This 4-year, fully funded PhD project centres on the automation and optimisation of the pre-production process for wire-based Directed Energy Deposition Additive Manufacturing (w-DEDAM). It is supported by EPSRC Industrial Cooperative Awards in Science & Technology (CASE) training grants and WAAM3D Ltd, our industry partner. Additionally, the industrial partner offers a 3-month placement annually during the project. The project aims to explore cutting-edge digital technologies for w-DEDAM pre-production, including machine learning, deep learning, Design for Additive Manufacturing (DfAM), and advanced optimisation algorithms.

Wire-based directed energy deposition additive manufacturing (w-DEDAM) systems have effectively constructed qualified parts, now extensively employed in many industrial applications. To ensure a stable, reliable, high-quality and environmentally sustainable deposition process, the pre-production process is crucial which includes multiple activities, in terms of pre-forming original Computer Aided Design (CAD) models, recognising and segmenting design features, simulating geometry and mechanical properties, defining build sequences, and planning paths with appropriate process parameters.

Currently, the entire pre-production process is heavily reliant on the expertise and experience of additive manufacturing (AM) engineers. The decisions have also been decided based on prior experience, which may result in various part quality, lead time, and the use of material. This current artificial process is also time-consuming and fraught with uncertainties, often prone to human errors during decision-making. Therefore, there is an urgent need to fully optimise and automate this pre-production process with the combination of expert knowledge and artificial intelligence (AI) driven digital tools.

This project aims to explore and discover a non-expert pre-production process for w-DEDAM which can be implemented automatically based on expert knowledge and AI-driven digital tools combined with multi-objective optimisation. It will routinely provide an optimal production solution in terms of productivity, minimal or no distortion and high quality.

The student will be based at the Welding and Additive Manufacturing Centre, known for its impactful research into advanced fusion-based processing/manufacturing methods and other relevant technologies. This project is closely linked to many ongoing academic and industry projects, ensuring the student will be part of a diverse and vibrant research community. Additionally, there will be opportunities to work with the Centre’s industrial partners, such as WAAM3D and WAAMMat.

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

  • Design for Additive Manufacturing (DfAM), including AM feature detection, segmentation and analysis, path planning, for w-DEDAM. 
  • Advanced data analytics methods, like machine learning and multiple objective optimisations. 
  • Techniques, requirements, and applications of metal additive manufacturing. 
  • Reviewing literature, planning, and managing research, writing technical report, paper, presenting in meetings, conferences, and teamwork.

You will be supported for international conferences. Also, the industry partner has agreed to support full access to the w-DEDAM software, in terms of path planning, process parameter generation, production simulation, and process monitoring with the support of professional system operating training section. A 3-month industrial placement is agreed to provide to the successful applicant every year during this project.

At a glance

  • Application deadline12 Feb 2025
  • Award type(s)PhD
  • Start date02 Jun 2025
  • Duration of award4 years
  • EligibilityUK
  • Reference numberSATM515

Entry requirements

Applicants should have an equivalent of first or second class UK honours degree in a related discipline or subject area (e.g., electrics, mechanical, mechatronics, and manufacturing,). This project would suit a candidate with a genuine interest in design for additive manufacturing and additive manufacturing automation. Previous experience with CAD model segmentation and analysis, simulation for metal additive manufacturing and/or multiple objective optimisations is also desirable. The candidate should be self-motivated, proactive, and good at communication and teamwork.

Funding

To be eligible for this funding, applicants must be classified as a home student. We require that applicants are under no restrictions regarding how long they can stay in the UK.

About the sponsor

Sponsored by EPSRC, 美姬阁 and WAAM3D, this DTP studentship will provide a bursary of up to 拢22,500 (tax free) plus fees* for four years.

美姬阁 Doctoral Network

Research students at 美姬阁 benefit from being part of a dynamic, focused and professional study environment and all become valued members of the 美姬阁 Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

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