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Study of surface integrity and dimensional accuracy in EDM using Fuzzy TOPSIS and sensitivity analysis

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(BQ) Surface integrity and dimensional accuracy remain critical concern in Electrical Discharge Machining (EDM). The current research work aims at investigating the influence of various EDM process parameters like pulse current (Ip), pulse-on time (Ton), tool work time (Tw) and tool lift time (Tup) on various aspects of surface integrity like white layer thickness (WLT), surface crack density (SCD) and surface roughness (SR). The dimensional accuracy, characterized by over cut (OC), has also been studied in the similar way.

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Nội dung Text: Study of surface integrity and dimensional accuracy in EDM using Fuzzy TOPSIS and sensitivity analysis

Measurement 63 (2015) 364–376<br /> <br /> Contents lists available at ScienceDirect<br /> <br /> Measurement<br /> journal homepage: www.elsevier.com/locate/measurement<br /> <br /> Study of surface integrity and dimensional accuracy in EDM<br /> using Fuzzy TOPSIS and sensitivity analysis<br /> S. Dewangan a, S. Gangopadhyay a,⇑, C.K. Biswas a,b<br /> a<br /> b<br /> <br /> AISI P20 tool steel<br /> <br /> Department of Mechanical Engineering, National Institute of Technology, Rourkela-769008, India<br /> Department of Petroleum Engineering, Universiti Teknologi Petronas, Ipoh, Malaysia<br /> <br /> a r t i c l e<br /> <br /> i n f o<br /> <br /> Article history:<br /> Received 28 November 2013<br /> Received in revised form 26 September 2014<br /> Accepted 27 November 2014<br /> Available online 8 December 2014<br /> Keywords:<br /> Electrical Discharge Machining (EDM)<br /> AISI P20 tool steel<br /> Fuzzy-TOPSIS<br /> Sensitivity analysis<br /> <br /> a b s t r a c t<br /> Surface integrity and dimensional accuracy remain critical concern in Electrical Discharge<br /> Machining (EDM). The current research work aims at investigating the influence of various<br /> EDM process parameters like pulse current (Ip), pulse-on time (Ton), tool work time (Tw)<br /> and tool lift time (Tup) on various aspects of surface integrity like white layer thickness<br /> (WLT), surface crack density (SCD) and surface roughness (SR). The dimensional accuracy,<br /> characterized by over cut (OC), has also been studied in the similar way. A response surface<br /> methodology (RSM) – based design of experiment has been considered for this purpose.<br /> The present study also recommends an optimal setting of EDM process parameters with<br /> an aim to improve surface integrity aspects after EDM of AISI P20 tool steel. This has been<br /> achieved by simultaneous optimization of multiple attributes (i.e. WLT, SCD, SR and OC)<br /> using Fuzzy-TOPSIS-based multi-criteria decision making (MCDM) approach. The optimal<br /> solution was obtained based on five decision makers’ preferences on the four responses<br /> (i.e. WLT, SCD, SR, and OC). From this analysis, an optimal condition of process parameters<br /> of Ip = 1 A, Ton = 10 ls, Tw = 0.2 s, and Tup = 1.5 s has been determined. Furthermore, sensitivity analysis was also carried out to study the sensitivity or robustness of five decision<br /> makers’ preference of optimal machining parameters. Form this study, decision makers’<br /> preference for surface crack density has been found to be the most sensitive response<br /> and therefore should be chosen first and analyzed very carefully.