Defect Development during the Preforming of Non-Crimp Fabrics
Lead Research Organisation:
University of Cambridge
Department Name: Engineering
Abstract
The research questions that this PhD is trying to address are by what mechanisms do the defects such as wrinkles develop during the preforming of textile reinforcements and how can these defects be characterised and minimised. Textile reinforcements are a critical area of research within composite manufacturing as they provide a pathway towards more cost effective and faster production of composite components for the aerospace and automotive industries. However, currently the defects that develop during the preforming step of the manufacturing process pose a significant hurdle and thus this PhD attempts to address this issue. The textile reinforcements that are considered are non crimp fabrics (NCFs) which are novel multi-layered textiles that are stitched together instead of being woven. The approach for this PhD is to develop a novel experimental preforming rig that is able to track and measure the development of wrinkles and the associated fibre strains in the NCFs using 3D digital image correlation (DIC), under a variety of boundary conditions. This data will then be used to develop relationships between the strains and wrinkle amplitudes in order to attempt to develop a wrinkling forming limit diagram (WFLD) that is able to characterise the onset of wrinkling for a particular textile reinforcement in terms of the strains induced. The research will highlight a plausible standardised methodology for experimentally obtaining the wrinkling limits of a particular fabric from a series of simple tests, which can then be applied to a range of fabrics and significantly simplify the fabric selection process and their subsequent manufacture. Furthermore, the understanding of wrinkle development during forming obtained in these experiments will be used to inform the development of a micromechanical model finite element (FE) model of the forming of NCFs. This model will be able to predict the locations and amplitudes of wrinkles and the associated strains, and can be used directly to develop the WFLD for a particular fabric, without the need for any experimental tests. Ultimately, the PhD will attempt to conduct an optimisation study based on both experimental and simulation data that is able to optimise the preforming process with regards to minimising defects. This project fits within the EPRSC research area of 'Materials engineering - composites.'
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509620/1 | 01/10/2016 | 30/09/2022 | |||
| 1946941 | Studentship | EP/N509620/1 | 01/10/2017 | 30/09/2021 | Johan Viisainen |
| Description | The research into the characterisation of wrinkling behaviour created new knowledge in that it led to a thorough understanding of the wrinkling behaviour of a biaxial non-crimp fabric, which was previously lacking. We were able to identify and provide direct evidence for the mechanisms behind the three types of wrinkling that occur the forming of this fabric. From this, it was then shown how the largest wrinkles seen during the forming of this NCF occur in the region of the fabric with the least shearing. Thus, the shear angle distribution cannot be used as an infallible indicator of wrinkling for all textile reinforcements. In addition, we highlighted how the stitching in the NCF dictates the deformation mode and shearing behaviour for the fabric in a particular region. Furthermore, the study shows how the size and extent of wrinkles generated vary significantly between different geometries but the associated wrinkling mechanisms and shear distributions are consistent. The second part of this research quantified the inherent variability in the wrinkling behaviour of textile reinforcement and how this variability is impacted by various changes in the preforming process conditions. This study highlighted especially how the variability in both amplitude and location becomes greater for an increased number of plies and for non-orthogonal relative orientations, which demonstrates the difficulties with attempting to control wrinkling behaviour during the industrial preforming process consisting of 10s of plies formed simultaneously. Previously, considerations of the variability in defect generation have been overlooked and this study displays how significant this can be for the forming of textile reinforcements, thus providing critical new insight. A new standardised method for applying a speckle pattern (stochastic arrangement of spots) onto textile fabrics was developed that allows for the deformation of the fabric to be quantified via 3D digital image correlation (DIC) without significantly affecting the deformation behaviour of the fabrics. A speckle pattern is required to allow tracking of an object's deformation with DIC. This new method consists of a controlled application of graphite spray for the base layer followed by flaw developer spray to create the speckles. It is a low-cost method that can be easily applied to any textile reinforcement and thus has a lot of potential for wide use in academic research and in industry for material characterisation. Researchers in this field reported how they had found the speckle pattern application difficult with fabrics without completely changing their behaviour and thus our approach is a significant development. This research has received support from the EPSRC Future Composites Manufacturing Research Hub. It has benefited from collaboration with academics from the University of Nottingham, who have been working with the same NCF material. This has already resulted in one joint journal publication and further collaborative work combining their forming simulations with the experimental work done at Cambridge. In addition, a separate side project stemmed out this work that developed into a collaboration with the University of British Columbia looking at the shear-tension coupling of NCFs and its effect on wrinkling. |
| Exploitation Route | The work is taken forward at the University of Cambridge by developing a machine learning algorithm that can use the experimental data, in combination with simulated forming data elsewhere, to predict the wrinkling patterns for arbitrary complex geometry. Once this concept can be shown to work, it can be used in industry to accurately identify the potential wrinkle areas of a potential part design before it is even made. Thus the component can be designed for manufacture and allow for more defect-free parts to manufactured, resulting in the wider adoption of composites across the automotive industry, and lower CO2 emissions from the transport sector. Furthermore, the extensive experimental data collected can be used to validate the accuracy of finite element forming models of NCFs that are developed at Cambridge or elsewhere. Because the DIC approach provides surface values of displacement, strains and shear angle, it allows for a one-to-one comparison to be made with finite element calculations, providing much more confidence in models that correlate well with the experimental data. |
| Sectors | Aerospace, Defence and Marine,Manufacturing, including Industrial Biotechology,Transport |
| URL | https://www.repository.cam.ac.uk/handle/1810/314995 |