- Email: [email protected]
Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review
Materials Today: Proceedings xxx (xxxx) xxx
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review Divya Marelli a, Singh S.K. b, Sateesh Nagari b, Ram Subbiah b a b
Design for Manufacturing, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India Mechanical Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India
a r t i c l e
i n f o
Article history: Received 10 December 2019 Received in revised form 11 January 2020 Accepted 13 January 2020 Available online xxxx Keywords: Wire EDM ANOVA Kerf width TAGUCHI Super alloys GRA PCA ANN
a b s t r a c t Wire-cut Electric Discharge Machining (WEDM) is an efficient machining process in numerous applications like space craft, defense, transportation vehicles, micro systems, farm machinery. It is used to machine conductive and hard materials like metal matrix composites, ceramic composites and super alloys. Super alloys exhibit excellent mechanical strength and creep resistance at high temperatures, good surface stability, and corrosion and oxidation resistance. The development of super alloys has primarily been driven by the aerospace and power industries. In this review of papers the optimization of WEDM machining parameters, responses by deploying different techniques like Taguchi Method, ANOVA, GRA, ANN, PCA. These techniques were utilized for examination of impact of wire-cut EDM process parameters on material removal rate (MRR) and surface roughness (SR) on super alloys. Parameters like pulse off time, pulse on time, servo voltage, peak current, kerf width are utilized to optimize the material removal rate (MRR) and surface roughness (SR). Taguchi orthogonal array was selected based on the process parameters. ANOVA is utilized for advancing parameters with the goal that greatest material removal rate and least surface roughness is acquired. Ó 2020 Elsevier Ltd. All rights reserved. Selection and of the scientific committee of the 10th International Conference of Materials Processing and Characterization.
1. Introduction Super alloys are an important group of high-temperature materials used in the hottest sections of jet and rocket engines where temperatures reach 1200–1400 °C. Super alloys are based on nickel, cobalt or iron with large additions of alloying elements to provide strength, toughness and durability at high temperatures [6]. These alloys are usually based on nickel–chromium. Added alloying elements include the refractory materials such as tungsten and molybdenum for solid-solution strengthening. Cobalt enhances strength as well as oxidation and hot corrosion resistance. Aluminum and titanium promote the formation of the major strengthening gamma-prime, c0 , phase. Superalloys typically contain between 15 and 60% of this strengthening phase. Niobium contributes to the formation of the precipitate gamma doubleprime, c00 ; this phase is the primary strengthening phase in alloys such as alloy 718 [7]. Nimonic composites are widely utilized for the manufacturing of aero engine parts due to its high specific strength [8]. During E-mail address: [email protected] (D. Marelli)
prolonged exposure to raised temperatures, numerous kinds of metal start to break, distort, corrode, fatigue, and so on. Nimonic compounds, in any case, are known for the maintenance of significant mechanical properties, such as sway quality, yield quality, and hardness, in temperatures as high as 1100°F, contingent upon the evaluation [9]. The chemical composition of nimonic alloys ranges from 38 to 76 wt% nickel, up to 27 wt% chromium and 20 wt% cobalt. Other refractory components, for example, tungsten (W), tantalum (Ta) and molybdenum (Mo) might be added to expand their quality and oxidation properties. Stainless steel (S304) is utilized as a work piece. Stainless Steel 304 is a nickel and chromium based alloy, because of their exceptional corrosion resistant, high ductility, non-magnetic and it holds solid stage up to 1400 °C. In this investigation, brass wire having 65% of copper and 35% of zinc is chosen as a tool because of its properties, accessibility and minimal cost [10]. Inconel is a nickel–iron based super composite that offers high mechanical quality with great texture capacity and it discovers application in gas turbines. Since it gets work hardened during conventional machining, getting complex shapes, high dimensional precision and great surface quality is truly challenging
https://doi.org/10.1016/j.matpr.2020.01.306 2214-7853/Ó 2020 Elsevier Ltd. All rights reserved. Selection and of the scientific committee of the 10th International Conference of Materials Processing and Characterization.
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
2
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
[11]. The performance of electric discharge machining (EDM) factors is arbitrary and prompts uneven evacuation of material in each discharge. Inconel 625 Chemical Composition: Carbon-0.10%, Chromium20.0–23.0%, Iron 5.00%, Silicon 0.50%, Manganese 0.50%, Sulphur 0.015%, Phosphorus 0.015%, Molybdenum 8.00–10.0%, Titanium 0.40%, Cobalt 1.00%, Columbium + Tantalum 3.15–4.15%, Aluminium 0.40%, Nickel Balance Inconel 750 Chemical Composition: Ni 70% min, Cr 14–17%, Ti 2.25–2.75%, Nb 0.70–1.20%, Mn 1% max, Si 0.50% max, Cu 0.50% max, C 0.08% max WEDM is a thermoelectric cutting procedure wherein material is expelled because of unremitting sparkles created among wire and workpiece isolated by a minute space which reaches from 0.025 mm to 0.075 mm in the presence of dielectric which might be deionized water or hydrocarbon oil. The material expulsion happens because of dissolving and dissipation. WEDM can machine any intricate profile with exactness and precision on the material regardless of its hardness. It utilizes a thin wire as an instrument with a width in the scope of 0.02–0.3 mm made up of copper, metal, tungsten, molybdenum or zinc covered wires with a brass core. It can machine material of thickness going from 1 mm to 500 mm relying on the machine utilized. The most significant exhibition characteristics in WEDM are MRR, surface completion, and kerf width [16].