<br /> Ó 2014 Elsevier Ltd. All rights reserved.<br /> <br /> 1. Introduction<br /> Electrical Discharge Machining (EDM) is an erosion process, whereby rapidly recurring spark is generated<br /> between the tool and the workpiece in order to remove<br /> the materials form the later. EDM is the one of the most<br /> versatile non-conventional machining processes since the<br /> effectiveness of EDM process is absolutely independent of<br /> mechanical properties of the workpiece material. Therefore, very hard and difficult-to-cut materials can be effectively machined into desired complex shape, if the work<br /> ⇑ Corresponding author. Tel.: +91 9439096336; fax: +91 661 2472926.<br /> E-mail address: soumya.mech@gmail.com (S. Gangopadhyay).<br /> http://dx.doi.org/10.1016/j.measurement.2014.11.025<br /> 0263-2241/Ó 2014 Elsevier Ltd. All rights reserved.<br /> <br /> piece materials are electrically conductive. During the process of EDM, material removal takes place due to melting<br /> and vaporization from the localized zone of the workpiece.<br /> Thermal energy liberated during EDM due to generation of<br /> spark leads to formation of thermally affected layers on the<br /> machined surface. The properties of such layers are different from parent workpiece material [1]. Therefore, surface<br /> integrity in EDM is an issue which requires considerable<br /> research attention. Surface integrity in EDM is usually<br /> characterized by surface roughness, formation of recast<br /> layer or white layer and surface cracks, residual stress<br /> and metallurgical modification of parent material [2].<br /> Therefore, if the surface integrity is not adequately<br /> addressed, the EDMed component would be more prone<br /> <br /> 365<br /> <br /> S. Dewangan et al. / Measurement 63 (2015) 364–376<br /> Table 2<br /> Linguistic variable for the important weight of each output.<br /> Fuzzy subset<br /> <br /> Fig. 1. Experimental setup.<br /> <br /> Table 1<br /> Machining parameters and their levels.<br /> Parameters<br /> <br /> Symbol<br /> <br /> Level<br /> <br /> Unit<br /> <br /> 1<br /> <br /> 2<br /> <br /> 3<br /> <br /> 3<br /> 80<br /> 0.6<br /> 0.75<br /> <br /> 5<br /> 150<br /> 1.0<br /> 1.5<br /> <br /> Control parameters<br /> Pulse current<br /> Pulse on Time<br /> Tool work time<br /> Tool lift time<br /> <br /> Ip<br /> Ton<br /> Tw<br /> Tup<br /> <br /> 1<br /> 10<br /> 0.2<br /> 0.0<br /> <br /> Fixed parameters<br /> Duty cycle<br /> Voltage<br /> Flashing pressure<br /> Sensitivity<br /> Inter electrode gap<br /> <br /> f<br /> V<br /> Fp<br /> SEN<br /> IEG<br /> <br /> 70<br /> 45<br /> 0.3<br /> 6<br /> 90<br /> <br /> A<br /> <br /> ls<br /> s<br /> s<br /> %<br /> V<br /> Kg f/cm2<br /> <br /> lm<br /> <br /> to failure during its intended application. Surface finish in<br /> EDM attracted significant research interest. Different<br /> mathematical models have been developed to correlate<br /> surface roughness with various EDM parameters like discharge current (Ip), pulse-on time (Ton), pulse-off time<br /> (Toff), duty cycle (Tau), polarity, input power, and thermal<br /> physical and electrical properties of workpiece and tool<br /> [3,4]. It has been found that process parameters like Ip<br /> and Ton played a major role in influencing EDMed surface<br /> roughness. For better surface finish, Ip and Ton should