Optimization of Machining Parameters for Tool Wear Rate and Material Removal Rate in CNC turning by Grey Relational Analysis

Transcription

1 Optimization of Machining Parameters for Tool Wear Rate and Material Removal Rate in CNC turning by Grey Relational Analysis Upinder Kumar* Assistant Professor, Department of Mechanical Engineering, Lovely professional University, Jalandhar-Delhi G.T. Road (NH-1), Phagwara, Punjab , India. Abhinavkumar Singh Assistant Professor, Department of Mechanical Engineering, Lovely professional University, Jalandhar-Delhi G.T. Road, (NH-1), Phagwara, Punjab , India. Rakesh Kumar Lecturer, Department of Mechanical Engineering, Lovely professional University, Jalandhar-Delhi G.T. Road (NH-1), Phagwara, Punjab , India. Abstract Tool wear and material removal rate (MRR) are the biggest intentions in modern Computer Numerical Controlled (CNC) machining. In the current study, L 9 orthogonal array for selecting the combinations of three process parameters (Cutting Speed, feed rate and depth of cut) have been used. The two response parameters in the current multi response parameter study are Tool Wear rate and Material Removal Rate. All the process parameters have 3 levels and in this study we are searching the single optimum condition on which both response parameters have optimum value. AISI 1045 is a medium carbon steel, which has a wide variety of applications in vehicle component parts and also in machine building industry, is used for current study of high speed turning. The most significant factor affecting MRR is Depth of Cut and most significant factor affecting Tool Wear Rate is Feed Rate. The optimum condition after simultaneous optimization comes out to be V2- F3-D3. The optimum value of the MRR and Tool Wear Rate comes out to be (mm³/sec.) and E-(1/mm²) respectively. Optimum results are finally verified with the help of confirmation experiments. Keywords: Optimization, Tool Wear Rate, Material Removal Rate, Grey Relational Analysis, Taguchi Method Nomenclature: CNC = Computer Numerical Controlled MRR = Material Removal Rate TWR = Tool Wear Ratio Introduction This paper is about experimentally investigating and optimizing the machining parameters for Tool Wear Rate and Material Removal Rate in CNC turning by Taguchi method and then simultaneous optimization of parameters for two responses namely MRR and Tool Wear Rate. The work material used for the present study is AISI 1045 medium carbon steel of composition { Carbon (3-0)%, Silicon (-)%, Magnesium (0-0.90)%, Phosphorus (0.05)%, Sulphur (0.05)% }. Its tensile strength is ( ) Mpa. This Carbon steel is suitable for shafts and machinery parts. It is mostly used in Automobile parts, in gears and machine building industry. Design of Experiment In this study, three machining parameters were selected as control factors, and each parameter was designed to have three levels, denoted 1, 2, and 3 as: Table 1: Turning parameters and levels Symbol Turning parameter Level 1 Level 2 Level 3 CS Cutting speed(m/min) FR Feed rate (rev/min) DOC Depth of cut(mm) Experimental Details Medium Carbon Steel (AISI 1045) of Ø: 24 mm, length: 17 mm were used for the turning experiments in the present study. The turning tests were carried out to determine the MRR and Tool Wear rate under various turning parameters. STALLION-100 HS CNC lathe, 3D sony color video electronic camera have been used for experimental investigations. Material Removal Rate MRR is calculated as:-mrr = Wi Wf mm³/sec ρs t Where, Wi=Initial weight of work piece in gm, Wf=Final weight of work piece in gm t=machining time in seconds ρs=density of mild steel (7.8 x 10ˉ³gm/mm³). 2771

2 Tool Wear Ratio (TWR): Tool Wear ratio is calculated by calculating Tool Wear length and dividing by Volume of Material Removed. S. N. Table 2: Mean values and S/N Ratio calculation for MRR Weight before turning (gm) Weight after turning (gm) Machining Time (sec.) MRR (mm³/sec) S/N Ratio (ƞ) Table 3: Means of Tool Wear Ratio and S/N Ratio Figure 1: Tool Wear Length The tool wearing length VB2 (mm) is scaled on a 3D Sony color video electronic camera. The abovefigure shows the sectional view of tool wear length after the relation is used to calculate Tool wear ratio[1]:- TWR( 1 mm 2) = Tool Wear Length(mm) Volume of Material Removed(mm 3 ) Results and discussion: In high speed turning operation, MRR and Tool Wear rate are important criterion. A series of turning tests are conducted to assess the effect of turning parameters on Material Removal Rate and Tool Wear Rate in turning of AISI Calculation of MRR and TWR: Experimental results of the Means of MRR and Tool Wear Rate for turning of AISI 1045 with different turning parameters are shown in Tables 2 and 3 which also gives Signal to Noise (S/N) ratio for MRR and TWR calculated by using Taguchi s approaches[1, 13]: Smaller-The-Better: S N = 10 log ) (( Yi2 ) n Larger-The-Better: S 1 = 10log (( N yi² )/n) Where, yi-ith observed value of the response, n-number of observations in a trial. S. N. Cutting Speed (m/min) Feed Rate (mm/rev) Depth of Cut (mm) Means of Tool Wear Ratio (1/mm²) S/N Ratio (ƞ) E E E E E E E E E Simultaneous Optimization of Material Removal Rate and Tool Wear Rate by using Grey Relational analysis coupled with Taguchi Method Step 1: Grey relational generation In grey relational generation, the normalized data corresponding to smaller-the-better (SB) criterion is calculated using the relation [14]: max yᵢ(k) yᵢ(k) xᵢ(k) = max yᵢ(k) min yᵢ(k) For larger-the-better (LB) criterion, the normalized data is calculated using the relation: yᵢ(k) min yᵢ(k) xᵢ(k) = max yᵢ(k) min yᵢ(k) Where xᵢ(k) is the value after the grey relational generation, min yᵢ(k) is the smallest value of yᵢ(k) for the k thresponse, and max yᵢ(k) is the largest value of yᵢ(k) for the k thresponse. An ideal sequence is x0(k) for the responses. The purpose of grey relational grade is to reveal the degree of relation between the sequences. Step 2: Reference sequence definition {Δ0i(k)} After the grey relational generation, all performance values are scaled into [0, 1]. The Δ0i(k) is calculated using relation: Δ0i=[x0(k) xi(k)]; wherex0(k) is the absolute value. 2772

