Design and Optimization Of Extrusion Process Using FEA And Taguchi Method

DOI : 10.17577/IJERTV1IS8227

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Design and Optimization Of Extrusion Process Using FEA And Taguchi Method

1Thella Babu Rao, 2A.Gopala Krishna

1Assistant Professor, Department of Mechanical Engineering, GITAM University, Hyderabad campus, Hyderabad, Andhra Pradesh, India 502 329.

2Associate Professor, Department of Mechanical Engineering, University College of Engineering, JNTUK, Kakinada, Andhra Pradesh, India – 533 003.

Abstract

This paper addresses design and optimization of extrusion process for aluminum 6061 alloy. The extrusion temperature and load has significant on quality and cost of the extruded parts respectively. Hence, development of economical process conditions is found as vital. Forward extrusion model is developed to analyze the process responses temperature, extrusion load, extrusion ratio and blank velocity for deferent process designs. Some of the most significant design parameters ram velocity, coefficient of friction and die angle are considered. Taguchis L9 design is employed to simulate the experiments for each set of chosen extrusion variables via Finite Element Analysis (FEA) solver. Analysis of variance (ANOVA) is adopted to check the significance of the input variables on the output responses. Then, the optimal process parameters are determined using Taguchis method.

Keywords: Extrusion, finite element analysis, temperature distribution, extrusion load, optimization, Taguchi.

  1. Introduction

    In recent years, extrusion process has been applied in manufacturing of variety of the components usually long, straight, semi nished metal products in the forms of bars, tubes, strips, and solid and hollow proles [1]. In addition, it is necessary to know the history of the process responses to control the variables of extrusion. Finite element analysis of extrusion became primal to predict the response history prior to the experimental work to in the present trend [2-4].

    During extrusion friction between the die and blank has significant effect on numerous process conditions [5]. Practically, low lam speeds are advisable since heating of blank due to friction and deformation leads to rise of temperature at the end of the stroke. It results premature heating of the blank [6]. The process at low ram speeds consumes much operational time and it leads to increased cost.

    The present work proposes an integrated FEA based approach to evaluate the extrusion process conditions numerically and to find the optimal process variables for the forward extrusion of Al 6061 alloy. The simulations are carried out for different ram velocities, coefficient of frictions and die angles. The simulation results of temperature, extrusion load, extrusion ratio and blank velocity are presented. Consequently optimization has been carried out for to minimize the temperature and to minimize extrusion load using Taguchis technique.

  2. Literature Survey

    Al6061 is one of the most widely used aluminum alloys in the range from transportation components to machinery equipments. This is due to its excellent corrosion resistance to atmospheric conditions as well as sea water. Many authors have been performed the investigations to understanding the thermo-mechanical behavior of the aluminum blanks as well as the dies during extrusion process using numerical methods [7-9].

    Zhou et al [10] studied the state of stress, strain and the temperature during extrusion with square to round dies. They concluded as the extrusion of aluminium through FEM simulation as a powerful tool to predict the thermo-mechanical changes occurring inside the billet material. Gang et al [11] investigated the influence of steps in the die flow of metal by means of 3D FEM transient state simulation. The results of this investigation revealed as the pocket step could be effectively used to balance metal ow during extrusion. Sutasn et al [12] investigated the effect of punch height on the V-bending angle. The FEM results of this investigation showing that the gap between the workpiece and the die, as well as the reversed bending zone resulted in a non-required bending angle. Luo et al [13] have successfully implemented the numerical methods to investigate the anisotropic ductile fracture of a 6260-T6 anisotropic aluminum alloy extrusion. Luis et al

    [14] were analyzed the temperature and the strain of aluminum alloy during equal channel angular extrusion by using FEM simulations. This analysis

    resulted as the increase of temperatures leads to lower stress in both the die and billet. The proposed models in this analysis allowed to accurate study of the stress inside the billet and die as a consequence of both thermal heating and plastic strain. Smith et al [15] executed the finite element investigations upon the inuence of pocket die designs on metal ow in aluminium extrusion. They found as the inverse linear relationship between pocket angle and velocity. Smaller pocket angles result in large exit velocities and large pocket angles result in smaller velocity.

