In the automotive industry, the transition towards lightweight steels aims to reduce exhaust emissions while ensuring crashworthiness. To provide sacrificial corrosion protection, Zn-based coatings are applied to these carbon steels via hot-dip galvanizing (HDG). However, the high temperatures involved in HDG (460 °C) can undesirably alter the microstructure of substrate steels. Therefore, the study introduces novel coating alloys with lower liquidus temperatures (TL), aimed at preserving the substrate microstructure during HDG. These alloys offer metallurgical advantages in a variety of steels that benefit from reduced exposure of the annealed microstructure to the “elevated” temperatures associated with molten Zn. New coating alloy development started with the creation of a comprehensive database, including coating alloy composition, corrosion current density (Icorr), corrosion potential (Ecorr), types of electrolytes, and their concentrations extracted from the literature and computed TL for each alloy, to train machine learning (ML) models. ML tools including the Citrination platform® and various open-source Python libraries were employed to develop Icorr, Ecorr, and TL models. A materials selection chart was also developed to visualize the current state and potential future opportunities, focusing on optimizing Icorr, Ecorr, and TL attributes of coatings. For CALPHAD (Calculation of Phase Diagrams) modeling, elements such as Zn, Mg, Al, Sn, Bi, Ga, and In were chosen after an initial screening. This screening assessed factors like the availability of thermodynamic databases, toxicity, flammability, and their capacity to offer sacrificial protection to steels while lowering the TL of an alloy. Over 300,000 unique conditions were assessed, analyzing more than twenty-one binary systems, thirty five ternary systems, four quaternary systems, and six quinary systems. This analysis aimed to identify suitable coating alloy compositions with low TL. Consequently, four alloys – Zn 2.17Mg 3.95Al, Zn 3.25Mg 3.61Al 20.18Sn, Zn 2.61Mg 3.96Al 10.26Sn 9.18Ga, and Zn 2.84Mg 3.48Al 10.07Sn 9.4Bi (wt%), – were selected for experimental studies. Differential scanning calorimetry (DSC) and potentiodynamic polarization testing were used to experimentally validate the corrosion and transformation behavior of these alloys. Further, the study delved into applying these alloys to substrate steels, analyzing the phase transformations and microstructures through computed phase diagrams, DSC, scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS), scanning transmission electron microscopy (STEM), and X ray diffraction (XRD). The findings of the study demonstrate that the novel alloys provide effective sacrificial protection to the substrate and exhibit significantly lower TL than pure Zn. A unique phenomenon of liquid phase separation was observed in ZnMgAlSnGa alloy at room temperature, therefore, this alloy was not further considered for application to steel. The ZnMgAl coating showed pro-eutectic Zn dendrites and a ternary eutectic comprising lamellae of Zn (η hcp), Cubic MgZn2-based solution, and binary Zn (η-hcp)/Al (? fcc) regions. The ZnMgAlSnBi coating exhibited Zn (η hcp), Al (? fcc), tetragonal Sn, orthorhombic Mg2Sn, and hexagonal Mg3Bi2-based solutions. The ZnMgAlSnBi coating showed significant dross formation in the coating alloy bath. The ZnMgAlSn coating showed the best combination of sacrificial substrate protection, low TL, and coating uniformity. The coating microstructure involved the Zn phase, an Al matrix with Zn precipitates, a Mg2Sn-based solution, and a Sn-rich phase. There was a uniform intermetallic layer at the ZnMgAlSn coating substrate interface with minimal Fe diffusion into the coating, and Al segregation at the interface. This study highlighted the significance of low TL coatings that could enable new substrate concepts, presenting a novel approach for steel processing during HDG, without the extra costs associated with modifying existing galvanizing lines.
advanced high strength steels, alloy design, electrochemical corrosion, galvanizing, machine learning, materials selection chart
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Colorado School of Mines. Arthur Lakes Library