A Machine Learning-Optimized Robot-Assisted Driving System for Efficient Flexible Forming of Composite Curved Components

Abstract

Flexible forming technology breaks through the traditional reliance on rigid molds in the hot-pressing process and demonstrates great potential for fabricating large, lightweight composite components with curved geometries. However, the precise actuation and error control of discrete units in flexible molds remain key technical challenges in the flexible forming of composites. This study proposes a high-precision and efficient method for the shape adjustment and error compensation of flexible multi-point molds. The proposed approach integrates the tangential offset unit configuration (TOUC) algorithm with an industrial robot to establish a robot-assisted precision driving system (RAPDS) for flexible molds. Furthermore, the main error-influencing factors of RAPDS are identified through correlation analysis and response surface modeling (RSM). Based on these findings, a backpropagation neural network (BPNN) is employed to predict adjustment errors, and heuristic algorithms guided by the structural characteristics of the BPNN are embedded into the framework to construct a bi-level optimization strategy that enhances model performance. The experimental results show that, compared with traditional methods, the robot-assisted flexible mold driving system improves the accuracy of shape adjustment by 31.0% and increases the production efficiency of composite components by 66.7%. Overall, this study develops a rapid, efficient, and highly precise flexible multi-point forming method for composite components, demonstrating strong potential for industrial applications.