|Funding for:||UK Students|
|Funding amount:||Not Specified|
|Placed On:||28th September 2022|
|Closes:||30th November 2022|
In the aerospace, automotive, and medical industries, accurate and reliable form and surface measurements are key to producing parts that are within tolerance and fit for purpose. Non-contact optical systems provide benefits over contact systems as they could be used in-process to measure and monitor the part during manufacture. In addition, for camera-based systems, artificial-intelligence (AI)-based image recognition algorithms could enhance these measurements further. Yet the limitations of optical systems, such as having lower accuracy, material sensitivity and higher requirements for data handling and storage, are slowing their wide adoption.
Combining multiple techniques to address these limitations provides multiple data sets that can be merged to improve the resulting information. However, this adds to the processing time and complexity of the system. For in-process fabrication monitoring, the system needs to gather and process the data within seconds so as not to delay the manufacturing line.
The aim of this project is to increase the data analysis speed of optical measurement systems to provide real time information output. By investigating both hardware and software improvements, this project will work on increasing the speed of data analysis for a joint fringe projection (FP) and photogrammetry (PG) system.
The project will springboard from development work within Taraz Metrology and the Manufacturing Metrology Team (MMT) who are looking at combined FP and PG data sets to measure part form. The PhD candidate will investigate the implementation of faster algorithms for point cloud fusion of the two data sets, potentially using machine learning. From there they will develop algorithms for data extraction using information rich metrology (IRM) software to provide real time data on the form of the object, for instance, using CAD data to compare the measured part to required tolerances, providing pass or fail feedback to the manufacturing line.
This project has funding available (including enhanced stipend) for UK-Home based applicants. Preferred start date is 1st December 2022 but this is negotiable for the right candidate. Informal queries can be directed to Dr Samanta Piano email@example.com or Dr Luke Todhunter firstname.lastname@example.org.
Please apply here https://www.nottingham.ac.uk/pgstudy/how-to-apply/apply-online.aspx
When applying for this studentship, please include the reference number (beginning ENG and supervisors name) within the personal statement section of the application. This will help in ensuring your application is sent directly to the academic advertising the studentship.
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