Autonomous and objective characterisation of composite precursors in manufacturing

Any composite manufacturing method requires an application of a carefully designed consolidation process to ensure the suppression of voids in the laminate, establish bonding in laminate layers and prevent dimensional or fibre-path defects. The optimisation of consolidation processes relies on the characterisation of the composite precursors’ deformability. There are multiple mechanisms occurring in consolidation and various experimental programmes have been suggested in the literature to describe these mechanisms and deduce relevant material properties. The selection of a testing methodology often relies on an initial hypothesis or prior knowledge regarding the deformation modes. This may be a source of significant errors. This research poses questions on the testing rationales, on subjectivity in material testing and on how data-rich programmes should be designed. The proposed solution to the consolidation characterisation problem is a real-time adaptive testing strategy that enables a “conversation with the material” – flexible autonomous steering of a testing programme reacting on the obtained output. Such system is implemented within this research and is called adaptive consolidation sensor framework. The system’s algorithm is set to analyse composite precursor’s feedback to compression in real time and to track characteristic signatures relevant to existing consolidation models. Based on that feedback the framework formulates a load programme for the test in a reactive manner. The developed testing system focuses on the identification of the underlying physical mechanisms rather than material properties identification in a rightly or wrongly assumed resin flow mode. To validate the proposed testing approach, a series of characterisation experiments for different material systems was performed. The obtained results highlight a way forward in terms of rethinking experiments for materials used in manufacturing and beyond.