Self-funded PhD or MSc by Research in optimising and automating pre-production for wire based Directed Energy Deposition (w-DEDAM) production

This project sits within digital manufacturing and additive manufacturing, with a particular focus on wire-based directed energy deposition (w-DEDAM). It integrates disciplines including artificial intelligence, computational modelling, and manufacturing systems engineering. The research addresses the transition from experience-driven to data-driven and automated production workflows, which is a central challenge in modern manufacturing. Its relevance today is significant, as industries are increasingly adopting additive manufacturing for high-value, complex components while demanding improved consistency, reduced lead times, and enhanced sustainability. Automating the pre-production stage through AI and optimisation directly supports the development of intelligent, scalable, and reliable manufacturing systems, aligning with the broader movement towards digital manufacturing.

The aim of this project is to develop an automated, non-expert pre-production framework for wire-based directed energy deposition additive manufacturing (w-DEDAM). The research focuses on integrating expert knowledge with artificial intelligence, multi-objective optimisation, and digital tools to systematically define build strategies, process parameters, and toolpaths. The project seeks to deliver a robust and scalable solution that consistently achieves high-quality parts, minimal distortion, and improved productivity, while reducing reliance on manual decision-making and enhancing overall efficiency in additive manufacturing workflows.

The student will be based at the Welding and Additive Manufacturing Centre (WAMC), a renowned hub for impactful research into advanced fusion-based processing and manufacturing methods. The Centre's contributions to industry are demonstrated through its extensive MSc and PhD research initiatives and its ongoing technology development programs in large-scale additive manufacturing. The student will become part of a diverse and dynamic research community at WAMC, fostering collaboration and innovation. Additionally, there will be opportunities to work with WAMC’s industrial partners.

The project is expected to deliver a validated, automated pre-production framework for w-DEDAM that reduces reliance on expert judgement and minimises human-induced variability. It will enable more consistent part quality, reduced distortion, and improved productivity through optimised build strategies and process parameters. From a technical perspective, the research will advance multi-modal data integration, AI-driven optimisation, and digital workflow implementation in additive manufacturing. It is also expected to contribute new methodologies for incorporating expert knowledge into data-driven systems. From an industrial perspective, the outcomes will support faster decision-making, shorter lead times, and more efficient material usage, enhancing the scalability and reliability of w-DEDAM for high-value applications. The project will also contribute to standardisation and digitalisation efforts within modern manufacturing systems.

The student is expected to acquire the following (including but not limited to) knowledge and skills from the research in this project:

• Fundamental understanding of w-DEDAM processes. 
• Pre-production workflow design, covering CAD model handling, feature recognition, build strategy definition, and toolpath planning.
• Multi-objective optimisation techniques, including formulation of optimisation problems, trade-off analysis, and algorithm development for manufacturing applications.
• Artificial intelligence and machine learning methods, particularly for data-driven modelling.
• Multi-modal data integration and management, combining geometric data, simulation outputs, experimental data, and expert knowledge into a unified framework.
• Digital manufacturing systems and platforms, with experience in developing or implementing integrated, automated workflows.