Development of new coating alloys
type: CreativeWork
description: 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 a…
abstract: 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 a…
author: Bardapurkar, Rohit
language: eng
language: English
keywords: advanced high strength steels, alloy design, electrochemical corrosion, galvanizing, machine learning, materials selection chart
date published: 2024
date modified: 2026-07-24T01:41:37Z
provider: Colorado School of Mines
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Colorado School of Mines. Arthur Lakes Library

PropertyValue
property ID: Handle
value: https://hdl.handle.net/11124/180694