COMPARATIVE STUDY OF THE MACHINING CHARACTERISTICS OF EN24 AND HARDENED EN24 DURING CNC TURNING

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 4, April 018, pp , Article ID: IJMET_09_04_063 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed COMPARATIVE STUDY OF THE MACHINING CHARACTERISTICS OF EN4 AND HARDENED EN4 DURING CNC TURNING R. K. Bhuyan Faculty of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India Pavan Kumar Akula, Guna Ranjan Chivukula, Brugumalla Mohan Sai UG Scholar, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India ABSTRACT The present paper is described the Fuzzy logic optimization technique to controlling the process parameter during CNC turning EN-4 material. To achieve the industrial application of this material with normal machining and after hardening it needs to optimize the process parameter for reliable and economical production of this process. For that Box Behnken Central composite design method is considered for the experiments by using the three input process parameters like speed, feed rate and depth of cut and the output process are material removal rate, chip thickness and surface roughness (R a ). To enhance the method specification fuzzy logic approach is engaged for a sole numerical index. Analysis of Variance (ANOVA) mode is used to check the consequence of the parameters. Lastly, confirmation test is conducted with the assistance of mathematical model established by Response surface methodology (RSM) to accomplish the necessary approach of current work. Key words: CNC Turning, EN4 material, Fuzzy logic method, ANOVA, RSM. Cite this Article: R.K. Bhuyan, Pavan Kumar Akula, Guna Ranjan Chivukula and Brugumalla Mohan Sai, Comparative Study of the Machining Characteristics of EN4 and Hardened EN4 during CNC Turning, International Journal of Mechanical Engineering and Technology 9(4), 018, pp INTRODUCTION In this contemporary world, everything from a pen to spacecraft is interlinked with manufacturing technology. We, humans, always desired for accurate products at an affordable cost. And with the development of technology, there are many solutions to our desire and one among them is CNC. New technology in automation for manufacturers is widely open and editor@iaeme.com

2 Comparative Study of the Machining Characteristics of EN4 and Hardened EN4 during CNC Turning computer numeric control (C.N.C) is one of them where a computer executes Pre - Programmed machine control commands given by external controller or human. One of the most frequently used machines for machining of raw material is CNC lathe (Turning Operation). The work material used in our research is EN4, which is a medium carbon steel with carbon composition of 0.36 to 0.44%. It is a high tensile alloy steel which is used in high-stress component applications such as aircraft landing gear, High strength shafts, punches and dies etc., the class is an effective combination of strength, ductility and wear resistance because of the existence of alloying elements like Nickel, Chromium, and Molybdenum. The other key feature involves easy to Heat treat, Temper and suitable for hightemperature applications.. LITERATURE REVIEW In the past researcher are employed various optimization technique to improve the machining characteristics during the CNC Turning of EN4 material. According to Amrit pal et.al [6] overviewed the effects of several input process criterion like Cutting speed, Feed rate and D.O.C on output parameters such as Roughness on surface, Rate of Material Removal and Coefficient of Chip reduction. From their work, they concluded that the feed rate is the most significant parameter for Roughness, Cutting speed for tool wear, Feed and D.O.C for M.R.R. Dinesh et.al [1] promoted an empirical model using Statistical Methodology (R.S.M) to predict Surface roughness and MRR values effectively and studied the interaction effects of various parameters on the output variables for EN4 alloy steel. Fernandez et.al [4] from his work stated that cryogenic fluids are the more likely way to ameliorate the cutting process of advanced material like Inconel 718 in a conservative way. Mahindra et.al [8] suggested that, Nose radius is the utmost crucial aspect for both surface roughness and M.R.R(Response Characteristics) while cutting environment is the most insignificant factor for response characteristics. He also showed that the process parameters like nose radius, cutting speed, D.O.C, feed rate, and the cooling condition affects the M.R.R by 40.68%,14.88%,0.96%,0.53%, and 0.03% respectively on EN4 Alloy steel. Mukherjee et.al [3] from his report concluded that the Depth of cut is the crucial factor on mrr followed by F for SAE-100 steel by means of Taguchi Procedure. Nikam et.al [7] conducted their developmental work through the use of L9 orthogonal array in a Speed LX00 Major CNC Lathe mechanical device. Finally, the optimum cutting condition was found, and the Surface Roughness values were evaluated by the Analysis of Variance (ANOVA). Roopa et.al [5] carried their research work to study Machining parameters effect on 'Ra' (Surface roughness) and 'MRR' before and after heat treatment of EN19 (medium carbon steel). They determined how MRR for feed and Depth of cut varies before and after heat treatment of work material by considering speed as a significant factor and concluded how the surface roughness varies after heat treatment. Singh et.al [] identified that for material removal rate, D.O.C, feed rate, and Speed are the most significant parameters while Nose Radius is a least significant parameter. He conjointly studied various methods for the processing parameters with maximum implementation of these parameters. Sujith [9] applied Taguchi method for optimizing the similar process parameters like in our paper and performed dry turning of the mild steel bar with TIN coated Carbide tools. He specified that with the rise of these process parameters (DOC, F, and N). MRR increases and he also conducted several experiments to demonstrate the optimal cutting parameters. Then finally he concluded that the enhancement of MRR from the initial cutting parameters to the optimal cutting parameters is around 347.%. Based on the literature study the target is to perform a preliminary examination on CNC lathe machine of EN4 medium carbon steel material before and after hardening by using editor@iaeme.com

