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Optimization of finishing parameters for magnetic compound fluid finishing (MCFF) of copper alloy

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Abstract

As an advanced finishing technology, magnetic compound fluid finishing (MCFF) is considered capable of achieving damage-free finishing of low-hardness materials such as copper alloys with appropriate finishing parameters. However, ignoring the influence of the material removal amount on the dimensional accuracy while optimizing finishing parameters may result in excessive material removal and a reduction in the workpiece’s dimensional accuracy. Thus, a novel finishing parameter optimization method considering dimensional accuracy is proposed in this paper. Firstly, the MCFF experiments are planned and carried out for modeling. Secondly, an MCFF model is established based on the integrated learning theory. The established model, with a prediction layer and a fusion layer, is a multi-layer neural network fusion model which can accurately predict the polished surface quality and material removal amount. Thirdly, the finishing parameters are optimized by the multi-objective particle swarm optimization algorithm, taking the effect of material removal on dimensional accuracy into account. Finally, the model’s prediction accuracy and the superiority of the optimized parameters are verified through experiments. The results demonstrate that the developed model can predict the finishing effect correctly, and a workpiece with high-quality polished surfaces and high dimensional accuracy can be obtained with the optimized finishing parameters.

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References

  1. Ein-Eli Y, Starosvetsky D (2007) Review on copper chemical–mechanical polishing (CMP) and post-CMP cleaning in ultra large system integrated (ULSI)—an electrochemical perspective. Electrochim Acta 52(5):1825–1838. https://doi.org/10.1016/j.electacta.2006.07.039

    Article  Google Scholar 

  2. Khan DA, Jha S (2019) Selection of optimum polishing fluid composition for ball end magnetorheological finishing (BEMRF) of copper. Int J Adv Manuf Technol 100(5):1093–1103. https://doi.org/10.1007/s00170-017-1056-9

    Article  Google Scholar 

  3. Pan B, Kang R, Guo J, Fu H, Du D, Kong J (2019) Precision fabrication of thin copper substrate by double-sided lapping and chemical mechanical polishing. J Manuf Process 44:47–54. https://doi.org/10.1016/j.jmapro.2019.05.035

    Article  Google Scholar 

  4. Das M, Jain VK, Ghoshdastidar PS (2012) Nanofinishing of flat workpieces using rotational–magnetorheological abrasive flow finishing (R-MRAFF) process. Int J Adv Manuf Technol 62(1–4):405–420. https://doi.org/10.1007/s00170-011-3808-2

    Article  Google Scholar 

  5. Jha S, Jain VK (2004) Design and development of the magnetorheological abrasive flow finishing (MRAFF) process. Int J Mach Tool Manu 44(10):1019–1029. https://doi.org/10.1016/j.ijmachtools.2004.03.007

    Article  Google Scholar 

  6. Jiao L, Wu Y, Wang X, Guo H, Liang Z (2013) Fundamental performance of Magnetic Compound Fluid (MCF) wheel in ultra-fine surface finishing of optical glass. Int J Mach Tool Manu 75:109–118. https://doi.org/10.1016/j.ijmachtools.2013.09.003

    Article  Google Scholar 

  7. Alam Z, Jha S (2017) Modeling of surface roughness in ball end magnetorheological finishing (BEMRF) process. Wear 374–375:54–62. https://doi.org/10.1016/j.wear.2016.11.039

    Article  Google Scholar 

  8. DeGroote JE, Marino AE, Wilson JP, Bishop AL (2007) Lambropoulos JC, Jacobs SD. Removal rate model for magnetorheological finishing of glass. Appl Opt 46(32):7927–7941. https://doi.org/10.1364/AO.46.007927

  9. Vahdati M, Rasouli SA (2016) Study of magnetic abrasive finishing on freeform surface. Transactions of the IMF 94(6):294–302. https://doi.org/10.1080/00202967.2016.1232822

    Article  Google Scholar 

  10. Guo J, Kum CW, Au KH, Tan ZEE, Wu H, Liu K (2016) New vibration-assisted magnetic abrasive polishing (VAMAP) method for microstructured surface finishing. Opt Express 24(12):13542–13554. https://doi.org/10.1364/OE.24.013542

    Article  Google Scholar 

  11. Sidpara A, Jain VK (2012) Nano–level finishing of single crystal silicon blank using magnetorheological finishing process. Tribol Int 47:159–166. https://doi.org/10.1016/j.triboint.2011.10.008

    Article  Google Scholar 

  12. Kum CW, Sato T, Guo J, Liu K, Butler D (2018) A novel media properties-based material removal rate model for magnetic field-assisted finishing. Int J Mech Sci 141:189–197. https://doi.org/10.1016/j.ijmecsci.2018.04.006

    Article  Google Scholar 

  13. Niranjan M, Jha S, Kotnala RK (2014) Ball end magnetorheological finishing using bidisperse magnetorheological polishing fluid. Mater Manuf Process 29(4):487–492. https://doi.org/10.1080/10426914.2014.892609

