization of EDM parameters to deposit HA-coating on biodegradable Mg-alloy, Vacuum (2018). Aharwal K R, Sitaram, CM Krishna, Optimization of Material Removal Rate and Surface Roughness in EDM Machining of Metal Matrix Composite using Genetic Algorithm, Materials Today: Proceedings 5 (2018) 5391–5397 B.P. Mishra, B.C. Routara, An experimental investigation and optimization of performance characteristics in EDM of EN-24 alloy steel using Taguchi Method and Grey Relational Analysis, Materials Today: Proceedings 4 (2017) 7438–7447 V.Vikram Reddy, P.Vamshi Krishna, B.Shiva kumar, M.Shashidhar, Optimization of Process Parameters during EDM of Stainless a. Steel 304 using Taguchi Method, IJETT- Volume 31 Number 2- January 2016 Bhaskar Chandra Kandpal, Jatinder Kumar, Hari Singh, Optimisation Of Process Parameters Of Electrical Discharge Machning Of Fabricated AA 6061/10% Al2 O3 Aluminium Based Metal Matrix Composite, Materials Today: Proceedings 5 (2018) 4413–4420 Rahul, Saurav Datta, Bibhuti Bhusan Biswal, Siba Sankar Mahapatra, Optimization of Electro-Discharge Machining Responses of Super Alloy Inconel 718: Use of Satisfaction Function Approach Combined with Taguchi Phlosophy, Materials Today: Proceedings 5 (2018) 4376–4383
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Madhukar, Subhransu Roy, Ashish K. Nath, Performance optimization of water-jet assisted underwater laser cutting of AISI 304 stainless steel sheet, Optics andLasersinEngineering83(2016)32–47 25) Chih-Wei Chang, Chun-Pao Kuo, Evaluation of surface roughness in laser-assisted machining of aluminum oxide ceramics with Taguchi method, International Journal of Machine Tools & Manufacture 47 (2007) 141–147 26) J. Ciurana, G. Arias, T. Ozel, Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel, Materials and Manufacturing Processes, 24: 358–368, 2009 27) K. Ravi Kumar, V.S. Sreebalaji, T. Pridhar, Characterization and Optimization of Abrasive Water Jet Machining Parameters of Aluminium /Tungsten Carbide Composites, Measurement (2017) 28) Padmakar J Pawar, Umesh S. Vidhate, Mangesh Y. Khalkar, Improving the quality characteristics of abrasive watser jet machining of marble material using multi-objective artificial bee colony algorithm, Journal of Computational Design and Engineering (2017) 29) Rajkamal Shukla, Dinesh Singh, Experimentation investigation of abrasive water jet machining parameters using Taguchi and Evolutionary optimization techniques, Swarm and Evolutionary Computation(2016) 30) Farhad Kolahan, A. Hamid Khajavi, Modeling and Optimization of Abrasive Waterjet Parameters using Regression Analysis, International Journal of Mechanical, Aerospace, Industrial, Mechatronic, and Manufacturing Engineering Vol:3, No:11, 2009 31) Tauseef Uddin Siddiqui, Mukul Shukla, Optimisation of surface finish in abrasive water jet cutting of Kevlar composites using hybrid Taguchi and response surface method, Int. J. 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Mater Des (2007)28:1159–1168 41) Lin YC, Cheng CH, Su BL, Hwang LR, Machining characteristics and optimization of machining parameters of SKH57 high speed steel using electrical-discharge machining based on Taguchi method. Material Manufacturing Process(2006) 21:922–929 42) Salman O, Kayacan M C, Evolutionary programming method for modeling the EDM parameters for roughness. Journal of Material Process Technologies(2008) 200:347–355 43) Karazi SM, Issa A, Brabazon D, Comparison of ANN and DoE for the prediction of laser-machined micro-channel dimensions. Optics and Lasers Engineering(2009) 47:956–964 44) Arindam Ghosal, Alakesh Manna, Response surface method based optimization of ytterbium fiber laser parameter during machining of Al/Al2O3-MMC. Optics and Laser Technology 46(2013) 67-76. 45) Jegaraj JJR, Babu NR, A soft computing approach for controlling the quality of cut with abrasive waterjet cutting system experiencing orifice and focusing tube wear. 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ICMPC-2019
Modern Optimization Techniques for Advanced Machining Processes – A Review Rohit Surebana*, Vinayak N Kulkarnib, V. N. Gaitondec a,b,c
