Midlands Aerospace Alliance - Aston University

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Aston is known for world-class research and teaching, and its graduate employment record is second to none – due in part to the fact that many students spend a year working in industry as part of their studies. The university's cutting-edge research provides the platform for a range of services relevant to aerospace companies throughout the Midlands, the UK and globally.

Photonics for smart structures

The Photonics Research Group at Aston includes over 50 people working at a number of well-equipped laboratories. Research areas include high-speed telecommunications, fibre Bragg gratings, femtosecond laser inscription, polymeric fibre design and fabrication, microwave photonics, biophotonics and optical sensing.

Fibre Bragg grating sensing is one of the most exciting fibre technology areas of recent years with widespread applications. It is particularly relevant to the aerospace industry since it allows easy monitoring of composite structures.

At Aston the technology has been developed via a number of DTI and MoD funded research programmes in partnership with companies such as BAE Systems, Deutsch, Marconi and QinetiQ. The recently completed EMPIRE programme developed connectors for smart composite structures – making the technology suitable for volume manufacturing.

Optical fibres can be used to monitor vibration, strain and temperature throughout a composite structure. Their small size enables them to be embedded within, or bonded to, structures making the system very robust. This technology can be applied to monitoring the in-service stresses of aircraft wings which allows operators to optimize servicing schedules according to work done rather than miles flown.

Optical fibre sensors have a number of further advantages over conventional electrical strain gauges. They are immune to electromagnetic fields and it is possible to interrogate many measurement points using a single fibre – greatly reducing the complexity of installation.

Polymer Processing & Performance Research Unit

The research of the PPP Research Unit covers different stages of the polymer lifecycle, from synthesis to processing. The group also has expertise in the performance of specialty additives and polymers in service during first and subsequent lives. Another research field is polymer oxidation and ageing, where the effects of light and heat are investigated to improve the reliability and performance of polymers.

In the aerospace industry exact knowledge of material lifetime issues is required. Services offered by PPP to the aerospace industry include:

the application of fundamental understanding to areas of polymer processing & performance;
the analysis & testing of properties, processability, stability, recyclability, and degradability, including the effects of additives.

The PPP laboratories are equipped with a full suite of polymer processing facilities and state-of-the art tools including spectroscopic (e.g. FTIR-microscopy, FTIR-ATR, Raman, UV-Vis-NIR, fluorescence and NMR), thermal and rheological (e.g. DMA, Hyper DSC, TGA and capillary rheometry), chromatographical characterization and testing techniques (e.g. HPLC and GC) as well as accelerated aging and weathering devices.

The Information Processing and Pattern Analysis Research Group

The Information Processing and Pattern Analysis Research Group is an internationally recognized centre of excellence in artificial neural networks and advanced pattern processing methods for engineering applications. The Group’s expertise in the nonlinear, dynamic and statistical processing of a wide range of data and applications problems has been applied to a number of areas relevant to the aerospace industry including: corrosion detection; condition monitoring, and fault prediction.

Past and current aerospace-related projects include:

vigilance monitoring in pilots (BAE Systems); novel signal processing and analysis methods were used to determine alertness from an EEG signal;
fault modelling in gas turbine engines; Bayesian belief networks are used to model dependencies between engine components, thus improving diagnosis of faults.