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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

Data-Driven Tensile Strength Prediction of Biodegradable Zinc Alloys

Author(s)

Abhishek Kansal, Ram Niwas, Siddharth Angraa, Aryan Kumar, Arooj sharma, Harshil Chaddha

On-Site Speaker (Planned)

Abhishek Kansal

Abstract Scope

Accurate prediction of tensile strength is essential for the development and application of Zn-based alloys in structural and biomedical fields. Conventional tensile testing is time-consuming and resource-intensive, often slowing alloy design and optimization. This study presents a machine learning (ML) framework for predicting the tensile strength of Zn-based alloys using alloy composition, processing parameters, and microstructural features as input variables. A comprehensive database was compiled from published literature and used to train and evaluate various ML algorithms, including Decision Tree, Random Forest, Gradient Boosting, and XGBoost. Model performance was assessed using standard statistical metrics. The results demonstrate that ML models can reliably predict tensile strength and serve as an efficient tool for accelerating the design and optimization of Zn-based alloys while reducing experimental effort and development time.

Proceedings Inclusion?

Planned:

Keywords

Biomaterials, Characterization, Machine Learning

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