School of Mechanical Engineering, KLE. Technological University, Hubballi
Abstract A review about the optimization techniques used for modern machining techniques are analyzed in this paper. The chief focus is kept on the modern optimization techniques employed to obtain the optimal machining parameters and during both nonconventional machining processes. The various Non-traditional machining techniques analyzed for this study are the Wire Electro Discharge Machining (WEDM), Laser Beam Machining (LBM), Abrasive Water Jet Machining (AWJM) and Electro Discharge Machining (EDM). These machining processes were analyzed with respect to different modern optimization techniques. The most widely used, frequently used and rarely used optimization techniques for the mentioned non-conventional machining processes are identified. The paper provides an overall idea about the optimization techniques and helps to choose the suitable technique for the researchers. © 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the 9th International Conference of Materials Processing and Characterization, ICMPC-2019 Keywords: optimization: Optimization Techniques, Wire Electro Discharge Machining (WEDM), Laser Beam Machining (LBM), Abrasive Water Jet Machining (AWJM) and Electro Discharge Machining (EDM)
1.
Introduction
With the advent of new and smart materials, a question of machining such materials with precision and efficiency are being raised in the industries. These materials are used widely in biomedical applications, automobile, aerospace, defence, and various other sectors. The machining of such materials with conventional technique does not meet the customer requirement because the finishing quality of the component is very minimal. This is because
* Corresponding author. Tel.:+919481050491 E-mail address: [email protected] 2214-7853© 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the 9th International Conference of Materials Processing and Characterization, ICMPC-2019
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of the difficulties in machining such materials; certain materials like NiTi work harden during machining via conventional machining method this result in accelerated flank wear, notching and cratering. Hence certain special and advanced machining processes are to be adopted for efficient machining. Industries adopting such machining practices have an added lifeline for its growth as they have the better quality of components. The various advanced (non- conventional) machining processes are Laser Beam Machining (LBM), Wire Electrode Discharge Machining (WEDM), Electrode Discharge Machining (EDM), Abrasive Water Jet Machining (AWJM), Water Jet Machining (WJM), Electromechanical Machining (ECM) and Ultrasonic Machining (USM) and also various custom-made versions of these processes. All the above processes work according to their own principle which makes them suitable for machining certain materials and may also have a certain limitation of their use. All these processes have various input parameters and the selection of these parameters plays a crucial role as it affects the characteristics and performance of the machined component. Vinayak N Kulkarni et al [1] analysed the effects of WEDM process parameters on NiTi alloys. Random selection of such input parameters will not serve the purpose and might worsen the situation. To avoid such unconventional way of selecting the input parameters certain optimization techniques are used. Many optimization techniques have been developed for good quality of advanced machined components; some of them are Taguchi's optimization Techniques, Genetic Algorithm (GA), Simulated Annealing (SA), Fuzzy logic, etc. these techniques have already proven their significance in other parameter optimization of the various manufacturing process. Industries have been adopted certain of these techniques for their manufacturing optimization. In this paper, an effort is made to identify all such advanced machining process and the optimization techniques in recent years. 2.
