Skip to main content
Log in

Multi-objective optimization of improved magnetic abrasive finishing of multi-curved surfaces made of SUS202 material

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Surface roughness is one of the most significant factors that influence the performance of the loss of power and the wear resistance due to friction in engineering. It is difficult to finish simultaneously multi-curved surfaces by conventional finishing process. Besides, this process has to satisfy the criterion of minimum surface defections, accuracy, and high quality surface finish. This study proposes the improved magnetic abrasive finishing (IMAF) in which a magnetic abrasive finishing process was assisted by ring-magnetic field finishing the multi-curved surfaces made of alloy SUS202 in less processing time. The multi-objective particle swarm optimization (MOPSO) algorithm with the knee point (a better solution) was applied to optimize surface roughness of workpiece. The objective functions of the MOPSO depend on the quality of the surface roughness of finishing materials for both simultaneous surfaces (R a1, R a2), which are determined from experimental approach. Finally, the effectiveness of the process is compared between the optimal results and the experimental data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Yamaguchi H, Shinmura T, Sekine M (2004) Uniform internal finishing of sus304 stainless steel bent tube using a magnetic abrasive finishing process. J Manuf Sci Eng 127:605–611

    Article  Google Scholar 

  2. Yamaguchi H, Shinmura T (2004) Internal finishing process for alumina ceramic components by a magnetic field assisted finishing process. Precis Eng 28:135–142

    Article  Google Scholar 

  3. Djavanroodi F (2013) Artificial neural network modeling of surface roughness in magnetic abrasive finishing process. Res J Appl Sci Eng Technol 6:8

    Google Scholar 

  4. El-Taweel TA (2007) Modelling and analysis of hybrid electrochemical turning-magnetic abrasive finishing of 6061 Al/Al2O3 composite. Int J Adv Manuf Technol 37:705–714

    Article  Google Scholar 

  5. Singh DK, Jain VK, Raghuram V (2004) Parametric study of magnetic abrasive finishing process. J Mater Process Technol 149:22–29

    Article  Google Scholar 

  6. Singh DK, Jain VK, Raghuram V, Komanduri R (2005) Analysis of surface texture generated by a flexible magnetic abrasive brush. Wear 259:1254–1261

    Article  Google Scholar 

  7. Wang Y, Hu D (2005) Study on the inner surface finishing of tubing by magnetic abrasive finishing. Int J Mach Tools Manuf 45:43–49

    Article  Google Scholar 

  8. Yin S, Shinmura T (2004) A comparative study: polishing characteristics and its mechanisms of three vibration modes in vibration-assisted magnetic abrasive polishing. Int J Mach Tools Manuf 44:383–390

    Article  Google Scholar 

  9. Singh D, Jain VK, Raghuram V (2006) Experimental investigations into forces acting during a magnetic abrasive finishing process. Int J Adv Manuf Technol 30:652–662

    Article  Google Scholar 

  10. Mulik R, Pandey P (2011) Magnetic abrasive finishing of hardened AISI 52100 steel. Int J Adv Manuf Technol 55:501–515

    Article  Google Scholar 

  11. Lee Y-H, Wu K-L, Jhou J-H, Tsai Y-H, Yan B-H (2013) Two-dimensional vibration-assisted magnetic abrasive finishing of stainless steel SUS304. Int J Adv Manuf Technol 69:2723–2733

    Article  Google Scholar 

  12. Yang L-D, Lin C-T, Chow H-M (2009) Optimization in MAF operations using Taguchi parameter design for AISI304 stainless steel. Int J Adv Manuf Technol 42:595–605

    Article  Google Scholar 

  13. Teimouri R, Baseri H (2013) Artificial evolutionary approaches to produce smoother surface in magnetic abrasive finishing of hardened AISI 52100 steel. J Mech Sci Technol 27:533–539

    Article  Google Scholar 

  14. Asiltürk İ (2012) Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression. Int J Adv Manuf Technol 63:249–257

    Article  Google Scholar 

  15. Jain VK, Kumar P, Behera PK, Jayswal SC (2001) Effect of working gap and circumferential speed on the performance of magnetic abrasive finishing process. Wear 250:384–390

