Geometric measurement is a core aspect of the recent revolution in product development and manufacturing known as Industry 4.0. An increasing number of technologies is now available to quickly and comprehensively measure the form and texture of parts after they have been manufactured, or even while they are being fabricated. Optical form and surface measurement instruments are now capable of scanning geometries into high-density point clouds; X-ray computed tomography can even obtain volumetric data capturing internal surfaces and other hard-to-access geometric features. As part geometries are getting more complicated, thanks in particular to the technological improvements of advanced fabrication technologies such as additive manufacturing, an increased responsibility falls on measurement, which is now called not only to capture geometric information, but is also required to report on accuracy of measurement, i.e. on how reliable each captured dataset actually is. The challenge of obtaining accuracy-related information, commonly referred to as the estimation of the uncertainty associated to measurement, i.e. “measurement uncertainty”, is currently unsolved at the worldwide level, and the availability of expertise in the field is highly sought for by the major worldwide manufacturers and by developers of the technologies of the future.
In this research project, the student will explore what we currently know about measurement uncertainty associated to high-density measurement of part geometry and surface texture. The student will acquire expertise in investigating and understanding error sources associated to state of the art measurement technologies, and will work towards the objective of increasing our understanding of how error propagates through the measurement process, ultimately affecting the primary dataset resulting from measurement, i.e. the point cloud or volumetric data. The student will then investigate how error in the point cloud/volumetric data may propagate through the algorithmic procedures commonly applied at the industrial level to verify whether a part conforms to geometric and dimensional specifications, ultimately investigating solutions for the accurate estimation of uncertainty associated to the verification process, thus providing a fundamental contribution towards the development of manufacturing solutions of the future.
The project will be supervised by Professor Richard Leach, from the Manufacturing Metrology Team (MMT). MMT is an international and diverse team that thrives on openness and cooperation – students work in teams to achieve joint goals in a friendly but professional cohort
Full fees and stipend are available.
The position is available for UK or EU candidates, but International applicants who can pay the difference between the Home and International Fees would also be welcome to apply.
Candidates must possess or expect to obtain, a high 2:1 or 1st class degree in science, engineering or computer science, or other relevant discipline.
Please send a copy of your covering letter, CV and academic transcripts to [email protected]. Please note, applications without academic transcripts will not be considered.