Abstract
Establishing the property contour-map using the classical physics-based models is easy to fall into the “dimension disaster” due to the complexity of experimental variables (composition and heat treatment), limiting the discovery of alloys with high hardness and good electrical conductivity (EC). Here, we propose the combination of a Gaussian process regression-based machine learning (ML) surrogate model and the phase diagram to search for compositions with better target properties in Cu-Co-Si alloys. The ML models trained from “discarded” experimental data with undesirable hardness or EC establish the connection between input variables (experimental variables and other features) and target properties. After five iteration loops, the highest EC and hardness of the alloys were optimized to 69.24% IACS and 207.39 HV, 20.8% and 14.7% higher than the best values in the initial training data. The optimum compositions of Cu-Co-Si alloys are discovered from the contour maps which consist of the properties of 12,000 alloys predicted by our ML models. Combined with the phase diagram and microstructure characterization, we infer that the moderate precipitation of Co2Si leads to high conductivity and the precipitation of Co2Si and CoSi strengthens the Cu matrix.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.