3 Step 3: Grey relational coefficient calculation The grey relational coefficient is used to determine how close xi(k) is to x0(k). The larger the grey relational coefficient, the closer xi(k) and x0(k) are. The grey relational coefficient ξi(k) is calculated as[14]: ξi(k) = Δmin + θδmax Δ0i(k) + θδmax Where Δ0i(k)=difference of the absolute value x0(k) and xi(k); θis the distinguishing coefficient 0 θ 1; Δmin=the smallest value of Δ0i and Δmax=the largest value of Δ0i.The value of θ is taken for both MRR and Tool Wear Ratio. Step 4: Grey relation grade (GRG) calculation After calculating the entire grey relational coefficient(ξi(k)), the grey relational grade (γi) is calculated using the following relation[14]: γi= 1 n ξi n k=1 (k) Where, n=number of process responses. The higher value of grey relational grade corresponds to intense relational degree between the reference sequence X0 (k) and the given sequence xi (k). The reference sequence X0(k) represents the best process sequence. Therefore, higher grey relational grade means that the corresponding parameter combination is closer to the optimal. Run No. Ideal Sequence (Xo) Table 4: Results of GRG Parameters Grey Relational Generation (xᵢ(k)) Reference Sequence Definition (Δ0i(k)) Grey Relation Coefficient (ξi(k)) MRR TWR MRR TWR MRR TWR Table 4 shows the calculation of Grey Relational parameters and values of GRG which is the overall representative of all the features of cutting process quality. Thus, the multi-criteria optimization problem has been transformed into a single equivalent objective problem using a combination of the Taguchi approach and Grey relational analysis. The higher is the value of grey relational grade, the corresponding factor combination is close to the optimal. Table 5: Grey Relational Grade (GRG) calculation and S/N ratio S.No. GRG S/N Ratio Rank To investigate significant contribution of process parameters on performance, characteristics Analysis of Variance (ANOVA) has been carried out. Study of the ANOVA table is done to explore the factors that require careful control. Table 6: Analysis of Variance of means of GRG Source DF SS MS P C Cutting Speed * Feed Rate ** Depth of cut *** Error Total Table 7: Response Table for GRG Level Cutting Speed Feed Rate Depth of Cut Delta Rank Table 6 shows the percentage contribution of each parameter i.e. feed rate is the largest (77.27%) followed by depth of cut and cutting speed. The response table shows the rank of each process parameter. Mean of Means cutting speed depth of cut Main Effects Plot for Means Data Means feed rate Figure 2: Main Effects Plots for means of GRG 2773

