Accelerated Design of High-Performance Alloys Enabled by a Coupled Materials Informatics and Additive Manufacturing Approach

The development of new materials, and in particular the design of new alloys, is key to the continued development and improvement of efficiencies in both emerging and traditional technologies. Industrial sectors such as aerospace and automotive, are reliant on new alloys to enable efficiency improvements that enhance their competitiveness in an open market. Similarly, emerging technologies, for example nuclear fusion and hydrogen energy, are limited by existing materials and necessitate the design of novel alloys to enable their commercial viability and expansion. Consequently, industrial R&D programs invest significant portions of yearly budgets on alloy design activities, along with years of development such that new products can enter service.

Emerging tools, such as materials informatics, that utilise machine learning, materials modelling and integrated computational materials engineering (ICME) concepts to accelerate the downselection of viable alloy candidates for experimental testing, are becoming increasingly sought after as a means through which empirical testing can be eliminated. However, the use of such tools is often limited by the availability of data in useful areas of compositional space, resulting in only marginal chemical iterations being achieved. Similarly, existing databases include sparse information on manufacturing and processing steps, further limiting the potential for the holistic development of materials systems that take into consideration both chemical constituents as well as manufacturing practices to achieve the required properties.

To address these limitations, the aim of the project proposed herein is to develop an ambitious, resource efficient and industry-agnostic accelerated alloy design methodology that utilises additive manufacturing for alloy fabrication from elemental powders using the unique capabilities available through the Royce Institute at the University of Sheffield. This approach will be combined with rapid characterisation using synchrotron X-ray diffraction (SXRD), and alloy downselection will be guided by materials informatics methods.

Key objectives for the work are:

•        Utilise existing machine learning methodologies for alloy development, using key mechanical and environmental performance indicators for downselection.

•        Establishing protocols for alloy manufacture using directed energy deposition from elemental powders

•        Create automated phase analysis algorithms that are capable of determining crystallographically-difficult to identify phases from high resolution diffraction data.

•        Formulate a high throughput testing protocol (incl. optimisation of sample geometries and automated data analysis algorithms) that includes tensile testing, thermal and microstructural analysis, and oxidation resistance.

Joining the Modern AlChEME group under the supervision of Dr Kathy Christofidou, you will be joining an internationally renowned, vibrant, early career-led, entrepreneurial group. As a member of a recently established group, we place particular emphasis on mentorship and career development to support your aspirations. You will have the opportunity to represent us and get involved with a variety of projects whilst also being exposed to our wide network of industrial and academic collaborators as well as gaining exposure to advanced manufacturing and characterisation techniques we pioneer.