From the 110,000 tonnes of composites produced in the UK each year, currently, only 15% will be reused or recycled at the end of their life and the rest are transferred to ever-growing landfills . Although recycling CFRP products use only 20% of the energy required to produce them in the first place, the recycled fibre resembles rough wire wool in terms of random alignment and lacks the initial tensile strength. However, this wool-like material can be processed further to closely achieve the appearance and strength of the firsthand fibers. Lightweight Manufacturing Centre (LMC), as a specialist technology centre within the National Manufacturing Institute of Scotland (NMIS), led by Prof. Ian Bomphary, has been leading a Sustainable Composite project to address this issue. NMIS along with National Composite Centre are the only two institutions in the UK pioneering recycling . As promising as the initiative may sound, they don't still have any Non-Destructive Testing (NDT) methods to evaluate the quality of reprocessed fibers, yarns, and woven CFRP mats rigorously.
While the inspection of dry fibres is almost unfeasible by most of the well-established NDT methods such as a) ultrasound testing due to the lack of coupling medium and strenuous results interpretation, b) X-rays due to safety requirements and concerns on the shop floor, and low resolution and angle-dependent results, c) thermography due to the lack of a monolithic structure and anisotropy of fiber sheets, the highly conductive woven dry carbon fibre plies are formed by a network of fiber yarns with directionally dependent electrical conductivity. This provides a unique opportunity to deploy electromagnetic NDT methods such as Eddy Current (EC) testing which neither require coupling medium nor contact with the test subject. Eddy current testing is a well-established testing method providing a superior detection sensitivity for surface and near-surface defects. The testing method reliability has even grown further with the development and introduction of new EC array technologies in the market. The EC arrays benefit from several coils ordered in rows, which provides a wider inspection coverage for the test subject. These probes are also manufactured in form of flexible sensors that can conform to geometric complexities. These flexible sensors are more stable than conventional pencil probes meaning that they produce less lift-off and tilt noise as compared to their predecessors. This makes them greatly suitable for automation, where the sensor can be delivered and manipulated on complex dry carbon fibre stacks using industrial robots.
Currently, dry fiber inspection research/technology using EC is very limited and seldom commercialized and deployed in the industry as the potential has not been extensively explored. However, the non-contact nature, sensitivity to electrically conductive fibre sheets, and the rich and broad range of information acquired by the Eddy currents' wide frequency spectrum could unlock unique characterization capabilities that can underpin the inspection before RTM process and the newly recycled/reprocessed fibres for their thickness, density, and quality consistency. EC reading, mostly influenced by fiber conductivity, can provide valuable information on fiber characteristics such as gaps, undulations, wrinkles, orientations, and rapture. The overarching project aims are to leverage dry fibre inspection capabilities EC to a) prevent the entry of defective fibre fabeics to the process and save cost on scrappage and prolonged repair undertakings after RTM and curing, and b) Inspect the recycled fiber quality for thickness, density, and continuity before reprocessing them into woven sheets and reusing them for manufacturing. As an RAE/Spirit lecturer in sensor development, my research has been particularly focused on the development and promotion of novel electromagnetic sensor technologies with high inspection reliability, which has been previously missing from CUE and UoS, to build on the past pioneering successful automated NDT developments for inspection of CFRPs achieved through several projects with Spirit AeroSystems.
To this end, model-based studies will be used for sensor design optimization for EC to balance the trade-off between the penetration depth and the sensitivity for the best inspection results. Different EC coil designs and layouts in form of arrays will be investigated to ensure the required sensitivity to different desired properties of dry fibres and woven fabrics. Therefore, as the core novelty, this project seeks to develop and robotically deploy a novel automation architecture, and EC sensor technology to yield superior inspection results with higher reliability and robustness. Therefore, a robotic EC sensor deployment strategy for the inspection of fibres/fabrics will be investigated and implemented, and the inspection data will undergo different signal processing stages and will be fused for different frequency channels via an intelligent inference system for defect detection and characterization to gain a more comprehensive understanding of the inspected component. While the novel EC sensor will significantly enhance the overall sensitivity of the NDE system to a broad range of fibre/fabric defects, the intelligent defect detection, and characterization unit will serve as the dedicated multi-tasking brain of the autonomous NDE system to: a) fuse the EC multi-frequency inspection data, b) render the processed signals in form of denoised images, and c) detect, and classify the defect indications.
Research-related & Transferrable Skills: The student will be incorporated within the NDE training programme that is exclusively offered to CDT FIND students by the leading academics in the field of NDE from the University of Manchester, Imperial College of London, University of Bristol, University of Nottingham, University of Warwick, and the University of Strathclyde. The training includes a series of networking, project management, interpersonal skill development, the theoretical background of frequently used NDE methods, and hands-on experience with these technologies. The student will be provided with training courses in robotics to gain knowledge about robot safety procedures, operation, and programming as well as MATLAB and LabVIEW programming courses to acquire the essential coding and system integration skills. The student will also have access to the £2.6M SEARCH facilities housing several advanced industrial robots and NDE equipment and the AIC established by Spirit AeroSystems at their Prestwick manufacturing facility.