Rapid alloy discovery and characterisation of additively manufactured Type 316 stainless steel and its advanced variants

Supervisory Team: Prof Bo Chen and Dr Andrew Hamilton This PhD project aims to accelerate bulk alloy discovery for additive manufacturing by leveraging smart design and 3D printing of material libraries to dramatically increase the throughput of characterisation and testing. Integrated with AI-assisted workflows, the project will streamline data curation and enable rapid exploration of structural material systems. In the field of additive manufacturing (AM), researchers are increasingly designing alloy compositions specifically tailored to the unique thermal history inherent in AM processes. This shift presents a critical challenge in the era of Materials 4.0: how can we efficiently explore new alloys? This question lies at the core of the broader mission to accelerate the discovery of structural materials. A major bottleneck in alloy development is the time-consuming and resource-intensive nature of microstructural characterisation and mechanical testing, which are commonly conducted on a one-sample-at-a-time basis. This PhD project aims to overcome that bottleneck by streamlining the materials innovation workflow. You will integrate computational screening of candidate alloys (benchmarked against 316 stainless steel) with 3D printing of compositional libraries in novel sample configurations designed to enable high-throughput characterisation and testing. A key innovation is the design and fabrication of bespoke multi-sample fixtures to support automated characterisation workflows. For example, you will fabricate in-house sample holders, facilitating automated data collection from X-ray diffraction and scanning electron microscopy. Such configuration will also allow for the extraction of multiple samples for small-scale testing. Your PhD will significantly accelerate materials experimentation by adopting the principles of parallelisation, automation, and AI-enhanced workflows. You will work with AI companion agents across the entire pipeline, including materials selection, testing coordination, and data curation. Your work will generate new insights into how high-throughput experimentation can be effectively combined with ML to drive a digital transformation in materials research. This project is fully funded by the University of Southampton, and you will be based at the university’s main campus (Highfield). You will benefit from access to cutting-edge research infrastructure, including the Additive Materials and Structures Research Laboratory, Testing and Structures Research Laboratory (TSRL), and Material Innovation Laboratory. You will thus receive training on the relevant experimental facilities.

First-class or upper second-class (2:1) honours degree (or international equivalent) in Mechanical Engineering, Materials Science, or a closely related discipline. Master’s desirable but not essential. Familiarity with additive manufacturing, microstructural characterisation (SEM, XRD, EBSD), AI/data-driven methods, Python programming, and strong problem-solving skills.

Apply online via the University of Southampton Postgraduate Programme portal (soton.ac.uk). Include research proposal, CV, two reference letters, and degree transcripts/certificates. Insert the name of supervisor Prof Bo Chen in the application form. For queries, contact feps-pgr-apply@soton.ac.uk.