University of Leeds Featured PhD Programmes
European Molecular Biology Laboratory (Heidelberg) Featured PhD Programmes

Measurement uncertainty in advanced manufacturing - ENG 1359

Faculty of Engineering

Applications accepted all year round Competition Funded PhD Project (European/UK Students Only)

About the Project

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), see 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.

Funding Notes

Funding Notes
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.

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here

The information you submit to University of Nottingham will only be used by them or their data partners to deal with your enquiry, according to their privacy notice. For more information on how we use and store your data, please read our privacy statement.

* required field

Your enquiry has been emailed successfully

Search Suggestions

Search Suggestions

Based on your current searches we recommend the following search filters.

FindAPhD. Copyright 2005-2020
All rights reserved.