Multi-source lay-up error analysis and lay-up pressure optimization for robotic automated fiber placement (AFP)

Abstract

Automated Fiber Placement (AFP) offers significant advantages in manufacturing large aircraft structures but is prone to defects impacting product quality and mechanical performance. Lay-up Pressure Error (LPE), influenced by various factors, notably lay-up pressure, affects AFP quality. Our study focuses on a heavy-duty robot with pre-positioned lay-up mechanisms for AFP. We analyze the impact of robot and end effector (AFP head) errors on LPE, developing analytical models for compaction rollers and prepreg to establish constitutive relationships. A Generalized Tool-tip Error (GTE) incorporating mold path point offsets is formulated. Additionally, models for joint torsion and bending deformation, considering end forces and robot gravity, are established. Mapping joint errors to AFP robot end-effector errors (ARE) is achieved using extended Jacobian matrices. We comprehensively analyze error effects on LPE and establish an optimization index for robot pose to mitigate LPE. Experimental results validate the effectiveness of our optimization method in enhancing lay-up pressure uniformity, accuracy, and overall quality while reducing defects.