3 R.K. Bhuyan, Pavan Kumar Akula, Guna Ranjan Chivukula and Brugumalla Mohan Sai fuzzy logic technique. Fuzzy logic is a potential tool to predict the result and to optimize the machining characteristics. The aim of this study is to remodel the multiple-characteristic problem to an identical sole feedback to empirically analyse the results of N, F and DOC on the mrr, r and R a. Also, the subsequent order mathematical models are established based on RSM to verify the importance of the models. Thus, the objective of the present work is carried out an experimental investigation on CNC turning of EN4. The aim of this paper is to focus on the fuzzy logic technique and ANOVA test is carried out to check the significance of machining parameter of the developed models. Finally, confirmation test is carried out to verify the error in the model based on the selected optimization technique. 3. EXPERIMENTAL PROCEDURE 3.1. Design of Experiment To study the effects of CNC turning process parameters, the experimentation planned to follow the Box Behnken Central composite design method. The experiments are followed by three process parameters i.e. N, F and D and their levels are shown in the Table 1. Table 1 Process parameters and their levels Parameters Symbols Units Levels -1(Low) 0 (Medium) 1(High) Cutting Speed N rpm Feed rate F mm/rev Depth of cut D mm Experimental Method & Result All the experiments are completed by using Model CNC Lathe which is manufactured by Lokesh Machines Limited with the carbide tool having nose radius mm. The machining is done by EN4 material before hardening and after hardening as shown in Fig.1. The material initial was 33HRC after hardening by oil quenching the hardness is changed to 58 HRC. Figure 1 Experimental set up-cnc turning MRR = (W1 W) / (density * t) (1) Where W1= Weight of specimen before CNC machining (mg), W=Weight of specimen after CNC machining (mg), t= time taken for machining (min).the R a values are measured by MITUTOYO (Roughness tester) at three different positions in the machining surface of the sample piece and the average of three measurements are recorded. The experiments result with their design of experiment as per the selection of the values is given in Table editor@iaeme.com

4 Comparative Study of the Machining Characteristics of EN4 and Hardened EN4 during CNC Turning Table Experimental responses using DOE Sl.no N F D MRR (mm3/min) r (mm) R a (Micron) The expt. result later hardening with DOE as per the selection of values is given in Table 3 Table-3 Experimental result after hardening r R Sl.no N F D MRR (mm3/min) a (mm) (Micron) Fuzzy Logic System Fuzzy logic idea is based on the concept of human common-sense reasoning i.e. the uncertainty decision-making in the situation of problems occurs. It is based on the assessment whether it is true or false, yes or no and high or low. It is described in terms of human linguistic as extremely small, very small, small, medium, high, very high, and extremely high etc. It has four different elementary ways and those are fuzzifier, knowledge base, inferences engine, and DE fuzzifier. In these four processes, each factor is transformed to numerical value varies from 0 to 1. After that the data of input and output factors are transformed in the shape of the membership function. This membership function is set by a definite array of borders value in the method of a fuzzy set and continuously exemplified by human linguistic. Based on the part describes the input-output membership function applied If-Then rules to the membership function to generate the result. Next is the inferences engine, built the performance evaluation of the pooled input-output membership function and execution of the operator. Lastly, the DE fuzzifier changes the fuzzy value into a single fuzzy cognitive rank known as Multi Performance Characteristic Index (MPCI) editor@iaeme.com

