Dynamic Optimization Of Variable Height Roll-die Forming Machine

Variable height roll-die forming machine is mainly composed of a fixed mold,rollers capable of moving in X and Z direction and rotating around Y direction,and the mechanism for above movement.In the forming process,the rollers controlled by servo motor can roll the sheet metal along the mold gradually and make it a desired shape.In order to reduce the vibration and impulse of the roll on the formed parts,Ensure the safety and accuracy of parts in forming,The paper carried out the dynamic analysis and optimization of the mechanical system.The variable section of the forming machine contained five passes,which are similar in structure,moving process and stress,Since the maximum forming force appeared in the fourth pass,the paper carried out the dynamic analysis of the fourth pass of the forming machine.Based on the Lagrange analytic dynamics principle,The paper deduced dynamic differential equation of the research object.According to the designed structural parameters,the paper obtained acceleration curve of the mechanical system.The improved particle swarm optimization algorithm combined with the interior point penalty function method and adaptive weight method is applied to the established mechanical model,and the optimization algorithm is programmed by Matlab software.The acceleration of the optimized system is compared with that before optimization.The results showed that the optimized structural parameters can reduce the maximum acceleration of the mechanical system.Finally,The paper imported 3D model of the forming machine into the ADAMS software,and carried out the dynamic simulation of the virtual prototype.Firstly,the simulation results before optimization are in good agreement with the theoretical analysis results,which verified the correctness of the mechanical system dynamics model established by the theoretical analysis;Secondly,the simulation results after optimization are in good agreement with the theoretical analysis results,which verified the validity of the mechanical system dynanics model optimized by the improved particle swarm optimization algorithm.