Physics-informed neural network for fatigue life prediction of irradiated austenitic and ferritic/martensitic steels

2025-08-24

AI Summary

Accurately predicting low-cycle fatigue (LCF) life of irradiated austenitic and ferritic/martensitic (F/M) steels under coupled high-temperature, cyclic loading, and irradiation conditions remains a significant challenge. To address this, we propose a physics-informed neural network (PINN) framework that explicitly incorporates fatigue physics—particularly the saturation behavior of LCF life at high strain amplitudes—into the loss function. Input features include strain amplitude, irradiation dose, and test temperature. The model is trained on 495 experimental data points and achieves superior predictive accuracy compared to random forest, XGBoost, gradient boosting, and conventional neural networks. Crucially, SHAP analysis is integrated to enhance model interpretability. This work presents the first physically consistent modeling of the irradiation–thermal–mechanical coupling-induced fatigue saturation effect in F/M steels, delivering high accuracy, strong generalizability across diverse irradiation and thermal conditions, and quantitative interpretability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This study proposes a Physics-Informed Neural Network (PINN) framework to predict the low-cycle fatigue (LCF) life of irradiated austenitic and ferritic/martensitic (F/M) steels used in nuclear reactors. These materials experience cyclic loading and irradiation at elevated temperatures, causing complex degradation that traditional empirical models fail to capture accurately. The developed PINN model incorporates physical fatigue life constraints into its loss function, improving prediction accuracy and generalizability. Trained on 495 data points, including both irradiated and unirradiated conditions, the model outperforms traditional machine learning models like Random Forest, Gradient Boosting, eXtreme Gradient Boosting, and the conventional Neural Network. SHapley Additive exPlanations analysis identifies strain amplitude, irradiation dose, and testing temperature as dominant features, each inversely correlated with fatigue life, consistent with physical understanding. PINN captures saturation behaviour in fatigue life at higher strain amplitudes in F/M steels. Overall, the PINN framework offers a reliable and interpretable approach for predicting fatigue life in irradiated alloys, enabling informed alloy selection.

Problem

• Predicting fatigue life of irradiated nuclear reactor steels
• Addressing complex degradation from cyclic loading and irradiation
• Improving accuracy over traditional empirical and ML models

Innovation

• Physics-Informed Neural Network with physical constraints
• Incorporates fatigue life constraints into loss function
• Outperforms traditional ML models and empirical approaches