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A part deformation control method via active pre-deformation based on online monitoring data

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Abstract

Pre-deformation of the workpiece in the elastic range is an effective deformation control method, as the machining deformation can be balanced by the pre-deformation. Different from the traditional pre-deformation control method based on experience or inaccurate prediction before machining, an innovative idea of online active pre-deformation control based on deformation monitoring data is proposed in this paper. The deformation monitoring data are used to analyze the workpiece state and predict the deformation of the next layer based on a cubic spline curve fitting approach, and then Kalman filter algorithm is used to control the error between the prediction deformation and the actual deformation so as to ensure the pre-deformation control accuracy. A real aircraft structural part is used to validate the proposed method, and the results show that the part deformation can be significantly reduced.

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References

  1. Denkena B, Boehnke D, León LD (2008) Machining induced residual stress in structural aluminum parts. Prod Eng 2(3):247–253

    Article  Google Scholar 

  2. Jr MCS, Machado AR, Sales WF, Barrozo MAS, Ezugwu EO (2016) Machining of aluminum alloys: a review. Int J Adv Manuf Technol 86(9-12):3067–3080

    Article  Google Scholar 

  3. Masoudi S, Amini S, Saeidi E (2015) Effect of machining-induced residual stress on the distortion of thin-walled parts. Int J Adv Manuf Technol 76(1):597–608

    Article  Google Scholar 

  4. Bowden DM, Halley JE (2001) Aluminium reliability improvement program-final report 60606. The Boeing Company, Chicago

    Google Scholar 

  5. Zhang G, Yu C, Shi RC, An L (2003) Experimental study on the milling of thin parts of titanium alloy (TC4). J Mater Process Technol 138(1):489–493

    Google Scholar 

  6. Cerutti X, Mocellin K (2014) Prediction of post-machining distortion due to residual stresses using FEM and a massive removal approach. Key Eng Mater 611-612(1):1159–1165

    Article  Google Scholar 

  7. Richter-Trummer V, Koch D, Witte A, Santos JFD, Castro PMSTD (2013) Methodology for prediction of distortion of workpieces manufactured by high speed machining based on an accurate through-the-thickness residual stress determination. Int J Adv Manuf Technol 68(9):2271–2281

    Article  Google Scholar 

  8. Li JG, Wang SQ (2017) Distortion caused by residual stresses in machining aeronautical aluminum alloy parts: recent advances. Int J Adv Manuf Technol 89(1-4):997–1012

    Article  Google Scholar 

  9. Denkena (2008) Milling induced residual stresses in structural parts out of forged aluminium alloys. Int J Mach Mach Mater 4(4):335–344

    Google Scholar 

  10. Rai JK, Xirouchakis P (2008) Finite element method based machining simulation environment for analyzing part errors induced during milling of thin-walled components. Int J Mach Tool Manu 48(6):629–643

    Article  Google Scholar 

  11. Huang X, Sun J, Li J (2015) Finite element simulation and experimental investigation on the residual stress-related monolithic component deformation. Int J Adv Manuf Technol 77(5):1035–1041

    Article  Google Scholar 

  12. Lin Y, Luo Y, Tang L (2012) Research on machining deformation control methodology for thin-wall arc shaped frame. Mach Des Manuf 2:107–109 (in Chinese)

    Google Scholar 

  13. Llanos I, Beristain A, Lanzagorta J L, Matzat H (2018) Case Study 2.3: Distortions in Aeronautical Structural Parts

  14. He ZY, Ren B (2010) Study on deformation of sheet metal parts during machining process. Equip Manuf Technol 4:101–103

    Google Scholar 

  15. Cerutti X, Mocellin K (2016) Influence of the machining sequence on the residual stress redistribution and machining quality: analysis and improvement using numerical simulations. Int J Adv Manuf Technol 83(1-4):489–503

    Article  Google Scholar 

  16. Li Y, Liu C, Hao X, James XG, Maropoulos PG (2015) Responsive fixture design using dynamic product inspection and monitoring technologies for the precision machining of large-scale aerospace parts. CIRP Ann Manuf Technol 64(1):173–176

    Article  Google Scholar 

  17. Hao X, Li Y, Chen G, Liu C (2018) 6+ X locating principle based on dynamic mass centers of structural parts machined by responsive fixtures. Int J Mach Tools Manuf 125:112–122

    Article  Google Scholar 

  18. Hao X, Li Y, Zhao Z, Liu C (2019) Dynamic machining process planning incorporating in-process workpiece deformation data for large-size aircraft structural parts. Int J Comput Integr Manuf 32(2):136–147

    Article  Google Scholar 

  19. Chatelain JF, Lalonde JF, Tahan AS (2012) Effect of residual stresses embedded within workpieces on the distortion of parts after machining. Int J Mech 6:43–51

    Google Scholar 

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Funding

The reported research was funded by the National Natural Science Foundation of China (Ref. 51775278), and the National Natural Science Foundation of China–Chinese Aerospace Science and Technology Corporation on Advanced Manufacturing (Ref: U1537209).

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Correspondence to Yingguang Li.

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Hao, X., Li, Y., Li, M. et al. A part deformation control method via active pre-deformation based on online monitoring data. Int J Adv Manuf Technol 104, 2681–2692 (2019). https://doi.org/10.1007/s00170-019-04127-w

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  • DOI: https://doi.org/10.1007/s00170-019-04127-w

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