With the proposal of Made in China 2025 and the country's strong support for high-end manufacturing, the development of China's robot industry is very agile, and industrial robots are constantly changing the customs of welding robots.
Based on the ten-kilogram arc welding robot, the finite element analysis and structural optimization of the robot were carried out based on the policy of light weight, high stiffness and high speed function. First of all, according to the needs of the research content, the pre-processing operation of the arc welding robot with a load of ten kilograms was completed. The parameter model of the robot was simplified, the structural diagram and D-H parameter table of the robot were established, and the production of the part diagram and the assembly of the whole machine were completed. The kinematic analysis and verification were carried out to solve the Jacobian matrix, which laid the foundation for the proposal of related functional indexes in the future. The working space of the robot is analyzed, which can be used as an optimization constraint or one of the criteria for evaluating the optimization effect.
KUKA robots
Secondly, the dynamic analysis of the robot was carried out in ADAMS, and the pose of each part of the robot in the required working space was found when the working state was bad, and the finite element analysis of the critical parts was carried out at the position. The topology optimization method was selected to lightweight design the boom structure of the robot, and the boom was topology optimized to achieve the purpose of reducing the mass and improving the stiffness together.
Thirdly, according to the requirements of the robot for motion flexibility and speed stability, some functional indicators were proposed, including the function index of working space interval, the function index of static stiffness and the function index of global speed induction. The static stiffness function and the velocity induction function were confirmed as the optimization guidelines for the final optimization model*, and then the coupling between the optimization policies was analyzed to confirm the relationship between the policies.
The sensitivity analysis of the optimization variables was carried out to obtain the sensitivity of each optimization policy to its variables, and the constraints were confirmed according to the actual situation. In order to prevent the occurrence of local* optimal solutions and improve the efficiency and convergence of the solution, the NSGA-II. optimization algorithm was selected to solve the optimization model.
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Teach Pendant Device Method Login Administrator
Click Get Operational > Auxiliary Software
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One. Outline:
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