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A novel method for workpiece deformation prediction by amending initial residual stress based on SVR-GA

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Abstract

High-precision manufactured thin-walled pure copper components are widely adopted in precision physics experiments, which require workpieces with extremely high machining accuracy. Double-sided lapping is an ultra-precision machining method for obtaining high-precision surfaces. However, during double-sided lapping, the residual stress of the components tends to cause deformation, which affects the machining accuracy of the workpiece. Therefore, a model to predict workpiece deformation derived from residual stress in actual manufacturing should be established. To improve the accuracy of the prediction model, a novel method for predicting workpiece deformation by amending the initial residual stress slightly based on the support vector regression (SVR) and genetic algorithm (GA) is proposed. Firstly, a finite element method model is established for double-sided lapping to understand the deformation process. Subsequently, the SVR model is utilized to construct the relationship between residual stress and deformation. Next, the GA is used to determine the best residual stress adjustment value based on the actual deformation of the workpiece. Finally, the method is validated via double-sided lapping experiments.

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References

  1. Zhang S (2019) Research on ultra-precision machining of pure copper sheet with large diameter-thickness ratio. Dissertation, Zhejiang University of Technology, Zhejiang

  2. Pan B, Kang RK, Guo J et al (2019) Precision fabrication of thin copper substrate by double-sided lapping and chemical mechanical polishing. J Manuf Process 44:47–54

    Article  Google Scholar 

  3. Li BH, Gao HJ, Deng HB et al (2019) Investigation on the influence of the equivalent bending stiffness of the thin-walled parts on the machining deformation. Int J Adv Manuf Tech 101(5/8):1171–1182

    Article  Google Scholar 

  4. Li YL, Zhou K, Tan PF et al (2017) Modeling temperature and residual stress fields in selective laser melting. Int J Mech Sci 136:24–35

    Article  Google Scholar 

  5. Wang ZJ, Chen WY, Zhang YD et al (2015) Study on the machining distortion of thin-walled part caused by redistribution of residual stress. Chin J Aeronaut 18(2):175–179

    Article  Google Scholar 

  6. Richter-Trummer V, Koch D, Witte A et al (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 Tech 68(9/12):2271–2281

    Article  Google Scholar 

  7. Guo H, Zuo DW, Wu HB et al (2009) Prediction on milling distortion for aero-multi-frame parts. Mat Sci Eng A Struct 499(1/2):230–233

    Article  Google Scholar 

  8. Gao HJ, Zhang YD, Wu Q et al (2017) An analytical model for predicting the machining deformation of a plate blank considers biaxial initial residual stress. Int J Adv Manuf Tech 93(1/4):1473–1486

    Article  Google Scholar 

  9. Cerutti X, Mocellin K (2015) Parallel finite element tool to predict distortion induced by initial residual stress during machining of aeronautical parts. Int J Mater Form 8(2):255–268

    Article  Google Scholar 

  10. Cerutti X, Arsene S, Mocellin K (2016) Prediction of machining quality due to the initial residual stress redistribution of aerospace structural parts made of low-density aluminium alloy rolled plates. Int J Mater Form 9(5):677–690

    Article  Google Scholar 

  11. Huang XM, Sun J, Li JF (2015) Finite element simulation and experimental investigation on the residual stress-related monolithic component deformation. Int J Adv Manuf Tech 77(5/8):1035–1041

    Article  Google Scholar 

  12. Yang Y, Li M, Li KR (2014) Comparison and analysis of main effect elements of machining distortion for aluminum alloy and titanium alloy aircraft monolithic component. Int J Adv Manuf Tech 70(9/12):1803–1811

    Article  Google Scholar 

  13. Dong HY, Ke YL (2006) Study on machining deformation of aircraft monolithic component by FEM and experiment. Chin J Aeronaut 19(3):247–254

    Article  Google Scholar 

  14. Righetti VAN, Campos TMB, Robatto LB et al (2020) Non-destructive surface residual stress profiling by multireflection grazing incidence X-ray diffraction: a 7050 Al alloy study. Exp Mech 60(2):1–6

