International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
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Cutting parameter optimization for minimizing machining distortion of thin

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Cutting parameter optimization for minimizing machining distortion of thin

  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 62 CUTTING PARAMETER OPTIMIZATION FOR MINIMIZING MACHINING DISTORTION OF THIN WALL THIN FLOOR AVIONIC COMPONENTS USING TAGUCHI TECHNIQUE Garimella Sridhar #1 , P. Ramesh Babu *2 # Research Scholar, College of Engineering, OU, Hyderabad, India * Associate Professor, College Of Engineering, OU, Hyderabad, India ABSTRACT Distortion of thin wall thin floor aluminium components during and after machining is one of the main challenges faced by aerospace manufacturing industries. These parts have to be machined from prismatic blanks to features with walls and floors as thin as 1mm. So, in this experimental study series of machining experiments were carried out using Taguchi design of experiments to find the effect of important machining parameters (speed, feed, depth of cut, width of cut, tool path layout) which influence distortion of the parts during machining and optimize them for minimizing distortion. An L’16 orthogonal array, signal-to- noise (S/N) ratio and ANOVA are utilized in this study. By this approach both the optimum parameters and main parameters which influence distortion can be found. Optimum parameters are finally verified with the help of confirmation experiment. 1. INTRODUCTION Distortion of thin wall thin floor components is one of the major challenges facing manufacturing industries. Machining these thin wall thin floor components from prismatic blocks, removing most of the material, almost to sheet metal configurations, resulting in distorted parts, leading to rejection and reworks is causing great economic loss to manufacturers. Literature survey reveals many factors which effect the distortion during manufacture of these thin wall thin floor parts. Right from design configuration, material to machine, clamping configuration to machining parameters viz., speed, feed, depth of cut, width of cut, tool path strategy, tool geometry used and their cumulative effect can cause distortion[1]. Important parameters which are controllable easily by any machinist during manufacturing of these low rigidity parts are feed, speed, depth of cut, width of cut and tool path strategy. The related published works on machining of these thin wall thin floor parts is as under. INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 4, Issue 4, July - August (2013), pp. 62-69 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 63 In depth studies were conducted by Budak on peripheral milling of flexible titanium plates and cutting force and chatter stability models were developed [2]. Static surface form errors due to deflection during peripheral milling of low rigidity walls and material removal simulation studies were carried out by Tsai, et al., Ratchev, et al., and Wan, et al [3-5]. In the last decade focus was shifted to analyze the distortion during metal removal process in-Toto. The effect of initial residual stresses on part distortion was studied by Wang & Padmanaban, and Wang et al [6-7]. Optimization of fixture design for machining thin walled work pieces was studied by Lie, et al.,[8]. Simulation studies of machining distortion on thin walled aircraft structures and validation by experiments was done by DONG Hui-yue, et al., Yun-bo BI, et al., and Yong YANG., et al [9-11]. Though simulation studies and validation experiments were carried to understand distortion during material removal process, much experimental work was not done to understand the effects of machining parameters directly on distortion. Yang and Tarng used Taguchi experimental design to find optimum cutting parameters to increase tool life and surface finish in turning S45C steel [12]. Ramanujam, et al., optimized multi- machining parameters during turning of composites using Taguchi and Desirability Function Analysis [13]. Sanjit, et al., used Principal Component Analysis based Taguchi method in optimizing the milling process parameters in improving surface finish and increasing the Material Removal Rate [14]. Kuram, et al., used Taguchi and ANOVA technique in optimizing the cutting fluids and machining parameters to reduce tool wear and cutting forces [15]. Sadasiva Rao., et al., used Taguchi based Grey Relational Analysis in optimizing multiple characteristics during Face milling process [16]. In this present work the effects of controllable machining parameters viz., Speed, Feed, Depth of Cut, Width