SIMULTANEOUS OPTIMIZATION OF µ-edm PARAMETERS FOR MACHINING INCONEL 718 SUPER ALLOY

Transcription

1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 5, May 2018, pp , Article ID: IJMET_09_05_047 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed SIMULTANEOUS OPTIMIZATION OF µ-edm PARAMETERS FOR MACHINING INCONEL 718 SUPER ALLOY Thella Babu Rao Associate Professor, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur District, Andhra Pradesh K. Dinesh Kumar Reddy, K. Bala Srikar, M. Prasanna Krishna and Ch. Pavan Kumar UG Student, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur District, Andhra Pradesh ABSTRACT: This investigation integrated Gray Relational Analysis (GRA) and Taguchi s robust optimization techniques for simultaneous optimization of micro-electric discharge machining (µ-edm) parameters for drilling micro holes on Inconel 718 super alloy. Voltage, current, duty cycle and pulse-on time are chosen as the most significant µ-edm variables while the tool wear rate, recast layer thickness, material removal rate (MRR) are treated as the process quality indicating responses. The machining experiments are planned according to Taguchi s L27 design of experiments. Since the measured process are conflicting in nature for the chosen process variables, the problem is treated as the multi-objective optimization problem to simultaneously minimize the tool wear rate and recast layer thickness and maximise the metal removal rate. As the Taguchi s maximum quality optimization alone cannot deal with the simultaneous optimization of multiple process quality responses, the GRA is introduced in this work to consolidate the three process responses as single process quality indicator such as gray relational grade. Consequently the derived gray relational grade was maximised by implementing the Taguchi optimization approach. Further, analysis of variance was used to analyse the significance of the process variables on the process overall performance. Keywords: µ-edm, Inconel-718, multi-response optimization, gray relational analysis, Taguchi, ANOVA Cite this Article: Thella Babu Rao, K. Dinesh Kumar Reddy, K. Bala Srikar, M. Prasanna Krishna and Ch. Pavan Kumar, Simultaneous Optimization of µ-edm Parameters for Machining Inconel 718 Super Alloy, International Journal of Mechanical Engineering and Technology, 9(5), 2018, pp editor@iaeme.com

2 Simultaneous Optimization of µ-edm Parameters for Machining Inconel 718 Super Alloy 1. INTRODUCTION In the present manufacturing industries the nickel-based super alloys are being extensively used for a variety of aerospace and industrial applications like jet engine casing, turbine blades of aero engine, turbo chargers, pump bodies and some automobile parts too. The superior mechanical and corrosion resistance properties at elevated temperatures than the other conventional materials made them potential for these applications [1-2]. However machining of these materials into complex contours with micro features become complex task to the today s manufacturing engineers due to their work hardening property and its tendency to react with tool at elevated temperatures which resulting in the sever tool wear progression [3]. These consequences drew the use of non-traditional machining techniques to machine them in the production of precision components with high dimensional accuracy and machined surface integrity. Micro-electric discharge machining (µ-edm) is an unconventional machining process in manufacturing dies, tools, and precision components for automotive and aerospace industries. µ-edm process use micro size electrodes to remove material in terms of microns [4]. In µ-edm process the work piece is being machined, there will be significant wear, which affects the shape accuracies of the machined component [5]. Ferraris et al. [6] Investigated the EDM drilling of micro holes with insulated tools and they introduced the concept where a non-conductive material like parylenehas been coated on the circumference of the electrode for reducing the occurrences of secondary discharges while µ- EDM process [6-7]. Tool wear rate of coated electrodes are found to be higher than that of uncoated copper electrode because of peeling off of coating during machining [6]. The tool wear of EDM decreases with increasing the discharge energy and for the high tool rotation the wear rate will be higher than compared to that of the lower tool rotation [8]. µ-edm generates spark erosion, leads to drill micro-holes of diameters slight more than the tool electrode diameter. This undesired variance between the diameter of tool electrode and the drilled micro-hole is called overcut. The overcut of micro-hole is another accuracy aspect in µ-edm process. Jesudas et al. [9]. Investigated the influence of µ-edm variables which affects the overcut and they concluded that the overcut gets increased with increase in voltage as the energy that dissipated from the tool electrode will increase if the voltage raises-up. Also if spark gap is increased then the overcut decreases because, if the tool electrode gap increases, the thermal energy discharged from the tool electrode towards the specimen is less, resulting in lower overcut. In µ-edm process, current, voltage, Pulse-on time, pulse-off time and duty cycle are the notable process control variables while the dimensional accuracy, material removal rate, recast layer, electrode wear rate are the performance indicators of the µ-edm process. Siva et al. [10] studied the µ-edm process variables effect and noted that the MRR is directly affected by the current whereas the voltage is directly affecting overcut and taper angle as well. The Material removal rate increased with lower surface integrity, melting temperature and thermal conductivity of alloy. This shows the inverse relation between MRR and melting temperature, thermal conductivity, surface integrity [11-12]. The section of process parameters in µ-edm plays a vital role to manufacture a quality product at optimal conditions of material removal rate, tool wear rate, radial recast layer thickness and reduced overcut. Therefore, this investigation aimed to present integrated Gray Relational Analysis (GRA) and Taguchi s robust optimization techniques for simultaneous optimization of µ-edm parameters for drilling micro holes on Inconel 718 super alloy editor@iaeme.com

