EXPERIMENTAL STUDY OF MATERIAL REMOVAL RATE, SURFACE ROUGHNESS & MICROSTRUCTURE IN ELECTROCHEMICAL MACHINING OF INCONEL 825

Transcription

1 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India EXPERIMENTAL STUDY OF MATERIAL REMOVAL RATE, SURFACE ROUGHNESS & MICROSTRUCTURE IN ELECTROCHEMICAL MACHINING OF INCONEL 82 A.Mohanty 1*, GangadharuduTalla 2, S. Dewangan 3, S.Gangopadhyay 4 1 Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha aveekmohanty@gmail.com 2 Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha gangadhar.talla@gmail.com 3 Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha shaileshdewangan123@gmail.com 4 Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha soumya.mech@gmail.com Abstract Electrochemical machining (ECM) is one of the widely used non-traditional machining processes especially used for producing intricate or complex geometry on difficult-to-machine materials. Owing to this capability of machining any electrically conductive materials irrespective of its hardness, this process can be utilized to machine Ni-based super alloys which are termed as difficult-to-cut materials due its properties such as low thermal conductivity, high strain hardening tendency and high mechanical properties which although makes it very useful for high temperature applications but on the other hand it possess a significant challenge to machine by any conventional machine. The present study investigates the effect of process parameters such as electrolytic concentration, voltage and feed rate on performance characteristics such as material removal rate (MRR) and surface roughness (SR) when ECM of Inconel 82 by copper tool in an aqueous solution NaCl solution. ANOVA were employed to investigate the influence of process parameters on performance characteristics during machining. It was observed that MRR increased with increase in voltage whereas SR decreased. Voltage was found to be significantly affecting the MRR and SR. An attempt has also been made to study the microstructure of machined surface at different conditions and to correlate it with multiple performance characteristics. Keywords: Inconel 82, Microstructure, Taguchi, ANOVA 1. Introduction Electrochemical machining (ECM) is one of the most popularly known and widely used non-traditional machining process belonging to electrochemical category wherein the material removal takes place by anodic dissolution of workpiece in an electrolytic solution.ecm process is independent of hardness of workpiece material with very less tool wear and stress free surface generation, generally preferred when highly finished machined surface is required along with less tool wear.owing to above mentioned advantages ECMfinds its use into various sectors such as defense,automotive, aerospace, electronic industries etc. Machining parameters and other factors primarily decides the accuracy and precision of the machining.so it necessitates one to find optimal condition for obtaining better process performance along with process economics.with regards to this, various researchers have carried out electrochemical machining of differentmaterials and applied various optimization techniques to find optimal condition.bhattacharya and 174-1

