Please wait a minute...
Frontiers of Mechanical Engineering

ISSN 2095-0233

ISSN 2095-0241(Online)

CN 11-5984/TH

Postal Subscription Code 80-975

2018 Impact Factor: 0.989

Front. Mech. Eng.    2015, Vol. 10 Issue (4) : 424-432    https://doi.org/10.1007/s11465-015-0366-6
RESEARCH ARTICLE
Simulation of abrasive flow machining process for 2D and 3D mixture models
Rupalika DASH,Kalipada MAITY()
Mechanical Engineering Department, National Institute of Technology, Rourkela 769008, India
 Download: PDF(1767 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Improvement of surface finish and material removal has been quite a challenge in a finishing operation such as abrasive flow machining (AFM). Factors that affect the surface finish and material removal are media viscosity, extrusion pressure, piston velocity, and particle size in abrasive flow machining process. Performing experiments for all the parameters and accurately obtaining an optimized parameter in a short time are difficult to accomplish because the operation requires a precise finish. Computational fluid dynamics (CFD) simulation was employed to accurately determine optimum parameters. In the current work, a 2D model was designed, and the flow analysis, force calculation, and material removal prediction were performed and compared with the available experimental data. Another 3D model for a swaging die finishing using AFM was simulated at different viscosities of the media to study the effects on the controlling parameters. A CFD simulation was performed by using commercially available ANSYS FLUENT. Two phases were considered for the flow analysis, and multiphase mixture model was taken into account. The fluid was considered to be a Newtonian fluid and the flow laminar with no wall slip.