2. Literature review Sachin Ashok Sonawane et al performed a chain of experiments are executed on 5-axis ePULSE-40 sprint cut WEDM manufactured by Electronica Machine Tool Ltd., India. To ensure parallelism and perpendicularity of the workpiece concerned with the machine table a dial pointer is utilized. The material applied to the examination is the Nimonic-75 alloy of 30 mm 30 mm 5 mm thick which is clung to the negative extremity. The compound piece of Nimonic-75 amalgam is Carbon-0.08–0.15%, Chromium 18–21%, Copper-0.5% max, Iron-5% max, Manganese-1%, Silicon-1%, Titanium-0.2–0.6% and equalization Nickel. A square of measurement 10 mm 10 mm 5 mm is cut from the workpiece with the assistance of a metal wire of 0.25 mm diameter connected to the active polarity. The debris from the machining region is evacuated by de-ionized water which is utilized as a dielectric with a flushing pressure of 15 kg/cm2. The conductivity of the dielectric is held at a steady value of 20 mS/cm at 22 °C. Testing is done by zero wire offset. The servo feed value is fixed at 2120 units. In this study, six machining factors are considered, they are, pulse on time, servo voltage, peak current, pulse off time, wire feed rate, and cable tension. To decide on the suitable scope of machining factors, a series of pilot phase tests were led by moving each factor in turn while keeping different elements stable at some value. Considering the thickness of the workpiece and the deviation of machining characteristic, MRR about the cutting variables, the levels of the machining components are fixed. Taguchi’s L27 orthogonal array is utilized to achieve the analyses, and every trial is performed thrice consequently, overall 81 operations. The machining attributes required for the study are surface roughness, overcut and MRR [1]. Durairaj et al used Taguchi Technique to design the experiments [17], orthogonal arrays were generally utilized in planning experiments. It is utilized to decrease the number of trials should have been performed than the full factorial experiment. In view of the machine tool, cutting tool and work piece capability, the process parameters and the level for the process parameters were selected such as Gap Voltage, Wire Feed, Pulse ON schedule (Ton),
Pulse OFF time (Toff)., each parameter at four levels. L16 orthogonal array is used for conducting the series of experiments. The designed combination of input parameters and its corresponding surface roughness and kerf width were obtained. Taguchi technique was intended to optimize single performance of process parameters with high quality and lower cost. The test results are presently changed into a sign to-commotion (S/N) proportion [18]. Since surface hardness and kerf width is desired to be least, so Lower the Better characteristic is utilized for S/N ratio estimation. The ideal setting would be the one which could accomplish least S/N ratio [19]. Grey theory has been generally utilized in engineering analysis, and it uncovers the possibility to solve the setting of ideal machining parameters related with a procedure with multiple output parameters. Presently, the multiple objective optimization issues have been changed into a single equivalent objective function optimization problem utilizing this methodology. From the grey relational grade values acquired, the means of the grey relational grades at various degrees of procedure parameters were determined [2]. Amitesh Goswami et al demonstrated a cause and effect diagram for recognizing the potential factors that may influence the machining characteristics (MRR and SR) was built. From the cause and effect diagram and the literature on WEDM, all out six quantities of information parameters were chosen for this examination. In this work, L27 orthogonal array with six control factors viz., Ton, Toff, IP, WF, WT, SV and three associations viz. Ton Toff, Ton IP and Toff IP have been examined. Modified linear graph is utilized for the allocation of columns to the input parameters and interactions in the orthogonal array. Signal to noise ratio was acquired utilizing Minitab 16 programming. Higher is better (HB) for MRR and lower is better (LB) for SR were taken for acquiring optimum machining attributes. The S/N proportion can be determined as a logarithmic transformation of the loss function Multi-response optimization using utility concept is used in the study. Utility concept is different characteristics are combined and evaluated by term composite index. Such a composite index represents the utility of the item. In this paper it is assumed that the overall utility is the sum of utilities of individual quality characteristics [12]. Naveen Babu et al demonstrated that sheet of 6 mm thickness, Inconel 750 was analysed in this investigation. To perform the experiments L9 orthogonal array of Taguchi’s method was selected. The variables which are having significant impact on the performance of electric discharge machining was distinguished. They are (I) Pulse On schedule (ms), Pulse Off time (ms) (iii) Voltage (V) and (iv) current (Å) and these components were controlled during machining. A series of trial experiments were undertaken to determine the functional range of the factors. As the individual factor range was less, three level plan was utilized. Each bit of the size is 100 10 mm. For each machining the time taken was noted down. Likewise, after each machining weight was gauged. The material removal rate is determined [4]. Subrahmanyam et al performed tests on a wire-cut EDM machine with particular like design fixed column, moving table type with a size of the work piece 250 350 mm, type of interpolation is linear, power supply 3 stage, AC 415 V, 50 Hz. The work material is INCONEL 625 of size 30 15 2 mm plate. Wire diameter 0.25 mm made of Brass is utilized for the test. The wire is tensioned between the lower and upper guide for getting higher precision. Dielectric liquid is deionized water with 12 to 16 TDS (absolute broke up solids). For measurement of the Surface roughness, surface roughness analyzer was utilized. During the machining, both of the working surfaces may have present smooth and irregularities which causes least and the most extreme gap in the middle of the tool and workpiece [13]. At a
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
3
given instant at the minimum point, suitable voltage is developed, produce electrostatic field for the discharge of electrons from the cathode there electrons quickened towards the anode [20]. After high-speed electrons crash into the dielectric atoms, breaking them into negative and positive particles. Due to that sparkle is created with high temperature causes softening and vaporization of material from the work piece. The mean qualities for the outcomes got from the gadget are arranged as one of the yield parameters by utilizing Taguchi’s methodology [5].
3. Results and discussions Sachin Ashok Sonawane et al found that as the pulse on time and peak current enhances values of surface roughness, overcut and MRR enhances. Additionally, with increment in the estimations of pulse off time and servo voltage these values diminish. It is seen that quality attributes rely on measure of discharge energy released at the machining zone. The discharge energy is modulated by pulse on time and peak current [25]. With increment in pulse off time, the discharge frequency decreases within a given period likewise discharge gap gets widened because of increment in servo voltage. This creates low amount of heat energy which further outcomes into less volume of material removal and development of small, shallow craters on the cut surface. As the wire feed rate increases, contact term of wire and workpiece diminishes. This results into less amount of heat energy led into the workpiece prompting decline in the quality attributes. The impact of wire tension is seen as negligible. The optimal settings of the machining parameters are recognized as pulse on time 1(110) pulse off time 3(51) servo voltage 3(40) peak current 3(230) wire feed rate 3(5) cable tension 3(8). The optimal parameters settings are predicted and confirmation trials are conducted. ANOVA examination is done on the composite primary component to decide the significance of each process parameter on the responses of ANOVA. The individual percentage contribution of each machining parameter in the process execution is introduced in S/N proportion plot for ideal parameters settings for the composite primary component (CPC) [1] (Figs. 1–3). Durairaj et al demonstrated that as S/N Ratio decreases up to a short period at that point increases correspondingly to gap voltage. The S/N Ratio diminishes up to a specific point correspondingly to the wire feed. It is be seen that S/N Ratio increments up to a brief period at that point diminishes slowly when the pulse off time and wire feed increments. Regarding the increase in pulse on time, the S/N Ratio diminishes up to a short period and afterward increments step by
Fig. 1. Influence of machining factors on surface roughness [1].
Fig. 2. Graph showing values of CPC (W) [1].