2 EXPERIMENTAL STUDY OF MATERIAL REMOVAL RATE, SURFACE ROUGHNESS &MICROSTRUCTURE IN ELECTROCHEMICAL MACHINING OF INCONEL 82 Sorkel (2006)investigated the use of response surface methodology (RSM) for the maximization of MRR and minimization of overcut during electrochemical machining of EN19 steel and developed mathematical model to analyze the effects of the process parameters.hoecheng et al. (2003) found MRR increases with increasing electrical voltage, molar concentration of electrolyte, time of electrolysis and reduced initial electrode gap when electrochemical machining SKD 61 stainless steel using NaNO 3 as an electrolyte. Senthil Kumar et al. (2013) carried out electrochemical machining of LM2 Al/10% SiC p composites to obtain empirical relation between process parameters and responses using RSM. They found MRR to be greatly affected by voltage and tool feed while surface roughness being affected by electrolytic concentration.similarobservations were reported was also obtained by others researchers (Uttawar and Chopde (2013), Goswami et al. (2013) and Rao and Padmanbha (2012)). Joao et al. (2006)carried out electrochemical machining of SAE-XEV-F valve steel to study the influence of intervening variables with two different electrolytes. It was observed NaCl electrolyte resulted in higher MRR while NaNO 3 resulted in lower surface roughness and overcut. Current density at side gap of machined hole reduced resulting in improvement of machine accuracy and process stability in ECM of mild steel by dual pole [8]. To determine optimal machining parameters in ECM of hardened steel Asokan et al. (2008) developed multiple regression models and artificial neural network (ANN) model.they concluded ANN to be better prediction model.chakradhar and VenuGopal (2011) used L9 orthogonal array of Taguchi and used grey relation analysis to determine the optimal machining parameters while ECM of EN-31 steel. Tang and Yang (2013) used NaNO 3 and NaClO 3 electrolyte composite to study ECM on SS00Cr12Ni9MO4Cu2.Grey relation analysis was used to find out optimal condition.senthilkumar et al. (2011)have investigated the ECM of AL/1% SiCp through non-dominated sorting genetic algorithm-ii (NSGA II).They considered MRR and surface roughness as responses and developed multiple regression analysis.acharya et al. (2013) adopted RSM to study the influence of electrolyte flow rate, interelectrode gap, voltage and current on MRR and SR. NSGA II was employed to find the optimal condition when ECM hardened steel. From the above literature survey itcan be concluded that modeling and optimization in ECM was carried out on different grades of steels and composites materials.however there is hardly any research work on ECM characteristics of Nickel based superalloys. Today they are widely used in the field of defense, aerospace, aviation, nuclear plants and marine owing to their mechanical properties like high yield strength, high ultimate tensile strength, hot hardness and resistance to creep and corrosion. However some of the properties like poor thermal conductivity, chemical reactivity and work hardening tendency make these superalloys difficult-to cut using conventional machining techniques. Although EDM has been extensively used for machining some of these superalloys, particularly Inconel 718, the potential of ECM in machining of the same still remains unexplored. Inconel 82 is particularly suited for ECM operation because of its superior resistance to corrosion under hostile environment. Therefore the current investigation aimed at study and optimization of process variables (electrolytic concentration, voltage and tool feed rate) for MRR of Inconel 82.Emphasis will also be given on the effect of machining parameters on as machined surfaces. 2. Experimental details 2.1 Experimental setup All the experiments were carried out on the ECM setup (MAKE: METATECH), the photograph of which is shown in Figure 1.It mainly consists of three parts: machiningchamber, control panel and electrolyte circulation system. The machining takes place in the chamber while various process parameters are regulated through control panel.electrolyte is pumped through the reservoir where electrolyte flow rate can also be varied. Figure 1 Machining chamber 2.2 Selection of work piece, tool material and electrolyte Cylindrical block of 4 mm diameter and 6 mm thickness made up of Inconel 82 was selected as workpiece. Chemical composition and of Inconel 82 is Ni 38 46%, Fe 22 min %, Cr %, Mo %, Cu 1. 3%, Ti %.The tool made up of copper with circular cross section was selected as 174-2

3 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India anode.a central hole through the tool was used to axially feed the electrolyte into the machining zone. The outer diameter of the tool was 9mm with central hole of 3mm diameter for the passage of electrolyte. Aqueous solution of NaCl with varying concentration was taken as electrolyte. 2.3 Machining parameters and responses The machining efficiency depends largely on machining parameters. So the judicious selections of parameters are of prime importance. From the literature review the process parameters like voltage,tool feed rate and concentration of electrolyte have chosen for current study since they were find to have significant influence on MRR and SR The MRR can be defined as rate of dissolution of material from the workpiece. MRR of Inconel 82 has been considered as one of the performance measures was calculated by following expression: MRR= Initial and final weights of the workpiece were measured by electronic weighing balance machine of accuracy of g. Surface roughness is the variation or irregularity of a machined surface from its ideal atomic value. Arithmetic mean of surface roughness (R avg ) which is the average of all the peaks and valleys height in a specified range was used as a measure of surface roughness.en ISO 4287 standard was used to measure SR with Talysurf (Model: Taylor Hobson) with parameters cutoff length (L c ) as 0.8 mm and sample length (L n ) of 4 mm. 2.4 Design of experiment The experiment was planned as per 3 levels L9 Taguchi orthogonal array. The design was generated and analyzed by using MINITAB 16 statistical software.threefactors at three levels were considered for the experimentation.the L9 orthogonal array (OA) for the MRR is represented by Table 1. The machining was carried out for fixed duration of 10 minutes for all the experimental run. Run C V f MRR SR (g/lit) (V) (mm/min) Table 1 Taguchi L 9 OA for MRR 3. Influence of cutting parameters on responses measured Traditional Experimental design such as full factorial utilizes large number of experimental run when factors are more. Thus they are of much time consuming and complicated. But Taguchi design of experiment uses small number of runs to study the effect of process parameters by using orthogonal array in its design. Taguchi method mainly focuses on the average performance characteristics data close to the ideal target data rather than any other data within specified range, thereby improving the quality of product. Taguchi method is easy and time savvy, thus can be directly applied to any engineering situations. Taguchi design employs statistical tool called ANOVA (Analysis of variance) developed by Sir Ronald Fisher in order to determine significance and percentage contribution of individual process parameter on the performance characteristics or responses measured. The influence of different cutting parameters on different performance characteristics are explained in following section:- 3.1 Main effect plot The main effect plot is the graph of the average or means of response at each level of the factor or input parameter. The main effect plot helps one to determine the influence of individual input parameters on the responses measured, by disregarding the effect of any other input parameter present. The main effect plots of each response are explained below: Material removal rate (MRR) Although the material removal rate in electrochemical process is very low as compared to that of conventional machining but it s still a preferable option for machining of difficult-to-cut materials such as Nickel-based superalloy, Inconel 82 being belonging to same family into intricate and complex shapes. The productivity of ECM can be determined through MRR, so it is necessary to know the influence of the machining parameters on the MRR during ECM of Inconel 82. Figure 2 shows the main effect plot of the MRR depicting the effect of various machining parameters on MRR. As seen from the above graph obtained, the MRR increased with increase in both voltage and feed rate. This is due to the fact that with increase in voltage the current increases in the inter electrode gap thus increasing the MRR. Feed rate is another important parameter. Increase in feed rate results in decrease of the conducting path between the workpiece and the tool hence resulting in high current density thus enhancing the rapid anodic dissolution. An overall increase in the 174-3

