Multi-Objective Optimization of Shot-Peening Parameters Using Modified Taguchi Technique

Document Type : Research Paper

Authors

Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran

10.22034/jsm.2020.1908867.1641

Abstract

Shot-peening is a surface treatments utilized extensively in the industry to enhance the performance of metal parts against fatigue. This paper aimed to find the optimal parameters of the shot-peening process based on the finite elements model and the Taguchi method. The effects of three peening parameters (shot diameter, shot velocity, coverage percentage) are investigated on residual stress and roughness using Taguchi method. A new Taguchi technique is proposed by combining it with desirability function to optimize the shot-peening parameters that simultaneously provide two or more responses in an optimal mode. The results show that the coverage percentage has the most influence on the surface stress and maximum compressive stress whereas the velocity and diameter of the shot are the most effective parameters on the depth of compression stress. The shot velocity is the main factor of the surface roughness due to the shot peening. Through the proposed structure, optimal conditions can be obtained for surface stress and roughness simultaneously with high-coverage and low-velocity. Eventually, results reveal the effectiveness of the proposed strategy in stand point of saving time and cost.

Keywords

  1.  A.E. Handbook, 1986, Society of Automotive Engineers, SAE Publications.
  2.  Fathallah R., Sidhom H., Braham C., Castex L., 2003, Effect of surface properties on high cycle fatigue behaviour of shot peened ductile steel, Materials Science and Technology 19: 1050-1056.
  3.  Eleiche A., Megahed M., Abd-Allah N., 2001,The shot-peening effect on the HCF behavior of high-strength martensitic steels, Journal of Materials Processing Technology 113: 502-508.
  4.  Hills D., Waterhouse R., Noble B., 1983, An analysis of shot-peening, The Journal of Strain Analysis for Engineering Design 18: 95-100.
  5.  Al-Obaid Y., 1995, Shot-peening mechanics: experimental and theoretical analysis, Mechanics of Materials 19: 251-260.
  6.  Obata M., Sudo A., 1993, Effect of shot-peening on residual stress and stress corrosion cracking for cold worked austenitic stainless steel, Proceedings of the ICSP-5 Conference, Oxford, UK.
  7.  Dorr T., Hilpert M., Beckmerhagen P., Kiefer A., Wagner L., 1999, Influence of shot-peening on fatigue performance of high-strength aluminum-and magnesium alloys, 7th ICSP American Shot-peening Society.
  8.  Hong T., Ooi J., Shaw B., 2008, A numerical simulation to relate the shot-peening parameters to the induced residual stresses, Engineering Failure Analysis 15: 1097-1110.
  9.  Meguid S., Shagal G., Stranart J., Daly J., 1999, Three-dimensional dynamic finite element analysis of shot-peening induced residual stresses, Finite Elements in Analysis and Design 31: 179-191.
  10.  Guagliano M., 2001, Relating Almen intensity to residual stresses induced by shot-peening: a numerical approach, Journal of Materials Processing Technology 110: 277-286.
  11. Kim T., Lee J.H., Lee H., Cheong S.-k., 2010, An area-average approach to peening residual stress under multi-impacts using a three-dimensional symmetry-cell finite element model with plastic shots, Materials & Design 31: 50-59.
  12.  Ghasemi A., Hassani-Gangaraj S.M., Mahmoudi A., Farrahi G., Guagliano M., 2016, Shot-peening coverage effect on residual stress profile by FE random impact analysis, Surface Engineering 32: 861-870.
  13.  Miao H., Larose S., Perron C., Lévesque M., 2009, On the potential applications of a 3D random finite element model for the simulation of shot-peening, Advances in Engineering Software 40: 1023-1038.
  14.  Mahmoudi A., Ghasemi A., Farrahi G., Sherafatnia K., 2016, A comprehensive experimental and numerical study on redistribution of residual stresses by shot-peening, Materials & Design 90: 478-487.
  15.  Nam Y.-S., Jeon U., Yoon H.-K., Shin B.-C., Byun J.-H., 2016, Use of response surface methodology for shot-peening process optimization of an aircraft structural part, The International Journal of Advanced Manufacturing Technology 87: 2967-2981.
  16.  AlSumait A., Li Y., Weaser M., Niji K., Battel G., Toal R., 2019, A Comparison of the Fatigue Life of Shot-Peened 4340M Steel with 100, 200, and 300% Coverage, Journal of Materials Engineering and Performance 28: 1780-1789.
  17.  Petit-Renaud F., Evans J., Metcalfe A., Shaw B., 2008, Optimization of a shot-peening process, Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 222: 277-289.
  18.  Romero J.S., Rios E., Fam Y., Levers A., 2002, Optimisation of the Shot-Peening Process in Terms of Fatigue Resistance, University of Sheffield.
  19.  Vielma A., Llaneza V., Belzunce F., 2014, Shot-peening intensity optimization to increase the fatigue life of a quenched and tempered structural steel, Procedia Engineering 74: 273-278.
  20.  Unal O., 2016, Optimization of shot-peening parameters by response surface methodology, Surface and Coverages Technology 305: 99-109.
