Prediction of multiaxial fatigue life with a data-driven knowledge transfer model

IF 5.7 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue Pub Date : 2024-10-04 DOI:10.1016/j.ijfatigue.2024.108636

Lei Gan , Zhi-Ming Fan , Hao Wu , Zheng Zhong

引用次数: 0

Abstract

A data-driven model is presented for accurate prediction of multiaxial fatigue life based upon the principle of transfer learning (TL). The Tradaboost framework is explored to adjust the weights of training data from different sources, actuating information transfer from domain knowledge to the data-driven modeling of multiaxial fatigue life. Subsequently, extensive experimental results tested under the proportional and non-proportional circle loadings are collected for model evaluation. The results demonstrate that the proposed model is more accurate than domain knowledge-based, conventional data-driven, and comparable TL-based models, with a low data requirement, showcasing good applicability for multiaxial fatigue life assessment.

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利用数据驱动的知识转移模型预测多轴疲劳寿命

本文基于迁移学习（TL）原理，提出了一种数据驱动模型，用于准确预测多轴疲劳寿命。研究探索了 Tradaboost 框架，以调整来自不同来源的训练数据的权重，实现从领域知识到多轴疲劳寿命数据驱动模型的信息转移。随后，收集了在比例和非比例圆载荷下测试的大量实验结果，用于模型评估。结果表明，所提出的模型比基于领域知识的模型、传统的数据驱动模型和类似的基于 TL 的模型更精确，而且对数据的要求较低，在多轴疲劳寿命评估方面具有良好的适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Fatigue 工程技术-材料科学：综合

CiteScore

10.70

自引率

21.70%

发文量

619

审稿时长

58 days

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.