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Optimization of spinning process parameters for the large-diameter thin-walled cylinder based on the drum shape

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Abstract

Thinning spinning has the advantage of enhancing the performance of a product, and it is an effective method for manufacturing large-diameter ultrathin cylindrical parts. However, the rigidity of a large-diameter ultrathin wall cylinder is poor, the spinning process and forming quality are sensitive to the process parameters, and the drum-shaped result of the spinning process is prone to instability, which can cause the spinning process to be unbalanced. Therefore, it is a great challenge to select the appropriate process parameters for achieving the long-range stable spinning of large-diameter ultrathin wall cylinders. Based on the analysis of the thin-walled cylinder spinning mechanism, this study analyzed the effects of key process parameters on the drum shape of a C-276 thin-walled cylinder spinning process by serial experiments. The results of serialization experiments show that the thinning rate has a great influence on the spinning process state, the feed rate is secondary, and the speed has the least influence. On the basis of experimental analysis, the optimized process parameters of thin-walled cylinder spinning are obtained by using the support vector machine method: the wall thickness reduction rate cannot exceed 45% in single-pass spinning; 30~40% is a reasonable choice interval; the feed rate can range from 0.8 to 1.0 mm/r; and the rotation speed can range from 80 to 100 r/min. Finally, a comparative experiment and stability test of thin-walled cylinder thinning spinning over a long range is carried out, which verifies that the optimized spinning parameters can be used for the long-range stable spinning of large-diameter ultrathin cylinder parts.

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References

  1. Guan S, Gao YJ (2008) Brief analysis on fabrication process of AP1000 reactor coolant pump can. China Nucl Power (01):49–53 (in chinese)

  2. Music O, Allwood JM, Kawai K (2010) A review of the mechanics of metal spinning. J Mater Process Technol 210:3–23

    Article  Google Scholar 

  3. Xu W, Wu H, Ma H et al (2018) Damage evolution and ductile fracture prediction during tube spinning of titanium alloy. Int J Mech Sci 135:226–239

    Article  Google Scholar 

  4. Wu H, Xu W, Shan D, Jin BC (2019) Mechanism of increasing spinnability by multi-pass spinning forming – analysis of damage evolution using a modified GTN model. Int J Mech Sci 159:1–19

    Article  Google Scholar 

  5. Joseph Davidson M, Balasubramanian K, Tagore GRN (2008) An experimental study on the quality of flow-formed AA6061 tubes. J Mater Process Technol 203(1):321–325

    Article  Google Scholar 

  6. Ekinovic S, Đukic H, Plancic I, Begovic E (2015) Assessment of the surface topography of Al 99.5% tubular products formed by cold flow forming technology. Procedia Eng 132:389–396

    Article  Google Scholar 

  7. Molladavoudi HR, Djavanroodi F (2011) Experimental study of thickness reduction effects on mechanical properties and spinning accuracy of aluminum 7075-O, during flow forming. Int J Adv Manuf Technol 52(9–12):949–957

    Article  Google Scholar 

  8. Debin S, Guoping Y, Wenchen X (2009) Deformation history and the resultant microstructure and texture in backward tube spinning of Ti–6Al–2Zr–1Mo–1V. J Mater Process Technol 209(17):5713–5719

    Article  Google Scholar 

  9. Razani NA, Jalali Aghchai A, Mollaei DB (2014) Flow-forming optimization based on hardness of flow-formed AISI321 tube using response surface method. Int J Adv Manuf Technol 70(5–8):1463–1471

    Article  Google Scholar 

  10. Fazeli AR, Ghoreishi M (2009) Investigation of effective parameters on surface roughness in thermomechanical tube spinning process. Int J Mater Form 2(4):261–270

    Article  Google Scholar 

  11. Abedini A, Rash Ahmadi S, Doniavi A (2014) Roughness optimization of flow-formed tubes using the Taguchi method. Int J Adv Manuf Technol 72(5–8):1009–1019

    Article  Google Scholar 

  12. Xia Q, Xiao G, Long H, Cheng X, Yang B (2014) A study of manufacturing tubes with nano/ultrafine grain structure by stagger spinning. Mater Des 59:516–523

    Article  Google Scholar 

  13. Xia Q, Shima S, Kotera H, Yasuhuku D (2005) A study of the one-path deep drawing spinning of cups. J Mater Process Technol 159(3):397–400