3 Thella Babu Rao, K. Dinesh Kumar Reddy, K. Bala Srikar, M. Prasanna Krishna and Ch. Pavan Kumar 2. EXPERIMENTATION: The machining experiments were conducted on CNC EDM machine to drill holes of diameter 800µm on a 3mm thick Inconel 718 (IN718) alloy. The possible machining details are listed in Table1. During machining, pulse-on time, current, voltage and duty cycle are chosen as the most significant process variables at three levels as listed in Table 2. The machining experiments are conducted according to the Taguchi s L 27 design of experiments to minimise the number of experimental trials. Table 1 Machining conditions (a) Work piece (anode): Inconel 718 (IN718) alloy (b) Tool (cathode): copper of diameter 800μm (c) Work piece dimensions: 30mm 20mm 3mm (d) Angle of cut: vertical (e) Location of work piece: centre on the table (g) Average voltage gap maintained: 40 V (h) Die-electric temperature: 25 C (i) Die-electric fluid: Kerosene An extraordinarily outlined experimental strategy is required to evaluate the impacts of machining parameters on performance characteristics. Ordinary exploratory design methods are as well complex and troublesome to utilize [13]. In addition, more number of tests needs to be carried out when number of machining variables are more [13,27].Taguchi's orthogonal array design of experiments is effective for the permitted number of experiments. The present work concentrates on evaluating the outcome of drilling parameters in particular pulse on time, current(i), voltage(v), Duty cycle percentage(%) every parameter was varied at different levels on various parameter for 27 experiments. The selected process variables are tabulated in Table 2 and the design of experimental matrix is tabulated in Table 3. S. No. Table 2 Variable factors along with their levels Variable Units Level Pulse-on time µs Current Amps Voltage Volts Duty cycle % GRAY RELATIONAL ANALYSIS: In case of complicated multi-variable systems, the relationships among various variables are not clear. Such systems are usually called as gray inferring poor, deficient, and undetermined information. Gray relational analysis is an effecting measurement method in gray system hypothesis that analyses unknown association between one primary factor and all the other components in a given system. When tests are ambiguous or when the test strategy cannot be carried out precisely, gray analysis makes a difference to compensate for the deficiencies in measurable relapse [13, 14]. Data pre-processing in gray relational analysis is regularly required since the range and unit in one information sequence may vary from the others. In expansion, it could be a process of transferring the initial grouping to a comparable sequence. For data pre-processing within the gray relational analysis, the lower surface roughness and the higher MRR are the editor@iaeme.com

4 Simultaneous Optimization of µ-edm Parameters for Machining Inconel 718 Super Alloy sign of superior execution in milling process. At that point, it incorporates a characteristic of the higher is better if the target value of original grouping is infinite. The unique arrangement can be normalized as following [13, 15, and 18]. Y i (p) = Where i=1, n; p=1, m. n is the no of experimental values and m is the no of variables. X i (p) represents the original order, Y i (p) represents the order after the data pre-processing, Max Xi(p) represents the major value of X i (p), min X i (p) represents the lowest value of X i (p), and X is the preferred value. S. No Table 3 Design of Experiments data using L27 orthogonal array T-On (µs) Current (amps) Voltage (volts) Duty cycle (%) MRR (g/min) TWR (mm 3 /min) RCL (mm) When the lower is the better is a typical of the original order, the original order must be normalized as succeeding [13, 15and 18] Yi (p) = Gray Relational Grade is the measure of pertinence between the two sequences or two orders in Gray Relational Analysis. Followed by the local gray relational measurement (1) (2) editor@iaeme.com

