Abstract Scope |
Data-driven design, discovery, and development (D5)TM was utilized in this study to identify novel corrosion-resistant coating alloys with low liquidus temperature (TL) for galvanizing of new lightweight automotive sheet steels. Machine-learning (ML) algorithms trained on a database containing TL data (computed via CALPHAD modeling) and experimental corrosion data (collected from the literature) were employed to predict properties of new alloy coatings. A “Materials Selection Map” was developed to visualize the current state of design space and potential future opportunities related to the key performance criteria: corrosion-current (Icorr), corrosion-potential (Ecorr), and TL. Based on computed and predicted results, ZnMgAl, ZnMgAlSn, ZnMgAlSnBi, ZnMgAlSnGa alloys were selected for experimental verification of the selected performance criteria. Differential scanning calorimetry (DSC) was used for TL validation, and potentiodynamic polarization testing was performed to study the corrosion behavior of alloys. Finally, sequential learning was utilized to optimize the ML models. |