Advanced machining process parameters optimization
The advanced machining processes considered in the present work are Wire Electric Discharge Machining (WEDM), Electric Discharge Machining (EDM), Laser Beam Machining (LBM), and Water Jet Machining. 2.1. Optimization of Wire Electric Discharge Machining (WEDM) process parameters Wire Electric Discharge Machining (WEDM) is one of the advanced machining processes in which highly intricate shapes can be machined. It is one of a non-contact type of machining process. The WED machine uses wire of predefined sizes as the electrode and die-electric fluid for machining purpose. A very minute gap is maintained between the wire electrode and the work piece, a high voltage is passed through the wire, a dielectric field is generated between the electrode and the work piece which is greater than the dielectric constant of the workpiece to be machined, thus eroding the material. The dielectric fluid used acts a coolant and also it washes away the debris formed on the machined surface. Various types of electrode wires can be used for the machining of the WEDM process like copper, zinc coated copper, brass, and zinc coated brass wires. The output response for the WEDM process varies with the type of wire used in the process. Hence the optimal input parameters such as pulse on/off time, servo voltage and wire feed also change with the type of wire being used. The input parameters that are studied for this process are Wire Feed (WF), pulse off time (Toff), Pulse on time (Ton) and the Servo Voltage (SV) and the most studied output responses are Material Removal Rate (MRR) and Surface Roughness (SR). Himadri Majumder and Kalipada Maity [2] used a multivariate hybrid approach known as the Multivariate VIKOR-Fuzzy approach to predict and optimize WEDM machining responses for machining of Nitinol alloy. The study was designed using Design of Experiments and Taguchi's L28 orthogonal array. Five controlled parameters were considered for high, medium and low levels. A General Regression Neural Network (GRNN) Model was developed in order to control and forecast the insufficient data. It is a one-pass learning algorithm. The data set is preprocessed to predict the responses which involve smoothing. The input parameters Discharge current (I), Pulse on time (Ton), Wire Tension (WT), Wire-speed (WS) and Flushing Pressure are considered as the input layer for the GRNN model and the output responses were Mean Roughness (Ra), Root Mean Square roughness (Rq), Maximum
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peak to valley height (Rz) and Micro Hardness (MH). The optimal input parameters were obtained by the VIKORFuzzy method and a confirmation test was carried out, the error obtained were well below 12% with the GRNN model having ±5% error was a very well fitted model. This optimization method yielded the result under the conflicting response criteria. The ANOVA results show that the pulse-on time was found to be affecting the most. A. Conde et.al [3] studied the optimization of machining of Nitinol by WEDM using Simulated Annealing (SA), the machining predictions were carried out using Elman-based Layer Recurrent Neural Network (LRNN) which predicted with an average deviation of 6µm for the wire path radius which implies its good performance. The network was trained using industrial information, the hidden layer had the Bayesian regularization with tangent hyperbolic activation function and the weights were applied according to the Nguyen-Widrow algorithm. The deviation between the machined and the predicted values were well below 5.6µm. Thus by amalgamating the predictions of the developed LRNN with the Simulated Annealing optimization technique, the wire paths of the variable radius can be generated thus the radial deviations due to the wire deformations can be minimized with the present method as it follows an iterative process that minimizes the error of LRNN solution. An optimization technique known as Modified Flower Pollination Algorithm (FPA) which is known to outperform the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) was studied by Sreenivasa Rao M et.al [4] for WEDM parameters for the machining of Inconel-690 alloy. The effect of input parameters on the responses such as Material Removal Rate (MRR) and Surface Roughnes (SR) was studied. The experiments were carried out according to face-centered Central Composite Design and the percentage contribution of the process parameters on the