of Cut and Tool Path layout on machining distortion are analyzed by way of machining experiments adopting Taguchi experimental approach (L’16 orthogonal array) and ANOVA technique and find optimum cutting parameters which minimize distortion for the first time. 2. EXPERIMENTAL WORK 2.1 Work piece and Work piece material The work material selected for the study was aluminum alloy 2014A T651. This is alloy with copper as principle alloying element which is used in avionic structures. A representative thin wall thin floor work piece as shown in figure1 was used for experiments. The physical & chemical properties are shown in Table 1 & Table 2 respectively. Figure 1 Representative part used for experiment (All Dimensions in mm)
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 64 2.2 Experimental Setup The machining experiments were carried on CNC 3-axis vertical machining centre (VMC MICRON VCP 600 Haidenhain controller ITNC 530 as shown in figure 2. The cutting tool used is 2 flutes solid carbide slot drill ø 10mm. New tool is used for machining each experimental work piece. The work piece was clamped from underneath using a vacuum fixture made for machining experimental work piece as shown in figure 3. Table 1 Physical properties of alloy Table 2 Chemical properties of alloy Figure 2 Vertical Machining centre used for experiments Sl. No PROPERTY VALUE 1 Yield strength 380 Mpa (minimum) 2 Tensile strength 405 Mpa (minimum) 3 Hardness Rockwell B 82 4 Density 2.80 g/cc 5 Poisson’s Ratio 0.2 to 1.2 6 Elongation 4 to 7 % 7 Modulus of Elasticity 72.4 GPa Sl. No ELEMENT PERCENTAGE (%) 1 Copper 3.8 to 4.8 2 Magnesium 0.2 to 0.8 3 Silicon 0.6 to 0.9 4 Iron 0.7 max 5 Manganese 0.2 to 1.2 6 Aluminum Reminder
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 65 Figure 3 Figure 4 Figures showing Vacuum fixture with work piece used for experiments The machining parameters considered for experiments are 5 factors i.e., Speed, Feed, Depth Of Cut (DOC), Width Of Cut (WOC) and Tool path layouts as shown in Figures 6 with each parameter having 4 levels as shown in table 3. The quality characteristic i.e., response which is of main focus in these experiments is Distortion and. The distortion is measured by using CMM the distortion taken as quality characteristic is maximum deviation from the flat surface in millimeters as shown in figure 5. Figure 5 Maximum Distortion Figure 6 Tool Path Layouts Table 3 Experimental Design showing factors and levels used in experiments FACTORS LEVELS 1 2 3 4 FEED F (mm/TOOTH) 0.05 0.1 0.15 0.2 SPEED V (m/min) 100 150 200 250 DEPTH OF CUT D (mm) 0.4 0.8 1.2 1.4 WIDTH OF CUT Ae (% of D) (mm) 50 60 70 80 TOOL PATH LAYOUTS T ZIGZAG (Z) ONE WAY (O) PARALLEL SPIRAL [INSIDEOUT] (P) CONSTANT OVERLAP [INSIDEOUT] (S)
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 66 3. RESULTS AND DISCUSSIONS 3.1 Analysis of Signal to Noise Ratio In Taguchi analysis Signal to Noise ratio (S/N) is used to know the deviation of quality characteristic from desired value. There are four types of characteristics viz., Lower the Better (LB), Nominal the Best (NB), Higher the better (HB) and Smaller the Better (SB). In this current experiments Smaller the Better (SB) is used as least distortion is desirable characteristic. The SB is calculated by the following equation ݊ ൌ െ10log ቂ ଵ ௡ ሼ∑ ‫ݕ‬௜ ଶ௡ ௜ୀଵ ሽቃ (1) Where n is number of experiments and yi is ith value measured in a run. The values of maximum distortion and S/N ratio calculated using equation (1) are listed in Table 4. Figure 7 shows the main effects plot for S/N ratios. It can be seen from Figure 7 and Table 5 that the optimum parameters for minimizing the distortion are feed 0.05 feed/ tooth, speed 150 m/min., depth of cut 0.4mm, width of cut is 70% of the diameter of the cutter and Tool path is constant overlap. Table 4 Values of S/N ratios for distortion Experiment No. FEED (mm/ tooth) SPEED (m/min.) DEPTH OF CUT (mm) WIDTH OF CUT (mm) TOOL PATH DISTORTION (mm) Signal to Noise ratio (S/N) 1 0.05 100 0.4 5 ZIG ZAG 0.16 15.92 2 0.05 150 0.8 6 ONE WAY 0.26 11.70 3 0.05 200 1.2 7 PARALLEL SPIRAL 0.28 11.06 4 0.05 250 1.6 8 CONSTANT OVER LAP 0.19 14.42 5 0.1 100 0.8 7 CONSTANT OVER LAP 0.24 12.40 6 0.1 150 0.4 8 PARALLEL SPIRAL 0.12 18.42 7 0.1 200 1.6 5 ONE WAY 0.39 8.18 8 0.1 250 1.2 6 ZIG ZAG 0.44 7.13 9 0.15 100 1.2 8 ONE WAY 0.41 7.74 10 0.15 150 1.6 7 ZIG ZAG 0.12 18.42 11 0.15 200 0.4 6 CONSTANT OVER LAP 0.11 19.17 12 0.15 250 0.8 5 PARALLEL SPIRAL 0.81 1.83 13 0.2 100 1.6 6 PARALLEL SPIRAL 0.52 5.68 14 0.2 150 1.2 5 CONSTANT OVER LAP 0.44 7.13 15 0.2 200 0.8 8 ZIG ZAG 0.27 11.37 16 0.2 250 0.4 7 ONE WAY 0.10 20.00
