Learning based control systems for additive manufacturing

About the Project

Supervisory Team: Prof Bing Chu & Dr Thomas Andritsch

This project explores learning based control systems for additive layer manufacturing (3D printing) in electrical engineering. Using machine learning and advanced control theory, it aims to optimise printing processes, reduce defects, and enhance component performance, bridging a key gap between AM technology and reliable, high-quality materials for the electrical power sector.

Additive Layer Manufacturing (AM) is a computer-controlled manufacturing process, which creates 3D objects by subsequent deposition of layers. AM has developed rapidly over the past decade, but there is a lack of relevant work in the field of electrical and electronics engineering, as much of the published work focuses on mechanical, thermal and chemical properties.

In contrast to conventional manufacturing methods, such as injection moulding or extrusion moulding, AM deposits a material layer by layer on a build plate, which then results in a different material structure. A solid block of AM prepared material (metals, ceramics, polymers...) would, for example, still have noticeable layers as the polymer of the previous layer would cool down (and potentially crystallise) before the next layer would be deposited in the same location.

Various materials and processing parameters affect the parts created by AM, such as nozzle size, printing speed, patterning, processing temperature, melt-flow index etc. which makes the aim of void reduction for AM components suitable for electronic engineering a complex control challenge.

This project aims to develop intelligent control systems for AM, leveraging recent advances in control theory and machine learning. The goal is to design adaptive and data-driven control strategies that enhance the functional quality of components for use in the electrical power sector. Ultimately, the project seeks to bridge a critical gap in the integration of AM into electrical and electronics engineering, paving the way for more reliable, efficient, and innovative manufacturing solutions.

You must have a UK 2:1 honours degree or its international equivalent.

Full scholarships include tuition fees, a tax-free stipend at the UKRI rate for up to 3.5 years (totalling £20,780 for 2025/26, rising annually). UK, EU and Horizon Europe students are eligible for scholarships. Chinese Scholarship Council funded students are eligible for fee waivers. Funding for other international applicants is very limited and highly competitive. Overseas students who have secured or are seeking external funding are welcome to apply.

You need to:

• choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Electronic & Electrical Engineering (7092)
• add name of the supervisor in section 2 of the application

Applications should include:

• research proposal
• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (if applicable)