    Google Scholar 

  15. Robinson JS, Hossain S, Truman CE et al (2010) Residual stress in 7449 aluminium alloy forgings. Mater Sci Eng A Struct 527(10/11):2603–2612

    Article  Google Scholar 

  16. Fu SL, Feng PF, Ma Y et al (2020) Initial residual stress measurement based on piecewise calculation methods for predicting machining deformation of aeronautical monolithic components. Int J Adv Manuf Tech 108(7/8):2063–2078

    Article  Google Scholar 

  17. Wang Q, Wang B, Ma DC et al (2006) Residual stress measurement for ion-implanted NiTi alloy by using moiré interferometry and hole-drilling combined method. Chin J Aeronaut S1:27–30

    Google Scholar 

  18. Zhang Z, Yang YF, Li L et al (2015) Assessment of residual stress of 7050–T7452 aluminum alloy forging using the contour method. Mater Sci Eng A Struct 644:61–68

    Article  Google Scholar 

  19. Korsunsky AM, Sebastiani M, Bemporad E (2010) Residual stress evaluation at the micrometer scale: analysis of thin coatings by FIB milling and digital image correlation. Surf Coat Tech 205(7):2393–2403

    Article  Google Scholar 

  20. Irastorza-Landa A, Grilli N, Swygenhoven HV (2017) Laue micro-diffraction and crystal plasticity finite element simulations to reveal a vein structure in fatigued Cu. J Mech Phys Solids 104(1):157–171

    Article  Google Scholar 

  21. Du JX, Yang YF, Zhu JP et al (2019) Research on aluminum alloy electrolysis process for stress delamination measurement. Tool Tech 53(7):50–55

    Google Scholar 

  22. Barile C, Casavola C, Pappalettera G et al (2014) Remarks on residual stress measurement by hole-drilling and electronic speckle pattern interferometry. Sci World J 2014:487149. https://doi.org/10.1155/2014/487149

  23. Guo J, Fu HY, Pan B et al (2019) Recent progress of residual stress measurement methods: a review. Chin J Aeronaut 34(2):54–78

    Article  Google Scholar 

  24. Na MG, Kim JW, Lim DH et al (2008) Residual stress prediction of dissimilar metals welding at NPPs using support vector regression. Nucl Eng Des 238(7):1503–1510

    Article  Google Scholar 

  25. Zhang P, Yin ZY, Jin YF et al (2019) A novel hybrid surrogate intelligent model for creep index prediction based on particle swarm optimization and random forest. Eng Geol 265:105328. https://doi.org/10.1016/j.enggeo.2019.105328

    Article  Google Scholar 

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Acknowledgments

The authors acknowledge the financial support provided by the National Key Research and Development Program (Grant No. 2018YFA0702900), the Science Challenge Project (Grant No. TZ2016006), and the National Natural Science Foundation of China (Grant No. 51975096).

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Authors and Affiliations

  1. Key Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, 116024, Liaoning, People’s Republic of China

    Jiang Guo, Bin Wang, Zeng-Xu He, Bo Pan & Ren-Ke Kang

  2. Institute of Mechanical Manufacturing Technology, China Academy of Engineering Physics, Mianyang, 621999, Sichuan, People’s Republic of China

    Dong-Xing Du & Wen Huang

Authors
  1. Jiang Guo
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  2. Bin Wang
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  3. Zeng-Xu He
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  4. Bo Pan
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  5. Dong-Xing Du
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  6. Wen Huang
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  7. Ren-Ke Kang
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Correspondence to Jiang Guo or Ren-Ke Kang.

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Guo, J., Wang, B., He, ZX. et al. A novel method for workpiece deformation prediction by amending initial residual stress based on SVR-GA. Adv. Manuf. 9, 483–495 (2021). https://doi.org/10.1007/s40436-021-00368-9

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  • DOI: https://doi.org/10.1007/s40436-021-00368-9

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