  3. Taguchis Method

Taguchis methods are one of the widely used methods to solve the optimization problems of multiple objectives and are influenced by multiple variables. It was developed to minimize the cost and time of experimentations when the process has large number of combinations [16] by providing a systematic approach to the design of experiments. Hence, it became an extensively adopted method to solve some complex problems in manufacturing [17-21]. In this method, the performance characteristic is represented by signal-to-noise (S/N) ratio and the largest value of S/N ratio is required. These are logarithmic function of desired output and serve as objective function in the optimization process. There are three types of S/N ratio as the lower-the-better, the higher-the-better, and the nominal-the-better. The S/N ratio with a lower-the-better characteristic that can be expressed as [22]:

  1. Lower the better

    body. The model geometry of extrusion process is as shown in Fig.1. The physical properties of work material are listed Table 2. L9 Taguchis orthogonal array with nine experiments has been selected. The model was developed by selecting the extrusion process followed by defining the necessary process parameters. The convection heat transfer coefficient at the billet die interface was selected as 0.1 N/sec/mm/C. Geometrically identical meshes for the thermal equations have been considered for the computation of extrusion temperature. Table 3 shows the simulation results for each set of input variables using DEFORM-3D.

    Fig.2 shows the temperature distribution obtained for the first set of experimental run corresponding parameters as punch velocity 1mm/sec, coefficient of friction 0.1 µ and die angle 30o. At these levels of input parameters the predicted extrusion temperature is 86.80C. It can be observed that high temperature region is occurred at the exit of the die from the center of the blank. Fig.3 shows the effective stress distribution during extrusion. The maximum effective stress developed is 437 MPa the process variables for first FEA run. Fig. 4 shows the variation of extrusion load with respect to time for the 4th run in Table 3. The maximum extrusion load obtained in this case is 272 kN. Fig. 5 shows the velocity of blank during extrusion.

    S 10log

    N

    1 n 2

    y

    i

    n i 1

    (1)

    Die

    Billet

  2. Higher the – better

    S 10log

    N

    1 n 1

    y

    i

    n i 1 2

    (2)

  3. Normal the – better

    Punch

    S 10log

    N

    1 2

    y

    i

    ns i 1

    (3)

    Fig. 1 FEA Modelling of Extrusion process

    Where, yi is the ith value of measured response, n is the total number of runs and s is the standard deviation.

  4. Finite Element Modelling

In this investigation DEFORM-3D [23] software is used to simulate the turning process. The process parameters used for the present numerical computer simulations are given in Table

  1. The initial temperatures of billet and die are set at 25oC and the billet is assumed to be as plastic in a circular shaped. The die is classified as rigid

    Table 1 Extrusion parameters and levels Parameter Code Levels

    Punch Velocity

    Ev

    1

    1.5

    2

    (mm)

    Coefficient of

    COF

    0.1

    0.15

    0.2

    friction (µ) Die Angle

    30

    45

    60

    1 2 3

    (deg.)

    Fig.2 Temperature distribution

    Fig.4 Load Vs Time plot

    Table 2 Physical properties of work material Properties

    Work material Al6061

    Density (kg/m3) 2690

    Youngs Modulus (N/mm2 ) 69000

    Poissons ratio 0.293

    Coefficient of thermal expansion (1/0C)

    23.6E-06

    Fig.3 Distribution of Effective stress

    Specific heat (N/mm2/0C) 2.39

    Thermal conductivity (W/m/0C) 180

    Fig.5 Velocity of the blank

    1. Implementation of Taguchi

      The objective of this optimization problem is to minimize the die exit temperature and extrusion

      optimal set of extrusion process parameters has

      X1

      2

      2.9E3

      2.9E3

      1.4E3 240.6 0.004

      been calculated using Eq. 2. The obtained values of

      X2

      2

      48.06

      48.06

      24.03 3.99 0.201

      S/N values are listed in Table 4. These optimal

      X3

      2

      882.36

      882.36

      441.18 73.19 0.013

      process parameters are depicted in Figs. 7 and 8.

      Error

      2

      12.06

      12.06

      6.03

      load. Therefore, On the basis of maximization of S/N ratio for extrusion temperature and load, the

      Ad Adj

      Source DF Seq SS jSS MS F P

      This figure reveals the set of optimal parameter levels. Therefore, based on the S/N the set of optimal cutting parameters for temperature is Pv = 2 mm/sec, COF = 0.15µ and die angle = 30o and the set of optimal cutting parameters for extrusion load is Pv = 2 mm/sec, COF = 0.1µ and die angle = 60o.

