To meet the increasingly complex machining needs much better, this paper proposes a 2CPU/2UPC multi-mode mechanism that can achieve the two machining processes of surface drilling and milling. The screw theory is used to verify the degree of freedom and full-cycle motion of the mechanism in various modes. The position inverse solution and velocity Jacobian matrix of the mechanism in each mode are solved. The Monte Carlo method is used to solve the workspace of the mechanism in various modes. The stiffness model of the mechanism in each mode is established, and the stiffness distribution is solved. Finally, the multi-objective optimization model is established and a set of optimal structural parameters is obtained by optimizing the structural parameters of the mechanism through the combination of Isight and Matlab. By comparing the results before and after optimization, the performance of the optimized mechanism has been comprehensively improved.

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Kinematic Analysis and Performance Optimization of 2CPU/2UPC Multi-mode Mechanism

  • XueYuan Gao,
  • XinWang Liu,
  • Shihua Li

摘要

To meet the increasingly complex machining needs much better, this paper proposes a 2CPU/2UPC multi-mode mechanism that can achieve the two machining processes of surface drilling and milling. The screw theory is used to verify the degree of freedom and full-cycle motion of the mechanism in various modes. The position inverse solution and velocity Jacobian matrix of the mechanism in each mode are solved. The Monte Carlo method is used to solve the workspace of the mechanism in various modes. The stiffness model of the mechanism in each mode is established, and the stiffness distribution is solved. Finally, the multi-objective optimization model is established and a set of optimal structural parameters is obtained by optimizing the structural parameters of the mechanism through the combination of Isight and Matlab. By comparing the results before and after optimization, the performance of the optimized mechanism has been comprehensively improved.