Fig. 3. S/N ratio plot of CPC (W) [1].
step. The S/N proportion will be diverted with expanding and diminishing when the Gap Voltage increments. The outcomes got from the Grey relational analysis Optimization technique to get the minimum Surface Roughness and least Kerf Width are appeared in Table 1. From Fig. 4, it can seen that for a specific value of input parameter the corresponding range of event of surface roughness can be determined. From Fig. 5, it tends to be seen that for a specific value of input parameter the relating scope of event of kerf width can be determined [2]. Amitesh Goswami et al demonstrated that by an increase in pulse on time, discharge energy produced will be higher, consequently more powerful is the explosion, and this outcomes in expanded MRR [23]. Higher cutting velocity brings about higher estimation of surface harshness [24]. Expanding the beat on schedule and peak current builds the quantity of electrons striking the work surface along these lines dissolving out increasingly material from the work surface per release. So also, MRR diminishes with an increased pulse off time and sparkle gap set voltage. SEM micrograph shows that few craters have been observed, while machining was performed at high value of pulse off time, sparkle gap set voltage and low value of pulse on time. From SEM micrograph unmistakably there is an expansion in size of garbage with decline in pulse off time and sparkle gap set voltage and; increment in pulse on time when contrasted [3]. It is clear that all the factors significantly affect material removal rate. Pulse on time (46.09%) is seen as the major influencing factor for the MRR followed by pulse off time (32.97%), spark
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
4
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
Table 1 Grey Relational Grade value for corresponding levels [2]. Grey relational grade mean S. no
Process parameter
Level 1
Level 2
Level 3
Level 4
1 2 3 4
Gap voltage Wire feed Pulse-on time Pulse-off time
0.5778 0.6148 0.6347 0.6187
0.5780 0.5269 0.4324 0.5562
0.5851 0.6106 0.6006 0.5857
0.5191 0.5076 0.5924 0.4993
Fig. 4. Range of occurrence of surface roughness [2].
Fig. 5. Range of occurrence of kerf width [2].
gap set voltage (6.68%), peak current (1.45%), wire feed (0.84%) and wire tension (0.44%). A low value (1.49%) of experimental error has been observed. All the three interactions (Ton Toff, Ton IP and Toff IP) have seen as critical. So also the outcome for SR has been arranged. For SR, just four components: pulse on time (66.70%), spark gap set voltage (11.73%), pulse off time (9.09%) and peak current (3.75%) have been seen as noteworthy. An interaction has additionally been found between pulse on time and peak current. Surface roughness is mostly effected by pulse on time. The brass wire of cutting tool quickens depletion, which prompts in creation of built up layer. This increased built up layer brings about more unpleasant surface. Wire feed rate and wire tension strain has irrelevant impact on surface harshness. Henceforth, in Wire EDM of Nimonic 80A, higher machining rates couldn’t be acquired without giving up surface quality. The examination of the microstructure of machined work surface was performed for appraisal of the surface quality utilizing WEDM process. The Specimens were seen with scanning electron microscope (Hitachi S-3400 N) with an accelerating voltage of 10.0 kV. Three examples were chosen for microstructure perception, one example was machined at trial condition comparing to high info vitality where beat on time is set at its most significant level, beat off time being at least level, while top current has middle of the road level. Another two examples were machined at exploratory condition relating to low and moderate input energy rate (Figs. 6–8).
Naveen Babu et al utilized the ANN method which offers a more noteworthy prescience than some other model, such as, linear and exponential regression. The utilization of ANN is acknowledged by researchers as an prediction tool because of its remarkable capability of learning algorithm and coordinating of input and output association, in any event, for other than linear and complicated systems [14,15]. In Particle swarm optimisation (PSO), every particle is viewed as an individual among the population in dimensional solution space. Every particle is initiated with the position and velocity randomly and first position of every particle is assessed dependent on the target functions [21]. At that point gbest is distinguished among the outcomes yielded by the all particles in the populations. Based gbest, the pbest particle is fixed. Next velocity is added to the p best particle and made new population which characterizes the new (second) position of all particles. On the other hand gbest is distinguished among the outcomes yielded by the new made population which depends on ongoing pbest particle. This procedure is proceeded until the stop criteria take an action on the algorithm either number of iterations or no changes on the g best value At long last the model is accomplished with RMSE and assurance coefficient for MRR is 0.0053881 and 0.99995 and for SR is 0.0038324 and 0.99992 respectively. To execute the optimisation in PSO the accompanying parameters were utilized [22]. The
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
5
Fig. 6. a) Microstructure of the sample machined at low input energy rate, b) Microstructure of the sample machined at high input energy rate c) Microstructure of the machined sample [3].
Fig. 7. Multi Response Data (Utility) Main Effects Plots (MRR and SR) [3]. Fig. 9. Experimental no. vs experimental value of MRR and SR [4].
Fig. 8. Main effects plot (MRR and SR) [3].
population size, inertia weight, learning factors, such as, cognitive factor (C1) and social factor (C2) are 10, 0.1, 2 and 2 respectively. At last, it was seen that the improved value of MRR is 22.110471 mm3/min and its enhanced process parameters Pulse on time, Pulse off time, Voltage, and Current are 109.9931 ms, 62.971967 ms, 79.998425 V and 10.653109 Å individually. In addition it was discovered that the streamlined estimation of Surface Roughness is 2.3320223 mm and its optimized process factors Pulse on time, Pulse off time, Voltage, and Current are 100.12864 ms, 55.010629 ms, 20.794803 V and 10.505231 Å respectively Subrahmanyam et al demonstrated that the impact of machining parameter, i.e., Discharge current, Pulse on time and voltage on
Fig. 10. Configuration of three layered NN [4].
Fig. 11. Factors importance (%) on MRR and SR.[4].
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
6
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 12. (a) Main effects plot for Means; (b) Main effects plot for S/N ratios [5].
Fig. 13. (a) Main effects plot for means; (b) Main effects plot for S/N ratios [5].
material removal rate (MRR), surface roughness (SR) of Inconel 625 machined work piece with Brass Wire tool utilizing Taguchi strategy F-ratio builds up whether the process parameter is critical or not at a specific confidence level. The higher value of F-ratio shows that any little variation of the process parameter can make a huge effect on the performance characteristics. Increment in pulse off time builds MRR because increasing pulse off time number of electrons striking the work surface in a discharge thus increases the erosion of material from the work surface per discharge. As per this, Pulse-off time is seen as the most critical factor influencing MRR with the contribution of 61.90% (Figs. 12,13).
4. Conclusion Principal Component Analysis (PCA) based technique take into consideration association between various quality outputs and converts this into uncorrelated components known as principal components. These principal components reduce the number of aspects and trims down the convolution of the multicharacteristic problems. The optimal settings of the machining parameters based on the principal component analysis for the WEDM of Nimonic-75 alloy obtained are pulse-on time 110 ms, pulse-off time 51 ms, servo voltage 40 V, peak current 230 Amp., wire feed rate 5 m/min and cable tension 8 g (Figs. 9–11).
