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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Machine Learning Framework for Predicting and Interpreting Corrosion Behavior of Zinc Alloys
Author(s) Abhishek Kansal, R Durga Prasad Reddy, Pradeep kumar, Siddharth Angraa, Harshil Chaddha, Aryan kumar, Arooj sharma
On-Site Speaker (Planned) R Durga Prasad Reddy
Abstract Scope Accurate prediction of the corrosion rates of Zn-based alloys is essential for their development and application in structural, coating, and biomedical fields. Conventional corrosion testing methods are often time-consuming and labor-intensive, slowing alloy design and optimization. This study presents a data-driven machine learning (ML) framework for predicting the corrosion behavior of Zn-based alloys using their elemental compositions, mechanical properties, and metallurgical phases. Several ML algorithms, including Decision Tree, Random Forest, Gradient Boosting, and XGBoost, were trained and evaluated using a comprehensive dataset compiled from published literature. Model performance was assessed through statistical evaluation metrics to ensure reliability and generalization. The findings demonstrate that ML models can accurately predict corrosion behavior and serve as efficient tools for accelerating the development of Zn-based alloys with tailored corrosion properties, reducing experimental effort and development time.
Proceedings Inclusion? Planned:
Keywords Biomaterials, Machine Learning, Characterization

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