5 R.K. Bhuyan, Pavan Kumar Akula, Guna Ranjan Chivukula and Brugumalla Mohan Sai In this modelling the input is signified by three dialectal variables like minimum, medium, and maximum for five linguistic output variables such as very small, small, medium, large and very large. The shapes of the membership function are in the form of triangular membership function. By using MATLAB R014 environment 15 experiments rules are implemented in the form of IF-THEN CONTROL rules with their membership function are executed to find the single numerical index known as MPCI. The result of MPCI and ranked the order based on its largest single numerical index value is shown in Table 4. It is observed that the experiment no 5 is the highest MPCI value having before hardening and after hardening as shown in the Fig. and 3. Figure Fuzzy Logic Model Figure 3 Membership function for the MPCI Table 4 MPCI attribute before and after hardening Before Hardening Rank After Hardening Rank editor@iaeme.com

6 Comparative Study of the Machining Characteristics of EN4 and Hardened EN4 during CNC Turning 4. ANALYSIS OF VARIANCE (ANOVA) The investigational results are analysed with ANOVA test. It is a statistically based method and the objective is to verify and tested the individual performance of process and their combination. The results ANOVA test MRR, r and Ra for before hardening and after hardening are shown in Tables 5, 6 and 7 and 8,9,10 respectively. From the table it is analysed that if the P value is less than 0.05, Then the values are significant and if P value is more than 0.05, then the values are not significant. Also, it is found that the R value for the MRR, r and Ra before hardening 98.%, 97.61%, 90.68% and after hardening are 97.78%, 98.94%, 94.83% respectively. The R value closed to 1 which is more desirable for the selected model. Table 5 ANOVA TABLE for MRR Before Hardening Linear Not Significant Square Significant Interaction Not Significant Residual Error Total Table 6 ANOVA TABLE for r Chip Thickness Before Hardening Linear Significant Square Significant Interaction Not Significant Residual Error Total Table 7 ANOVA TABLE for Ra Surface Roughness Before Hardening Linear Not Significant Square Not Significant Interaction Not Significant Residual Error Total Table 8 ANOVA TABLE for MRR After Hardening Linear Significant Square Significant Interaction Significant Residual Error Total Table 9 ANOVA TABLE for r Chip Thickness After Hardening: Linear Significant Square Significant Interaction Significant Residual Error Total editor@iaeme.com

7 R.K. Bhuyan, Pavan Kumar Akula, Guna Ranjan Chivukula and Brugumalla Mohan Sai Table 10 ANOVA TABLE for Ra Surface Roughness After Hardening: Linear Significant Square Significant Interaction Not Significant Residual Error Total RESPONSE SURFACE METHODOLOGY RSM is used to govern a suitable estimation for efficient relationship between the response parameter and process parameters. In RSM the model is represent in the form mathematical quadratic equation. This equation involves linear and quadratic interactions of process variables. In RSM the second order quadratic mathematical equation is represent by in the form of Y 0 1N F 3D 4N 5F 6D 7N * F 8N * D 9F * D () Where 0 is a constant 1,, 3 linear, 4, 5, 6 squares, 7, 8, 9 interaction are the coefficients are evaluated by the using Minitab Software. The final equations for the responses are given for the material before hardening and after hardening Before Hardening MRR N 7614F 643D N D 11D 14N*F 4.19N*D F*D r N 8F D N D 0.000N*F N*D F*D N*F N*D F*D F 14F Ra N F D N After Hardening D MRR N F D N 5.5D N*F N*D F*D r E 04N F D 3.15E 07N D 0.003N*F N*D 1.017E 14F*D N*F N*D 3.0F*D F Ra N F 3.88D E 06N 10500F 1F F (3) (4) (5) (6) (7) (8) 6. CONFORMATION TEST The experiments are further analysed by conducting by confirmation test. The predicted values are for MRR, r, Ra for before hardening and after hardening are evaluated by considering the equation (3) to (8). To evaluate the predicted values before and after hardening of the model the corresponding process parameters of the highest ranked of MPCI i.e. the experiment No.5 shown in Table 4 is consider. The corresponding process parameter for experiment no 5 are speed is 800 rpm., feed rate 0.15 mm/rev and depth of cut 0.3mm. The percentage of error is evaluated by considering the equation 9 and its experiments value 56 editor@iaeme.com

