用于摩擦疲劳寿命预测的数据辅助物理信息神经网络 (DA-PINN)

IF 3.4 Q1 ENGINEERING, MECHANICAL
Can Wang, Qiqi Xiao, Zhikun Zhou, Yongyu Yang, Gregor Kosec, Lihua Wang, Magd Abdel Wahab
{"title":"用于摩擦疲劳寿命预测的数据辅助物理信息神经网络 (DA-PINN)","authors":"Can Wang,&nbsp;Qiqi Xiao,&nbsp;Zhikun Zhou,&nbsp;Yongyu Yang,&nbsp;Gregor Kosec,&nbsp;Lihua Wang,&nbsp;Magd Abdel Wahab","doi":"10.1002/msd2.12127","DOIUrl":null,"url":null,"abstract":"<p>In this study, we present for the first time the application of physics-informed neural network (PINN) to fretting fatigue problems. Although PINN has recently been applied to pure fatigue lifetime prediction, it has not yet been explored in the case of fretting fatigue. We propose a data-assisted PINN (DA-PINN) for predicting fretting fatigue crack initiation lifetime. Unlike traditional PINN that solves partial differential equations for specific problems, DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction accuracy. The DA-PINN method, employed in this study, consists of two main steps. First, damage parameters are obtained from the finite element method by using critical plane method, which generates a data set used to train an artificial neural network (ANN) for predicting damage parameters in other cases. Second, the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models, which predict fretting fatigue lifetime. The results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is constructed. Additionally, the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.</p>","PeriodicalId":60486,"journal":{"name":"国际机械系统动力学学报(英文)","volume":"4 3","pages":"361-373"},"PeriodicalIF":3.4000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msd2.12127","citationCount":"0","resultStr":"{\"title\":\"A data-assisted physics-informed neural network (DA-PINN) for fretting fatigue lifetime prediction\",\"authors\":\"Can Wang,&nbsp;Qiqi Xiao,&nbsp;Zhikun Zhou,&nbsp;Yongyu Yang,&nbsp;Gregor Kosec,&nbsp;Lihua Wang,&nbsp;Magd Abdel Wahab\",\"doi\":\"10.1002/msd2.12127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In this study, we present for the first time the application of physics-informed neural network (PINN) to fretting fatigue problems. Although PINN has recently been applied to pure fatigue lifetime prediction, it has not yet been explored in the case of fretting fatigue. We propose a data-assisted PINN (DA-PINN) for predicting fretting fatigue crack initiation lifetime. Unlike traditional PINN that solves partial differential equations for specific problems, DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction accuracy. The DA-PINN method, employed in this study, consists of two main steps. First, damage parameters are obtained from the finite element method by using critical plane method, which generates a data set used to train an artificial neural network (ANN) for predicting damage parameters in other cases. Second, the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models, which predict fretting fatigue lifetime. The results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is constructed. Additionally, the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.</p>\",\"PeriodicalId\":60486,\"journal\":{\"name\":\"国际机械系统动力学学报(英文)\",\"volume\":\"4 3\",\"pages\":\"361-373\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msd2.12127\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"国际机械系统动力学学报(英文)\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/msd2.12127\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"国际机械系统动力学学报(英文)","FirstCategoryId":"1087","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/msd2.12127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0

摘要

在本研究中,我们首次将物理信息神经网络(PINN)应用于摩擦疲劳问题。虽然 PINN 近来已被应用于纯疲劳寿命预测,但还没有人探索过它在摩擦疲劳情况下的应用。我们提出了一种数据辅助 PINN(DA-PINN),用于预测摩擦疲劳裂纹起始寿命。与针对特定问题求解偏微分方程的传统 PINN 不同,DA-PINN 将实验或数值数据与物理方程相结合,作为损失函数的一部分,以提高预测精度。本研究采用的 DA-PINN 方法包括两个主要步骤。首先,使用临界面法从有限元法中获得损伤参数,生成用于训练人工神经网络(ANN)的数据集,以预测其他情况下的损伤参数。其次,将预测的损伤参数与实验参数相结合,形成 DA-PINN 模型的输入数据集,该模型可预测摩擦疲劳寿命。结果表明,DA-PINN 在预测精度方面优于 ANN,并且在构建损伤参数数据集后无需高昂的计算成本。此外,DA-PINN 模型中损失函数方法的选择对其性能起着至关重要的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A data-assisted physics-informed neural network (DA-PINN) for fretting fatigue lifetime prediction

A data-assisted physics-informed neural network (DA-PINN) for fretting fatigue lifetime prediction

In this study, we present for the first time the application of physics-informed neural network (PINN) to fretting fatigue problems. Although PINN has recently been applied to pure fatigue lifetime prediction, it has not yet been explored in the case of fretting fatigue. We propose a data-assisted PINN (DA-PINN) for predicting fretting fatigue crack initiation lifetime. Unlike traditional PINN that solves partial differential equations for specific problems, DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction accuracy. The DA-PINN method, employed in this study, consists of two main steps. First, damage parameters are obtained from the finite element method by using critical plane method, which generates a data set used to train an artificial neural network (ANN) for predicting damage parameters in other cases. Second, the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models, which predict fretting fatigue lifetime. The results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is constructed. Additionally, the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.50
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信