    2. ANOVA

      Analysis of variance is employed for the obtained FEA results. Tables 5 and 6 represent the ANOVA of extrusion temperature and load respectively. From Table 5, it can be found those extrusion velocity and die angle are signicant for extrusion temperature. From Table 6, the extrusion velocity is signicant for affecting load.

      Table 3 Simulation results of Extrusion

      Total 8 3844.15

      Table 6 ANOVA analysis for Extrusion load

      Adj

      X1

      2

      38.7E3

      38.7E3

      19.3E3 21.79

      0.044

      X2

      2

      8144.7

      8144.7

      4072.3 4.58

      0.179

      X3

      2

      10.4E3

      10.4E3

      5212.3 5.86

      0.146

      Error

      2

      1778

      1778

      889

      Source DF Seq SS Adj SS MS F P

      Total 8 59088

      42

      41

      40

      39

      38

      X1

      X2

      1.0

      1.5

      X3

      2.0

      0.10 0.15

      0.20

      Sl. No

      Pv (mm/s)

      COF

      (µ)

      (deg.)

      T

      ( 0c )

      P

      42

      41

      40

      39

      38

      Mean of SN ratios

      ( kN)

      1. 1

      0.1

      30

      86.8

      124

      2. 1

      0.15

      45

      80.9

      229

      3. 1

      0.2

      60

      70.4

      309

      4. 1.5

      0.1

      45

      105

      272

      5. 1.5

      0.15

      60

      88.1

      347

      6. 1.5

      0.2

      30

      114

      312

      7. 2

      0.1

      60

      106

      393

      8. 2

      0.15

      30

      134

      368

      X1 X2 X3 Y1 Y2

      9. 2 0.2 45 130 382

      Table 4 S/N ratio for responses

      1. 38.77039

      41.86843

      2. 38.15897

      47.19671

      3. 36.95145

      49.79917

      4. 40.42379

      48.69138

      5. 38.89952

      50.80659

      6. 41.13810

      49.88309

      7. 40.50612

      51.88785

      8. 42.54210

      51.31696

      Exp.No. (Y1) (Y2)

      9. 42.27887 51.64127

      Table 5 ANOVA analysis for Temperature

      30

      45

      60

      Signal-to-noise: Larger is better for Temperature

      0.10 0.15 0.20

      2.0

      1.5

      X3

      1.0

      Mean of SN ratios

      Fig.6 S/N ratios plot for Temperature

      52

      50

      48

      46

      X1

      X2

      30

      45

      60

      Signal-to-noise: Larger is better for Load

      52

      50

      48

      46

      Fig.7 S/N ratios plot for Extrusion load

    3. Conclusions

      The finite element analysis of temperature and extrusion load during the extrusion of aluminum 6061 alloy is carried out in the present work. The optimal set of extrusion variables for the chosen responses was obtained. The experimental runs were conducted based on the Taguchs L9 design matrix. ANOVA is employed to identify the significance of variables on the responses. Also, the percentage of contributions of each variable on each response was represented graphically. The set of optimal process variables was obtained with the help of Taguchi optimization method on the basis of maximum S/N ratio. These optimal process variables help to extrude the chosen aluminum

      alloy with minimum Die exit temperature and using minimum extrusion load. Hence, the quality of the extruded product improved with minimum energy.

      References:

      1. Gang Liu, Jie Zhou, Jurek Duszczyk, Finite Element Simulation of Magnesium Extrusion to Manufacture a Cross-Shaped Prole, Journal of Manufacturing Science and Engineering, ASME, JUNE 2007, Vol. 129.

      2. Gang Fang, Jie Zhou, Jurek Duszczyk, Effect of pocket design on metal ow through single-bearing extrusion dies to produce a thin-walled aluminium prole, journal of matrials processing technology, 199 (2008) 91101.

      3. G. Liu, J. Zhou, J. Duszczyk, Prediction and verication of temperature evolution as a function of ram speed during the extrusion of AZ31 alloy into a rectangular section, Journal of Materials Processing Technology 186 (2007) 191199.

