A federated learning–driven data fusion strategy for the hardenability prediction of gear steel
TL;DR
A federated learning–driven data fusion strategy incorporating a multi-regularized attention residual network model for hardenability prediction (MRAN-J9) is proposed, providing a practical and reliable solution for hardenability prediction in complex industrial application scenarios.
Abstract
Hardenability is a critical indicator for evaluating the mechanical performance and service reliability of gear steel. However, conventional Jominy end-quench testing is labor-intensive and time-consuming, and data sharing among different companies is often restricted, which further complicates hardenability assessment. To address these challenges, a federated learning–driven data fusion strategy incorporating a multi-regularized attention residual network model for hardenability prediction (MRAN-J9) is proposed. In this strategy, collaborative models are trained on heterogeneous data from multiple sources, improving predictive accuracy while preserving the privacy of each participant’s raw data. Additionally, stable predictive performance is evaluated on a completely independent external validation dataset containing 755 samples [the coefficient of determination (R2) = 0.88, root mean square error (RMSE) = 0.99, Rockwell hardness (HRC)], demonstrating the generalization capability and predictive stability. The results confirm the feasibility and effectiveness of federated learning for privacy-preserving multi-party collaborative modeling. Furthermore, integrating the MRAN-J9 model facilitates the effective exploitation of distributed multi-source data, providing a practical and reliable solution for hardenability prediction in complex industrial application scenarios.