5 Thella Babu Rao, K. Dinesh Kumar Reddy, K. Bala Srikar, M. Prasanna Krishna and Ch. Pavan Kumar defines as if there is only one sequence X i (p) acts as reference order and the rest orders serve as normalised sequences, once the data pre-processing is approved out, In the i th experiment, the gray relational coefficient ξ i (p) for the p th performance characteristics can be determined as: ξ i (p) = (3) Here, 0i is set to be the deviation order of the normalisation order and the reference order. 0i = X 0 (p) Y i (p) (4) Table 4 The performance characteristics after data pre-processing: Normalised Sequence Deviation Sequence MRR TWR RCL MRR TWR RCL X 0 (p) represents the reference order and Y i (p) represents the normalised sequence. ζ is distinctive coefficient ζ [0,1]. A value of ζ is the lesser and the notable ability is the greater. ζ=0.33 as the process parameters are three and they are considered to be equally important. Once the gray relational coefficient is determined, it is normally used to calculate the gray relational grade from the mean values of the gray relational coefficients. [15, 18]. The gray relational grade is determined by: editor@iaeme.com

6 Simultaneous Optimization of µ-edm Parameters for Machining Inconel 718 Super Alloy γ i = mean(ξ i (p)) (5) The gray relational grade γ i denotes the range of association between the reference order and the normalised order. The value of gray relational grade is equivalent to 1 if the two orders are identically coincident. Table 5 The obtained gray relational coefficients and gray relational grade for 27 Normalised sequences. S. No. T-On (µs) IP (amps) V (Volt) Duty Cycle (%) Gray Relational Coefficients MRR TWR RCL Gray Relational Grade Signal to Noise Ratio editor@iaeme.com

7 Gray Relational Grade Thella Babu Rao, K. Dinesh Kumar Reddy, K. Bala Srikar, M. Prasanna Krishna and Ch. Pavan Kumar Experiments Figure 1 Gray Relational Grades 4. RESULTS: In the drilling processes, the minimum tool wear rate as well as radial recast layer thickness whereas the maximum Material Removal Rate are the signs of superior performance. The data that pre-processing in the gray relational analysis, the TWR and RCL thickness are taken as the lower is better and Material removal rate are taken as the higher is better. The grades of 27 trials be the normalised sequences X i (p), I=1 27,p=1. The orders after data preprocessing with the help of Equations 1 and 2 are tabulated in Table 4 and X i (p)is represented as reference order and Y i (p) is represented as the normalised sequences. Table 4 shows the similar calculation process performed for I=1 27 and the results of all deviation sequences for I=1 27. The distinctive coefficient ζ is switched into Equation 3 to determine the gray relational coefficient. The gray relational coefficients and grade values for every trials of the L27 orthogonal array were determined by using the Equations. 3 5 (Table 5). From the executed investigational process, it is noticeably detected from Table 4 and Fig. 3 that the drilling parameters values of trial 14 th has the maximum gray relational grade. Hence, 14 th trial is the optimum machining parameter value maximum material removal rate simultaneously for least tool wear rate and radial recast layer thickness from the 27 trials. Figure 2 Response graph for signal to noise ratio In extension, to decide the finest drilling parameters for material removal rate, TWR and the RCL the results table of Taguchi process had used to compute the mean gray relational grade for every stage of the drilling parameters. This process will comprise of every level of editor@iaeme.com

8 Simultaneous Optimization of µ-edm Parameters for Machining Inconel 718 Super Alloy gray relational grades and their average from each vertical values in L27 orthogonal array. The gray relational grades for every stage of the drilling parameters were determined using the similar process. The highest value of the gray relational grade indicates that the normalised has a durable association with the reference order [14]. Ignoring the type of performance characteristics, the superior performance of the machining can be obtained at the maximum value of the gray relational grade [13]. Hence, it is clear that the maximum gray relational grade value will result in the optimum machining parameter. The best machining performance for the least TWR and RCL and the maximum Material Removal Rate was obtained for 80µs pulse on time, 3amps currents, 70volts voltage and 80% duty cycle combinations. The optimum drilling parameter can also be obtained from the signal to noise ratio, figure 2 indicates that the effect of drilling parameters on the performance (TWR, RCL and MRR). The prior values in the fig 2 shows the higher MRR, minimum TWR, RCL and the superior quality cut during the drilling operation. 5 CONCLUSIONS: The optimization of Micro Electrical Discharge machining process parameters while drilling micro holes on Inconel 718 alloy is successfully done by implementing the gray relational analysis associated with the Taguchi methods. In order to achieve the quality while machining, the maximum Material Removal Rate and the minimum Tool Wear Rate and Radial Recast Layer thickness were treated as the quality targets. From the obtained Gray Relational Grade results we came to know that the 14 th experiment gives us the Optimal process parameters at where the superior quality of drilling was observed on the material, Inconel 718, which will give desired output of minimum TWR, RCL and, maximum MRR. The current was the major factor among all other drilling parameters that used on the multiperformance characteristics. The significance of the governable factors on the multiperformance characteristics was in order of current < voltage < duty cycle < pulse on time. The investigation clearly indicates that the gray relational analysis helped efficiently the optimization of TWR, RCL and MRR in the drilling operation at multi quality requirements. REFERENCES [1] L. Lia, Y. B. Guob, X.T. Weia, W. Lib., Surface integrity characteristics in wire- EDM of Inconel 718 at different discharge energy, Procedia CIRP 6, pp [2] Sharman, A.R.C., Hughes, J.I., Ridgway, K., Workpiece surface integrity and tool life issues when turning Inconel 718 nickel based superalloy, Machining Sci. Tech. 8/3, pp [3] Vrushali, Bunde, Machining of Inconel 718 Alloy using EDM-A Review., International Journal of Engineering Research & Technology (IJRET), vol. 06, pp , 2017 [4] Shiv G. Kapoor, Soham S. Mujumdar, DavideCurreli, Effect of Dielectric Conductivity on Micro-EDM Plasma Characteristics using Optical Emission Spectroscopy, Journal of Micro- and Nano-Manufacturing, JMNM , 2018 [5] N. B. Gurule, S. A. Pansare, Potentials of Micro-EDM, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), pp [6] Ferraris, E., V. Castiglioni, F. Ceyssens, M. Annoni, B. Lauwers and D. Reynaerts, EDM Drilling of Ultra-high Aspect Ratio Micro Holes with Insulated Tools. CIRP Annals - Manufacturing Technology, 62: [7] M. Siva, M. Parivallal, M. Pradeep Kumar, Investigation on the effect of process parameters in micro electric discharge machining. Procedia Materials Science 5 ( 2014 ) editor@iaeme.com