responses have been estimated using ANOVA. It was found that the Modified FPA was faster in comparison with the present FPA. It had a better accuracy and speed due to its novel two-stage initialization concept. It's a multi-objective optimization technique and gives us a privilege to adjust the weights for the responses and get the result as per our requirement. However, the comparison of the results with the results of GA and PSO were not compared by the author. An alloy that is widely used other than iron is Aluminum. An aluminum-based composite was machined using WEDM and the responses were studied. To get the optimal input parameters Titus Thankachan et.al [5] used Taguchi coupled Grey Relational Analysis (GRA). Responses such as MRR and SR were studied for various Input parameters and the prediction was made by using Machine learning and neural network models. The experiments were carried out according to Taguchi's L32 Orthogonal Array as the study included 6 factors that prompt to be expensive if Full Factorial Design was followed. As Taguchi's optimization method is a single objective optimization technique, GRA method was used. The Sound to Noise ratio (SN ratio) was obtained for MRR based on the objective- higher the better, and the SN ratio for SR was decided based on the performance characteristicslower the better. ANN model predicted the responses close to the actual values and the for the multi-objective study i.e. for maximum MRR and Minimum SR the optimum process parameters obtained from GRA are Ton=125µs, Toff = 40µs and WF= 7m/min. Bikash Choudhuri et.al [6] studied optimization of Surface Roughness (SR) and Wire Consumption (WC) for machining of AISI H21 grade steel using Fuzzy-logic and Particle Swarm Optimization (PSO) through RSM Modeling. Through experimental results, it was found that the Ton is a significant parameter as with its increase leads to an increase in SR and decreased in WC respectively. To optimize this conflicting objective problem, the multi-performance criteria index (MPCI) is to be maximized by Combining RSM fitness function with Fuzzy-logic and PSO algorithm for optimization. The results yielded the Ton to be 0.5µs, Toff 9.5 µs, the current of 160A, SV to be 50V and wire tension of 1.4kg for which the machining yielded higher quality with lower cost. The optimization of machining of Aluminum 7075 with activated charcoal as reinforcement to fabricate Metal matrix composites using Grey-Fuzzy technique was studied by G. Ramanan et.al [7]. The Grey-Fuzzy Reasoning Grade was found out to be 0.761 which means it’s the optimize setting that gives good quality of machined product with low SR and high MRR. Ko-Ta Chiang et.al [8] studied the multi perform characteristics of WED machining on 6061 alloy reinforced with AL2O3 using Grey Relational Analysis. The MRR and SR were optimized considering the parameters such as WF, Ton, Toff and water flow. Machining of D2 tool was studied by S.S. Mahapatra et.al [9] using the Taguchi method. The MRR, SR, and kerf were optimized independently. It was found that Ton, I, Dielectric flow rate play a significant role in machining. Using multi-objective, multivariable, and as a non-linear problem the maximization of MRR and minimization of SR and kerf width was also studied and the confirmation tests were carried out in which the error was found to be 3.14%, 1.95% and 3.725 for MRR, SR and kerf width respectively. R.V. Rao et.al [10]
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used an Artificial Bee Colony (ABC) method to find the optimum parameter by modeling by RSM method. The ABC algorithm combines the source position gathered by onlooker bees. The ABC has a good convergence rate and accuracy. Vinayak N Kulkarni et.al [11] studied the optimization of multi-performance characteristics of NiTi alloys using Taguchi's utility and Quality loss function. It was found that both the methods provided the same optimized results for maximized MRR and minimized SR. The optimized values were Ton=115µs, Toff=25µs, WF= 6m/min and SV=40V. 2.2. Optimization of Electric Discharge Machining (EDM) process parameters Electric Discharge Machining (EDM) is an advanced machining process where the desired shapes are machined by electrical discharges (sparks), hence the name Spark Machining. The principle is the same as that of WEDM except it does not have wire as the electrode. Also, the input process parameters and the responses are the same as the WEDM process. The optimization of multiple