    Article  Google Scholar 

  16. Liu ZQ, Chen Y, Li YJ, Zhang X (2013) Comprehensive performance evaluation of the magnetic abrasive particles. Int J Adv Manuf Technol 68:631–640

    Article  Google Scholar 

  17. Singh S, Shan HS, Kumar P (2002) Parametric optimization of magnetic-field-assisted abrasive flow machining by the Taguchi method. Qual Reliab Eng Int 18:273–283

    Article  Google Scholar 

  18. Liao H-T, Shie J-R, Yang Y-K (2008) Applications of Taguchi and design of experiments methods in optimization of chemical mechanical polishing process parameters. Int J Adv Manuf Technol 38:674–682

    Article  Google Scholar 

  19. Palanikumar K (2006) Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling. Int J Adv Manuf Technol 36:19–27

    Article  Google Scholar 

  20. Hasçalık A, Çaydaş U (2007) Optimization of turning parameters for surface roughness and tool life based on the Taguchi method. Int J Adv Manuf Technol 38:896–903

    Article  Google Scholar 

  21. Liu S-T, Tong Z-Q, Tang Z-L, Zhang Z-H (2014) Design optimization of the S-frame to improve crashworthiness. Acta Mech Sinica 30:589–599

    Article  Google Scholar 

  22. Wang GG, Shan S (2006) Review of metamodeling techniques in support of engineering design optimization. J Mech Des 129:370–380

    Article  Google Scholar 

  23. Kurtaran H, Eskandarian A, Marzougui D, Bedewi NE (2002) Crashworthiness design optimization using successive response surface approximations. Comput Mech 29:409–421

    Article  MATH  Google Scholar 

  24. Forsberg J, Nilsson L (2006) Evaluation of response surface methodologies used in crashworthiness optimization. Int J Impact Eng 32:759–777

    Article  Google Scholar 

  25. Tran T, Hou S, Han X, Nguyen N, Chau M (2014) Theoretical prediction and crashworthiness optimization of multi-cell square tubes under oblique impact loading. Int J Mech Sci 89:177–193

    Article  Google Scholar 

  26. Tran T, Hou S, Han X, Tan W, Nguyen N (2014) Theoretical prediction and crashworthiness optimization of multi-cell triangular tubes. Thin-Walled Struct 82:183–195

    Article  Google Scholar 

  27. Hardy RL (1971) Multiquadric equations of topography and other irregular surfaces. J Geophys Res 76:1905–1915

    Article  Google Scholar 

  28. Dellino G, Lino P, Meloni C, Rizzo A (2009) Kriging metamodel management in the design optimization of a CNG injection system. Math Comput Simul 79:2345–2360

    Article  MathSciNet  MATH  Google Scholar 

  29. Clarke SM, Griebsch JH, Simpson TW (2005) Analysis of support vector regression for approximation of complex engineering analyses. J Mech Des 127:1077–1087

    Article  Google Scholar 

  30. Rajasekaran S, Gayathri S, Lee TL (2008) Support vector regression methodology for storm surge predictions. Ocean Eng 35:1578–1587

    Article  Google Scholar 

  31. Gun SR (1998) Support vector machines for classification and regression. Technical report Image speech and intelligent systems research group. University of Southampton, UK

    Google Scholar 

  32. Tran T, Hou S, Han X, Chau M (2015) Crushing analysis and numerical optimization of angle element structures under axial impact loading. Compos Struct 119:422–435

    Article  Google Scholar 

  33. Deb K, Gupta S (2011) Understanding knee points in bicriteria problems and their implications as preferred solution principles. Eng Optim 43:1175–1204

    Article  MathSciNet  Google Scholar 

  34. Branke J, Deb K, Dierolf H, Osswald M et al (2004) Finding knees in multi-objective optimization. In: Yao X, Burke E, Lozano J, Smith J, Merelo-Guervós J, Bullinaria J (eds) Parallel problem solving from nature—PPSN VIII. Springer, Berlin Heidelberg, pp 722–731

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to TrongNhan Tran.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, N., Tran, T., Yin, S. et al. Multi-objective optimization of improved magnetic abrasive finishing of multi-curved surfaces made of SUS202 material. Int J Adv Manuf Technol 88, 381–391 (2017). https://doi.org/10.1007/s00170-016-8773-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-8773-3

Keywords

Navigation