Design of High Entropy Superalloys Using a Hybrid Experimental-Based Machine Learning Approach: Steel Sector Application

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End users in high value and other manufacturing sectors report low confidence in the long-term performance of both coatings and their processing as a barrier to the introduction of new products dependent on surface engineering and advanced coatings. Responding to this market need this project aims to shift the paradigm from "apply the material you have" to "engineer the material you need" developing significant global markets through increased confidence in surface engineering technology. High entropy alloys (HEAs) provide a transformative opportunity in this direction enabling high-performance manufactured goods that are competitive in the international marketplace through extraordinary material properties and unique property combinations. The new High Entropy Superalloys will be engineered around the Al-Co-Cr-Cu-Fe-Ni alloy system promoting both the face-centred cubic phase and the wear resistant nano-oxides phase. To achieve rapid material development, the consortium will devise a hybrid R&D approach combining high throughput experimentation and a neural network software that would allow a computer to sift through more than a million possibilities in search of promising mixtures tailored to the operating environment characteristics. The new approach aims to "discover, manufacture, and deploy advanced materials twice as fast, at a fraction of the cost. The new technology will be first demonstrated to the steel sector that is under ever increasing pressure. Steel mills in the UK will benefit from the project outcomes helping them to maintain and increase their workforce. A successful project outcome will result in jobs created and retained in the supply chain. The project will have a major environmental impact through the substitution of carcinogenic hexavalent chrome and the reduction of cobalt based materials excessively used in hard facing applications worldwide.