An analytical equation for predicting corrosion rates of biodegradable Zn–0.45Mn–0.2Mg alloy via symbolic regression

Abstract

Corrosion rates of biodegradable Zn alloys are directly related to their post-implantation safety and effectiveness. However, highly accurate and interpretable “white-box” machine learning models for predicting their corrosion rates remain largely unexplored. This study proposes a data-driven method coupled with accelerated corrosion testing for predicting the corrosion rates of biodegradable Zn–0.45Mn–0.2Mg (wt%) alloy. A symbolic regression (SR) machine-learning model was established based on an analytical expression of the corrosion rate and four corrosion parameters. Outperforming five other machine-learning models, the SR model achieved a determination coefficient of 0.97 and prediction errors in the verification experiments of less than 10%. This study contributes to a paradigm shift from qualitative to quantitative analysis for corrosion research on biodegradable metals.

Keywords

symbolic regression / corrosion rate / accelerated corrosion / zinc alloys

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