The research for this fellowship will significantly enhance two key areas which are not currently covered by the UK metrology offering, and which are key to success in advanced manufacturing. The ambitious target is to provide manufacturers of components made using precision machining processes or additive manufacturing (AM), with the metrology instrumentation and post-measurement analysis techniques that will allow them to keep processes under tight control, therefore, improving:
- reducing scrap rates
- enhancing environmental sustainability
In both areas, this will require completely new approaches to instrument design, combining prior expertise and fundamental research to overcome the obstacles preventing current instrumentation from delivering with the required accuracy and measurement speed.
Summarisation of the research objectives:
- To carry out the fundamental research and development (R&D) for the next-generation of techniques for dimensional measurement of high-precision and AM components. Current commercial measurement methods will be reviewed and considered where possible, but the objective is to develop new techniques that promote high accuracy and measurement speed in industrial environments. Non-contact (optical and x-ray) methods will be given priority.
- The techniques developed will be compared with off-line techniques, which have higher accuracy but cannot be applied in industrial environments due to the speed of measurement. This will primarily be mechanical contacting systems. Calibration methods for the techniques will also be developed, based on finding the metrological characteristics of the instruments, and simple approaches to calibration in an industrial context will be developed. Traceability through NPL, where possible, will be assured.
- In-process techniques will be developed, based on the outputs of Objective One.
Often, when process-control, as opposed to a complete understanding of the process, is required, the measurement methodology can be simplified. This requires a high degree of process understanding and will be carried out for case studies, which will generate spin-off academic outputs.
This project is split into two separate but interconnected work packages (WPs).
Co - Investigators
Post-doctoral researches: Rong Su, Petros Stavroulakis, Wahyudin Syam, Nicola Senin
PhD students: Adam Thompson, Danny Sims-Waterhouse, Patrick Bointon, Lars Korner, Lewis Newton