preferably be low [5,6]. Effect of EDM parameters on white<br /> layer and surface crack formation for D2 and H13 tool steel<br /> was studied by Lee and Tai [7]. It was observed that white<br /> <br /> Respected fuzzy weight<br /> <br /> Tiny (T)<br /> Very Small (VS)<br /> Small (S)<br /> Medium (M)<br /> Large (L)<br /> Very Large (VL)<br /> Huge (H)<br /> <br /> (0.000, 0.000, 0.0769, 0.1538)<br /> (0.0769, 0.1538, 0.2307, 0.3076)<br /> (0.2307, 0.3076, 0.3845, 0.4612)<br /> (0.3845, 0.4614, 0.5383, 0.6152)<br /> (0.5383, 0.6152, 0.6921, 0.7690)<br /> (0.6921, 0.7690, 0.8459, 0.9228)<br /> (0.8459, 0.9228, 1.000, 1.000)<br /> <br /> layer thickness (WLT) and induced residual stress<br /> appeared to increase at higher value of Ip and Ton. Cracks<br /> found on the transverse plane of machined component<br /> was quantified it terms of surface crack density (SCD)<br /> which increased at lower Ip and decreased as Ton was<br /> increased. Similar observation was also made on AISI<br /> 1045 steel [8] and AISI D2 tool steel [1]. Pradhan [9] determined optimal setting of EDM parameters using RSM combined with grey relation analysis (GRA) as multi-objective<br /> optimization technique with an aim to achieve improved<br /> surface integrity during EDM of AISI D2 tool steel.<br /> Another issue of concern during EDM is overcut phenomenon due to sparking from lateral surface or corner<br /> of bottom surface of the tool electrode. This leads to<br /> dimensional inaccuracy of EDMed component. Pulse current and pulse-on time have been found to be major<br /> parameters in influencing overcut. Increase in both Ip and<br /> Ton resulted in rise in overcut [10–12] owing to higher<br /> amount of discharge energy associated with them. However, inverse relationship between Ip and overcut has also<br /> been reported during micro-EDM of Ti–6Al–4V alloy [13].<br /> It is evident that EDM is always characterized by multiple output responses. Therefore, multi-objective optimization has become one of the major areas of research in EDM<br /> for determining optimal process condition. In the recent<br /> years, fuzzy logic-based multi-criteria decision making<br /> approaches have become very popular in optimization of<br /> different manufacturing processes. Sivapirakasam et al.<br /> [14] applied Fuzzy-TOPSIS technique to optimize various<br /> responses like process time, relative electrode wear rate,<br /> process energy and consumption of dielectric fluid during<br /> EDM of tool steel. Grey-fuzzy logic-based optimization<br /> technique was utilized to optimize MRR, TWR and SR during EDM of SKD11 alloy steel [15]. Puhan et al. [16] integrated principal component analysis (PCA) and fuzzy<br /> inference system coupled with Taguchi method to find<br /> out optimal condition of EDM parameters.<br /> <br /> Fig. 2. Membership function of responses.<br /> <br /> 366<br /> <br /> S. Dewangan et al. / Measurement 63 (2015) 364–376<br /> <br /> Table 3<br /> Decision maker for responses with aggregated fuzzy weight.<br /> Responses<br /> <br /> Decision Maker (DM)<br /> <br /> Aggregated fuzzy weight<br /> <br /> DM-1<br /> <br /> DM-3<br /> <br /> DM-4<br /> <br /> DM-5<br /> <br /> VS<br /> S<br /> S<br /> M<br /> <br /> WLT<br /> SCD<br /> SR<br /> OC<br /> <br /> DM-2<br /> S<br /> VS<br /> M<br /> S<br /> <br /> S<br /> VS<br /> S<br /> S<br /> <br /> T<br /> S<br /> M<br /> VS<br /> <br /> S<br /> T<br /> M<br /> S<br /> <br /> (0.1538,<br /> (0.1230,<br /> (0.3230,<br /> (0.2307,<br /> <br /> 0.2153,<br /> 0.1846,<br /> 0.3999,<br /> 0.3076,<br /> <br /> 0.2922,<br /> 0.2615,<br /> 0.4768,<br /> 0.3845,<br /> <br /> 0.3691)<br /> 0.3348)<br /> 0.5537)<br /> 0.4612)<br /> <br /> Table 4<br /> Design matrix and the normalized response value.