8 Comparative Study of the Machining Characteristics of EN4 and Hardened EN4 during CNC Turning and predicted values and its error are shown in Table 11. It is found that the error of MRR, r and Ra before hardening are1.915%,4.651%,6.757% and after hardening 5.03%,3.175%, and 0.0% respectively. Percentage of Error = ((Experimental value- Predicted value) *100)/ Predicted value (9) Table 11 Compression result between before hardening and after hardening with highest value of MPCI Machining characteristics Optimum Parameters setting Predicted Values experimental values % of Error Before Hardening MRR N= R F= R a D= After Hardening MRR N= R F= R a D= CONCLUSIONS Based on the experimental and analytical result of multi objective optimization technique, following conclusion are drawn: The effect of machining parameters on the MRR, r, R a is evaluated with the help of Box Behnken C.C.D. method. The proposed Fuzzy logic method is an easy technique to optimize the multi-machining characteristics into single numerical index i.e. MPCI. The ANOVA test is carried out to observe the most significant machining characteristics during linear, square and interaction of process parameter during the process. A second-order mathematical model equation is used to develop the MRR, r, R a is from the highest MPCI data. This indicates that the developed model can be used effectively to predict the responses parameter. Finally, the corresponding process parameter of highest value MPCI is used for confirmation test to verify the percentage of error Therefore, the present approach is simple method to optimize the multi machining problem for any type of machining. REFERENCES [1] Dinesh, K. Raja Guru, V. Vijayan, Investigation and Prediction of Material Removal Rate and Surface Roughness in CNC turning of EN4 Alloy Steel -Vol.0, No.4 (016) Lodz University of Technology. [] Manpreet Singh, Er. Sanjeev Verma, Dr. Sanjiv Kumar Jain, A Literature Review on Machining of different materials with CNC. - International Journal of Emerging Research in Management and Technology, - ISSN: ( ) (Volume-3, Issue-8), August 014. [3] Sayak Mukherjee, Anurag Kamal, Kaushik Kumar, Optimization of Material Removal Rate During Turning of SAE 100 Material in CNC Lathe using Taguchi Technique, EL SEVIER - Procedia Engineering97 (014) editor@iaeme.com

9 R.K. Bhuyan, Pavan Kumar Akula, Guna Ranjan Chivukula and Brugumalla Mohan Sai [4] Fernandez, V. Garcia Navas, A. Sanda, I. Bengoetxea, Companion of Machining INCONEL 718 with Conventional and Sustainable Coolant December/ MM Science Journal. [5] Roopa K Rao, Vinay Murgod, Dr.AS.Deshpande, Dr. K K Jadhav, Analysis of the effect of Cutting Parameters on responses Surface Roughness and Material Removal Rate for EN19 work-pieces material with and without heat treatment. International Journal of Scientific & Engineering research, volume 6, Issue 1-January- 015 ISSN : (9-5518). [6] Amritpal Singh, Mr. Harjeet Singh, Mr. Rakesh Kumar, Review on Effects of Process Parameters in Hard Turning of Steels. IJIRST-International Journal for Innovative Research in Science and Technology, Volume-3, Issue 06-November 016. ISSN: [7] K.G.Nikam, S.S.Kadam, Optimization of Surface Roughness of EN8 Steel by enhancing Cutting Parameters and Insert Geometry in Turning Process. International of Science and Research (IJSR) ISSN: , Impact Factor (01): [8] Mahindra, Neeraj Agarwal, Optimization of Different M/C ing Parameters of En4 (Alloy steel) in CNC Turning by use of -Taguchi Method.- Int. J. of Eng. Res. & Applications- (IJERA), Vol.,Issue -5,01, PP , ISSN: [9] Sujit Kumar Jha, - Optimization of Process parameters for MRR During Turning Steel Bar using Taguchi Method and ANOVA, Int. J. Mech. Eng. &Rob. Res. 014, ISSN , Vol. 3, No.3, July, 014. [10] A. Thakur, S.Dewangan, Prediction of work hardening during machining Inconel 85 using Fuzzy Logic Method Procedia Materials science5 AMME [11] Ashish, Jyoti Bhardwaj, Maninder Singh, Sandeep Kumar Pal, Optimisation of Diff. Performance Param. i.e. - Surface Roughness, - Tool Wear Rate & MRR with the selection of various process param. in CNC Turning of EN4 Alloy Steel An Empirical Approach. (IJES) volume Issue 1 pages , ISSN : , ISBN : [1] Vishaldeep Singh, Hitesh Arora, Prashant K. Pandey and Rahul Wandra, A Methodology for Simulation and Verification of Tool Path Data for 3 - Axis and 5 - Axis CNC Machining, International Journal of Mechanical Engineering and Technology, 9(3), 018, pp editor@iaeme.com