4 EXPERIMENTAL STUDY OF MATERIAL REMOVAL RATE, SURFACE ROUGHNESS &MICROSTRUCTURE IN ELECTROCHEMICAL MACHINING OF INCONEL 82 MRR was also observed with increase in the concentration. Mean of Means CONC 40 FEED 0.2 Main Effects Plot for Means Data Means VOLTAGE Figure 2 Main effect plots for MRR 10 1 surface roughness but at high electrolytic concentration there is reduction in the surface roughness. Also noted was decrease in the surface roughness with increase in the voltage. The non-uniform removal of material during ECM of Inconel 82 at low voltage leads to high surface roughness but as the voltage increases uniform dissolution of material takes place resulting in lowering of surface roughness. 3.2 Analysis of Variance (ANOVA) ANOVA developed by Sir Ronald Fisher is a very powerful statistical tool to determine the significance of the process parameters on the responses measured. The F-test in the table assesses which process factors are significant and insignificant. Generally a large F-value signifies the higher significance of the process parameters on the performance characteristics. Percentage of contribution of each factor can also be deducted from the ANOVA table which is calculated by following expression: % contribution = Surface roughness (SR) The quality of the machined surface can be determined by its surface texture. During the ECM process the surface quality obtained is of high order such that no further finishing operation is required for the electrochemical machined surface. Mean of Means CONC 40 FEED 0.2 Main Effects Plot for Means Data Means VOLTAGE Figure 3 Main effect plots for SR From the main effect plot of the SR (Figure 3) it can be seen that when concentration of electrolyte increase from 30 g/lit to 40 g/lit there is increase in the 10 1 Source of variation Table 2 ANOVA table for MRR DOF Sum of squares F- value % contributi on C V F Error Total Source of variation Table 3 ANOVA table for SR DOF Sum of squares F- value % contribut ion C V F Error Total Table 2 here presents combined ANOVA table for all the response MRR. From the table it can be observed that the most significant factor contributing towards the MRR is voltage and feed with percentage contribution of 43.3 % and % respectively. In case of SR it was voltage and concentration of electrolyte with percentage contribution of 2.0 % and % respectively as per Table Response table for outputs 174-4

5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India Response table can also indicate which process parameters has greater influence on the responses measured by giving the process parameter a rank. Also one can infer the optimal condition from the response table. The highest value corresponding to the particular level in the response table is the optimal one for the MRR and it s the lower value for the SR. (a) (b) Table 4 Response table for MRR LEVEL CONC VOLTAGE FEED DELTA RANK From the response table of MRR (Table 4) it can be also inferred that the voltage is most influencing process parameter. The optimal condition was found at 0 g/lit concentration of electrolyte (level 3), 1v voltage (level 3) and 0.3 mm/min feed rate (level 3). Figure 4 SEM micrographs showing the microstructure of the as machined surfaces with different parameters. (a) 30 g/lit, V & 0.1 mm/m (b) 30 g/lit, 1V & 0.3 mm/min Figure also depicts the SEM micrographs showing the variation of the surface roughness with varying concentration and at a fixed voltage of V. It can be observed that initially with increase in concentration SR increases but later at high electrolyte concentration of 0 g/lit it decreases. Table Response table for SR LEVEL CONC VOLTAGE FEED DELTA RANK (a) (b) From Table it was found the optimal condition for SR to be at 30 g/lit concentration of electrolyte (level 1), 1v voltage (level 3) and 0.2 mm/min feed rate (level 2). 4 Microstructure of machined surfaces Figure 4 and Figure demonstrates the SEM micrographs depicting the morphology of as machined surfaces. It is evident that the ECM resulted in very distinct microstructure without any chemical treatment of the surface indicating electrolytic etching of Inconel 82 during the process. Figure 4 depicts the SEM micrographs showing the morphology of the machined surfaces obtained with different machining parameters. It can be concluded from the Figure 1 that at low voltage of V there is nonuniform anodic dissolution which leads to low MRR and high surface roughness. But at high voltage of 1 V the material removal is uniform, hence increase in MRR and decrease in SR were observed. (c) Figure SEM micrographs showing the microstructure of the as machined surfaces at (a) 30 g/lit, V & 0.1 mm/m (b) 40 g/lit, V & 0.2 mm/min and (c) 0 g/lit, V & 0.3 mm/min Conclusion This paper presented the use of Taguchi methodology in optimizing the ECM process parameters on MRR of Inconel 82.The following conclusions can be made based on the experimental results and analysis: i. The MRR increased with increase in voltage as well as tool feed rate. 174-