<br /> Run no.<br /> <br /> Pt type<br /> <br /> Ip<br /> <br /> Ton<br /> <br /> Tw<br /> <br /> Tup<br /> <br /> WLT (lm)<br /> <br /> SCD (lm/lm2)<br /> <br /> SR (lm)<br /> <br /> OC (mm)<br /> <br /> 1<br /> 2<br /> 3<br /> 4<br /> 5<br /> 6<br /> 7<br /> 8<br /> 9<br /> 10<br /> 11<br /> 12<br /> 13<br /> 14<br /> 15<br /> 16<br /> 17<br /> 18<br /> 19<br /> 20<br /> 21<br /> 22<br /> 23<br /> 24<br /> 25<br /> 26<br /> 27<br /> 28<br /> 29<br /> 30<br /> <br /> 0<br /> 1<br /> 1<br /> 1<br /> 1<br /> 0<br /> 1<br /> 1<br /> 1<br /> 1<br /> À1<br /> À1<br /> À1<br /> À1<br /> 0<br /> À1<br /> À1<br /> 0<br /> À1<br /> À1<br /> 1<br /> 1<br /> 1<br /> 0<br /> 1<br /> 0<br /> 1<br /> 1<br /> 1<br /> 1<br /> <br /> 2<br /> 3<br /> 1<br /> 1<br /> 3<br /> 2<br /> 3<br /> 1<br /> 3<br /> 1<br /> 2<br /> 2<br /> 2<br /> 3<br /> 2<br /> 2<br /> 1<br /> 2<br /> 2<br /> 2<br /> 1<br /> 3<br /> 3<br /> 2<br /> 1<br /> 2<br /> 1<br /> 3<br /> 3<br /> 1<br /> <br /> 2<br /> 3<br /> 1<br /> 3<br /> 1<br /> 2<br /> 3<br /> 3<br /> 1<br /> 1<br /> 2<br /> 2<br /> 1<br /> 2<br /> 2<br /> 3<br /> 2<br /> 2<br /> 2<br /> 2<br /> 3<br /> 3<br /> 1<br /> 2<br /> 1<br /> 2<br /> 3<br /> 3<br /> 1<br /> 1<br /> <br /> 2<br /> 3<br /> 3<br /> 3<br /> 3<br /> 2<br /> 1<br /> 1<br /> 1<br /> 1<br /> 2<br /> 1<br /> 2<br /> 2<br /> 2<br /> 2<br /> 2<br /> 2<br /> 2<br /> 3<br /> 3<br /> 3<br /> 3<br /> 2<br /> 3<br /> 2<br /> 1<br /> 1<br /> 1<br /> 1<br /> <br /> 2<br /> 3<br /> 3<br /> 1<br /> 1<br /> 2<br /> 1<br /> 3<br /> 3<br /> 1<br /> 1<br /> 2<br /> 2<br /> 2<br /> 2<br /> 2<br /> 2<br /> 2<br /> 3<br /> 2<br /> 3<br /> 1<br /> 3<br /> 2<br /> 1<br /> 2<br /> 1<br /> 3<br /> 1<br /> 3<br /> <br /> 12.452<br /> 28.379<br /> 3.755<br /> 7.479<br /> 15.633<br /> 15.209<br /> 26.882<br /> 10.271<br /> 17.469<br /> 6.954<br /> 18.243<br /> 13.717<br /> 7.299<br /> 23.166<br /> 14.66<br /> 17.302<br /> 4.972<br /> 17.993<br /> 16.305<br /> 13.001<br /> 9.595<br /> 29.842<br /> 16.553<br /> 16.435<br /> 3.146<br /> 18.275<br /> 9.267<br /> 24.615<br /> 19.594<br /> 6.684<br /> <br /> 0.0210<br /> 0.0078<br /> 0.0662<br /> 0.0703<br /> 0.0210<br /> 0.0066<br /> 0.0066<br /> 0.0605<br /> 0.0004<br /> 0.0202<br /> 0.0093<br /> 0.0110<br /> 0.0011<br /> 0.0009<br /> 0.0096<br /> 0.0186<br /> 0.0637<br /> 0.0134<br /> 0.0031<br /> 0.0156<br /> 0.0690<br /> 0.0092<br /> 0.0370<br /> 0.0027<br /> 0.0730<br /> 0.0071<br /> 0.0650<br /> 0.0069<br /> 0.0010<br /> 0.0230<br /> <br /> 4.86<br /> 7.13<br /> 1.73<br /> 1.73<br /> 3.40<br /> 5.20<br /> 5.86<br /> 2.00<br /> 3.66<br /> 2.06<br /> 4.66<br /> 5.26<br /> 3.20<br /> 6.06<br /> 5.06<br /> 5.06<br /> 2.89<br /> 5.40<br /> 4.66<br /> 4.80<br /> 1.73<br /> 5.53<br /> 3.26<br /> 4.80<br /> 1.66<br /> 4.96<br /> 1.86<br /> 6.60<br /> 3.00<br /> 1.82<br /> <br /> 0.1775<br /> 0.1950<br /> 0.0067<br /> 0.2100<br /> 0.0935<br /> 0.1684<br /> 0.1934<br /> 0.0180<br /> 0.2667<br /> 0.1017<br /> 0.1650<br /> 0.1450<br /> 0.1885<br /> 0.1650<br /> 0.1734<br /> 0.1955<br /> 0.0267<br /> 0.1409<br /> 0.1850<br /> 0.1217<br /> 0.1267<br /> 0.1934<br /> 0.1550<br /> 0.1617<br /> 0.1134<br /> 0.1610<br /> 0.1246<br /> 0.1950<br /> 0.1734<br /> 0.0417<br /> <br /> Rij<br /> WLT<br /> <br /> From the study of past literature, it is evident that some<br /> research work has been carried out to examine the influence of EDM parameters on surface integrity aspects and<br /> overcut phenomenon separately. However, it is also very<br /> essential to determine optimal parametric combination in<br /> order to achieve maximum dimensional accuracy (i.e. minimum overcut) as well as improved surface integrity.