    Article  Google Scholar 

  14. Bai Q, Yang H, Zhan M (2008) Finite element modeling of power spinning of thin-walled shell with hoop inner rib. Trans Nonferrous metal soc 18(1):6–13

    Article  Google Scholar 

  15. Zhang J, Zhan M, Yang H, Jiang Z, Han D (2012) 3D-FE modeling for power spinning of large ellipsoidal heads with variable thicknesses. Comput Mater Sci 53(1):303–313

    Article  Google Scholar 

  16. Tsivoulas D, Quinta Da Fonseca J, Tuffs M, Preuss M (2015) Effects of flow forming parameters on the development of residual stresses in Cr–Mo–V steel tubes. Mater Sci Eng A 624:193–202

    Article  Google Scholar 

  17. Sangkharat T, Dechjarern S (2017) Spinning process design using finite element analysis and Taguchi method. Procedia Eng 207:1713–1718

    Article  Google Scholar 

  18. Li XH, Liu YP, Yan YS, He XH (2012) Analysis on bulging during flow forming of tube with ultra-thin-wall and large diameter/thickness ratio. J Plast Eng 19(03):64–70 (in chinese)

    Google Scholar 

  19. Peng C, Wang L, Liao TW (2015) A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine. J Sound Vib 354:118–131

    Article  Google Scholar 

  20. Bakhshiani A, Mofid M, Khoei AR, McCabe SL (2003) Finite strain simulation of thin-walled tube under torsion using endochronic theory of plasticity. Thin Wall Struct 41(5):435–459

    Article  Google Scholar 

  21. Andrew AM (2001) An introduction to support vector machines and other kernel-based learning methods. Kybernetes 30(1):103–115

    MathSciNet  Google Scholar 

  22. Si L, Wang Z, Liu X, Tan C (2019) A sensing identification method for shearer cutting state based on modified multi-scale fuzzy entropy and support vector machine. Eng Appl Artif Intell 78:86–101

    Article  Google Scholar 

  23. Min JH, Young-Chan L (2005) Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst Appl 28(4):603–614

    Article  Google Scholar 

  24. Tan X, Bi W, Hou X, Wang W (2011) Reliability analysis using radial basis function networks and support vector machines. Comput Geotech 38(2):178–186

    Article  Google Scholar 

  25. Vecchio CD, Fenu G, Pellegrino FA, Foggia MD, Quatrale M, Benincasa L, Iannuzzi S, Acernese A, Correra P, Glielmo L (2019) Support vector representation machine for superalloy investment casting optimization. Appl Math Model 72:324–336

    Article  Google Scholar 

  26. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–39

    Article  Google Scholar 

  27. Fang HR, Gong J (2015) Data acquisition and pre-processing research of weird complex thermal structure components. J Beijing Jiaotong Univ 39(01):95–100 (in chinese)

    Google Scholar 

  28. Dong MX, Zheng KP (2004) A random filter algorithm for reducing noise error of point cloud data. J Image Graph (02):120–123 (in chinese)

  29. Ye L, Xie MH (2010) Third-order non-uniform rational B-spline curve fitting based on method of accumulating chord length. J HUAQIAO Univ (Nat Sci) 31(04):383–387 (in chinese)

    Google Scholar 

  30. Zhang T, Li XH, Wei Z, Chang SW (2017) Influence of process parameters on the flow forming quality of thin-walled tube with large diameter-thickness ratio. J Plast Eng 24(02):75–81 (in chinese)

    Google Scholar 

  31. Davidson DMJ, Balasubramanian K, Tagore GRN (2013) Surface roughness prediction of flow-formed AA6061 alloy by design of experiments. J Mater Process Technol 202:41–46

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 51775564), 973 programs (No.2015CB057305).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Central South University, Changsha, 410083, Hunan, China

    Xue Wen, Jianping Tan & Xinhe Li

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  1. Xue Wen
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  2. Jianping Tan
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  3. Xinhe Li
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Correspondence to Jianping Tan.

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Wen, X., Tan, J. & Li, X. Optimization of spinning process parameters for the large-diameter thin-walled cylinder based on the drum shape. Int J Adv Manuf Technol 108, 2315–2335 (2020). https://doi.org/10.1007/s00170-020-05507-3

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  • DOI: https://doi.org/10.1007/s00170-020-05507-3

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