Optimise and Automate Pre-production for Wire Based Directed Energy Deposition (w-DEDAM) Production PhD

Start date: 02 Jun 2025

Duration of award: 4 years

Eligibility: UK

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.

How to apply

For further information please contact:

Name: Dr. Jian Qin

Email: J.Qin@Cranfield.ac.uk

If you are eligible to apply for this studentship, please complete the online application form. 

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