<br /> Although AISI P20 tool steel has wide industrial applications in the manufacturing of plastic molds, frames for<br /> plastic pressure dies, hydro forming tools and many more,<br /> surface integrity and dimensional accuracy during EDM of<br /> AISI P20 tool steel has hardly been reported so far.<br /> Therefore, the current study aims at investigating the<br /> influence of different EDM process parameters on different<br /> surface integrity aspects like surface roughness (SR), white<br /> layer thickness (WLT) and surface crack density (SCD) and<br /> dimensional accuracy in terms of overcut (OC) phenomenon. The second major objective of the current study is<br /> to determine optimal setting of EDM parameters using<br /> <br /> SCD<br /> <br /> SR<br /> <br /> OC<br /> <br /> 0.138<br /> 0.316<br /> 0.042<br /> 0.083<br /> 0.174<br /> 0.169<br /> 0.299<br /> 0.114<br /> 0.194<br /> 0.077<br /> 0.203<br /> 0.153<br /> 0.081<br /> 0.258<br /> 0.163<br /> 0.192<br /> 0.055<br /> 0.200<br /> 0.181<br /> 0.145<br /> 0.107<br /> 0.332<br /> 0.184<br /> 0.183<br /> 0.035<br /> 0.203<br /> 0.103<br /> 0.274<br /> 0.218<br /> 0.074<br /> <br /> 0.111<br /> 0.041<br /> 0.349<br /> 0.371<br /> 0.111<br /> 0.035<br /> 0.035<br /> 0.319<br /> 0.002<br /> 0.107<br /> 0.049<br /> 0.058<br /> 0.006<br /> 0.005<br /> 0.051<br /> 0.098<br /> 0.336<br /> 0.071<br /> 0.016<br /> 0.082<br /> 0.364<br /> 0.049<br /> 0.195<br /> 0.014<br /> 0.385<br /> 0.037<br /> 0.343<br /> 0.036<br /> 0.005<br /> 0.121<br /> <br /> 0.206<br /> 0.302<br /> 0.073<br /> 0.073<br /> 0.144<br /> 0.220<br /> 0.248<br /> 0.085<br /> 0.155<br /> 0.087<br /> 0.197<br /> 0.223<br /> 0.135<br /> 0.256<br /> 0.214<br /> 0.214<br /> 0.122<br /> 0.228<br /> 0.197<br /> 0.203<br /> 0.073<br /> 0.234<br /> 0.138<br /> 0.203<br /> 0.070<br /> 0.210<br /> 0.079<br /> 0.279<br /> 0.127<br /> 0.077<br /> <br /> 0.205<br /> 0.226<br /> 0.008<br /> 0.243<br /> 0.108<br /> 0.195<br /> 0.224<br /> 0.021<br /> 0.309<br /> 0.118<br /> 0.191<br /> 0.168<br /> 0.218<br /> 0.191<br /> 0.201<br /> 0.226<br /> 0.031<br /> 0.163<br /> 0.214<br /> 0.141<br /> 0.147<br /> 0.224<br /> 0.179<br /> 0.187<br /> 0.131<br /> 0.186<br /> 0.144<br /> 0.226<br /> 0.201<br /> 0.048<br /> <br /> Fuzzy-TOPSIS-based multi criteria decision making<br /> (MCDM) approach by simultaneously considering surface<br /> integrity and dimensional accuracy aspects. Sensitivity<br /> analysis would also be carried out to study the sensitivity<br /> or robustness of five decision makers’ preference on optimal machining parameters.