About the Project
This project will develop technology to create high-efficiency and reliability in Computer Numerical Control (CNC) machine operations using Acoustic Emission (AE), leading to an advanced tool control strategy.
Machine tools are subject to wear and possible failure during their operational life that can lead to poorly manufactured parts or scrapping of a component. The aim of this four year studentship is therefore to use AE to monitor the tool during the CNC process to move from the current approach of periodic wear measurements to an 'as required' approach leading to high-efficiency and importantly prevent tool failure. This project will develop a detailed understanding of AE signatures associated with the CNC process, released by the tool and the component, in normal operations and during defect development. Using Machine Learning a data interrogation approach that can identify wear will be developed leading to a control system capable of detecting tool damage that will automatically adjust the CNC process during manufacture. This will aid efficiency by removing the need to periodically measure tool wear and importantly prevent tool failure.
The prospective student will be embedded within the Tribology and Mechanical Performance research group. The group is home to leading academics in condition monitoring, tribology, manufacturing, signal processing and numerical analysis and currently supervises over 20 PhD studentships. The group currently leads research projects with leading industrial companies, including Renishaw Plc., who are sponsors of this studentship and have access to significant test and analysis facilities.
The prospective student will benefit from award-winning courses, workshops and careers advice organised by the Doctoral Academy at Cardiff University specifically aimed at researchers throughout their career to develop research and professional skills. In addition, the prospective student will have an opportunity for training and development offered by the partner and will undertake a placement at the partner.
Tuition fees at the UKRI rate and an annual stipend at least equivalent to current Research Council rates (£15,609 stipend for academic year 2020/21), plus a stipend enhancement. (min. £1200 pa). The studentship also includes significant support for travel/conferences/consumables. Up to 30% of fully-funded studentships (or % applicable to your scheme) are available to international applicants, and there is no requirement for the student to make up the fee difference.
The successful candidate will have a first degree at 2:1 level or higher, in Mechanical Engineering/Physics/Computer Science, ideally with practical experience of Machine Learning/Signal Processing. Applicants whose first language is not English will be required to demonstrate proficiency in the English language (IELTS 6.5 or equivalent)
For further information contact Professor Rhys Pullin, PullinR@Cardiff.ac.uk
Applicants should submit an application for postgraduate study via the Cardiff University webpages (http://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/engineering ) including;
· an upload of your CV
· a personal statement/covering letter
· two references (applicants are recommended to have a third academic referee, if the two academic referees are within the same department/school)
· Current academic transcripts
Applicants should select Doctor of Philosophy (Engineering), with a start date of October 2022.
In the research proposal section of your application, please specify the project title and supervisors of this project and copy the project description in the text box provided. In the funding section, please select "I will be applying for a scholarship / grant" and specify that you are applying for advertised funding, reference RP/iCASE2021
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