Experimental examination on wire electrical discharge machining of Stainless Steel (SS304) was performed utilizing brass wire of 0.25 mm. Taguchi technique, the optimized input parameter combinations to get the minimum surface roughness are 40 V gap voltage, 2 mm/min wire feed, 6 ls pulse on time, 10 ls pulse off time and correspondingly optimized conditions to get the minimum kerf width are 50 V gap voltage, 2 mm/min Wire Feed, 4 ls pulse on time, 6 ls pulse off time. Based on the Grey relational analysis, the optimised input parameter combinations to get both the minimum surface roughness and the nominal kerf width are 50 V gap voltage, 2 mm/min wire feed, 4 ls pulse on time and 4 ls pulse off time. The optimized value of surface roughness obtained through single response optimization has been as low as 0.16 mm with percent contribution of pulse-on time (66.70%) and spark gap set voltage (11.73%) have been found to dominate other factors such as pulse-off time (9.09%), peak current (3.75%), wire feed (0.22%) and wire tension (0.18%). Only one interaction (pulseon time peak current) has been found to be significant for roughness as response. The microstructure investigation of the samples machined at experimental condition corresponding to high energy input rate has revealed a strong co-relation between the surface quality and energy input rate. The samples machined at high energy input condition exhibited rougher surface with lot of builtedge layers, whereas the better surface quality was obtained under low energy input conditions.
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
The model was constructed between the process factors and output values by artificial neural network and the model was found that the root mean square error is as low as for MRR (0.0053881) and SR (0.0038324). Whereas the determination coefficient for MRR and SR is 0.99995 and 0.99992 respectively. The model trained by ANN was suitably incorporated with the evolutionary computational techniques of particle swarm optimization for optimizing the MRR and SR It is evident that when working on wire electric discharge machine with brass wire and INCONEL 625 as work piece, the above stated parameters gave the maximum material removal rate and minimum surface roughness. CRediT authorship contribution statement Marelli Divya: Conceptualization, Methodology, Writing original draft, Writing - review & editing. S.K. Singh: Supervision. N. Sateesh: Data curation, Visualization, Investigation, Supervision. Ram Subbiah: Supervision, Validation. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] Sachin Ashok Sonawane, M.L. Kulkarni, Optimization of machining parameters of WEDM for Nimonic-75 alloy using principal component analysis integrated with Taguchi method, J. King Saud Univ. Eng. Sci. 30 (3) (2018) 250–258, https://doi.org/10.1016/j.jksues.2018.04.001. [2] M. Durairaj, D. Sudharsun, N. Swamynathan, Analysis of process parameters in wire EDM with stainless steel using single objective taguchi method and multi objective grey relational grade, Procedia Eng. 64 (2013) 868–877, https://doi. org/10.1016/j.proeng.2013.09.163. [3] Amitesh Goswami, Jatinder Kumar, Optimization in wire-cut EDM of Nimonic80A using Taguchi’s approach and utility concept, Eng. Sci. Technol. Int. J. 17 (2014) (2014) 236–246. [4] K. Naveen Babu, R. Karthikeyan, A. Punitha, An integrated ANN – PSO approach to optimize the material removal rate and surface roughness of wire cut EDM on INCONEL 750, Mater. Today. Proc. 19 (2019) 501–505, https://doi.org/ 10.1016/j.matpr.2019.07.643.
7
[5] M. Subrahmanyam, T. Nancharaiah, Optimization of process parameters in wire-cut EDM of Inconel 625 using Taguchi’s approach, Mater. Today. Proc. (2019), https://doi.org/10.1016/j.matpr.2019.05.449. [6] Aerospace materials: past, present and future (2012). Introduction to aerospace materials, 15–38. [7] J.H. Weber, in encyclopedia of materials:science and technology, 2001. [8] E.O. Ezugwu, Key improvements in the machining of difficult-to-cut aerospace alloys, Int. J. Mach. Tools Manuf. 45 (2005) 1353–1367. [9] E.O. Ezugwu, J. Bonney, Y. Yamane, An overview of the machinability of aeroengine alloys, J. Mater. Process. Technol. 134 (2003) 233–253. [10] Manoj Kumar Sorabh, Neeraj Nirmal, A literature review on optimization of machining parameter in wire EDM, Int. J. Latest Res. Sci. Technol. 2 (1) (2013) 492. [11] D. Priyaranjan Sharma, S. Narendranath Chakradhar, Effect of wire diameter on surface integrity of wire electrical discharge machined Inconel 706 for gas turbine application, J. Manuf. Process. 24 (2016) 170–178. [12] W.B. Derek, Analysis for optimum decisions, Wiley, New York, 1982. [13] A.V.S. Ramprasad, Koona Ramji, G.l. Datta, An experimental study of wire EDM on Ti-6A1-4VAlloy, Sci. Direct Procedia Mater. Sci. 5 (2014) 2567–2576. [14] C.R. Alavala, Fuzzy Logic and Neural Networks: Basic Concepts and Applications, New Age International, Daryaganj, Delhi, 2008. [15] J.L. Pilkington, C. Prestonb, R.L. Gomesa, Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annual, Ind. Crop. Prod. 58 (2014) 15–24. [16] K.H. Ho, S.T. Newman, S. Rahimifard, R.D. Allen, State of the art in Wire electrical discharge machining (WEDM), Int. J. Mach. Tools Manuf. 44 (12–13) (2004) 1247–1259. [17] V.S. Gadakh, Parameteric Optimization of Wire Electrical Discharge Machining using Topsis Method, Adv. Prod. Eng. Manage. 7 (3) (2012) 157. [18] S.V. Sapakal, M.T. Telsang, Parametric Optimization of MIG Welding using Taguchi Design Method, Int. J. Adv. Eng. Res. Stud. 1 (IV) (2012) 28. [19] Saurav Datta, Siba Sankar Mahapatra, Modeling, simulation and parametric optimization of wire EDM process using response surface methodology coupled with grey-Taguchi technique, Int. J. Eng. Sci. Technol. 2 (5) (2010) 162. [20] J. Singh, M.R. Singh, Review on effects of process parameters in wire cut EDM and wire electrode development, Int. J. Innovat. Res. Insci. Technol. 2 (2) (2016). [21] S. Bharathi Raja, N. Baskar, Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation, Int. J. Adv. Manuf. Technol. 54 (2011) 445–463. [22] Xuesong Yan, Can Zhang, Wenjing Luo, Wei Li, Wei Chen, Hanmin Liu, Solve traveling salesman problem using particle swarm optimization algorithm, Int. J. Comput. Sci. 9 (6) (2012). [23] R.T. Yang, C.J. Tzeng, Y.K. Yang, M.H. Hsieh, Optimization of wire electrical discharge machining process parameters for cutting tungsten, J. Adv. Manuf. Tech. (2011). [24] S. Sarkar, S. Mitra, B. Bhattacharyya, Parametric optimisation of wire electrical discharge machining of g titanium aluminide alloy through an artificial neural network model, Int. J. Adv. Manuf. Tech. 27 (2006) 501–508. [25] M. Gostimirovic, P. Kovac, M. Sekulic, B. Skoic, Influence of discharge energy on machining characteristics in EDM, J. Mech. Sci. Technol. 26 (1) (2012) 173– 179.