<br /> 2. Experimental details<br /> 2.1. Equipment, machining process and workpiece material<br /> The experiments were conducted on Electronica Electraplus PS 50ZNC die sinking EDM equipment. Commercial<br /> grade EDM oil (with specific gravity of 0.763 and flash<br /> point of 94 °C) is used as dielectric fluid. The selected<br /> EDM parameters for the current research work include<br /> pulse current (Ip), pulse-on time (Ton), tool work time<br /> (Tw) and tool lift time (Tup). The sparking cycle consists of<br /> Tw and Tup. Tw is made up of multiple sparks each of which<br /> <br /> S. Dewangan et al. / Measurement 63 (2015) 364–376<br /> <br /> tempered for better toughness. A commercially pure<br /> (99.9% purity) and cylindrical shaped copper with 12 mm<br /> diameter was used as tool electrode. The workpiece<br /> (+ve polarity) and tool (Àve polarity) are shown in Fig. 1.<br /> <br /> Table 5<br /> Positive and negative ideal value.<br /> Sj+ and SjÀ<br /> WLT<br /> SÀ<br /> 1<br /> S+<br /> 1<br /> SCD<br /> SÀ<br /> 2<br /> S+<br /> 2<br /> SR<br /> À<br /> S3<br /> S+<br /> 3<br /> OC<br /> SÀ<br /> 4<br /> S+<br /> 4<br /> <br /> 367<br /> <br /> (0.0510, 0.0714, 0.0970, 0.1225)<br /> (0.0054, 0.0075, 0.0102, 0.0129)<br /> (0.0473, 0.0711, 0.1007, 0.1303)<br /> (0.0003, 0.0004, 0.0006, 0.0007)<br /> (0.0974, 0.1206, 0.1438, 0.1670)<br /> (0.0227, 0.0281, 0.0335, 0.0389)<br /> (0.0712, 0.0949, 0.1187, 0.1423)<br /> (0.0018, 0.0024, 0.0030, 0.0035)<br /> <br /> Table 6<br /> Closeness coefficient.<br /> Run no.<br /> <br /> diÀ<br /> <br /> d+<br /> i<br /> <br /> CCi<br /> <br /> 1<br /> 2<br /> 3<br /> 4<br /> 5<br /> 6<br /> 7<br /> 8<br /> 9<br /> 10<br /> 11<br /> 12<br /> 13<br /> 14<br /> 15<br /> 16<br /> 17<br /> 18<br /> 19<br /> 20<br /> 21<br /> 22<br /> 23<br /> 24<br /> 25<br /> 26<br /> 27<br /> 28<br /> 29<br /> 30<br /> <br /> 0.3821<br /> 0.2268<br /> 0.5684<br /> 0.3755<br /> 0.4825<br /> 0.3968<br /> 0.2854<br /> 0.5260<br /> 0.3765<br /> 0.5767<br /> 0.3945<br /> 0.4109<br /> 0.5123<br /> 0.3364<br /> 0.3936<br /> 0.3384<br /> 0.5101<br /> 0.3783<br /> 0.4053<br /> 0.4387<br /> 0.4324<br /> 0.2735<br /> 0.3939<br /> 0.4190<br /> 0.4731<br /> 0.3922<br /> 0.4412<br /> 0.2699<br /> 0.4601<br /> 0.6275<br /> <br /> 0.3554<br /> 0.5107<br /> 0.1692<br /> 0.3620<br /> 0.2550<br /> 0.3407<br /> 0.4521<br /> 0.2116<br /> 0.3611<br /> 0.1608<br /> 0.3430<br /> 0.3266<br /> 0.2252<br /> 0.4011<br /> 0.3439<br /> 0.3991<br /> 0.2274<br /> 0.3592<br /> 0.3322<br /> 0.2988<br /> 0.3051<br /> 0.4640<br /> 0.3437<br /> 0.3185<br /> 0.2644<br /> 0.3453<br /> 0.2963<br /> 0.4676<br /> 0.2774<br /> 0.1100<br /> <br /> 0.5181<br /> 0.3075<br /> 0.7707<br /> 0.5092<br /> 0.6543<br /> 0.5380<br /> 0.3869<br /> 0.7132<br /> 0.5104<br /> 0.7819<br /> 0.5349<br /> 0.5572<br /> 0.6946<br /> 0.4561<br /> 0.5337<br /> 0.4588<br /> 0.6916<br /> 0.5130<br /> 0.5495<br /> 0.5948<br /> 0.5864<br /> 0.3709<br /> 0.5340<br /> 0.5681<br /> 0.6415<br /> 0.5317<br /> 0.5983<br /> 0.3660<br /> 0.6239<br /> 0.8508<br /> <br /> is associated with pulse-on time (Ton) and pulse-off time<br /> (Toff). Tw is followed by Tup i.e. duration for which the tool<br /> will be lifted up to facilitate effective flushing of dielectric<br /> fluid across the spark gap. Obviously, there is no sparking<br /> during the period of Tup. Flushing was intermittent and carried out through a solenoid valve that is synchronized with<br /> the lifting of tool. The values of control parameters along<br /> with their levels and those of fixed parameters are provided in Table 1. The work piece material is AISI P20 tool<br /> steel with semi-circular shape (100 mm diameter and<br /> 10 mm thickness). The composition of AISI P20 tool steel<br /> is 0.4% C, 1% Mn, 0.4% Si, 1.2% Cr, 0.35% Mo, 0.25% Cu,<br /> 0.03% P, 0.03% S. The work piece is first