      4. J. Zhou, L. Li, J. Duszczyk, Computer simulated and experimentally veried isothermal extrusion of 7075 aluminium throug continuous ram speed variation, Journal of Materials Processing Technology 146 (2004) 203212.

[5]M. Schikorra, L. Donati, L. Tomesani, M. Kleiner, The role of friction in the extrusion of AA6060 aluminum alloy, process analysis and monitoring, Journal of Materials Processing Technology 191 (2007) 288292.

  1. Hu H.-J, A three-dimensional finite element simulation of the warm extrusion of AZ31 alloy billets, Journal of Manufacturing Processes 12 (2010) 6772.

  2. L. Li, J. Zhou, J. Duszczyk, Prediction of temperature evolution during the extrusion of 7075 aluminium alloy at various ram speeds by means of 3D FEM simulation, Journal of Materials Processing Technology 145 (2004) 360370.

  3. S.O.Onuh, M. Ekoja, M.B.Adeyemi, Effects of die geometry and extrusion speed on cold extrusion of aluminum and lead alloys, Journal of Materials Processing Technology, 132 (2003), 274-285.

  4. J. Zhou, L.Li, J. Duszczyk, 3D FEM simulations of the whole cycle of aluminum extrusion throughoput the transient state and the study state using the legrangian updated approach, Journal of Materials Processing Technology, 134 (2003), 383-397.

  5. T. Chanda, J. Zhou, J. Duszczyk, FEM analysis of aluminium extrusion through square and round dies, Materials and Design, 21 (2000), 323-335.

  6. Gang Fang, Jie Zhou, Jurek Duszczyk, FEM simulation of aluminium extrusion through two-hole multi-step pocket dies, journal of matrials processing technology, 209 (2009) 18911900.

  7. Sutasn Thipprakmas, Finite element analysis of punch height effect on V-bending angle, Materials and Design 31 (2010) 15931598.

  8. Meng Luo, Matthieu Dunand, Dirk Mohr, Experiments and modeling of anisotropic aluminum extrusions under multi-axial loading Part II: Ductile fracture, International Journal of Plasticity 3233 (2012) 3658

  9. C.J. Luis, R. Luri, J. Leon, Strain and temperature analysis of AA-1370 processed by ECAE at different temperatures, Journal of Materials Processing Technology 164165 (2005) 15301536.

  10. Q. Li, C.J. Smith, C. Harris, M.R. Jolly, Finite element investigations upon the inuence of pocket die designs on metal ow in aluminium extrusion Part I. Effect of pocket angle and volume on metal ow, Journal of Materials Processing Technology 135 (2003) 189196.

  11. Nooryusmiza Yusoff, M. Ramasamy, Suzana Yusup, Taguchis parametric design approach for the selection of optimization variables in a refrigerated gas plant, chemical engineering research and design, 89 (2011) 665675.

  12. Erol Kilickap, Modeling and optimization of burr height in drilling of Al-7075 using Taguchi method and response surface methodology, Int J Adv Manuf Technol (2010) 49:911 923.

  13. K.N. Ballantyne, R.A.van Oorschot, R.J. Mitchell, Reduce optimisation time and effort: Taguchi experimental design methods, Forensic Science International: Genetics Supplement Series 1 (2008) 78.

  14. Ilhan Asiltürk, Süleyman Neseli, Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis, Measurement 45 (2012) 785794.

  15. Saeed Maghsoodloo, Gultekin Ozdemir, Victoria Jordan and Chen-Hsiu Huang, Strengths and Limitations of Taguchi's Contributions to Quality, Manufacturing, and Process Engineering, Journal of Mam~Jacturing Systems, Vol. 23/No. 2, 2004.

  16. Julie Z. Zhang, Joseph C. Chen, E. Daniel Kirby, Surface roughness optimization in an end-milling operation using the Taguchi design method, Journal of Materials Processing Technology 184 (2007) 233239.

  17. Hsinn-Jyh Tzeng & Biing-Hwa Yan & Rong-Tzong Hsu & Han-Ming Chow, Finishing effect of abrasive flow machining on micro slit fabricated by wire-EDM, Int J Adv Manuf Technol (2007) 34:649 656.

  18. SFTC Defrom 3D, V.6.1, Scientific Forming Technologies Corporation.

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