responses for EDM was studied by Nakka Nagraju et.al [12] through Taguchi Methods and Fuzzy-logic for machining of AISI Stainless Steel with the Cylindrical copper electrode. In this study, the author also included the study of electrode gap from the workpiece. For ease of calculations, the multiple responses were converted into single response index by using Fuzzy logic called as Multiple Performance Characteristic Index (MPCI) which was further optimized using Taguchi optimization Technique. The responses MRR, TWR, and SR were optimized through MPCI. The optimum values were found out to be Ton=500µs, SV=45V, Discharge Current =10A and the electrode gap of 150µ-*m. To improve the machining efficiency of dry EDM and to avoid short circuit at lower boundary, a new technique of using two-phase dielectric medium i.e. liquid and gas has been studied by Nimo Singh Khundrakpam et.al [13]. The machining was carried out by using liquid dielectric medium and compressed air near the copper electrode and EN-8 workpiece and the surface roughness was studied as the response. The Multi-response optimal process parameter was found by using Taguchi orthogonal array with Grey relational analysis. Through ANOVA for grey relation grade, it was found that discharge current influences the output responses and also the S/N ratio provided the necessary optimal setting of process parameter for which the response is minimum which was also very near to the predicted optimal result and hence the study is validated. For the biomedical purpose the Mg-ZN-Mn alloy was to be coated with Hydroxyapatite (HA) [14] which is a bio-ceramic. A progressive dispersed smart model that understands restricted and un-restricted optimization conditions is Swarm Intelligence (SI) that is an inspiration if organic examples by shoaling singularities, shoaling and swarming. One of the optimization techniques which work on SI is the Particle Swarm Optimization (PSO). In PSO all the particles (Input parameters) in the swarm (various values) are inclined towards improved position; therefore the optimized value can be attained through mutual efforts of sample size. The objective was to maximize Micro Hardness (MH) and to minimize SR and Recast layer Thickness (RLT). Through the PSO method the low values of SR=0.70µm, RLT =11.85µm and high MH of 246HV were obtained through the optimized values of concentration of hydroxyapatite powder =5.28g/l, discharge current of 3.48A, Ton value of 40.33µs and Toff value of 109.29µs was obtained that was confirmed through the XRD pattern for MH. Another material widely used in aerospace is Aluminum Silicon Carbide (AlSiC) as it has a high strength to weight ratio. Aharwal K. R. et.al [15] studied the EDM process of AlSiC for higher MRR and lower SR by using Genetic Algorithm for optimization of input parameters. A linear regression equation was developed to predict the responses. To optimize MRR and SR single objective optimization method was adopted and a genetic algorithm was developed which yielded the optimum values for both the responses. The error percent when the value from GA and predicted value was compared was found out to be less than 7% and the SR value was found to be stable. B.P. Mishra et.al [16] studied the optimization of MRR and TWR both as sing objective and as a multi-objective function using Taguchi's optimization technique and Grey Relation Analysis respectively on machining of EN-24 alloy steel. The input current (I) and Ton have a significant contribution to MRR and TWR. The multi-objective functions for MRR and TWR were converted into single optimization problem by Grey Relational Analysis and were found out that at Ton=100µs, Toff=30µs, I=15A, flushing pressure=0.25kg/cm2 the responses were optimized. Vikram Reddy et.al
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[17] studied the optimization using the S/N ratio on the stainless steel 304 alloy. The relationship between the process parameters was established using non-linear regression equation. It was found that MRR and SR were decreased with increase in Toff time. The experimental MRR and SR were in comparison with predicted value at the optimum setting with error accounting to 2.93% and 3.14% respectively, whereas TWR had an error of 18.18%. Optimization of AA6061 with 10% Al2O3 was studied by Bhaskar Chandra Kandpal et.al [18], the experiment design was carried out as per Taguchi’s L9 orthogonal array and it was found that pulse current had the significant influence in MRR and through S/N ratio it was found that the pulse current of 14A, Ton=200µs and duty factor of 50% provided maximum MRR. The parameters for machining Inconel 718 alloy using Satisfaction Function Approach combined with Taguchi Philosophy was carried out by Rahul et.al [19] and the L25 orthogonal array was referred for experimentation. Through this method, the limitations of Multiresponse optimization techniques were overcome and the optimized process parameters were open circuit voltage of 80V, a current of 5A, Ton=100µs, flush pressure of 0.6 bar provided the optimized EWR, SR, Surface Crack Density and white Layer Thickness. 