heated to the temperature range of 843–898 °C in a controlled furnace and<br /> held for half an hour. Then, it is oil quenched and later<br /> <br /> 2.2. Measurement of responses<br /> In the current study, surface integrity was characterized<br /> by machined surface roughness, formation of white layer<br /> and surface cracks. While formation of white layer was<br /> investigated in the form of white layer thickness (WLT),<br /> surface cracks were quantified by means of surface crack<br /> density (SCD). Dimensional accuracy was characterized<br /> by overcut (OC) phenomenon. The measurement techniques for these output responses have been described<br /> briefly in the following section.<br /> 2.2.1. White layer thickness<br /> For the measurement of recast or white layer, each<br /> specimen was sectioned vertically followed by polishing<br /> of each specimen with different grades of polishing papers<br /> with deceasing grit size. The polished surface was then<br /> etched with Nital solution to reveal microstructure along<br /> with white layer. Images were then captured on five different locations of each specimen using optical microscope<br /> (with model: SCD313 BPD and make: Radical Instrument)<br /> with a magnification of 400X. These images were then<br /> used to determine white layer thickness (WLT). Recast area<br /> was measured using software (PDF X-change viewer) and<br /> then the area was divided by total length of optical microscopic images to get the average height of white layer (i.e.<br /> WLT).<br /> 2.2.2. Surface crack density<br /> In order to measure density of surface cracks, the top<br /> surface morphology of the EDMed surface was studied<br /> using scanning electron microscopy (SEM) at a magnification of 1000X. Randomly five sample areas were selected<br /> on each specimen and the length of cracks was measured<br /> using same software. The average crack length on each<br /> specimen was divided by area of each micrograph<br /> (10649.072 lm2) to measure the SCD. Similar measurement of SCD has been reported elsewhere [1,16].<br /> 2.2.3. Surface roughness<br /> The measurement of surface roughness (Ra value) of the<br /> EDMed surface was made with portable style type profilometer, Talysurf (Model: Taylor Hobson, Surtronic 3+),<br /> with cut-off length (Lc) of 0.8 mm, sample length (Ln) of<br /> 4 mm, and filter CR ISO.<br /> 2.2.4. Overcut<br /> The over cut which is a measure of dimensional accuracy is calculated by half the difference between the sizes<br /> of the cavity and the diameter of the tool after EDM process. The OC was measured on a tool makers microscope<br /> (Make: Carl Zeiss) with an accuracy of 0.001 mm using following equation.<br /> <br /> OC ¼<br /> <br /> Djt À Dt<br /> 2<br /> <br /> ð1Þ<br /> <br /> 368<br /> <br /> S. Dewangan et al. / Measurement 63 (2015) 364–376<br /> <br /> Fig. 3. Main effect plots for WLT.<br /> <br /> where Dj is the diameter of hole produced in the workpiece<br /> and Dt is the diameter of tool.