    Article  Google Scholar 

  14. Guo H, Wu Y, Lu D, Fujimoto M, Nomura M (2014) Ultrafine polishing of electroless nickel–phosphorus-plated mold with magnetic compound fluid slurry. Mater Manuf Process 29(11–12):1502–1509. https://doi.org/10.1080/10426914.2014.930953

    Article  Google Scholar 

  15. Barman A, Das M (2018) Nano-finishing of bio-titanium alloy to generate different surface morphologies by changing magnetorheological polishing fluid compositions. Precis Eng 51:145–152. https://doi.org/10.1016/j.precisioneng.2017.08.003

    Article  Google Scholar 

  16. Wang Y, Wu Y, Nomura M (2017) Fundamental investigation on nano-precision surface finishing of electroless Ni–P-plated STAVAX steel using magnetic compound fluid slurry. Precis Eng 48:32–44. https://doi.org/10.1016/j.precisioneng.2016.11.003

    Article  Google Scholar 

  17. Shimada K, Akagami Y, Fujita T, Miyazaki T, Kamiyama S, Shibayama A (2002) Characteristics of magnetic compound fluid (MCF) in a rotating rheometer. J Magn Magn Mater 252:235–237. https://doi.org/10.1016/S0304-8853(02)00646-7

    Article  Google Scholar 

  18. Guo H, Wu Y, Lu D, Fujimoto M, Nomura M (2014) Effects of pressure and shear stress on material removal rate in ultra-fine polishing of optical glass with magnetic compound fluid slurry. J Mater Process Technol 214(11):2759–2769. https://doi.org/10.1016/j.jmatprotec.2014.06.014

    Article  Google Scholar 

  19. Pan J, Guo M, Yan Q, Zheng K, Xiao X (2019) Research on material removal model and processing parameters of cluster magnetorheological finishing with dynamic magnetic fields. Int J Adv Manuf Technol 100(9):2283–2297. https://doi.org/10.1007/s00170-018-2747-6

    Article  Google Scholar 

  20. Mosavat M, Rahimi A (2019) Simulation and experimental study on the effect of abrasive size, rotational speed, and machining gap during ultra-precision polishing of monocrystalline silicon. Colloid Surface A 575:50–56. https://doi.org/10.1016/j.colsurfa.2019.05.005

    Article  Google Scholar 

  21. Fan Z, Tian Y, Liu Z, Shi C, Zhao Y (2019) Investigation of a novel finishing tool in magnetic field assisted finishing for titanium alloy Ti-6Al-4V. J Manuf Process 43:74–82. https://doi.org/10.1016/j.jmapro.2019.05.007

    Article  Google Scholar 

  22. Kala P, Kumar S, Pandey PM (2013) Polishing of copper alloy using double disk ultrasonic assisted magnetic abrasive polishing. Mater Manuf Process 28(2):200–206. https://doi.org/10.1080/10426914.2012.746704

    Article  Google Scholar 

  23. Kala P, Sharma V, Pandey PM (2017) Surface roughness modelling for double disk magnetic abrasive finishing process. J Manuf Process 25:37–48. https://doi.org/10.1016/j.jmapro.2016.10.007

    Article  Google Scholar 

  24. Khan DA, Jha S (2018) Synthesis of polishing fluid and novel approach for nanofinishing of copper using ball-end magnetorheological finishing process. Mater Manuf Process 33(11):1150–1159. https://doi.org/10.1080/10426914.2017.1328112

    Article  Google Scholar 

  25. More AK, Chanmanwar RM (2019) Experimental investigation of magnetorheological finishing on copper alloy. Materials Today: Proceedings 19:312–316. https://doi.org/10.1016/j.matpr.2019.07.215

    Article  Google Scholar 

  26. Zhang J, Hu J, Wang H, Kumar AS, Chaudhari A (2018) A novel magnetically driven polishing technique for internal surface finishing. Precis Eng 54:222–232. https://doi.org/10.1016/j.precisioneng.2018.05.015

    Article  Google Scholar 

  27. Mori T, Hirota K, Kawashima Y (2003) Clarification of magnetic abrasive finishing mechanism. J Mater Process Technol 143–144:682–686. https://doi.org/10.1016/S0924-0136(03)00410-2

    Article  Google Scholar 

  28. Jain VK (2008) Abrasive-based nano-finishing techniques: an overview. Mach Sci Technol 12(3):257–294. https://doi.org/10.1080/10910340802278133

    Article  Google Scholar 

  29. Rahimikhoob A (2010) Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renew Energ 35(9):2131–2135. https://doi.org/10.1016/j.renene.2010.01.029

    Article  Google Scholar 

  30. Li H, Li J, Han X (2019) Robot vision model based on multi-neural network fusion. In: 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) 2571-2577. https://doi.org/10.1109/ITNEC.2019.8729210

  31. Pavlyshenko B (2018) Using stacking approaches for machine learning models. In: 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP) 255–258. https://doi.org/10.1109/DSMP.2018.8478522

  32. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE T Evolut Comput 8(3):256–279. https://doi.org/10.1109/TEVC.2004.826067

    Article  Google Scholar 

  33. Zhai Q, Zhai W, Gao B (2021) Modeling of forces and material removal rate in ultrasound assisted magnetorheological polishing (UAMP) of sapphire. Colloid Surface A 628:127272. https://doi.org/10.1016/j.colsurfa.2021.127272

    Article  Google Scholar 

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Funding

This work was supported by the construction of machine tools and equipment CNC interconnection platform and big data center and application platform (Grant numbers 2021-0171-1-1).