2.3 Optimization of Laser Beam Machining (LBM) process parameters Laser Beam Machining (LBM) process is one of the non-traditional machining processes involved in machining of metallic and non-metallic components in the desired shape. The laser is made up of high-frequency monochromatic light which when directed on the surface produce high temperature due to which melting of material takes place and later the material is evaporated, it is used for precision machining of complicated shapes. The various input parameters involved for machining with LBM are the cutting speed (mm/s), pulse frequency (kHz), pulse power (W). The effect of laser process parameters on final geometry and surface roughness of micro-channel on Hastelloy C276 was studied my V. Chengal Reddy et.al [20]. Genetic Algorithm has been employed to optimize the process parameters i.e. scanning speed, pulse power, pulse frequency for milling of the workpiece and to lower its surface roughness. Full factorial design of experiments was adopted for the complete investigation and the Surface roughness was measured using Talysurf surface roughness tester and the milling depth was measured using an optical microscope. The GA algorithm was developed in MATLAB, the calculation run till 40 generation of chromosomes, which yielded the optimum response of surface roughness to me 1.386µm for the input parameters of scanning speed of 60mm/sec, pulse power of 60W and pulse frequency of 30 kHz and the milling depth to be 105.08µm for the setting parameters of speed of 50mm/sec, pulse power of 40W and pulse frequency of 20 kHz. As discussed earlier. Laser machining is not only restricted for metallic but also non-metallic components can be machined, G. Kibria et.al [21] studied the turning of Alumina (Al2O3) ceramic using pulsed Nd: YAG laser. Here the workpiece rotational speed also plays an important role, hence another parameter to optimize beside the pulse frequency, beam power, feed, and air pressure. The mathematical model to predict the response was developed by RSM method and was justified by regression analysis and Multi-objective optimization technique was adopted to optimize the input variables using MINITAB software. It was found that Surface roughness of 5.63µm and depth deviation of -0.0002mm was observed for 7.81 W of power, 5601.59 Hz of pulse frequency, workpiece rotating at 435.60rpm speed, 0.30 kgf/cm2 of air pressure and 0.443 mm/s of Y feed rate and the confirmation experiments were carried out which yielded an error of 4.78% and 4.76% for SR and depth which are acceptable results with Multi-objective optimized results. The need for the high rate of productivity and precision ask for the automated processes. One of the machining methods that help in increasing productivity is laser drilling, which can be automated and is also precise. The common defects of machining such as spatter, recast, Heat-Affected Zone (HAZ) and the taper are the limitations. For minimizing the HAZ, hole circularity and to maximize MRR the process parameters need to be optimized, Sumanta Panda et.al [22] used Grey Relational Analysis to optimize the process parameters i.e. pulse width, pulse frequency, assist gas flow rate, and its supply pressure. GRA is an effective way of analyzing the relationship between the variables when the data is less. This study decreased the try-error time and also consuming cost by recognizing the affecting variables. In order to reduce the HAZ Shashi Prakash et.al [23] studied the machining of polymethyl methacrylate under water. The adequacy of the model was checked by ANOVA, and the responses microchannel width, depth, burr width, and height were considered. The parameters were optimized individually by a single objective optimization process and then the responses were optimized at the same time using multiobjective optimization process. The multi-objective optimization process yielded the results as current=14.2142A,
