Problem Statement Rapid prototyping (RP) is an ever-growing field of research, gaining track and interest towards the advancements in automation and manufacturing. Given that the process from design to product in conventional manufacturing practice can be time and cost intensive, modern RP techniques such as 3D printing allow for faster and customised manufacturing of parts in shorter times, hence providing designers with the freedom and flexibility to modify and improve on the design during the whole development process.
One of the areas that has been benefiting much from the advancements in RP is the fabrication of biosensors for healthcare research and diagnostics. With the ability to provide a quick solution for a combination of bespoke biosensors and biomarkers catered to various requirements for prognostics and diagnostics, the use of RP has been proven critical in scaling diagnostics during times of crisis such as the COVID-19 pandemic. However, there also exist a few technological and research gaps in the current RP techniques, in particular 3D printing methods. For example, a majority of the 3D printers, be it commercial or custom-made, are operating based on an open-loop control architecture. As a result, the lack of an effective feedback control mechanism does not allow for precise motion control of the build plate and nozzle in the x-, y-, and z-axes. Given the wear-and-tear of parts due to the mechanical movements over time, the machines have to be constantly calibrated and serviced to ensure that the system is operating well. Furthermore, the lack of feedback control also affects the robustness of the system towards unwanted disturbances and noise in the electrical signals, which could affect the quality for advanced manufacturing and thus, compromising shape accuracy.
Proposed Innovative Solution
The project will first explore the various “in-the-loop”, i.e. software-, model-, and hardware-in-the-loop (SIL, MIL, HIL) techniques for the design and development of the control architecture. Then, using model-based controller-observer design methods with a vision-based sensor, e.g. CCD etc., for feedback, precise motion control can be achieved for the build plate and nozzle in the x-, y-, and z-axes. Thirdly, a combination of model-based and data-driven technique will also be explored to enhance the robustness of the control algorithm such that the system can compensate for the disturbances and noise in the electrical signals for advanced manufacturing. This technique also allows for a fault diagnosis scheme to detect potential wear-and-tear in the hardware mechanism due to prolonged operation of the system and for fault-tolerant to be performed to compensate the degradation in the performance of the system.
1)At least a 2:1 Honours degree (or equivalent) in Mechatronics Engineering, Control Engineering, Electrical and Electronic Engineering, Computer Science, Applied Mathematics, or a related subject.
2)A relevant Master’s degree and/or experience in one or more of the following will be an advantage: MATLAB, Simulink, engineering, control of nonlinear systems, optimisation, deep learning and machine learning.
3)Strong understanding of the mathematical sciences and its applications to engineering and automation.
4)Excellent written and spoken communication skills in English.
This project is part of the Advanced Biomedical Engineering Laboratory (BoDevices Lab) at Ulster University, which is a £7 million initiative for strategic partnership between Invest Northern Ireland (Invest NI), Ulster University, Randox Laboratories, and Heartsine Technologies. The Invest NI’s R&D support is part funded by ERDF under the EU Investment for Growth and Jobs Programme 2014–2020.
- To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
This project is funded by: Invest NI
The scholarship will cover tuition fees at the Home and EU rate and a maintenance allowance of £15,285 per annum for three years.