In this paper, the slewing support frame of gangue throwing machine is taken as the research object, and the its lightweight is carried out under the premise of ensuring its strength and stiffness performance. Firstly, the static analysis of the slewing support frame shows that there is room for optimization. The multi-target response surface optimization was carried out with the thickness of the four main plates of the slewing support frame as the design variable, the maximum deformation and stress of the structure as the constraint, and the structural quality as the optimization goal. After optimization, the mass is reduced by 37.9%, and the stress and maximum deformation meet the allowable requirements. After optimization, the modal verification shows that the inherent low-order frequency of the slewing support frame is much higher than the internal and external excitation frequency, which excludes the risk of resonance. The research results confirm the feasibility of the multi-objective optimization method.

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Lightweight Design of Slewing Support Frame of Gangue Throwing Machine Based on Multi-Objective Optimization Algorithm

  • Zhijie Ren,
  • Wenxiao Guo,
  • Tianlong Zhu

摘要

In this paper, the slewing support frame of gangue throwing machine is taken as the research object, and the its lightweight is carried out under the premise of ensuring its strength and stiffness performance. Firstly, the static analysis of the slewing support frame shows that there is room for optimization. The multi-target response surface optimization was carried out with the thickness of the four main plates of the slewing support frame as the design variable, the maximum deformation and stress of the structure as the constraint, and the structural quality as the optimization goal. After optimization, the mass is reduced by 37.9%, and the stress and maximum deformation meet the allowable requirements. After optimization, the modal verification shows that the inherent low-order frequency of the slewing support frame is much higher than the internal and external excitation frequency, which excludes the risk of resonance. The research results confirm the feasibility of the multi-objective optimization method.