A digital twin for smart 3D printing/additive manufacturing

Summary

This project offers an exciting opportunity on developing a smart 3D printing/additive manufacturing technology through computer vision, machine learning and Digital Twin (DT). Digital twin implementation will provide a benefit such as real-time monitoring and controlling the 3D printing manufacturing process. The aim of this project is to apply computer vision for real-time defect monitoring and feature engineering, leveraging machine learning for real-time defect detection and process parameter optimisation, and controlling 3D printers in an optimised environment through Digital Twin (DT). Process-level DT will allow real-time monitoring of deposition process, temperature, humidity and vibrations. Based on the monitored data, the robust optimising algorithm will be proposed to optimise process parameters such as speed, nozzle temperature, and extrusion distance.

This research will be part of the Cognitive Robotics Lab within Intelligent Systems Research Centre (ISRC). The focus of ISRC is to develop fundamentally new AI algorithms in areas such as predictive modelling and analytics, computer vision, and evolutionary algorithms. Whereas the lab is currently focusing on building a smart manufacturing technology. The lab has advanced resources with all 3D printing facilities such as Raise3D Pro2 Plus, Raise3D E2, AnkerMake M5. The lab is also equipped with advanced sensors required like process monitoring camera, temperature sensor, Humidity sensor and Vibration Sensor.

The aim of the project aligns with the field of computer vision, a primary focus of the Cognitive Robotics Lab. As a member of the Cognitive Robotics Lab, the successful candidate will join a network of academic faculty, research professionals, and fellow students with diverse expertise. They will also have access to interdisciplinary research teams within the ISRC. This opportunity will equip the candidate with advanced skills to become a leading expert in digital technology for 3D printing/additive manufacturing.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

These scholarships, funded via the Department for the Economy (DfE) and the Vice Chancellor’s Research Scholarships (VCRS), are open to applicants worldwide, regardless of residency or domicile.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies.