University of Bath URSA project: Application of Machine Learning for Sustainable Manufacturing

This project is one of a number that are in competition for funding from the University of Bath URSA competition.

Project

This project investigates the use and implements new machine learning techniques in order to enhance the sustainability of manufacturing processes in terms of reduced resource consumption and CO₂ emissions.

UK Manufacturing has an annual output of £183bn and provides 2.5m jobs in the UK [1]. Whilst the UK remains the world’s 9^th largest manufacturer, the productivity and growth has remained sluggish in recent years. According to the Made Smarter report [2], the application of digital technologies such as artificial intelligence and sensor-based control can increase productivity by 25% and reduce CO₂ emissions by at least 4.5%.

Sensor data collection from manufacturing processes such as machining has provided an insight into manufacturing processes. Indirect measurement methods have proved to be helpful in providing information about the process condition potentially enabling optimisation of manufacturing processes and systems. However, new major challenges have arisen: multiple sensor data collection leads to a large amount of data which need to be processed in real time; beyond processing, real time decision making is required to adapt the processes and systems; in-depth understanding of the system is required to ensure decisions are feasible and optimum. On the other hand, sustainability is multifaceted and multi-dimensional relying on many input parameters. Parameters such as raw materials, tooling, consumables as well as the process parameters, the final product, recyclability and circularity need to be considered which makes decision making and optimisation extremely challenging.

These provide an opportunity for applying advanced machine learning methods and techniques for real time processing and analysing sensor data, predicting system behaviour and decision making in order to reduce manufacturing CO₂ emissions whilst ensuring part quality and economic viability of the processes.

In this project you will design a sensor network for monitoring and data collection from manufacturing processes, design and apply machine learning methods for data analysis and decision making and use these to minimise the CO₂ emissions from manufacturing processes. This project will build on the ongoing EPSRC and Innovate UK funded research at the University of Bath on sustainable and intelligent manufacturing processes and systems.

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 by February 2023 in order to be considered for funding.

Enquiries and Applications

Informal enquiries are encouraged! Direct these to Dr Alborz Shokrani - [Email Address Removed]

Please make a formal application should via the University of Bath’s online application form for a PhD in Mechanical Engineering

When completing the form, please identify your application as being for the URSA studentship competition in Section 3 Finance (question 2) and 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.

Funding Eligibility

To be eligible for funding, you must qualify as a Home student. The eligibility criteria for Home fee status are detailed and too complex to be summarised here in full; however, as a general guide, the following applicants will normally qualify subject to meeting residency requirements:

• UK nationals (living in the UK or EEA/Switzerland)
• Irish nationals (living in the UK or EEA/Switzerland)
• Those with Indefinite Leave to Remain
• EU nationals with pre-settled or settled status in the UK under the EU Settlement Scheme.

This is not intended to be an exhaustive list. Additional information may be found on our fee status guidance webpage, on the GOV.UK website and on the UKCISA website.

Equality, Diversity and Inclusion

We value a diverse research environment and strive to be an inclusive university, where difference is celebrated and respected. We encourage applications from under-represented groups. In particular, we are welcoming applications from candidates with Refugee, Asylum Seeker, or Humanitarian Protection in the UK to our Doctoral Sanctuary Studentship in Engineering and Design.

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.

The Disability Service ensures that individuals with disabilities are provided the support that they need. If you state if your application that you have a disability, the Disability Service will contact you as part of this process to discuss your needs.

Keywords: Applied Statistics; Artificial Intelligence; Communications Engineering; Computer Vision; Data Analysis; Electrical Engineering; Electronic Engineering; Engineering Mathematics; Machine Learning; Manufacturing Engineering