The University of Bath is inviting applications for this project from students who can self-fund their studies.
Project
This project investigates the application of sensor data collection and deep learning signal processing and decision making to enhance the sustainability of machining processes for manufacturing aerospace parts. Short tool life in machining means that statistical process control is applied and cutting tools are discarded prematurely to avoid tool wear induced damages on to the workpiece. About 30-50% of the useful remaining life of the tool is lost due to premature tool disposal. With the global cutting tool market estimated at $34bn annually, up to $17bn worth of cutting tools are discarded. Cutting tools are metal matrix composites consisting of tungsten carbide and cobalt binders. A significant amount of energy and resources are used for producing tools which are not necessarily recycled after use. Being able to detect the tool wear at the right time not only maximises the use but also provides an opportunity for remanufacturing and reconditioning of the cutting tools.
In this project, acoustic emission and vibration sensors will be used to collect data from machining. Deep learning methods such as convolutional long short term memory networks will be designed to predict future signals from the sensors based on the past data and convolutional neural network classifiers will be trained to detect end of tool life prior to tool failure. The impact of tool reconditioning and remanufacturing on tool performance and energy footprint of machining will be assessed and analysed. Traditionally, optimisation has been concentrated on increasing output and productivity. With an increasing concerns regarding sustainability of manufacturing processes, multi objective optimisation would allow for optimising machining parameters for minimising environmental impacts whilst increasing productivity.
Candidate Requirements
Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent). A master’s level qualification would also be advantageous.
Non-UK applicants must meet our English language entry requirement.
Enquiries and Applications
Informal enquiries are welcomed and should be directed to Dr Alborz Shokrani - [Email Address Removed]
Formal applications should be made via the University of Bath’s online application form for a PhD in Mechanical Engineering
When completing the form, please quote the project title and lead supervisor’s name in the ‘Your research interests’ section.
More information about applying for a PhD at Bath may be found on our website.
Equality, Diversity and Inclusion
We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.
If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.
Keywords
Control Systems; Electronic Engineering; Manufacturing Engineering; Mechanical Engineering; Mechatronics
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