9 Thella Babu Rao, K. Dinesh Kumar Reddy, K. Bala Srikar, M. Prasanna Krishna and Ch. Pavan Kumar [8] N. B. Gurule, S. A. Pansare, Potentials of Micro-EDM, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), pp [9] AsmaPerveen, M. P. Jahan, An Experimental Study on the Effect of Operating Parameters during the Micro-Electro-Discharge Machining of Ni Based Alloy. International Scholarly and Scientific Research & Innovation 10(11) 2016 [10] Jesudas T, Ramesh S, Senthilkumar K, Arunachalam RM, Study and optimization of overcut on most influencing process parameters of Micro-EDM. International Journal of Applied Engineering Research, ISSN Vol. 10 No.50 (2015) [11] Siva, M. Parivallal, M. Pradeep Kumar, Investigation on the effect of process parameters in micro electric discharge machining. Procedia Materials Science 5 ( 2014 ) [12] Saeed Daneshmand, Ehsan FarahmandKahrizi, EsmailAbedi, M. Mir Abdolhosseini (2013). Influence of Machining Parameters on Electro Discharge Machining of NiTi Shape Memory Alloys, Int. J. Electrochem. Sci., Vol. 8, 2013 [13] S. L. Chen, M. H. Lin, S. F. Hsieh, J. S. Huang, H. C. Lin, Electrical discharge machining of TiNiCr and TiNiZr ternary shape memory alloys, Material science and engineering, pp. A , 2007 [14] NihatTosun&HasimPihtili, Gray relational analysis of performance characteristics in MQL milling of 7075 Al alloys. Int J AdvManufTechnol (2010) 46: [15] Fung CP (2003) Manufacturing process optimization for wear property of fiber-reinforced polybutylene terephthalate composites with grey relational analysis. Wear 254: [16] Lo SP (2002) the application of an ANFIS and grey system method in turning tool-failure detection. Int J AdvManufTechnol19: [17] Tosun N, Cogun C, Tosun G (2004) A study on kerf and material removal rate in wire electrical discharge machining based on Taguchi method. J Mater Process Technol 152: [18] Tosun N (2006) Determination of optimum parameters for multiperformance characteristics in drilling by using grey relational analysis. Int J AdvManufTechnol 28(5 6): [19] Lin ZC, Ho CY (2003) Analysis and application of grey relation and ANOVA in chemical mechanical polishing process parameters. Int J AdvManufTechnol 21: [20] Hulas Raj Tondaya and Anand Mukut Tiggab. Empirical Assessment and Modeling of MRR and Surface Roughness Acquired from Wire Electrical Discharge Machining of Inconel 718. International Journal of Mechanical Engineering and Technology, 8(1), 2017, pp [21] M Manohar, Jomy Joseph, T Selvaraj and D Sivakumar, Development of Models Using Genetic Programming for Turning Inconel 718 with Coated Carbide Tools, International Journal of Design and Manufacturing Technology (IJDMT), Volume 4, Issue 1, January- April (2013), pp editor@iaeme.com