Continuous stiffness optimization of mobile robot in automated fiber placement

The low stiffness of series robots limits their application in high-load precision manufacturing, such as automated fiber placement (AFP). This paper presents a stiffness optimization method to enhance the stiffness of plane-mobile robots in continuous fiber placement by simultaneously adjusting the robot's posture and the base position. A stiffness performance index suitable for evaluating the comprehensive stiffness of the robot during the AFP process is proposed, which is based on the fluctuation characteristics of the contact force in fiber placement. To maximize this index and the normal stiffness, the multi-objective particle swarm optimization algorithm (MOPSO) is used to solve the two-objective optimization model under multiple constraints. The constrained area of the mobile robot base corresponding to a given path point is determined by the fixed-height slice of the robot's reachable point cloud. A novel method combining global discrete solution and local continuous solution (GD-LC) is proposed to solve the model efficiently, which reduces the search dimension of the MOPSO algorithm. Experimental results from fiber placement on an aircraft mold show that the proposed method can significantly improve the stiffness performance of the AFP robot, and the force-induced deformation after continuous stiffness optimization is reduced by 70.01 % on average. The optimized laying quality further validates the engineering value of the proposed method.