Future-Proof Adaptive Path Correction in Automated Fibre Placement: A Concept Demonstration
Year: 2025
Type: conference-paper
Abstract: Automated Fibre Placement (AFP) involves the robotic placement of pre-preg carbon fibre composite material in slit-tape segments. This technology is key to intelligent automated manufacturing in composites. Despite its high production value in aerospace, automotive, renewables, and defence, AFP lacks real-time control, a core Industry 4.0 principle. The demand for high production quality imposes strict defect detection and rectification requirements, typically performed both manually and via intelligent sensors. Consequently, AFP processes generate significant waste in scrapped material and lost machine time—up to 63% of cycle time is spent on defect inspection and rework.To address these inefficiencies, we propose an upgraded online control method for AFP, enabling real-time monitoring and correction of deposition paths. This paper demonstrates a proof-of-concept for in-process trajectory monitoring and control of an AFP system using an upgraded online control method via KUKA’s Robot Sensor Interface (RSI) and the ITRA framework, within a 12-degrees-of-freedom system. This approach enables low-latency, real-time programming of deposition paths, within the robot’s standard six degrees of freedom but also across six external axes, including the material deposition axis. AFP programming uniquely links deposition control with external axis control, requiring integration into path planning.Furthermore, we show feasibility of combining the use of real-time sensor data in sync with real-time trajectory execution on the robot, enabling a rapidly reacting system for the control of positional defects during material deposition. By synchronising real-time sensor data with trajectory execution, this approach enables a more adaptive and responsive AFP system, reducing defects and improving efficiency. Future developments should explore localised, online path planning for closed-loop control, advancing AFP integration within Industry 4.0 ecosystems.
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4410298049