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review Divya Marelli a, Singh S.K. b, Sateesh Nagari b, Ram Subbiah b a b
Design for Manufacturing, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India Mechanical Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India
a r t i c l e
i n f o
Article history: Received 10 December 2019 Received in revised form 11 January 2020 Accepted 13 January 2020 Available online xxxx Keywords: Wire EDM ANOVA Kerf width TAGUCHI Super alloys GRA PCA ANN
a b s t r a c t Wire-cut Electric Discharge Machining (WEDM) is an efficient machining process in numerous applications like space craft, defense, transportation vehicles, micro systems, farm machinery. It is used to machine conductive and hard materials like metal matrix composites, ceramic composites and super alloys. Super alloys exhibit excellent mechanical strength and creep resistance at high temperatures, good surface stability, and corrosion and oxidation resistance. The development of super alloys has primarily been driven by the aerospace and power industries. In this review of papers the optimization of WEDM machining parameters, responses by deploying different techniques like Taguchi Method, ANOVA, GRA, ANN, PCA. These techniques were utilized for examination of impact of wire-cut EDM process parameters on material removal rate (MRR) and surface roughness (SR) on super alloys. Parameters like pulse off time, pulse on time, servo voltage, peak current, kerf width are utilized to optimize the material removal rate (MRR) and surface roughness (SR). Taguchi orthogonal array was selected based on the process parameters. ANOVA is utilized for advancing parameters with the goal that greatest material removal rate and least surface roughness is acquired. Ó 2020 Elsevier Ltd. All rights reserved. Selection and of the scientific committee of the 10th International Conference of Materials Processing and Characterization.
1. Introduction Super alloys are an important group of high-temperature materials used in the hottest sections of jet and rocket engines where temperatures reach 1200–1400 °C. Super alloys are based on nickel, cobalt or iron with large additions of alloying elements to provide strength, toughness and durability at high temperatures [6]. These alloys are usually based on nickel–chromium. Added alloying elements include the refractory materials such as tungsten and molybdenum for solid-solution strengthening. Cobalt enhances strength as well as oxidation and hot corrosion resistance. Aluminum and titanium promote the formation of the major strengthening gamma-prime, c0 , phase. Superalloys typically contain between 15 and 60% of this strengthening phase. Niobium contributes to the formation of the precipitate gamma doubleprime, c00 ; this phase is the primary strengthening phase in alloys such as alloy 718 [7]. Nimonic composites are widely utilized for the manufacturing of aero engine parts due to its high specific strength [8]. During E-mail address: [email protected] (D. Marelli)
prolonged exposure to raised temperatures, numerous kinds of metal start to break, distort, corrode, fatigue, and so on. Nimonic compounds, in any case, are known for the maintenance of significant mechanical properties, such as sway quality, yield quality, and hardness, in temperatures as high as 1100°F, contingent upon the evaluation [9]. The chemical composition of nimonic alloys ranges from 38 to 76 wt% nickel, up to 27 wt% chromium and 20 wt% cobalt. Other refractory components, for example, tungsten (W), tantalum (Ta) and molybdenum (Mo) might be added to expand their quality and oxidation properties. Stainless steel (S304) is utilized as a work piece. Stainless Steel 304 is a nickel and chromium based alloy, because of their exceptional corrosion resistant, high ductility, non-magnetic and it holds solid stage up to 1400 °C. In this investigation, brass wire having 65% of copper and 35% of zinc is chosen as a tool because of its properties, accessibility and minimal cost [10]. Inconel is a nickel–iron based super composite that offers high mechanical quality with great texture capacity and it discovers application in gas turbines. Since it gets work hardened during conventional machining, getting complex shapes, high dimensional precision and great surface quality is truly challenging
https://doi.org/10.1016/j.matpr.2020.01.306 2214-7853/Ó 2020 Elsevier Ltd. All rights reserved. Selection and of the scientific committee of the 10th International Conference of Materials Processing and Characterization.
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
2
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
[11]. The performance of electric discharge machining (EDM) factors is arbitrary and prompts uneven evacuation of material in each discharge. Inconel 625 Chemical Composition: Carbon-0.10%, Chromium20.0–23.0%, Iron 5.00%, Silicon 0.50%, Manganese 0.50%, Sulphur 0.015%, Phosphorus 0.015%, Molybdenum 8.00–10.0%, Titanium 0.40%, Cobalt 1.00%, Columbium + Tantalum 3.15–4.15%, Aluminium 0.40%, Nickel Balance Inconel 750 Chemical Composition: Ni 70% min, Cr 14–17%, Ti 2.25–2.75%, Nb 0.70–1.20%, Mn 1% max, Si 0.50% max, Cu 0.50% max, C 0.08% max WEDM is a thermoelectric cutting procedure wherein material is expelled because of unremitting sparkles created among wire and workpiece isolated by a minute space which reaches from 0.025 mm to 0.075 mm in the presence of dielectric which might be deionized water or hydrocarbon oil. The material expulsion happens because of dissolving and dissipation. WEDM can machine any intricate profile with exactness and precision on the material regardless of its hardness. It utilizes a thin wire as an instrument with a width in the scope of 0.02–0.3 mm made up of copper, metal, tungsten, molybdenum or zinc covered wires with a brass core. It can machine material of thickness going from 1 mm to 500 mm relying on the machine utilized. The most significant exhibition characteristics in WEDM are MRR, surface completion, and kerf width [16].