<br /> 3. Analysis methods<br /> 3.1. Experimental design using RSM<br /> Response surface methodology (RSM) consists of mathematical and statistical techniques that can be utilized for<br /> modeling and analysis of problems. This methodology is of<br /> particular interest when output is influenced by several<br /> variables and the goal is to determine relationship<br /> between the output and the input variables [17,18]. Moreover, RSM helps to extract significant amount of information from small number of experimental runs. Since EDM<br /> involves a large number of process variables, in the current<br /> study, experiment has been designed using RSM using<br /> face-centered central composite design (CCD) with four<br /> variables (Ip, Ton, Tw and Tup). This scheme of design yields<br /> a total of 30 runs (as shown in Table 4) in three blocks,<br /> where the cardinal points used are sixteen cube points,<br /> eight axial points and six center points [19]. RSM-based<br /> design of experiment helps to conveniently study the influence of different process parameters on output responses<br /> (i.e. SR, WLT, SCD and OC) in the form of mean effect of<br /> plot.<br /> 3.2. Fuzzy TOPSIS<br /> After studying the influence of different machining<br /> parameters on surface integrity and dimensional accuracy<br /> aspects, further attempt would be made to determine an<br /> optimal setting of EDM parameters using technique for<br /> order of preference by similarity to ideal solution (TOPSIS)<br /> method. TOPSIS method for multiple attribute optimization was proposed by [20]. This method was used in fuzzy<br /> environments [21] and fits human thinking under actual<br /> <br /> environment. Fuzzy numbers such as triangular and trapezoidal are primarily used for modeling the uncertainty<br /> under multiple engineering environments. While triangular fuzzy numbers have been widely studied, trapezoidal<br /> numbers are sometimes preferred due to its computational<br /> simplicity [22]. The advantage is that trapezoidal fuzzy<br /> number of the form (a, b, c, d) presents most fundamental<br /> class of fuzzy numbers with linear membership function<br /> over the triangular fuzzy number (a, b, c) [22–24]. Therefore, this technique can be utilized for modeling linear<br /> uncertainty in various engineering applications [24–26].<br /> The fuzzy linguistic variable is designated using trapezoidal fuzzy number that is shown in Fig. 2. According to this<br /> figure, the value of fuzzy weight numbers are denoted by<br /> linguistic values that are shown in Table 2. The similar type<br /> of calculation in triangular fuzzy number is described by<br /> Sivapirakasam et al. [14]. The five decision makers give<br /> their decisions of responses for each attribute weight in<br /> linguistic term that are shown in Table 3. The average<br /> fuzzy weight of the decision makers for each response<br /> parameter is shown in same table. The experimental<br /> design matrix along with normalized response (Rij) is<br /> shown in Table 4. Rij is the normalized value and this normalized matrix can be calculated by Eq. (2).<br /> <br /> xij<br /> Rij ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi<br /> P30 2<br /> i¼1 xij<br /> <br /> ð2Þ<br /> <br /> where xij is the experimental value of the ith attribute of<br /> the jth experimental run.<br /> In the next step, all attributes of normalized matrix<br /> (Rij’s) are multiplied by fuzzy weights. Then resultant<br /> matrix is called weighted performance matrix which is<br /> denoted by Sij (for ith experimental run and jth response).<br /> Similar methodology was adopted by different researchers<br /> [14,27,28]. Now positive ideal solution (S+) and negative<br /> ideal solution are expressed by the following equations<br /> and their values are provided in Table 5.<br /> <br />
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