Machine Learning-guided accelerated discovery of structure-property correlations in lean magnesium alloys for biomedical applications
Sreenivas Raguraman, Maitreyee Sharma Priyadarshini, Tram Nguyen, Ryan, McGovern, Andrew Kim, Adam J. Griebel, Paulette Clancy, Timothy P. Weihs

TL;DR
This study uses machine learning and rapid characterization techniques to understand how thermal processing affects microstructure, strength, and corrosion resistance in magnesium alloys for biomedical implants.
Contribution
It introduces a combined approach of correlation analysis and LASSO for identifying microstructural factors influencing properties in magnesium alloys.
Findings
Grain size refinement enhances strength.
Ternary Ca2Mg6Zn3 phase influences corrosion behavior.
Optimal properties achieved by controlling microstructure.
Abstract
Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability, biocompatibility, and impressive mechanical characteristics. However, their rapid in-vivo degradation presents challenges, notably in upholding mechanical integrity over time. This study investigates the impact of high-temperature thermal processing on the mechanical and degradation attributes of a lean Mg-Zn-Ca-Mn alloy, ZX10. Utilizing rapid, cost-efficient characterization methods like X-ray diffraction and optical, we swiftly examine microstructural changes post-thermal treatment. Employing Pearson correlation coefficient analysis, we unveil the relationship between microstructural properties and critical targets (properties): hardness and corrosion resistance. Additionally, leveraging the least absolute shrinkage and selection operator (LASSO), we…
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