Interpolating CTS specimens’ mode I and II stress intensity factors using artificial neural networks

IF 5 2区工程技术 Q1 ENGINEERING, MECHANICAL

Theoretical and Applied Fracture Mechanics Pub Date : 2024-11-19 DOI:10.1016/j.tafmec.2024.104761

R. Baptista , V. Infante , L.F.P. Borrego , E.R. Sérgio , D.M. Neto , F.V. Antunes

{"title":"Interpolating CTS specimens’ mode I and II stress intensity factors using artificial neural networks","authors":"R. Baptista , V. Infante , L.F.P. Borrego , E.R. Sérgio , D.M. Neto , F.V. Antunes","doi":"10.1016/j.tafmec.2024.104761","DOIUrl":null,"url":null,"abstract":"<div><div>Fracture mechanics parameters, such as the stress intensity factor (SIF), are fundamental for the analysis of fracture, fatigue crack growth and crack paths. SIFs of a cracked body can be determined either experimentally or numerically. Analytical solutions of SIF are very useful, but their determination from discrete values can be extremely complex when there are many independent variables. In this paper, artificial neural networks (ANN) are proposed to predict mode I and II stress intensity factors in a CTS specimen under mixed mode loading conditions. Trained with numerical data, the performance of different network architectures and backpropagation algorithms was assessed. Using at least 10 neurons, in the hidden layers, made it possible for the designed solution to match the performance of analytical solutions. Increasing the number of neurons, allowed the model performance to improve up to 90%, when compared with previous analytical solutions. This increases the quality of fracture and fatigue studies done with the CTS sample.</div></div>","PeriodicalId":22879,"journal":{"name":"Theoretical and Applied Fracture Mechanics","volume":"134 ","pages":"Article 104761"},"PeriodicalIF":5.0000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Theoretical and Applied Fracture Mechanics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167844224005111","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}

引用次数: 0

Abstract

Fracture mechanics parameters, such as the stress intensity factor (SIF), are fundamental for the analysis of fracture, fatigue crack growth and crack paths. SIFs of a cracked body can be determined either experimentally or numerically. Analytical solutions of SIF are very useful, but their determination from discrete values can be extremely complex when there are many independent variables. In this paper, artificial neural networks (ANN) are proposed to predict mode I and II stress intensity factors in a CTS specimen under mixed mode loading conditions. Trained with numerical data, the performance of different network architectures and backpropagation algorithms was assessed. Using at least 10 neurons, in the hidden layers, made it possible for the designed solution to match the performance of analytical solutions. Increasing the number of neurons, allowed the model performance to improve up to 90%, when compared with previous analytical solutions. This increases the quality of fracture and fatigue studies done with the CTS sample.

查看原文本刊更多论文

利用人工神经网络插值 CTS 试样的模式 I 和模式 II 应力强度因子

应力强度因子（SIF）等断裂力学参数是分析断裂、疲劳裂纹生长和裂纹路径的基础。裂纹体的 SIF 可以通过实验或数值方法确定。SIF 的解析解非常有用，但当存在许多独立变量时，从离散值确定 SIF 可能会非常复杂。本文提出了人工神经网络（ANN）来预测混合模式加载条件下 CTS 试样的模式 I 和模式 II 应力强度因子。通过使用数值数据进行训练，评估了不同网络结构和反向传播算法的性能。在隐藏层中至少使用 10 个神经元，可使设计方案与分析方案的性能相匹配。与之前的分析解决方案相比，增加神经元数量可使模型性能提高 90%。这提高了对 CTS 样品进行断裂和疲劳研究的质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Theoretical and Applied Fracture Mechanics 工程技术-工程：机械

CiteScore

8.40

自引率

18.90%

发文量

435

审稿时长

37 days

期刊介绍： Theoretical and Applied Fracture Mechanics'' aims & scopes have been re-designed to cover both the theoretical, applied, and numerical aspects associated with those cracking related phenomena taking place, at a micro-, meso-, and macroscopic level, in materials/components/structures of any kind. The journal aims to cover the cracking/mechanical behaviour of materials/components/structures in those situations involving both time-independent and time-dependent system of external forces/moments (such as, for instance, quasi-static, impulsive, impact, blasting, creep, contact, and fatigue loading). Since, under the above circumstances, the mechanical behaviour of cracked materials/components/structures is also affected by the environmental conditions, the journal would consider also those theoretical/experimental research works investigating the effect of external variables such as, for instance, the effect of corrosive environments as well as of high/low-temperature.