Fully funded Four Year PhD Studentship sponsored by AMRC with Boeing and EPSRC

University of Sheffield

Nonlinear Normal Modes for Reduced Order Modelling of Machining Processes

Project Description

Vibration signatures of machines during machining processes can be used in order to monitor tool wear states and can also be used to extract quality indicators for the process. The signals from common vibration sensors will also capture information about the underlying linear dynamics of the machine, which is not particularly useful for machine diagnostics; of much more interest is the nonlinear dynamics of the cutting processes themselves. Recently, within the Sheffield Dynamics Research Group (DRG), we proposed a new means of nonlinear normal modal (NNM) decomposition, which offers the promise of more informative reduced order models for nonlinear system identification and diagnostics. Although the basic feasibility of the method was established, much remains to be done in order to refine and validate the approach. Most importantly from a machining viewpoint, the method needs to be tested on cases where harsh nonlinearity (friction, impact) is present. The proposed project here is to extend the new NNM method to the sort of nonlinearities commonly occurring in machining. This will include refinement of the basic algorithm in terms of speed of implementation. It will be a fundamental research project, largely based on simulated data; however, it is expected that some experimental validation will be carried out.

We are looking for an outstanding person with an interest and expertise in some or all of the following fields: machine learning, time series analysis, nonlinear dynamics. Applicants should possess a good (1 or 2.1) honours degree in Civil/Mechanical/Aerospace Engineering /Computer Science/Mathematics or other relevant discipline.

The award provides a generous stipend of £18,000 pa and includes a pot of £5,000 for research expenses. The PhD students will join an annual cohort with EngD students with structured technical training, research mini-projects, and professional skills development focussed particularly on the 1st year.

Entry requirements and eligibility

Due to EPSRC residency requirements, this project is open only to UK and EU applicants who have been resident in the UK for at least 3 years immediately preceding the start of the course.

Applicants must have, or expect to get, a good Masters-level degree (e.g. 1st or 2.i MEng degree or MSc with Merit) or an exceptional BEng, in a relevant science or engineering subject such as applied mathematics, statistics, physics, electrical and electronic engineering, systems and control engineering, mechanical engineering, materials science and engineering, or computer science.

Candidates must also be able show that their English language proficiency is at a level which allows them to successfully complete the EngD. All applicants require an English language qualification, typically a GCSE or an IELTS test (a score of 7 or above is required, with a minimum of 6 in each component).

If in doubt about any aspect of Eligibility, please email idc-machining-science@sheffield.ac.uk for clarification.

Partners: University of Sheffield AMRC with Boeing & EPSRC
Start date: 03 September 2018

Duration: 4 years

Applications deadline: 30 November 2017

check eligibility requirements before applying.

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