# Predicting grain growth kinetic in steels using machine learning and XAI for mechanical properties

## TL;DR

This paper uses machine learning and explainable AI to predict grain growth in steels, improving mechanical properties prediction during processing.

## Contribution

A novel ML framework integrating XAI for predicting grain growth kinetics in steels with high accuracy and experimental validation.

## Key findings

- XGBoost model achieved an R2 value of 0.9728 in predicting grain growth.
- Temperature, initial grain size, and holding time were identified as dominant factors.
- Predicted grain sizes matched experimental results and showed Hall–Petch behavior in hardness.

## Abstract

Understanding and controlling grain growth kinetics in steels is crucial for optimizing mechanical properties during thermomechanical processing. However, traditional empirical models often fail to account for the complex, nonlinear interactions between alloying elements and processing parameters. In this study, we introduce a novel machine learning (ML) based framework that predicts austenitic grain growth behaviour directly from chemical composition and process conditions, utilizing a comprehensive dataset of 1039 experimentally validated samples. Among various algorithms tested, the XGBoost model demonstrated exceptional predictive capability, achieving an R2 value of 0.9728 after hyperparameter optimization. Feature selection methods (Pearson correlation, CfsSubset, ReliefF) and SHAP-based explainable AI analyses were employed to identify the most influential parameters, revealing temperature, initial grain size, and holding time as dominant factors. Experimental validation was conducted on 316L stainless steel samples annealed at 1100 °C. The predicted grain sizes showed strong agreement with experimental measurements, and the observed hardness variations followed the expected Hall–Petch behaviour. This study demonstrates the first integrated ML and experimental approach for predicting grain growth kinetics in steels, offering a powerful tool for alloy design and process optimization. Future work will extend this framework to additional process variables and alloy systems.