In-process optical surface measurement sensor using machine learning - ENG 1367

In this project, the student will develop the sensing technology to realise the next generation of digital manufacturing: Industry 4.0. An essential part of manufacturing is quality control, which is achieved through measurement. One of the most important measurands for quality control is the surface - the fine-scale topography is critical when considering tolerances, assembly and ultimately functionality. It is estimated that surface effects cause 10% of manufactured parts to fail and contribute significantly to UK GDP. Mostly in manufacturing, measurements are taken after manufacture or by slowing down the process – compromising the all-important throughput. The student will combine optical measurement techniques with machine learning to produce enhanced measurement systems that are an integral, real-time and constantly learning part of the manufacturing process – not only fast enough but becoming faster over time. The project will be focused on the optical sensor development and testing, but the balance of modelling and experimental methods can be chosen to suit the background of the student.
The project will be supervised by Professor Richard Leach, from the Manufacturing Metrology Team (MMT), see http://www.nottingham.ac.uk/research/manufacturing-metrology. MMT is an international and diverse team that thrives on openness and coopertation – students work in teams to achieve joint goals in a friendly but professional cohort. The project is supported by Zygo Corporation as an industrial partner and provides the PhD candidate with an enhanced stipend as well as opportunities for collaboration with a major international supplier of metrology equipment.