6 EXPERIMENTAL STUDY OF MATERIAL REMOVAL RATE, SURFACE ROUGHNESS &MICROSTRUCTURE IN ELECTROCHEMICAL MACHINING OF INCONEL 82 ii. The SR initially increased with concentration but it decreased at higher concentration. iii. The voltage was found to be significant factor affecting the MRR and SR. iv. The percentage contributions of most significant process parameters i.e. voltage towards the MRR and SR was found to be and 2.0 respectively. v. Optimal condition for MRR was found to be at 0 g/lit concentration of electrolyte(level 3), 1v voltage (level 3) and 0.3 mm/min feed rate (level 3). vi. The optimal condition for SR was found to be at 30 g/lit concentration of electrolyte (level 1), 1v voltage (level 3) and 0.2 mm/min feed rate (level 2). References Acharya, B.R., Mohanty, C.P., and Mahapatra, S.S. (2013), Multi-objective Optimization of Electrochemical Machining of Hardened Steel Using NSGAII, Procedia Engineering, Vol. 1, pp Asokan, P., Kumar, R.R., Jeyapaul, R., and Santhi, M. (2008) Development of muti-objective optimization models for electrochemical machining process. International Journal of Advance Manufacturing Technology, 39, pp Bhattacharya, B. and Sorkhel, P.B. (2006), Investigation for controlled electrochemical machining through response surface methodology-based approach,journal of Materials Processing Technology, Vol. 86, pp Chakradhar,D. and Gopal, A.V. (2011) Multi-objective optimization of Electrochemical machiningof EN31 steel by Grey Relational Analysis, International Journal of Modeling and Optimization, Vol. 1(2), pp Goswami, R., Chouhan, R. and Chaturvedi, V. (2013), The implementation of Taguchi approach on ECM process parameters for mild steel and aluminium, IJREAS, ISSN: , Vol. 3(2). Hocheng, H., Sun, Y. H., Lin, S. C., & Kao, P. S. (2003), A material removal analysis of electrochemical machining using flat-end cathode, Journal of Materials Processing Technology, Vol. 140(1), Neto, J.C.S., Silva, E.M. and Silva, M.B. (2006), Intervening variables in electrochemical machining, Journal of Materials Processing Technology, Vol. 179, pp Rama Rao, S., and G. Padmanabha (2012), Application of Taguchi methods and ANOVA in optimization of process parameters for metal removal rate in electrochemical machining of Al/% SiC composites, International Journal of Engineering Research and Applications (IJERA), Vol. 2(3), pp Senthilkumar, C., Ganesan, G. and Karthikeyan, R. (2011), Paramateric optimization of electrochemical machining of Al/1% SiC p composites using NSGA-II, Trans. Nonferrous Met. Soc.China, Vol. 21, pp Senthilkumar, C., Ganesan, G. and Karthikeyan, R. (2013), Influence of input parameters on characteristics of Electrochemical machining process, International Journal of Science and Engineering, Vol. 11(1), pp Tang, L. and Yang, S. (2013) Experimental investigation on the electrochemical machining of 00Cr12Ni9MoCu2 material and multi-objective parameters optimization, Int J AdvManufTechnol, Vol. 67, pp , Uttarwar, S.S. and Chopde, I.K. (2013), Experimental Study of Effect of Parameter variations on output parameters for Electrochemical Machining of SS AISI 202, IOSR-JMCE, ISSN: , Vol. (), pp Zhu, D. and Xu, H.Y. (2002), Improvement of electrochemical machining accuracy by using dual pole tool, Journal of Materials Processing Technology, Vol. 129, pp