High entropy alloy design for dissolvable tools using machine learning

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Dissolvable Alloys is an emerging area of special metallic alloys that dissolve under specific operating conditions. These alloys have found wide spread use in oil and gas, biomedical, and space applications. This proposal aims to use Machine Learning (ML) approaches to define new alloy systems for application of dissolvable tools. These tools must provide a unique combination of strength and desirable corrosion rates. To date, aluminum alloys and magnesium alloys have been used for such applications. High Entropy alloys (HEAs) and near stoichiometric HEAs (all referred to here as HEAs) are a new generation of materials that show promise to provide such a combination of properties. Given the enormous possibilities in tunning the structures and compositions of HEAs, this proposal aims to develop a machine learning model that will combine the results in the public domain and patents with the desired application goals for these dissolvable alloys. Such knowledge will be combined with thermodynamic database tools to identify new alloy compositions for HEAs. Experimental work and ML modelling will be interactively carried out. An experimental validation will then be undertaken using a hybrid investment casting additive manufacturing approach developed at the University of Alberta. Mechanical and corrosion properties will subsequently be evaluated to validate the ML model. With sufficient data acquired, ML models for predicting the mechanical and corrosion properties of HEAs will be set up and tested. The deliverables of the project will be new materials for dissolvable materials, machine learning based tools and methods for directed materials space research based on specific material requirements.