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Influence of uncertain parameters on machining distortion of thin-walled parts

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Abstract

Thin-walled parts refer to lightweight structural parts comprised of thin plates and stiffeners. During the machining process of thin-walled parts, machining distortion often occurs due to uncertain factors such as varying stiffness, cutting force, cutting temperature, residual stress, and other factors. This paper studied the minimization of the failure probability of machining distortion by controlling the uncertainties of inputs. For this, a fuzzy inference model for the machining system was proposed to determine the effects of uncertain factors on the machining distortion errors, which was composed of rule frame and result frame. In the rule frame, machining parameters, outline size, and wall thickness were used as inputs. In the result frame, linear stiffness, cutter path, as well as cutting force were taken as the input parameters. The values of machining distortion were the output, taken into a threshold function. Comprehensive matching was defined to measure the importance of uncertain inputs to outputs. Machining distortion will exceed the specification (failure) with the increase in comprehensive matching. Therefore, the comprehensive matching index evaluates the effects of the uncertainties on the machining distortion and quantifies the effects of given uncertain parameters. Two engineering examples were employed to illustrate the accuracy and efficiency of the proposed approach. It revealed that the comprehensive matching of cutting force to the failure probability of machining distortion was the maximum, 0.040, which was 12 to 13 times greater than that of linear stiffness or cutter path.

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Data availability

All data generated or analyzed during this study are included in this manuscript.

Abbreviations

MD:

Machining distortion

X 1 :

Stiffness

X 2 :

Length

X 3 :

Cutter path

X 4 :

Cutting force

k 1 :

Outline width

k 2 :

Wall thickness

k 3 :

Depth of cut

k 4 :

Width of cut

k 5 :

Cutting speed

k 6 :

Feed rate

CF :

Certainty factor

ST i :

Evidence importance

ST rule :

Rule importance

DRSS:

Distributed receptorbased sourceappointment statistical

BRRT:

Bayesian recursive regression tree

EILP:

Enhanced-interval linear programming

References

  1. Li XY, Li L, Yang YF, Zhao GL, He N, Schmidt E (2021) Variance-based sensitivity analysis for the influence of residual stress on machining deformation. J Manuf Process 68:1072–1085

    Article  Google Scholar 

  2. Li XY, Li L, Yang YF, Zhao GL, He N, Ding XC, Shi YW, Fan LX, Lan H, Jamil M (2020) Machining deformation of single–sided component based on finishing allowance optimization. Chinese J Aeronaut 33(9):2434–2444

    Article  Google Scholar 

  3. Yue CX, Zhang JT, Liu XL, Chen ZT, Liang SY, Wang LH (2022) Research progress on machining deformation of thin-walled parts in milling process. Acta Aeronautics et Astronautica Sincia 43(4):525164

    Google Scholar 

  4. Sridhar G, Babu PR (2013) Cutting parameter optimization for minimizing machining distortion of thin wall thin floor avionic components using Taguchi technique. Int J Mech Eng Technol 4(4):71–78

    Google Scholar 

  5. Xue LF, Chen WF, Feng T, Ma WT (2014) Synchronous optimization of clamping force and cutting parameters for thin-walled parts. Adv Mater Res 900:623–626

    Article  Google Scholar 

  6. Hu QW, Qiao LH, Zhang HW (2013) Optimization of thin-walled part milling parameters based on finite element and orthogonal dominance analysis. J Mech Eng 49(21):176–184

    Article  Google Scholar 

  7. Cong JM, Wu R, Wu BH, Wang JT (2019) Prediction of deformation induced by residual stress in milling of thin-walled part and optimization of cutting parameters. Mech Sci Technol Aerosp Eng 38(2):205–210

    Google Scholar 

  8. Vipindas K, Kuriachen B, Mathew J (2019) Investigations into the effect of process parameters on surface roughness and burr formation during micro end milling of Ti–6Al–4V. Int J Adv Manuf Tech 100(5–8):1207–1222

    Article  Google Scholar 

  9. Li ZL, Zhu LM (2019) Compensation of deformation errors in five-axis flank milling of thin-walled parts via tool path optimization. Precis Eng 55:77–87

    Article  Google Scholar 

  10. Wang J, Ibaraki S, Matsubara A, Shida K (2015) FEM-based simulation for workpiece deformation in thin-wall milling. Int J Atuo Tech 9(2):122–128

    Google Scholar 

  11. Ma W, Wang R, Zhou XZ, Xie X (2021) The finite element analysis–based simulation and artificial neural network–based prediction for milling processes of aluminum alloy 7050. P I Mech Eng B-J Eng 235(1–2):265–277

    Google Scholar 

  12. Tan L, Yao CF, Ren JX, Zhang DH (2017) Effect of cutter path orientations on cutting forces, tool wear, and surface integrity when ball end milling TC17. Int J Adv Manuf Tech 88(9–12):2589–2602

    Article  Google Scholar 

  13. Fei J, Lin B, Xiao J, Ding M, Yan S, Zhang X, Zhang J (2018) Investigation of moving fixture on deformation suppression during milling process of thin-walled structures. J Manuf Process 32:403–411