  21.  Bhuvaraghan B., Srinivasan S.M., Maffeo B., Prakash O., 2011, Constrained probabilistic multi-objective optimization of shot-peening process, Engineering Optimization 43: 657-673.
  22.  Baragetti S., 1997, Shot-peening optimisation by means of DOE: Numerical simulation and choice of treatment parameters, International Journal of Materials and Product Technology 12: 83-109.
  23.  Seddik R., Bahloul A., Atig A., Fathallah R., 2017, A simple methodology to optimize shot-peening process parameters using finite element simulations, The International Journal of Advanced Manufacturing Technology 90: 2345-2361.
  24.  Roy R.K., 2001, Design of Experiments Using the Taguchi Approach, New York, John Willey & Sons.
  25.  George P., Pillai N., Shah N., 2004, Optimization of shot-peening parameters using Taguchi technique, Journal of Materials Processing Technology 153: 925-930.
  26.  Khany S.E., 2015, An experimental study of the effect of shot peening on the low carbon steel and identification of optimal process parameters, Materials Today: Proceedings 2(4-5): 3363-3370.
  27.  Maleki E., Okan U., Kashyzadeh K.R., 2019, Efficiency analysis of shot peening parameters on variations of hardness, grain size and residual stress via taguchi approach, Metals and Materials International25(6): 1436-1447.
  28.  Shiau G.H., 1990, A study of the sintering properties of iron ores using the Taguchi’s parameter design, Journal of the Chinese Statistical Association 28: 253-275.
  29.  Tong L.I., Su C.T., Wang C.H., 1997, The optimization of multi-response problems in the Taguchi method, International Journal of Quality and Reliability Management 14(4): 367-380.
  30.  Logothetis N., Haigh A., 1988, Characterizing and solving multi-response processes by the Taguchi method, Quality and Reliability Engineering International 4(2): 159-169.
  31.  Pignatello J.J.,1993, Strategies for robust multi-response quality engineering, IIE Transactions 25: 5-15.
  32.  Tong L.-I., Su C.-T., 1997, Optimizing multi-response problems in the Taguchi method by fuzzy multiple attribute decision making, Quality and Reliability Engineering International 13: 25-34.
  33.  Su C.T., Tong L.I., 1997, Multi-response robust design by principal component analysis, Total Quality Management 8(6): 409-416.
  34.  Antony J., 2000, Multi-response optimization in industrial experiments using Taguchi’s quality loss function and principal component analysis, Quality and Reliability Engineering International 16: 3-8.
  35.  Liao H.C., Chen Y.K., 2002, Solving multi-response problem in the Taguchi method by DEA based ranking method, International Journal of Quality & Reliability Management 19(7): 825-837.
  36.  Wong Y.H.W., Beasley J.E., 1990, Restricting weight flexibility in DEA, The Journal of the Operational Research Society 41: 829-835.
  37.  Baker R.C., Talluri S., 1997, A closer look at the use of data envelopment analysis for technology selection, Computers and Industrial Engineering 28: 101-108.
  38.  Kim T., Lee H., Jung S., Lee J.H., 2012, A 3D FE model with plastic shot for evaluation of equi-biaxial peening residual stress due to multi-impacts, Surface and Coverages Technology 206: 3125-3136.
  39.  Gangaraj S.M.H., Guagliano M., Farrahi G.H., 2014, An approach to relate shot peening finite element simulation to the actual coverage, Surface and Coatings Technology243: 39-45.
  40.  Kim T., Lee H., Hyun H.C., Jung S., 2013, Effects of rayleigh damping, friction and rate-dependency on 3D residual stress simulation of angled shot peening, Materials & Design 46: 26-37.
  41.  Yang F., Yukui G., 2016, Predicting the peen forming effectiveness of Ti-6Al-4V strips with different thicknesses using realistic finite element simulations, Journal of Engineering Materials and Technology138(1):011004.
  42.  Kirk D., 2005, Theoretical principles of shot-peening coverage, Shot Peener 19: 24-28.
  43.  Singh L., Khan R.A., Aggarwal M.L., 2013, Multi performance characteristic optimization of shot peening process for AISI 304 austenitic stainless steel using grey relational analysis with principal component analysis and Taguchi method, American Journal of Engineering Research 2(10): 160-172.
  44.  Gariépy A., Miao H.Y., Lévesque M., 2017, Simulation of the shot peening process with variable shot diameters and impacting velocities, Advances in Engineering Software114: 121-133.
  45.  Wagner L., 1999, Mechanical surface treatments on titanium, aluminum and magnesium alloys, Materials Science and Engineering: A 263: 210-216.
  46.  ISO 4287, 1997, Geometrical Product Specifications (GPS)–Surface Texture: Profile Method–Terms, Definitions and Surface Texture Parameters.
  47.  Derringer G., Suich R., 1980, Simultaneous optimization of several response variables, Journal of Quality Technology 12: 214-219.
  48.  Hassanzadeh M., Moussavi Torshizi S.E., 2020, Multi-objective optimization of shot-peening parameters using design of experiments and finite element simulation: a statistical model, Journal of Applied and Computational Mechanics, 22055/JACM.2020.33102.2152.