4 cutting speed Interaction Plot for GRG Data Means feed rate depth of cut Figure 3: Interaction Plot for GRG cutting speed feed rate depth of cut The main effects plot shows that the optimum combination is V2-F3-D3 for the GRG, whereas the interaction plot shows the interaction between two parameters at the time for GRG. Determination of Optimum Condition: Both the response and S/N ratio are used to derive the optimum conditions. Since for quality characteristic, Grey Relational Grade larger the better approach is desirable, the largest is the ideal level for a parameter. The S/N ratio is always highest at the optimum condition. The graphs of Figures 2 and 3 are used to determine the optimum process parameters combination. The optimum combination is therefore V2-F3-D3. Predictive Equation: The predicted values of GRG, MRR and TWR at the optimal levels are calculated by using the relation[14]: o ň = nm + (nim nm) i=1 Where, ň-predicted response value after optimization, nm- Total mean value of quality characteristic, nim- Mean value of quality characteristic at optimum level of each parameter and o- Number of main machining parameters that affect the response parameter. Applying this relation, predicted values of GRG, MRR and TWR at the optimum conditions are calculated as: i) ň(grg)=140 + [ ( ) + ( ) + ( ) ]= ii). ň(mrr)= [ ( ) + ( ) +( )= mm³/sec iii).ň(twr)= [ ( ) + ( ) + ( ]= Here ň(twr) is the value of S/N ratio of TWR and the corresponding value of TWR is E- (1/mm²). The robustness of this parameter optimization is verified experimentally. This requires the confirmation run at the predicted optimum conditions. The experiment is conducted twice at the predicted optimum conditions. Verifications For Material Removal Rate (MRR): The calculated value of MRR at the optimum condition (V2- F3-D3) is mm³/sec. The error in the predicted optimum value (721.76) and the experimentally calculated value (695) is only 4.3%. For Tool Wear Ratio (TWR): The calculated value of TWR at the optimum condition (V2-F3- D3) is E- (1/mm²). The error in the predicted optimum value (2.5142E-) and experimental value (2.4021E-) is only 4.45%. Hence very good agreement between the actual and the predicted results is observed. Since the percentage error is less than 5%, it confirms excellent reproducibility of the results. The results show that using the optimal parameter setting (V2-F3- D3) a higher Material Removal Rate is achieved with lower Tool Wear Ratio.. Results The effect of three machining parameters i.e. Cutting speed, feed rate and depth of cut and their interactions are evaluated using ANOVA and with the help of MINITAB 16 statistical software. The purpose of the ANOVA in this study is to identify the important turning parameters in prediction of MRR and Tool Wear rate. Some important results comes out are given below in table 16 shown below shows that optimal values of surface roughness and material removal rate that lie between the optimal ranges. Table 8: Optimal values of machining and response parameters CP OV OL POV EOV OR CS V2- MRR= MRR= <MRR> F F D D3 TWR=2.5142E- TWR=2.4021E- Where, CP-Cutting Parameters, OV- Values of Parameters OL- Optimum Levels of Parameters, POV- Predicted Optimum value EOV- Experimental Optimum Value, OR- Optimum Range of MRR and TWR E- < TWR> E- Conclusion In this study, the Grey relational based Taguchi method was applied for the multiple performance characteristics of turning operations. A grey relational analysis of the Material Removal Rate and the Tool wear ratio obtained from the Taguchi method reduced from the multiple performance characteristics to a single performance characteristic which is called the grey relational grade. Therefore, the optimization of the complicated multiple performance characteristics of the processes can be greatly simplified using the Grey relational based Taguchi method. It is also shown that the performance characteristics of 2774

5 the high speed turning operations, such as the Material Removal Rate and the Tool Wear Ratio are greatly enhanced by using this method. [14] C. L. Lin, Use of the Taguchi method and grey relational analysis to optimize turning operations with multiple performance characteristics, Materials and Manufacturing Processes, 19(2), , References [1] H. Singh and P. Kumar, Tool wear optimization in turning operation by taguchi method, International Journal of Engineering and Material Science, Vol.11, pp , [2] T. Srikanth and V. Kamala, A Real Coded Genetic Algorithm for Optimization of Cutting Parameters in Turning. IJCSNS Int. J. Comput. Sci. Netw. Secur, 8(6), pp , [3] I. P. Arbizu and C. J. L. Pérez, Surface roughness prediction by factorial design of experiments in turning processes. J. Mater. Process. Technol., pp : , [4] C. C. Tsao andh. Hocheng, Evaluation of the thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network J. Mater. Process. Technol., pp. 203(1-3): , [5] J. Z. Zhang, J. C. Chen and E. D. Kirby, Surface roughness optimization in an end-milling operation using the Taguchi design method J. Mater. Process. Technol, pp. 184(1-3): , 20. [6] Munteanu, Application of taguchi method for cutting force in turning high density polyethylene, academic journal of manufacturing engineering, vol. 8, issue 3, [7] P. Selvarajand P. Chandarmohan, Optimization of surface roughness of AISI 304 austenitic stainless steel in dry turning operation using Taguchi design method, Journal of Engineering Science and Technology, Vol. 5, No. 3, pp , [8] K. Kamarudin and E. A. Rahim, Optimizing Surface Roughness and Flank Wear on Hard Turning Process Using Taguchi Parameter Design, Proceedings of the World Congress on Engineering, 20 [9] U. Cayda, Machinability evaluation in hard turning of AISI 4340 steel with different cutting tools using statistical techniques, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, [10] S. Thamizhmanii, S. Saparudin and S. Hasan, Analysis of Surface Roughness by Using Taguchi Method, Achievements in Materials and Manufacturing Engineering, Volume 20, Issue 1-2, pp , 20. [11] T. S. Lan, C. Y. Lo, M. Y. Wang and A. Y. Yen, Multi Quality Prediction Model of CNC Turning Using Back Propagation Network, Information. Proceeding of American society of mechanical engineers, [12] C. Dhavamani and T. Alwarsamy, Review on optimization of machining, International Journal of Academic Research, Vol. 3. No. 3, II Part, [13] W. H. Yang &Y. S. Tarng, Design optimization of cutting parameters for turning operations based on the Taguchi method. Journal of Materials Processing Technology, 84, pp ,