2. Literature review Sachin Ashok Sonawane et al performed a chain of experiments are executed on 5-axis ePULSE-40 sprint cut WEDM manufactured by Electronica Machine Tool Ltd., India. To ensure parallelism and perpendicularity of the workpiece concerned with the machine table a dial pointer is utilized. The material applied to the examination is the Nimonic-75 alloy of 30 mm 30 mm 5 mm thick which is clung to the negative extremity. The compound piece of Nimonic-75 amalgam is Carbon-0.08–0.15%, Chromium 18–21%, Copper-0.5% max, Iron-5% max, Manganese-1%, Silicon-1%, Titanium-0.2–0.6% and equalization Nickel. A square of measurement 10 mm 10 mm 5 mm is cut from the workpiece with the assistance of a metal wire of 0.25 mm diameter connected to the active polarity. The debris from the machining region is evacuated by de-ionized water which is utilized as a dielectric with a flushing pressure of 15 kg/cm2. The conductivity of the dielectric is held at a steady value of 20 mS/cm at 22 °C. Testing is done by zero wire offset. The servo feed value is fixed at 2120 units. In this study, six machining factors are considered, they are, pulse on time, servo voltage, peak current, pulse off time, wire feed rate, and cable tension. To decide on the suitable scope of machining factors, a series of pilot phase tests were led by moving each factor in turn while keeping different elements stable at some value. Considering the thickness of the workpiece and the deviation of machining characteristic, MRR about the cutting variables, the levels of the machining components are fixed. Taguchi’s L27 orthogonal array is utilized to achieve the analyses, and every trial is performed thrice consequently, overall 81 operations. The machining attributes required for the study are surface roughness, overcut and MRR [1]. Durairaj et al used Taguchi Technique to design the experiments [17], orthogonal arrays were generally utilized in planning experiments. It is utilized to decrease the number of trials should have been performed than the full factorial experiment. In view of the machine tool, cutting tool and work piece capability, the process parameters and the level for the process parameters were selected such as Gap Voltage, Wire Feed, Pulse ON schedule (Ton),
Pulse OFF time (Toff)., each parameter at four levels. L16 orthogonal array is used for conducting the series of experiments. The designed combination of input parameters and its corresponding surface roughness and kerf width were obtained. Taguchi technique was intended to optimize single performance of process parameters with high quality and lower cost. The test results are presently changed into a sign to-commotion (S/N) proportion [18]. Since surface hardness and kerf width is desired to be least, so Lower the Better characteristic is utilized for S/N ratio estimation. The ideal setting would be the one which could accomplish least S/N ratio [19]. Grey theory has been generally utilized in engineering analysis, and it uncovers the possibility to solve the setting of ideal machining parameters related with a procedure with multiple output parameters. Presently, the multiple objective optimization issues have been changed into a single equivalent objective function optimization problem utilizing this methodology. From the grey relational grade values acquired, the means of the grey relational grades at various degrees of procedure parameters were determined [2]. Amitesh Goswami et al demonstrated a cause and effect diagram for recognizing the potential factors that may influence the machining characteristics (MRR and SR) was built. From the cause and effect diagram and the literature on WEDM, all out six quantities of information parameters were chosen for this examination. In this work, L27 orthogonal array with six control factors viz., Ton, Toff, IP, WF, WT, SV and three associations viz. Ton Toff, Ton IP and Toff IP have been examined. Modified linear graph is utilized for the allocation of columns to the input parameters and interactions in the orthogonal array. Signal to noise ratio was acquired utilizing Minitab 16 programming. Higher is better (HB) for MRR and lower is better (LB) for SR were taken for acquiring optimum machining attributes. The S/N proportion can be determined as a logarithmic transformation of the loss function Multi-response optimization using utility concept is used in the study. Utility concept is different characteristics are combined and evaluated by term composite index. Such a composite index represents the utility of the item. In this paper it is assumed that the overall utility is the sum of utilities of individual quality characteristics [12]. Naveen Babu et al demonstrated that sheet of 6 mm thickness, Inconel 750 was analysed in this investigation. To perform the experiments L9 orthogonal array of Taguchi’s method was selected. The variables which are having significant impact on the performance of electric discharge machining was distinguished. They are (I) Pulse On schedule (ms), Pulse Off time (ms) (iii) Voltage (V) and (iv) current (Å) and these components were controlled during machining. A series of trial experiments were undertaken to determine the functional range of the factors. As the individual factor range was less, three level plan was utilized. Each bit of the size is 100 10 mm. For each machining the time taken was noted down. Likewise, after each machining weight was gauged. The material removal rate is determined [4]. Subrahmanyam et al performed tests on a wire-cut EDM machine with particular like design fixed column, moving table type with a size of the work piece 250 350 mm, type of interpolation is linear, power supply 3 stage, AC 415 V, 50 Hz. The work material is INCONEL 625 of size 30 15 2 mm plate. Wire diameter 0.25 mm made of Brass is utilized for the test. The wire is tensioned between the lower and upper guide for getting higher precision. Dielectric liquid is deionized water with 12 to 16 TDS (absolute broke up solids). For measurement of the Surface roughness, surface roughness analyzer was utilized. During the machining, both of the working surfaces may have present smooth and irregularities which causes least and the most extreme gap in the middle of the tool and workpiece [13]. At a
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
3
given instant at the minimum point, suitable voltage is developed, produce electrostatic field for the discharge of electrons from the cathode there electrons quickened towards the anode [20]. After high-speed electrons crash into the dielectric atoms, breaking them into negative and positive particles. Due to that sparkle is created with high temperature causes softening and vaporization of material from the work piece. The mean qualities for the outcomes got from the gadget are arranged as one of the yield parameters by utilizing Taguchi’s methodology [5].
3. Results and discussions Sachin Ashok Sonawane et al found that as the pulse on time and peak current enhances values of surface roughness, overcut and MRR enhances. Additionally, with increment in the estimations of pulse off time and servo voltage these values diminish. It is seen that quality attributes rely on measure of discharge energy released at the machining zone. The discharge energy is modulated by pulse on time and peak current [25]. With increment in pulse off time, the discharge frequency decreases within a given period likewise discharge gap gets widened because of increment in servo voltage. This creates low amount of heat energy which further outcomes into less volume of material removal and development of small, shallow craters on the cut surface. As the wire feed rate increases, contact term of wire and workpiece diminishes. This results into less amount of heat energy led into the workpiece prompting decline in the quality attributes. The impact of wire tension is seen as negligible. The optimal settings of the machining parameters are recognized as pulse on time 1(110) pulse off time 3(51) servo voltage 3(40) peak current 3(230) wire feed rate 3(5) cable tension 3(8). The optimal parameters settings are predicted and confirmation trials are conducted. ANOVA examination is done on the composite primary component to decide the significance of each process parameter on the responses of ANOVA. The individual percentage contribution of each machining parameter in the process execution is introduced in S/N proportion plot for ideal parameters settings for the composite primary component (CPC) [1] (Figs. 1–3). Durairaj et al demonstrated that as S/N Ratio decreases up to a short period at that point increases correspondingly to gap voltage. The S/N Ratio diminishes up to a specific point correspondingly to the wire feed. It is be seen that S/N Ratio increments up to a brief period at that point diminishes slowly when the pulse off time and wire feed increments. Regarding the increase in pulse on time, the S/N Ratio diminishes up to a short period and afterward increments step by
Fig. 1. Influence of machining factors on surface roughness [1].
Fig. 2. Graph showing values of CPC (W) [1].