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 67 3.2 Analysis of Variance (ANOVA) Analysis of variance is used to determine the contribution of each factor under consideration which influences the distortion due to machining. Table 6 shows the summary of ANOVA results for distortion. It can be seen from the ANVOA analysis that depth of cut has major influence on distortion contributing 55.72%. The next factor which has major impact on distortion is Width of cut contributing 25.31%. The way of cutting i.e., tool path layout has also a contribution of 9.61% to distortion and planning the tool path is also important in minimizing distortion followed by cutting speed contributing 6.87%. From these experimental results it is found that feed has negligible effect on distortion contributing only 2.49%. Table 5 Importance of parameters with S/N ratio values for distortion FACTORS LEVELS 1 2 3 4 A(feed) *13.27 11.53 11.79 11.04 B(speed) 10.43 *13.91 12.44 10.84 C(depth of cut) *18.37 9.32 8.26 11.67 D(width of cut) 8.26 10.92 *15.46 12.98 E(tool path) 13.20 11.90 9.24 *13.28 *indicate optimized parameters to minimize distortion 4321 17.5 15.0 12.5 10.0 4321 4321 4321 17.5 15.0 12.5 10.0 4321 A MeanofSNratios B C D E Main Effects Plot for SN ratios Data Means Signal-to-noise: Smaller is better Figure 7 S/N ratio values for distortion Table 6 ANOVA values for distortion FACTOR DOF AVERAGE S/N VALUES SUM OF SQUARES MEAN SQUARE PERCENTAGE OF CONTRIBUTION LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 4 FEED 3 13.27 11.53 11.79 11.04 11.072 3.691 2.49 SPEED 3 10.43 13.91 12.44 10.84 30.476 10.159 6.87 DOC 3 18.37 9.32 8.26 11.67 247.339 82.446 55.72 WOC 3 8.26 10.92 15.46 12.98 112.353 37.451 25.31 TOOL LAYOUT 3 13.20 11.9 9.24 13.28 42.662 14.221 9.61 ERROR 0 0 TOTAL 15 443.903 100
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 68 3.3 Results of Confirmation Experiment Confirmation experiments were done taking the optimum factors obtained by Taguchi analysis as per Table 5, the results of the experiment given in Table 7. It can be seen that the distortion 0.05mm of the component which is very less. Table 7 Optimum parameters showing distortion Feed A1 Speed B2 Depth of cut C1 Width of cut D3 Tool Path E4 Distortion 0.05mm/tooth 150m/min 0.4mm 7mm Constant overlap 0.05mm 3.4 Observations In the experiments conducted it has been observed that the location of the maximum distortion is not same in all the experiments and is varying. This is due to redistribution of stresses while equilibrating after machining. In Experiments 1 and 2 twist was observed in the components. In Experiments 14 and 15 the distortion was observed only along the direction. In Experiments 8 and 9 distortion was observed only in one area. In Experiment 12 and 13 distortion was observed all over the component. In Experiments 4, 5 and 6 U shaped distortion was observed. 4. CONCLUSION Taguchi method has been applied to find significant controllable machining parameters which influence the distortion during machining and optimum machining parameters to minimize distortion. Based on results achieved it can be concluded that depth of cut followed by width of cut main contributing factors influencing distortion. 5. REFERENCES 1. J-F. Chatelin, J-F. Lalone & A.S Tahan, Comparasion of the Distortion of Machined parts resulting form residual stresses with in work pieces, Recent Advances in Manufacturing Engineering, ISBN:978-1-61-804-031-2,PP 79-84. 2. Erhan Budak, Mechanics and Dynamics of Thin walled Structures, PhD thesis, Department of Mechanical Engineering, The University of British Columbia, 1994. 3. Tsai, J.S., Liao, C.L. Finite-element modeling of static surface errors in the peripheral milling of thin-walled workpieces, Journal of Materials Processing technology, 94(2-3):235-246. [doi:10.1016/S0924-0136(99)00109-0] ., 1999. 4. Ratchev, S., Govender, E., Nikov, S., Phuah, K., Tsiklos, G., Force and deflection modelling in milling of low-rigidity complex parts. Journal of Materials Processing Technology, 143- 144(12):796-801. [doi:10.1016/ S0924-0136(03)00382-0], 2003. 5. Wan, M., Zhang, W.H., Qiu, K.P., Gao, T., Yang, Y.H., Numerical prediction of static form errors in peripheral milling of thin-walled workpieces with irregular meshes. Journal of Manufacturing Science and Engineering, 127(1):13-22, [doi:10.1115/1.1828055], 2005. 6. Wang, S.P., Padmanaban. S, A New Approach for FEM Simulation of NC Machining Processes. Proceedings of the 8th International Conference on Numerical Methods in Industrial Forming Processes, Columbus, Ohio, p.1371-1376., 2004. 7. Wang, Z.J., Chen, W.Y., Zhang, Y.D., Study on the machining distortion of thin-walled part caused by redistribution of residual stress. Chinese Journal of Aeronautics, 18(2):175-179 (in Chinese), 2005.
