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.
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References
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
Yamaguchi H, Shinmura T (2004) Internal finishing process for alumina ceramic components by a magnetic field assisted finishing process. Precis Eng 28:135–142
Djavanroodi F (2013) Artificial neural network modeling of surface roughness in magnetic abrasive finishing process. Res J Appl Sci Eng Technol 6:8
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
Singh DK, Jain VK, Raghuram V (2004) Parametric study of magnetic abrasive finishing process. J Mater Process Technol 149:22–29
Singh DK, Jain VK, Raghuram V, Komanduri R (2005) Analysis of surface texture generated by a flexible magnetic abrasive brush. Wear 259:1254–1261
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
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
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
Mulik R, Pandey P (2011) Magnetic abrasive finishing of hardened AISI 52100 steel. Int J Adv Manuf Technol 55:501–515
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
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
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
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
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
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
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
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
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
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
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
Wang GG, Shan S (2006) Review of metamodeling techniques in support of engineering design optimization. J Mech Des 129:370–380
Kurtaran H, Eskandarian A, Marzougui D, Bedewi NE (2002) Crashworthiness design optimization using successive response surface approximations. Comput Mech 29:409–421
Forsberg J, Nilsson L (2006) Evaluation of response surface methodologies used in crashworthiness optimization. Int J Impact Eng 32:759–777
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
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
Hardy RL (1971) Multiquadric equations of topography and other irregular surfaces. J Geophys Res 76:1905–1915
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
Clarke SM, Griebsch JH, Simpson TW (2005) Analysis of support vector regression for approximation of complex engineering analyses. J Mech Des 127:1077–1087
Rajasekaran S, Gayathri S, Lee TL (2008) Support vector regression methodology for storm surge predictions. Ocean Eng 35:1578–1587
Gun SR (1998) Support vector machines for classification and regression. Technical report Image speech and intelligent systems research group. University of Southampton, UK
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
Deb K, Gupta S (2011) Understanding knee points in bicriteria problems and their implications as preferred solution principles. Eng Optim 43:1175–1204
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
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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
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DOI: https://doi.org/10.1007/s00170-016-8773-3