Author information

Authors and Affiliations

  1. Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China

    Jinwei Fan, Xingfei Ren, Ri Pan, Peitong Wang & Haohao Tao

Authors
  1. Jinwei Fan
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  2. Xingfei Ren
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  3. Ri Pan
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  4. Peitong Wang
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  5. Haohao Tao
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Contributions

JF and XR provided ideas for this study, wrote codes and manuscripts. RP assisted in the revision of the article and the compilation of codes. PW and HT were responsible for the experiment in this study. All authors contributed to this study.

Corresponding author

Correspondence to Jinwei Fan.

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Ethics approval

The research does not involve ethical issues.

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All the authors agreed to publish this paper.

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The authors declare no competing interests.

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Appendix

Appendix

Non-dominated optimal solutions recorded in REP.

No

W (μm)

h (mm)

n (rpm)

t (min)

Ra (μm)

MRA (g)

1

3.4

1

1000

150

0.0303

0.2625

2

3.4

1

780

142

0.0310

0.2520

3

3.4

1.1

660

150

0.0316

0.2626

4

3.4

1

750

132

0.0327

0.2182

5

3.4

1

680

130

0.0331

0.2271

6

3.4

1.1

760

119

0.0336

0.2351

7

3.4

1.1

710

121

0.0339

0.2278

8

3.4

1

660

104

0.0339

0.2268

9

3.4

1.3

840

130

0.034

0.2244

10

3.4

1.3

510

121

0.0343

0.2117

11

1.6

1.2

470

80

0.0351

0.1980

12

2.6

1.4

310

103

0.0352

0.2229

13

1.6

1.7

200

109

0.0360

0.1989

14

3.4

1.2

810

110

0.0360

0.2132

15

1.6

1.4

310

85

0.0364

0.1627

16

1.6

1.8

430

99

0.0381

0.1662

17

1.6

1.7

340

78

0.0383

0.1235

18

1.6

1.8

310

72

0.0385

0.1501

19

1.6

1.7

200

80

0.0388

0.1427

20

1.6

1.5

380

79

0.0397

0.1181

21

1.6

1.5

360

90

0.0398

0.1414

22

1.6

1.9

430

90

0.0410

0.1302

23

1.6

1.9

360

85

0.0416

0.0929

24

2.6

1.8

360

72

0.0425

0.1012

25

1.6

1.7

390

93

0.0427

0.1184

26

1.6

2

290

79

0.0437

0.0825

27

1.6

1.9

340

85

0.0445

0.1076

28

1.6

1.9

300

74

0.0452

0.0860

29

1.6

1.9

400

71

0.0458

0.0760

30

1.6

2

390

94

0.0471

0.0845

31

1.6

1.7

200

60

0.0471

0.0723

32

1.6

1.9

400

81

0.0477

0.0815

33

1.6

2

370

62

0.0482

0.0747

34

1.6

1.9

220

60

0.0509

0.0700

35

1.6

2

250

65

0.0510

0.0557

36

1.6

1.9

280

64

0.0517

0.0621

37

1.6

1.8

200

55

0.0541

0.0573

38

1.6

2

220

61

0.0550

0.0560

39

1.6

2

360

60

0.0574

0.0492

40

1.6

1.9

200

47

0.0581

0.0498

41

1.6

1.9

270

59

0.0590

0.0461

42

1.6

1.8

210

58

0.0602

0.0440

43

1.6

1.8

210

40

0.0608

0.0429

44

1.6

2

200

50

0.0634

0.0384

45

1.6

1.8

200

34

0.0662

0.0401

46

1.6

1.8

210

30

0.0684

0.0359

47

1.6

2

220

44

0.0686

0.0370

48

1.6

2

210

30

0.0700

0.0328

49

1.6

1.9

200

38

0.0708

0.0320

50

1.3

2

200

33

0.0769

0.0266

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Fan, J., Ren, X., Pan, R. et al. Optimization of finishing parameters for magnetic compound fluid finishing (MCFF) of copper alloy. Int J Adv Manuf Technol 121, 2181–2195 (2022). https://doi.org/10.1007/s00170-022-09436-1

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  • DOI: https://doi.org/10.1007/s00170-022-09436-1

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