A study of the pyramid of testing for forming of non-crimp fabrics for aerostructures

Abstract

This thesis presents an assessment on the various scales and tests used to characterise NCF forming. Through applying wrinkle characterisation methods appropriate to each scale and test, key trends for both geometry (process) and material parameters are extracted. Correlation between results across the scales is used to assess the limitations of each scale and test method. Firstly, the approach started at the material level with bias extension testing. A direct comparison with wrinkles observed during forming experiments showed that the bias extension test overpredicts wrinkle heights. Therefore, the bias extension was considered unsuitable on its own for predicting preform quality in an NCF forming process where excess length is generated due to part geometry. Secondly, forming tooling was designed to explore the effect of geometry features relevant to aerostructures and NCF material parameters. Results showed that whereas location and shape of the wrinkling are driven by geometry, wrinkle size and its metrics (amplitude, wavelength, aspect ratio) are driven by the NCF architecture. Finally, in situ X-ray computed tomography experiments were designed to track the forming process in space and time, capturing the evolution of wrinkles from initial forming until consolidation in single and mixed orientation ply stacks. Results show the most critical stage of the process where change in wrinkle size, shape and location occurs is the initial 0.1 bar of vacuum. The remaining pressures until vacuum, mainly show consolidation, with wrinkle size decreasing but wrinkle shape and location remaining largely unchanged. Mixed orientation ply stacks showed differences in wrinkle shape and size between the plies during the vacuum stages, however overall similar wrinkle shapes in the final consolidated preform. Overall, the importance of using several characterisation methods for forming and not reducing wrinkling characterisation to a single data point were key observations from this work.

Date of Award	20 Jun 2023
Original language	English
Awarding Institution	University of Bristol
Sponsors	Airbus (United Kingdom)
Supervisor	James Kratz (Supervisor) & Vincent Karel Maes (Supervisor)