Adaptive Real-Time Characterisation of Composite Precursors in Manufacturing

Experimental data accompanying the paper of A.Koptelov et al on new testing framewor

Adaptive real-time characterisation of composite precursors in manufacturing

Experimental data accompanying the paper of A.Koptelov et al on new testing framewor

Revising testing of composite precursors:A new framework for data capture in complex multi-material systems

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 paper poses questions on the testing rationales, on subjectivity in material testing and on how data-rich programmes should be designed. Two approaches are suggested – the first one 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. This framework focuses on the identification of the underlying physical mechanisms rather than material properties identification in a rightly or wrongly assumed flow mode. The second approach examines favourable combinations of tests to maximise information obtained whilst minimising the amount of testing. The obtained results highlight a way forward in terms of rethinking experiments for materials used in manufacturing and beyond

A deep learning approach for predicting the architecture of 3D textile fabrics

In this paper, a deep learning approach to 3D textile geometry simulations is presented. Two different network architectures with convolutional and recurrent properties are explored. The deep neural networks were trained to generate a fully compacted 3D textile unit cell based on the weave initial architecture. The AI training was conducted on a set of precomputed weaving case studies generated by digital element based weaving simulation software. The proposed strategy demonstrated effectiveness in estimation of 3D textile architectures. The designed system was able to operate within 10% error for stiffness properties prediction. The main benefit of the proposed approach over conventional modelling is its computational efficiency. Rapid weaving simulations provide an opportunity to explore the effects of different yarn architectures, matrix materials, and manufacturing techniques on the mechanical properties of woven composites, leading to a better understanding of their behaviour and their potential for use in new applications