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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2023, Vol. 44 ›› Issue (4): 426805.doi: 10.7527/S1000-6893.2021.26805

• Material Engineering and Mechanical Manufacturing • Previous Articles     Next Articles

Layout optimization of auxiliary support for thin-walled blade based on GA-SVR

Zhiyang ZHENG1,2, Yang ZHANG1,2, Zhao ZHANG1,2, Baohai WU1,2(), Ying ZHANG1,2   

  1. 1.Key Laboratory of High Performance Manufacturing for Aero Engine,Ministry of Industry and Information Technology,Northwestern Polytechnical University,Xi’an 710072,China
    2.Engineering Research Center of Advanced Manufacturing Technology for Aero Engine,Ministry of Education,Northwestern Polytechnical University,Xi’an 710072,China
  • Received:2021-12-10 Revised:2021-12-14 Accepted:2021-12-21 Online:2023-02-25 Published:2022-01-11
  • Contact: Baohai WU E-mail:wubaohai@nwpu.edu.cn
  • Supported by:
    National Key R&D Program of China(2020YFB1710400);Natural Science Basic Research Program of Shaanxi(2021JM-054);National Natural Science Foundation of China(52005413);Natural Science Basic Research Program of Shaanxi(2020JQ-183)

Abstract:

As a typical thin-walled workpiece with complex curved surface, the blade is prone to deflection, that is, elastic deformation, which seriously affects the machining accuracy of the blade. As to this problem, a four degree-of-freedom rotary auxiliary support mechanism is designed to increase the machining rigidity of the blade, and a layout optimization method of auxiliary support for thin-wall blade based on GA is proposed. Firstly, a finite element simulation model for blade milling is established considering material removal and the coupling effect between milling force and elastic deformation. Secondly, the layout scheme of auxiliary support is taken as design variable, the largest value and standard deviation of overall machining elastic deformation are taken as the quality evaluation index of the layout. The sample set is constructed by Latin hypercube design and finite element simulation model. The surrogate forecast model is trained with Support Vector machine Regression (SVR). Then, elite strategy Genetic Algorithm (GA) is used to optimize the auxiliary support layout of thin-walled blade. Finally, the milling and CMM experiments of thin-walled blade are carried out, and the results show that the optimal auxiliary support layout scheme can suppress the blade elastic deformation by 57.6%.

Key words: thin-walled blade, elastic deformation, deformation prediction and control, SVR surrogate model, support layout optimization

CLC Number: