Huiya Liao , Jun Pan , Xihui Su , Xingyue Sun , Xu Chen
{"title":"A path-dependent adaptive physics-informed neural network for multiaxial fatigue life prediction","authors":"Huiya Liao , Jun Pan , Xihui Su , Xingyue Sun , Xu Chen","doi":"10.1016/j.ijfatigue.2024.108799","DOIUrl":null,"url":null,"abstract":"<div><div>In this work, a path-dependent adaptive physics-informed neural network (PINN) is proposed for multiaxial fatigue life prediction. Several critical plane models are incorporated into the loss function, with weights optimized through a genetic algorithm and meta-learning framework which considers path-dependent non-proportional information. With training and transfer learning in 316LN, 316L, and 304 stainless steels, the GRU-PINN meta-learning model shows the best overall performance in multiaxial fatigue predictions. Compared with other models, the GRU-PINN model achieves the lowest root mean square error (<em>RMSE</em>) and predicted lives are located most in 1.5-factor bands.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"193 ","pages":"Article 108799"},"PeriodicalIF":6.8000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Fatigue","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0142112324006583","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/30 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
In this work, a path-dependent adaptive physics-informed neural network (PINN) is proposed for multiaxial fatigue life prediction. Several critical plane models are incorporated into the loss function, with weights optimized through a genetic algorithm and meta-learning framework which considers path-dependent non-proportional information. With training and transfer learning in 316LN, 316L, and 304 stainless steels, the GRU-PINN meta-learning model shows the best overall performance in multiaxial fatigue predictions. Compared with other models, the GRU-PINN model achieves the lowest root mean square error (RMSE) and predicted lives are located most in 1.5-factor bands.
期刊介绍：
Typical subjects discussed in International Journal of Fatigue address:
Novel fatigue testing and characterization methods (new kinds of fatigue tests, critical evaluation of existing methods, in situ measurement of fatigue degradation, non-contact field measurements)
Multiaxial fatigue and complex loading effects of materials and structures, exploring state-of-the-art concepts in degradation under cyclic loading
Fatigue in the very high cycle regime, including failure mode transitions from surface to subsurface, effects of surface treatment, processing, and loading conditions
Modeling (including degradation processes and related driving forces, multiscale/multi-resolution methods, computational hierarchical and concurrent methods for coupled component and material responses, novel methods for notch root analysis, fracture mechanics, damage mechanics, crack growth kinetics, life prediction and durability, and prediction of stochastic fatigue behavior reflecting microstructure and service conditions)
Models for early stages of fatigue crack formation and growth that explicitly consider microstructure and relevant materials science aspects
Understanding the influence or manufacturing and processing route on fatigue degradation, and embedding this understanding in more predictive schemes for mitigation and design against fatigue
Prognosis and damage state awareness (including sensors, monitoring, methodology, interactive control, accelerated methods, data interpretation)
Applications of technologies associated with fatigue and their implications for structural integrity and reliability. This includes issues related to design, operation and maintenance, i.e., life cycle engineering
Smart materials and structures that can sense and mitigate fatigue degradation
Fatigue of devices and structures at small scales, including effects of process route and surfaces/interfaces.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