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pulse frequency=5kHz, pulse width=15% and cutting speed=0.2175mm/s, and the responses were found to be channel width=15.2601mm, channel depth=155.1885mm, burr height=11.7782 mm and burr width=36.5329mm, the confirmation test was carried out and the error was below 5% for burr width and height and 3.05% for channel width. The channel depth yielded an error of 9.07%. Normally the laser machining process uses gas to assist the machining process, Suvradip Mullick et.al [24] studied the laser machining of AISI304 Stainless steel plate by a water-jet assist. The experiments were carried out according to the Box-Benken design of experiments, the process efficiency was to be maximized and thus the dependency of cut quality was also studied around those values of input parameters. The study does not include any optimization technique but only include the investigation about the maximum efficiency. Chih-Wei Chang et.al [25] studied the laser-assisted machining for the precision manufacturing of aluminum oxide ceramic parts and the optimum setting was estimated using the Taguchi method. The primary objective of the study was to find an optimal setting of input parameters to reduce surface roughness hence the experiment was carried out as per Taguchi's L9 Orthogonal array and the S/N ratio was calculated. It was found that the surface roughness is minimized at the optimum machining conditions of Rotational speed=1500rpm, feed=0.013mm/rev, depth of cut=0.2mm and frequency=40 kHz. As laser machining is tool-free and high precision machining process with good MRR, J. Ciurana et.al [26] studied the process parameters optimization using PSO for 3-D geometrical machining of AISI H13 hardened tool steel. The modelling was carried out using Neural Network. The experimental results had large variations in dimensional quality; ANN was used to predict the parameters and their output. The multi-objective optimization PSO was efficient enough to provide parameters that give minimal SR and better dimensional quality. 2.3. Optimization of Abrasive Water Jet Machining (AWJM) process parameters Abrasive waterjet machining is a modification of waterjet machining (WJM) process. It uses a nozzle of diameter 0.05mm to 1mm with pressure as high as 1400MPa, AWJM uses abrasive particles such as silicon carbide or aluminum oxide, abrasive particles along with water rush on to the surface of the workpiece to be machined cutting it to the desired shape. These abrasive particles help in higher Material Removal Rate than WJM. This process can machine metallic, non-metallic, and composite materials particularly heat sensitive materials of various thicknesses, with cutting speed as high as 7.5m/min. in this process the burr produced is very minimal and also no heat is generated while machining. K. Ravi Kumar et.al [27] studied the machining of 2, 4, 6, 8 and 10% tungsten carbide reinforced composite specimen using multi-response optimization technique form the model developed using the RSM method. The responses that were a target are the MRR to be maximized and to minimize the SR; these were optimized simultaneously through a set of 20 process parameters that were derived by RSM. Not just the input parameters were studied but also the percentage of Tungsten carbide in the composite also affects the machinability. Both the MRR and SR were given equal weights in order to have equal importance. The optimum values were found to be standoff distance=4.22mm, transverse speed-223.28mm/min, and percentage of tungsten carbide=2.10%. As AWJM can be employed in machining non-metallic components, it is used in the marble industry for cutting purpose. But the drawback is that it leaves striation marks, and also difficulty related to dimensional inaccuracy. Padmakar J Pawar et.al [28] studied the parameter optimization of AWJM on marble using Multi-objective Artificial Bee colony (ABC) algorithm. The responses kerf width and kerf taper need to be minimized were as the striation free surface need to be maximized. The ABC method uses the greedy selection characteristics of onlooker bees to select the best source and also the random selection of scout bees to maintain diversity in the solution. The Multi-objective optimization approach gave 14 non-dominant solutions thus providing a ready reference for best-operating parameters. The objective was obtained