    Article  Google Scholar 

  14. Rex F, Hariharasakthisudhan P, Andrews A, Abraham BP (2022) Optimization of flexible fixture layout to improve form quality using parametric finite element model and mixed discrete-integer genetic algorithm. Proc Inst Mech Eng C J Mech Eng Sci 236(1):16–29

    Article  Google Scholar 

  15. Li C, Wang ZQ, Tong H, Tian SG, Yang L (2022) Optimization of the number and positions of fixture locators for curved thin-walled parts by whale optimization algorithm. J Phys Conf Ser 2174(1):012013

    Article  Google Scholar 

  16. Milad K, Maryam GS, Abdolreza O (2019) Multi-objective optimization of auto-body fixture layout based on an ant colony algorithm. Proc Inst Mech Eng C J Mech Eng Sci 234(6):1137–1145

    Google Scholar 

  17. Barcenas L, Ledesma-Orozco E, Van-der-Veen S, Reveles-Arredondo F, Rodríguez-S´anchez EA, (2020) An optimization of part distortion for a structural aircraft wing rib: an industrial workflow approach. CIRP J Manuf Sci Technol 28:15–23

    Article  Google Scholar 

  18. Feng KX, Lu ZZ, Pang C, Yun WY (2018) An efficient computational method of a moment-independent importance measure using quantile regression. Mech Syst Signal Pr 109:235–246

    Article  Google Scholar 

  19. Che YL, Wang XR, Lv XQ, Hu Y (2020) Probabilistic load flow using improved three point estimate method. Int J Elec Power 117:105618

    Article  Google Scholar 

  20. Lee CS, Wang MH (2011) A fuzzy expert system for diabetes decision support application. IEEE T Syst Man Cy B 41(1):139–153

    Article  Google Scholar 

  21. 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 

  22. Aliustaoglu C, Ertunc HM, Ocak H (2009) Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mech Syst Signal Pr 23(2):539–546

    Article  Google Scholar 

  23. Lucas PJF (2001) Certainty-factor-like structures in Bayesian belief networks. Knowl Based Syst 14(7):327–335

    Article  Google Scholar 

  24. Jondeau E, Rockinger M (2003) Conditional volatility, skewness, and kurtosis: existence, persistence, and comovements. J Econ Dyn Control 27(10):1699–1737

    Article  MathSciNet  MATH  Google Scholar 

  25. Banerjee PS, Chaudhuri PJ, SRB, (2013) Fuzzy membership function in a trust based AODV for MANET. Int J Comput Netw Inf Secur (IJCNIS) 5:27–34

    Google Scholar 

  26. Fan LX, Tian H, Li L, Yang YF, Zhou NG, He N (2020) Machining distortion minimization of monolithic aircraft parts based on the energy principle. Metals Basel 10(12):1586

    Article  Google Scholar 

  27. Shi FT, Cao P, Gan YN, Zhou CJ, Zhou GC (2020) Calculating precision analysis of static characteristics of multi-rib T-beam structure. IJST-T Civ Eng 44(3):813–824

    Google Scholar 

  28. Dun YC, Zhu LD, Wang SH (2019) Investigation on milling force of thin-walled workpiece considering dynamic characteristics of workpiece. J Mech Sci Technol 33(9):4061–4079

    Article  Google Scholar 

  29. Altenbach H, Eremeyev VA (2019) On nonlinear dynamic theory of thin plates with surface stresses. Contrib Adv Dyn Continuum Mech 114:19–26

    Article  MathSciNet  Google Scholar 

  30. Kasabov NK, Song Q (2002) DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE T Fuzzy Syst 10(2):144–154

    Article  Google Scholar 

  31. Soize C (2013) Bayesian posteriors of uncertainty quantification in computational structural dynamics for low-and medium-frequency ranges. Comput Struct 126:41–55

    Article  Google Scholar 

  32. Li XY, Yang YF, Li L, Zhao GL, He N (2020) Uncertainty quantification in machining deformation based on Bayesian network. Reliab Eng Syst Safe 203:107113

    Article  Google Scholar 

  33. Gologlu C, Sakraya N (2008) The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method. J Mater Process Tech 206(1–3):7–15

    Article  Google Scholar 

  34. Zhou F, Guo H C (2010) A coupled simulation-optimization model for nonlinear systems under uncertainty. Science Press

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Funding

This work is supported by the Shandong Natural Science Foundation (Grant No. ZR2022QE043) and Scientific research project of young outstanding talents of Qingdao university (Grant No. DC2200000908).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Qingdao University, Ningxia Road 308, 266075, Qingdao, People’s Republic of China

    Xiaoyue Li, Hao Qi & Qiang Tao

  2. Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, People’s Republic of China

    Liang Li

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  1. Xiaoyue Li
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  2. Hao Qi
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  4. Liang Li
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Contributions

Xiaoyue Li: conceptualization, methodology, writing—original draft, writing—review and editing, supervision. Hao Qi: data curation, collecting documents, writing—review and editing. Qiang Tao: validation, methodology. Liang Li: conceptualization, supervision.

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Correspondence to Xiaoyue Li.

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Li, X., Qi, H., Tao, Q. et al. Influence of uncertain parameters on machining distortion of thin-walled parts. Int J Adv Manuf Technol 127, 3773–3788 (2023). https://doi.org/10.1007/s00170-023-11713-6

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