Keywords abrasive flow machining (AFM)      computational fluid dynamics (CFD) modeling      mixture model     
Corresponding Author(s): Kalipada MAITY   
Online First Date: 26 November 2015    Issue Date: 03 December 2015
 Cite this article:   
Rupalika DASH,Kalipada MAITY. Simulation of abrasive flow machining process for 2D and 3D mixture models[J]. Front. Mech. Eng., 2015, 10(4): 424-432.
 URL:  
https://academic.hep.com.cn/fme/EN/10.1007/s11465-015-0366-6
https://academic.hep.com.cn/fme/EN/Y2015/V10/I4/424
Fig.1  Workpiece and fixture
Fig.2  Finite elements domain for the CFD analysis
Fig.3  (a) Arrangement of workpieces with fixtures [10]; (b) 3D meshed model for FLUENT analysis of the passage
Fig.4  Symmetry considered for FLUENT analysis
Parameter Value
2D 3D
Workpiece material Brass Aluminium
Density of the polishing media/(kg·m−3) 1219 1219
Viscosity of media/(Pa·s) 760, 640, 510 760, 640, 510
Density of silicon carbide/(kg·m–3) 3170 3170
Inlet pressure/MPa 2.5 2.5
Volume fraction values 0.4 0.2
Mesh size of the abrasive particle/µm 250 150
Tab.1  Simulation parameters for the 2D and 3D model
Fig.5  Finite elements domain for the CFD analysis [8]
Extrusion pressure/MPa Max. velocity of flow/(m·s−1) Max. static pressure/MPa Max. dynamic pressure/Pa Wall shear/MPa
2.5 0.1028 2.5 0.0594 0.17
5.0 0.3060 5.0 0.2450 0.38
Tab.2  Variation of velocity, static pressure, strain rate and shear stress at two different extrusion pressures (data from FLUENT analysis)
Fig.6  Velocity contour from the simulated result
Fig.7  Dynamic pressure contour from the simulated result
Volume fraction Strain rate/s−1
0.30 4.06×105
0.45 5.01×105
0.60 6.56×105
0.85 14.10×105
Tab.3  Strain rates at different volume fractions
Fig.8  Variation of strain rate at different volume fractions
Viscosity/(Pa·s) Depth of indentation/μm
50000 0.11000
75000 0.11278
100000 0.11400
300000 0.15110
500000 0.11540
Tab.4  Depth of indentation at different viscosities
Extrusion pressure/MPa Depth of indentation/μm
2.5 0.05766
3.5 0.09402
5.0 0.11420
7.5 0.17830
10.0 0.19770
Tab.5  Depth of indentation at different extrusion pressures
Fig.9  MRR for experimental and simulated results
Fig.10  Dynamic pressure contours at different viscosities. (a) 510 Pa·s; (b) 640 Pa·s; (c) 760 Pa·s
Fig.11  Strain rate contours at different viscosities. (a) 510 Pa·s; (b) 640 Pa·s; (c) 760 Pa·s
Fig.12  Velocity contours at different viscosities. (a) 510 Pa·s; (b) 640 Pa·s; (c) 760 Pa·s
vmMass-averaged velocity
rmMixture density
akVolume fraction of phase k
nNumber of phases
FBody force
μmViscosity of the mixture
vdr,kDrift velocity of secondary phase k
bRadius of the projected area
d?Depth of indentation
dgDiameter of the spherical abrasive grain
FiForce of indentation
sStress acting in normal direction on the grain
HwHardness of the material
A?Cross-sectional area of the groove
VaVolume of material removal
Tab.1  
1 Tzeng  H Y, Yan  B, Hsu  T, . Self-modulating abrasive medium and its application to abrasive flow machining for finishing micro channel surfaces. International Journal of Advanced Manufacturing Technology, 2007, 32(11–12): 1163–1169 
https://doi.org/10.1007/s00170-006-0423-8
2 Gorana  V K, Jain  V K, Lal  G K. Prediction of surface roughness during abrasive flow machining. International Journal of Advanced Manufacturing Technology, 2006, 31(3–4): 258–267
https://doi.org/10.1007/s00170-005-0197-4
3 Wang  A, Liang  K, Liu  C, . High precision polishing method in 3-D surface and elastic abrasive gel development. In: Proceedings of 4th Asia Pacific Forum on Precision Surface Finishing and Deburring Technology. Taichung: Metal Industries Research & Development Centre, 2005, 123–128
4 Tom  K. Advanced abrasive flow technologies. In: Proceedings of 4th Asia Pacific Forum on Precision Surface Finishing and Deburring Technology. Taichung: Metal Industries Research & Development Centre, 2005, 129–138
5 Jain  V K, Adsul  S G. Experimental investigations into abrasive flow machining (AFM). International Journal of Machine Tools & Manufacture, 2000, 40(7): 1003–1021 
https://doi.org/10.1016/S0890-6955(99)00114-5
6 Gorana  V K, Jain  V K, Lal  G K. Experimental investigation into cutting force and active grain density during abrasive flow machining. International Journal of Machine Tools & Manufacture, 2004, 44(2–3): 201–211
https://doi.org/10.1016/j.ijmachtools.2003.10.004
7 Jain  R K, Jain  V K, Kalra  P K. Modeling of abrasive flow machining process: A neural network approach. Wear, 1999, 231(2): 242–248
https://doi.org/10.1016/S0043-1648(99)00129-5
8 Jain  R K, Jain  V K, Dixit  P M. Modeling of material removal and surface roughness in abrasive flow machining process. International Journal of Machine Tools & Manufacture, 1999, 39(12): 1903–1923
https://doi.org/10.1016/S0890-6955(99)00038-3
9 Kumar Jain  R, Jain  V K. Simulation of surface generated in abrasive flow machining process. Robotics and Computer-integrated Manufacturing, 1999, 15(5): 403–412 doi:10.1016/S0736-5845(99)00046-0
10 Kenda  J, Pusavec  F, Kermouche  G, . Surface Integrity in abrasive flow machining of hardened tool steel AISI D2. In: Proceedings of 1st CIRP Conference on Surface Integrity (CSI). Procedia Engineering, 2011, 19: 172–177
https://doi.org/10.1016/j.proeng.2011.11.097
11 Gorana  V K, Jain  V K, Lal  G K. Forces prediction during material deformation in abrasive flow machining. Wear, 2006, 260(1–2): 128–139
https://doi.org/10.1016/j.wear.2004.12.038
12 Rabinowicz  E, Dunn  L A, Russell  P G. A study of abrasive wear under three body conditions. Wear, 1961, 4(5): 345–355
https://doi.org/10.1016/0043-1648(61)90002-3
13 Fang  L, Zhao  J, Sun  K, et al. Temperature as sensitive monitor for efficiency of work in abrasive flow machining. Wear, 2009, 266(7–8): 678–687
https://doi.org/10.1016/j.wear.2008.08.014
[1] Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG. An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings[J]. Front. Mech. Eng., 2018, 13(2): 301-310.
[2] Yulun CHI, Haolin LI. Simulation and analysis of grinding wheel based on Gaussian mixture model[J]. Front Mech Eng, 2012, 7(4): 427-432.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed