Adaptive Automation and Defect Control in Forming Processes of Composite Materials

Composite forming processes often face inaccuracies due to material variation and process factors like modulus values, temperature, and tool speed. Achieving consistent quality and accurate predictions under these conditions is a significant challenge in advanced manufacturing.



This research suggests a robust framework for improving finite element (FE) simulation accuracy and allowing for real-time defect control using point-cloud analysis. A parametrisation and optimisation method, backed by genetic algorithms, makes simulations more efficient even with limited computing power. It also uses scanned point clouds to identify common forming defects such as wrinkles, bridges and gaps. Both the simulation and control methods are tested and compared, connecting the simulated model and physical testing to enhance process reliability.