at stand-off distance 0.75 to 1 mm and 1.75 to 2.5 mm, traverse speed = 50 to 95 mm/min, water jet pressure of 140 to 230 MPa, Abrasive flow rate: 200 to 900 g/sec. Rajkamal Shukla et.al [29] studied the best optimization technique out of seven techniques i.e. particle swarm optimization (PSO), firefly algorithm (FA), Artificial Bee Colony (ABC), Simulated Annealing (SA), Black Hole (BH), biogeography based (BBO) and non-dominated sorting genetic algorithm (NSGA), for machining of AA631-T6 material. The process parameters considered were transverse speed, standoff distance and mass flow rate with kerf top width and taper angle as the response. Taguchi's experimental design was applied to get the best combination of parameters. After examining results from all the optimization techniques, it was found that the NSGA method had good agreement with the experimental result. Farhad Kolahan et.al [30] studied the optimization of AWJ cutting parameters using
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Simulated Annealing algorithm on machining of Aluminium 6063-T6 alloy through Taguchi method and regression modelling. The values attained from the SA algorithm were efficient and accurate to determine cutting parameters to have the desired depth of cut. The surface finish of AWJ cut Kevlar composites was studied by T.U. Siddiqui et.al [31] using a Hybrid Taguchi and response surface method (HTRSM), and a second-order Response surface model was developed using Central Composite Rotatable Design (CCRD). The HTRSM model showed that low values of surface finish were obtained at high water jet pressure, low abrasive flow rate and low traverse speed is desirable for optimum surface finish as shown in below Table 1. Table 1. Other works on Advanced Machining Process Parameter Optimization Author Year Process
Work Material
Optimization Technique
Kung and Chiang [32]
2008
WEDM
Al2O3-based ceramic
RSM
Rao and Pawar [33]
2009
WEDM
Oil hardened and nitrided steel (OHNS)
ABC
Chen et.al. [34]
2010
WEDM
Tungsten
ANN integrated with SA approach
Amini et.al [35]
2011
WEDM
TiB2 nano-composite ceramic
Kondayya and Krishna [36]
2011
WEDM
Hard metal alloys and MMC
amalgamation of Taguchi method, ANN and Genetic algoritm Genetic programming and NSGA-II
Manna and Bhattacharyya [37]
2006
CNC-based WEDM
Al/SiC-MMC
Keskin et.al [38]
2006
EDM
Steel
Kansal et.al [39]
2007
Powder-mixed EDM
AISI-D2 die steel
Tzeng and Chen [40]
2007
EDM
Tool steel SKD11
Taguchi-fuzzy-based approach
Lin et.al [41]
2006
EDM
SKH-57 high-speed steel
Taguchi method
Salman and Kayacan [42]
2008
EDM
DIN1.2379gradecold work steel
Karazi et.al [43]
2009
LBM
Micro-channels of glass
Genetic expression programming (GEP), Taguchi method ANN
Ghosal and Manna [44]
2012
LBM
Al/Al2O3 MMC
RSM
Jegaraj and Babu [45]
2007
AWJ cutting
6063-T6aluminium alloy
Neuro-fuzzy approach
Srinivasu and Babu [46]
2008
AWJ cutting
6063-T6aluminium alloy
Neuro-genetic approach
Caydas and Hascalik [47]
2008
AWJM
AA7075aluminium alloy
Taguchi method and ANN
Zain et.al [48]
2011
AWJM
AA7075aluminium alloy
Wenjun et.al [49]
2011
AWJM
C-17 steel
incorporated approach of ANN and SA Arbitrary Lagrange–Euler algorithm
Taguchi method and Gauss elimination method Multiple regression and design of experiments (DOE Taguchi method
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3.
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Conclusions In this review article, the optimization aspects and methods adopted for various advanced machining processes like WEDM, EDM, LBM, and AWJM are taken into account. A thorough literature review about the parameter optimization of these processes has been summarised. The various processes and the work material used by the researcher have been presented. The following observation has been made based on the review work. Lot of work has been conducted on WEDM and EDM processes when compared with other processes. The most widely used modelling technique was found to be the RSM technique. The most commonly used optimization technique was Taguchi’s optimization. Some researchers combined Taguchi’s method with other methods like SA, GRA to obtain multiple optimizations at a single time. Very few works have been carried out on the Artificial Bee Colony optimization technique. Very less work on Water jet machining process parameters optimization have been carried out. References 1) 2) 3) 4) 5)
6) 7) 8) 9) 10) 11) 12) 13) 14) 15) 16) 17) 18) 19)
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