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UOS

Rapid 3D Printing of Multi-Material Critical Defence Components

✓ Funded (Competition) ⏰ Closing Soon 🎓 Manufacturing Engineering 🎓 Mathematical Modelling 🎓 Thermodynamics graph neural networks multi-material additive manufacturing directed energy deposition computational thermodynamics phase kinetics modeling defence components mechanical testing advanced microscopy

Investigate multi-material metal additive manufacturing to enable rapid single shot printing of defence components. Combine computational modeling, experimental testing, and machine learning to overcome alloy joining challenges and optimize production logistics.

AI-generated overview

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Why This Research Matters

This research will enhance the UK’s capacity to rapidly manufacture critical defence components with complex multi-material architectures, improving national security and manufacturing flexibility. It addresses metallurgical challenges that currently limit the scalability of additive manufacturing for defence applications and supports strategic priorities in resilience and advanced manufacturing.

Alloy and microstructure design

Project Description

Project Overview

This project focuses on advancing multi-material metal additive manufacturing (AM) for critical defence components such as hot chambers, nozzles, tanks, and injectors. It aims to overcome metallurgical incompatibility issues between alloys like aluminium, titanium, steel, and nickel superalloys using transitional alloy layers and directed energy deposition (DED) AM systems.

What You Will Do

  • Develop and optimize new multi-material joining strategies (e.g., Ni/Al, Ni/Ti, Ni/Fe, Fe/Al, Fe/Ti) using computational thermodynamics and phase kinetics modeling for crack-free transitional layers.
  • Manufacture and test multi-material specimens at the Henry Royce Institute with characterization techniques such as advanced microscopy, X-ray CT, and mechanical testing at the University of Southampton.
  • Create a graph neural network-based logistics framework to optimize distribution of materials, stocks, and production sites across the UK under different scenarios.

Expected Outcomes

The project will deliver optimized joining processes enabling single shot multi-material component printing, validated through experimental testing and advanced characterization. A novel machine learning logistics framework will facilitate effective production and distribution planning at national scales.

Why This Matters

Rapid and flexible manufacturing is critical for UK national security to produce defence components on demand and across dispersed locations. Overcoming multi-material joining barriers will transform manufacturing resilience and defence readiness, directly aligning with EPSRC and MOD priorities in advanced manufacturing and national resilience.

Entry Requirements

Undergraduate degree with at least UK 2:1 honours (or international equivalent). Skills in computational thermodynamics, computer programming, additive manufacturing, physical modelling, and AI modelling are expected.

How to Apply

Apply via the University of Southampton online portal selecting: Research, academic year 2026/27, full or part-time, Faculty of Engineering and Physical Sciences. Search for "complex integrated systems" and select PhD. Include your CV, 2 academic references, and degree transcripts/certificates. Add the supervisor's name in section 2 of the application.

Eligibility

UK/Home
EU
International

Supervisor Profile

PP
Prof Pedro Rivera
University of Southampton
10031 Citations
62 h-index
Google Scholar

Prof Pedro Eduardo Jose Rivera-Diaz-del-Castillo is a leading expert in materials science and engineering, specializing in multi-material manufacturing and metallurgy. His research encompasses computational thermodynamics, phase kinetics, and the integration of advanced manufacturing techniques with machine learning. He holds a strong international research presence with over 10,000 citations and a robust h-index, reflecting his influential contributions to materials engineering and additive manufacturing.

Key Publications

2015 744 citations
Modelling solid solution hardening in high entropy alloys
2015 468 citations
A model for the microstructure behaviour and strength evolution in lath martensite
2017 314 citations
Understanding martensite and twin formation in austenitic steels: A model describing TRIP and TWIP effects
2019 271 citations
Modelling strengthening mechanisms in beta-type Ti alloys
2014 270 citations
The influence of silicon in tempered martensite: Understanding the microstructure–properties relationship in 0.5–0.6 wt.% C steels

Research Contributions

Development of models explaining solid solution hardening and microstructure behavior in advanced alloys.
This work enables the prediction and optimization of strength and behavior of high entropy alloys and martensitic steels.
Insights into martensite and twin formation mechanisms and their effects in austenitic steels.
Improves the understanding of TRIP and TWIP steel strengthening, aiding alloy design in automotive and structural applications.
Analysis of strengthening mechanisms in beta-type titanium alloys and the role of silicon in tempered martensite steels.
Supports the design of stronger, more resilient titanium and steel alloys for aerospace and industrial use.

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