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高强小弯曲半径钛管热场辅助数控弯曲工艺参数确定性优化

  • Ying Fu
  • , Zhao Zhang
  • , Heng Yang
  • , Jun Ma
  • , Jing Chao Yang
  • , Guang Jun Li
  • , Heng Li
  • China Aviation Industry Corporation
  • Northwestern Polytechnical University Xian

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

Taking the advanced heat field-assisted numerical control bending process of titanium tube with high strength and small bending radius as the research object, the thermal-mechanical coupled finite element model of whole process of heat field-associated NC bending was established, and the local loading method of thermal field in the numerial control bending was optimized. The double response surface function relation expression of the maximum cross-section deformation degree Q and the maximum wall thickness thinning rate t was established based on the optimized parameters of forming temperature, mandrel diameter, mandrel extension and boost coefficient. The deterministic optimization of process parameters based on double response surface method was realized using the non-dominated sorting multi-objective optimization genetic algorithm. The results show that the temperature difference of different positions of the die is reduced by the temperature field of the optimized bending die, and the heating efficiency is improved. The optimal combination of heat field forming parameters was obtained. The maximum deformation degree is reduced by 27.8%, the maximum wall thickness thinning rate is reduced by 17.5%, and the ultimate bending radius of the high-strength titanium tube is reduced to 1.5D from 3D at room temperature, which improve the forming capacity of high-strength titanium tube.

投稿的翻译标题Process parameters deterministic optimization of heat field-assisted numerical control bending for titanium tube with high strength and small bending radius
源语言繁体中文
页(从-至)60-69
页数10
期刊Suxing Gongcheng Xuebao/Journal of Plasticity Engineering
28
4
DOI
出版状态已出版 - 28 4月 2021

关键词

  • Deterministic optimization
  • Heat field-assisted bending
  • High strength titanium tube
  • Response surface model

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