Fig. 3. S/N ratio plot of CPC (W) [1].
step. The S/N proportion will be diverted with expanding and diminishing when the Gap Voltage increments. The outcomes got from the Grey relational analysis Optimization technique to get the minimum Surface Roughness and least Kerf Width are appeared in Table 1. From Fig. 4, it can seen that for a specific value of input parameter the corresponding range of event of surface roughness can be determined. From Fig. 5, it tends to be seen that for a specific value of input parameter the relating scope of event of kerf width can be determined [2]. Amitesh Goswami et al demonstrated that by an increase in pulse on time, discharge energy produced will be higher, consequently more powerful is the explosion, and this outcomes in expanded MRR [23]. Higher cutting velocity brings about higher estimation of surface harshness [24]. Expanding the beat on schedule and peak current builds the quantity of electrons striking the work surface along these lines dissolving out increasingly material from the work surface per release. So also, MRR diminishes with an increased pulse off time and sparkle gap set voltage. SEM micrograph shows that few craters have been observed, while machining was performed at high value of pulse off time, sparkle gap set voltage and low value of pulse on time. From SEM micrograph unmistakably there is an expansion in size of garbage with decline in pulse off time and sparkle gap set voltage and; increment in pulse on time when contrasted [3]. It is clear that all the factors significantly affect material removal rate. Pulse on time (46.09%) is seen as the major influencing factor for the MRR followed by pulse off time (32.97%), spark
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
4
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
Table 1 Grey Relational Grade value for corresponding levels [2]. Grey relational grade mean S. no
Process parameter
Level 1
Level 2
Level 3
Level 4
1 2 3 4
Gap voltage Wire feed Pulse-on time Pulse-off time
0.5778 0.6148 0.6347 0.6187
0.5780 0.5269 0.4324 0.5562
0.5851 0.6106 0.6006 0.5857
0.5191 0.5076 0.5924 0.4993
Fig. 4. Range of occurrence of surface roughness [2].
Fig. 5. Range of occurrence of kerf width [2].
gap set voltage (6.68%), peak current (1.45%), wire feed (0.84%) and wire tension (0.44%). A low value (1.49%) of experimental error has been observed. All the three interactions (Ton Toff, Ton IP and Toff IP) have seen as critical. So also the outcome for SR has been arranged. For SR, just four components: pulse on time (66.70%), spark gap set voltage (11.73%), pulse off time (9.09%) and peak current (3.75%) have been seen as noteworthy. An interaction has additionally been found between pulse on time and peak current. Surface roughness is mostly effected by pulse on time. The brass wire of cutting tool quickens depletion, which prompts in creation of built up layer. This increased built up layer brings about more unpleasant surface. Wire feed rate and wire tension strain has irrelevant impact on surface harshness. Henceforth, in Wire EDM of Nimonic 80A, higher machining rates couldn’t be acquired without giving up surface quality. The examination of the microstructure of machined work surface was performed for appraisal of the surface quality utilizing WEDM process. The Specimens were seen with scanning electron microscope (Hitachi S-3400 N) with an accelerating voltage of 10.0 kV. Three examples were chosen for microstructure perception, one example was machined at trial condition comparing to high info vitality where beat on time is set at its most significant level, beat off time being at least level, while top current has middle of the road level. Another two examples were machined at exploratory condition relating to low and moderate input energy rate (Figs. 6–8).
Naveen Babu et al utilized the ANN method which offers a more noteworthy prescience than some other model, such as, linear and exponential regression. The utilization of ANN is acknowledged by researchers as an prediction tool because of its remarkable capability of learning algorithm and coordinating of input and output association, in any event, for other than linear and complicated systems [14,15]. In Particle swarm optimisation (PSO), every particle is viewed as an individual among the population in dimensional solution space. Every particle is initiated with the position and velocity randomly and first position of every particle is assessed dependent on the target functions [21]. At that point gbest is distinguished among the outcomes yielded by the all particles in the populations. Based gbest, the pbest particle is fixed. Next velocity is added to the p best particle and made new population which characterizes the new (second) position of all particles. On the other hand gbest is distinguished among the outcomes yielded by the new made population which depends on ongoing pbest particle. This procedure is proceeded until the stop criteria take an action on the algorithm either number of iterations or no changes on the g best value At long last the model is accomplished with RMSE and assurance coefficient for MRR is 0.0053881 and 0.99995 and for SR is 0.0038324 and 0.99992 respectively. To execute the optimisation in PSO the accompanying parameters were utilized [22]. The
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
5
Fig. 6. a) Microstructure of the sample machined at low input energy rate, b) Microstructure of the sample machined at high input energy rate c) Microstructure of the machined sample [3].
Fig. 7. Multi Response Data (Utility) Main Effects Plots (MRR and SR) [3]. Fig. 9. Experimental no. vs experimental value of MRR and SR [4].
Fig. 8. Main effects plot (MRR and SR) [3].
population size, inertia weight, learning factors, such as, cognitive factor (C1) and social factor (C2) are 10, 0.1, 2 and 2 respectively. At last, it was seen that the improved value of MRR is 22.110471 mm3/min and its enhanced process parameters Pulse on time, Pulse off time, Voltage, and Current are 109.9931 ms, 62.971967 ms, 79.998425 V and 10.653109 Å individually. In addition it was discovered that the streamlined estimation of Surface Roughness is 2.3320223 mm and its optimized process factors Pulse on time, Pulse off time, Voltage, and Current are 100.12864 ms, 55.010629 ms, 20.794803 V and 10.505231 Å respectively Subrahmanyam et al demonstrated that the impact of machining parameter, i.e., Discharge current, Pulse on time and voltage on
Fig. 10. Configuration of three layered NN [4].
Fig. 11. Factors importance (%) on MRR and SR.[4].
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
6
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 12. (a) Main effects plot for Means; (b) Main effects plot for S/N ratios [5].
Fig. 13. (a) Main effects plot for means; (b) Main effects plot for S/N ratios [5].
material removal rate (MRR), surface roughness (SR) of Inconel 625 machined work piece with Brass Wire tool utilizing Taguchi strategy F-ratio builds up whether the process parameter is critical or not at a specific confidence level. The higher value of F-ratio shows that any little variation of the process parameter can make a huge effect on the performance characteristics. Increment in pulse off time builds MRR because increasing pulse off time number of electrons striking the work surface in a discharge thus increases the erosion of material from the work surface per discharge. As per this, Pulse-off time is seen as the most critical factor influencing MRR with the contribution of 61.90% (Figs. 12,13).
4. Conclusion Principal Component Analysis (PCA) based technique take into consideration association between various quality outputs and converts this into uncorrelated components known as principal components. These principal components reduce the number of aspects and trims down the convolution of the multicharacteristic problems. The optimal settings of the machining parameters based on the principal component analysis for the WEDM of Nimonic-75 alloy obtained are pulse-on time 110 ms, pulse-off time 51 ms, servo voltage 40 V, peak current 230 Amp., wire feed rate 5 m/min and cable tension 8 g (Figs. 9–11).
Experimental examination on wire electrical discharge machining of Stainless Steel (SS304) was performed utilizing brass wire of 0.25 mm. Taguchi technique, the optimized input parameter combinations to get the minimum surface roughness are 40 V gap voltage, 2 mm/min wire feed, 6 ls pulse on time, 10 ls pulse off time and correspondingly optimized conditions to get the minimum kerf width are 50 V gap voltage, 2 mm/min Wire Feed, 4 ls pulse on time, 6 ls pulse off time. Based on the Grey relational analysis, the optimised input parameter combinations to get both the minimum surface roughness and the nominal kerf width are 50 V gap voltage, 2 mm/min wire feed, 4 ls pulse on time and 4 ls pulse off time. The optimized value of surface roughness obtained through single response optimization has been as low as 0.16 mm with percent contribution of pulse-on time (66.70%) and spark gap set voltage (11.73%) have been found to dominate other factors such as pulse-off time (9.09%), peak current (3.75%), wire feed (0.22%) and wire tension (0.18%). Only one interaction (pulseon time peak current) has been found to be significant for roughness as response. The microstructure investigation of the samples machined at experimental condition corresponding to high energy input rate has revealed a strong co-relation between the surface quality and energy input rate. The samples machined at high energy input condition exhibited rougher surface with lot of builtedge layers, whereas the better surface quality was obtained under low energy input conditions.
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306
D. Marelli et al. / Materials Today: Proceedings xxx (xxxx) xxx
The model was constructed between the process factors and output values by artificial neural network and the model was found that the root mean square error is as low as for MRR (0.0053881) and SR (0.0038324). Whereas the determination coefficient for MRR and SR is 0.99995 and 0.99992 respectively. The model trained by ANN was suitably incorporated with the evolutionary computational techniques of particle swarm optimization for optimizing the MRR and SR It is evident that when working on wire electric discharge machine with brass wire and INCONEL 625 as work piece, the above stated parameters gave the maximum material removal rate and minimum surface roughness. CRediT authorship contribution statement Marelli Divya: Conceptualization, Methodology, Writing original draft, Writing - review & editing. S.K. Singh: Supervision. N. Sateesh: Data curation, Visualization, Investigation, Supervision. Ram Subbiah: Supervision, Validation. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] Sachin Ashok Sonawane, M.L. Kulkarni, Optimization of machining parameters of WEDM for Nimonic-75 alloy using principal component analysis integrated with Taguchi method, J. King Saud Univ. Eng. Sci. 30 (3) (2018) 250–258, https://doi.org/10.1016/j.jksues.2018.04.001. [2] M. Durairaj, D. Sudharsun, N. Swamynathan, Analysis of process parameters in wire EDM with stainless steel using single objective taguchi method and multi objective grey relational grade, Procedia Eng. 64 (2013) 868–877, https://doi. org/10.1016/j.proeng.2013.09.163. [3] Amitesh Goswami, Jatinder Kumar, Optimization in wire-cut EDM of Nimonic80A using Taguchi’s approach and utility concept, Eng. Sci. Technol. Int. J. 17 (2014) (2014) 236–246. [4] K. Naveen Babu, R. Karthikeyan, A. Punitha, An integrated ANN – PSO approach to optimize the material removal rate and surface roughness of wire cut EDM on INCONEL 750, Mater. Today. Proc. 19 (2019) 501–505, https://doi.org/ 10.1016/j.matpr.2019.07.643.
7
[5] M. Subrahmanyam, T. Nancharaiah, Optimization of process parameters in wire-cut EDM of Inconel 625 using Taguchi’s approach, Mater. Today. Proc. (2019), https://doi.org/10.1016/j.matpr.2019.05.449. [6] Aerospace materials: past, present and future (2012). Introduction to aerospace materials, 15–38. [7] J.H. Weber, in encyclopedia of materials:science and technology, 2001. [8] E.O. Ezugwu, Key improvements in the machining of difficult-to-cut aerospace alloys, Int. J. Mach. Tools Manuf. 45 (2005) 1353–1367. [9] E.O. Ezugwu, J. Bonney, Y. Yamane, An overview of the machinability of aeroengine alloys, J. Mater. Process. Technol. 134 (2003) 233–253. [10] Manoj Kumar Sorabh, Neeraj Nirmal, A literature review on optimization of machining parameter in wire EDM, Int. J. Latest Res. Sci. Technol. 2 (1) (2013) 492. [11] D. Priyaranjan Sharma, S. Narendranath Chakradhar, Effect of wire diameter on surface integrity of wire electrical discharge machined Inconel 706 for gas turbine application, J. Manuf. Process. 24 (2016) 170–178. [12] W.B. Derek, Analysis for optimum decisions, Wiley, New York, 1982. [13] A.V.S. Ramprasad, Koona Ramji, G.l. Datta, An experimental study of wire EDM on Ti-6A1-4VAlloy, Sci. Direct Procedia Mater. Sci. 5 (2014) 2567–2576. [14] C.R. Alavala, Fuzzy Logic and Neural Networks: Basic Concepts and Applications, New Age International, Daryaganj, Delhi, 2008. [15] J.L. Pilkington, C. Prestonb, R.L. Gomesa, Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annual, Ind. Crop. Prod. 58 (2014) 15–24. [16] K.H. Ho, S.T. Newman, S. Rahimifard, R.D. Allen, State of the art in Wire electrical discharge machining (WEDM), Int. J. Mach. Tools Manuf. 44 (12–13) (2004) 1247–1259. [17] V.S. Gadakh, Parameteric Optimization of Wire Electrical Discharge Machining using Topsis Method, Adv. Prod. Eng. Manage. 7 (3) (2012) 157. [18] S.V. Sapakal, M.T. Telsang, Parametric Optimization of MIG Welding using Taguchi Design Method, Int. J. Adv. Eng. Res. Stud. 1 (IV) (2012) 28. [19] Saurav Datta, Siba Sankar Mahapatra, Modeling, simulation and parametric optimization of wire EDM process using response surface methodology coupled with grey-Taguchi technique, Int. J. Eng. Sci. Technol. 2 (5) (2010) 162. [20] J. Singh, M.R. Singh, Review on effects of process parameters in wire cut EDM and wire electrode development, Int. J. Innovat. Res. Insci. Technol. 2 (2) (2016). [21] S. Bharathi Raja, N. Baskar, Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation, Int. J. Adv. Manuf. Technol. 54 (2011) 445–463. [22] Xuesong Yan, Can Zhang, Wenjing Luo, Wei Li, Wei Chen, Hanmin Liu, Solve traveling salesman problem using particle swarm optimization algorithm, Int. J. Comput. Sci. 9 (6) (2012). [23] R.T. Yang, C.J. Tzeng, Y.K. Yang, M.H. Hsieh, Optimization of wire electrical discharge machining process parameters for cutting tungsten, J. Adv. Manuf. Tech. (2011). [24] S. Sarkar, S. Mitra, B. Bhattacharyya, Parametric optimisation of wire electrical discharge machining of g titanium aluminide alloy through an artificial neural network model, Int. J. Adv. Manuf. Tech. 27 (2006) 501–508. [25] M. Gostimirovic, P. Kovac, M. Sekulic, B. Skoic, Influence of discharge energy on machining characteristics in EDM, J. Mech. Sci. Technol. 26 (1) (2012) 173– 179.
Please cite this article as: D. Marelli, S. S.K., S. Nagari et al., Optimisation of machining parameters of wire-cut EDM on super alloy materials–A review, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.01.306