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<p>Difficult-to-bend metal tubes are widely used in various high-end fields such as aerospace due to their excellent performance, which is bent with heat-assisted rotary draw bending (RDB). The bending springback seriously affects their axial accuracy. Normally it is compensated by the bending angle passively, resulting in low accuracy and low robustness. To address this issue, an active prediction-compensation framework for difficult-to-bend metal tubes based on the alert collaborative sand cat swarm optimization algorithm (AcSCSO) is proposed to actively eliminate the springback defects generated during heat-assisted RDB. The feasibility of the proposed active prediction-compensation framework is validated with a Ti-3.5Al-2.5&#xa0;V titanium alloy tube warm bending. The optimization results of AcSCSO are compared with the compensation results of other optimization algorithms. The comparison results show that AcSCSO performs best in active compensation of bending process parameters. Finally, the effects of process parameters on springback defects in bent tubes are analyzed. Reasonable forming temperature and mandrel frontage are beneficial to reduce the tube springback angle.</p>

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Springback active prediction-compensation framework: difficult-to-manufacturing metal tubes intelligent bending based on alert collaborative sand cat swarm algorithm

  • 期刊论文

  • 发表日期：

    2025年2月24日

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  • Zheyi Li,
  • Zili Wang,
  • Shuyou Zhang,
  • Jianrong Tan,
  • Le Wang

摘要

Difficult-to-bend metal tubes are widely used in various high-end fields such as aerospace due to their excellent performance, which is bent with heat-assisted rotary draw bending (RDB). The bending springback seriously affects their axial accuracy. Normally it is compensated by the bending angle passively, resulting in low accuracy and low robustness. To address this issue, an active prediction-compensation framework for difficult-to-bend metal tubes based on the alert collaborative sand cat swarm optimization algorithm (AcSCSO) is proposed to actively eliminate the springback defects generated during heat-assisted RDB. The feasibility of the proposed active prediction-compensation framework is validated with a Ti-3.5Al-2.5 V titanium alloy tube warm bending. The optimization results of AcSCSO are compared with the compensation results of other optimization algorithms. The comparison results show that AcSCSO performs best in active compensation of bending process parameters. Finally, the effects of process parameters on springback defects in bent tubes are analyzed. Reasonable forming temperature and mandrel frontage are beneficial to reduce the tube springback angle.