Rapid Alloy Discovery and Characterisation of Additively Manufactured Type 316 Stainless Steel and Its Advanced Variants

Project Overview

This PhD project aims to accelerate alloy discovery for additive manufacturing (AM) by leveraging smart design and 3D printing of material libraries to significantly increase throughput in characterisation and mechanical testing. By integrating AI-assisted workflows, the project streamlines data curation and enables rapid exploration of structural material systems tailored to AM processes.

Current challenges include the time-consuming nature of microstructural characterisation and mechanical testing, typically done sample-by-sample. This project overcomes this by integrating computational alloy screening with novel 3D printed compositional libraries and bespoke multi-sample fixtures, enabling automated characterisation technologies like X-ray diffraction and scanning electron microscopy.

What You Will Do

You will combine computational screening workflows benchmarked against 316 stainless steel with additive manufacturing of multi-sample libraries. You will design and fabricate in-house sample holders for automated data collection and extraction for small-scale testing. AI companion agents will support the entire pipeline including materials selection, test coordination, and data curation. This approach adopts principles of parallelisation and automation to accelerate materials experimentation markedly.

Expected Outcomes

The PhD will generate new insights into combining high-throughput experimentation with machine learning to enable a digital transformation of materials science. It will enhance understanding of alloy design and testing workflows specific to AM, ultimately reducing development time for new structural alloys.

Why This Matters

Additive manufacturing allows tailored alloys optimized for unique AM thermal histories, but efficient exploration of new alloys remains a bottleneck. This project addresses critical challenges in Materials 4.0, pushing forward materials innovation through AI-enhanced, automated workflows and advancing the field of structural material development.