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 69 8. Liu, S.G., Zheng, L., Zhang, Z.H., Wen, D.H, Optimal fixture design in peripheral milling of thin-walled workpiece. International Journal of Advanced Manufacturing Technology, 28(7- 8):653-658. [doi:10.1007/s00170-004-2425-8] ., 2006. 9. DONG Hui-yue,KE Ying-lin., Study on Machining Deformation of Aircraft Monolithic Component by FEM and Experiment, Chinese Journal of Aeronautics, Vol. 19, No.3, Aug. 2006. 10. Yun-bo BI, Qun-lin CHENG, Hui-yue DONG and Ying-lin KE, Machining distortion prediction of aerospace monolithic components, Journal of Zhejiang University SCIENCE, ISSN 1862-1775, PP 661-668, 2009. 11. Yong YANG, Yu-Ling WANG, Analysis and control of machining distortion for aircraft monolithic component aided by computer, Third International Conference on Information and Computing, [DOI 10.1109/ICIC.2010.256], 2010. 12. W.H. Yang, Y.S. Tarng, Design optimization of cutting parameters for turning operations based on Taguchi method, Journal of Material Processing Technology, 84(1998) 122-129, 1998. 13. R. Ramanujam, R. Raju and N. Muthukrishnan, Taguchi Multi-machining Characteristics Optimization in Turning of A1-15%SiCp Composites using Desirability Function Analysis., Journal of Studies on Manufacturing (Vol. 1-2010/Iss.2-3), pp. 120-125, 2010. 14. Sanjit Moshat, Saurav Datta, Asish Bandyopadhyay and Pradip Kumar Pal., Optimization of CNC end milling process parameters using PCA – based Taguchi method., International Journal of Engineering, Science and Technology., Vol.2, No.1, pp. 92-102, 2010. 15. E. Kuram, B.T. Simsek, B. Ozcelik, E. Demirbas and S. Askin., Optimization of the Cutting Fluids and Parameters Using Taguchi and ANOVA in Milling., Proceedings of the World Congress on Engineering Vol. II, 2010. 16. Sadasiva Rao T., Rajesh V., Venu Gopal A., Taguchi based Grey Relational Analysis to Optimize Face Milling Process with Multiple Performance Characteristics., International Conference on Trends in Industrial and Mechanical Engineering (ICTIME’2012) March 24- 25, 2012 Dubai. 17. Vijaya Kumar Gurram and Venkataramaiah patti, “Selection of Optimum Parameters to Develop an Aluminium Metal Matrix Composite with Respect to Mechanical Properties by using Grey Relational Analysis”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 462 - 469, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. 18. Vishal Francis, Ravi.S.Singh, Nikita Singh, Ali.R.Rizvi and Santosh Kumar, “Application of Taguchi Method and Anova in Optimization of Cutting Parameters for Material Removal Rate and Surface Roughness in Turning Operation”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 47 - 53, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. 19. Vipin Kumar Sharma, Qasim Murtaza and S.K. Garg, “Response Surface Methodology & Taguchi Techquines to Optimization of C.N.C. Turning Process”, International Journal of Production Technology and Management (IJPTM), Volume 1, Issue 1, 2010, pp. 13 - 31, ISSN Print: 0976- 6383, ISSN Online: 0976 – 6391. 20. Ajeet Kumar Rai, Richa Dubey, Shalini Yadav and Vivek Sachan, “Turning Parameters Optimization for Surface Roughness by Taguchi Method”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 203 - 211, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

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